Intelligent management and remote quality control system for immunohistochemical multi-class positive control

Abstract

The application relates to the field of pathological examination information technology, and discloses an intelligent management and remote quality control system for immunohistochemical multi-category positive controls. In the system, a structured database of multi-category controls is established by a positive control database module; an optimal control section is automatically matched based on five-dimensional weighted scores of antibody fitness and positive positioning matching degree by a rule engine module; a remaining valid number of days is dynamically calculated and expiration blocking is performed by a warning submodule; quantitative indexes such as a repetition rate and a good rate are calculated and a quality control report is generated by a quality control data analysis and report module; and after quality control data is desensitized and packaged, the remote sharing module uploads and receives multi-center deviation analysis results. The application solves the problems in the related art that immunohistochemical positive control selection depends on artificial experience, effective period management lacks automatic means, and quality control data cannot be compared horizontally across centers.

Description

Technical Field

[0001] This invention relates to the field of information technology in pathological testing, and more specifically, to an intelligent management and remote quality control system for multiple categories of positive controls in immunohistochemistry. Background Technology

[0002] In routine pathological diagnosis, immunohistochemistry laboratories need to use positive control slides alongside clinical slides to verify the effectiveness of the staining process. Common types of positive controls include amniotic membrane roll controls, lymphatic system controls, ER controls, PR controls, and HER2 controls, among others.

[0003] Currently, laboratories generally rely on manual methods to manage the aforementioned diverse categories of positive control resources. Regarding control slide selection, information on various types of controls is stored in a scattered manner with inconsistent formats and a lack of unified quantitative matching criteria. The selection of control slides primarily depends on the personal experience of the operators. In terms of expiration date monitoring, the management of control slide expiration dates relies on paper records or manual experience, lacking dynamic automated monitoring methods, and the misuse of expired control slides occurs frequently. Regarding quality control data management, the interpretation results after staining are stored in different systems, making it difficult to achieve full-process traceability at the slide level, and the quantification of quality control indicators is insufficient. In terms of multi-center quality control collaboration, the quality control data formats are inconsistent between laboratories, and there is a lack of standardized deviation analysis methods for cross-sectional comparisons. These factors collectively lead to low management efficiency of immunohistochemistry positive controls, insufficient quantification of quality control indicators, and difficulties in multi-center quality control collaboration. Summary of the Invention

[0004] This invention provides an intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls, solving the technical problems in related technologies such as reliance on manual experience in selecting immunohistochemical positive controls, lack of automated means for managing expiration dates, and inability to conduct cross-center horizontal comparisons of quality control data.

[0005] This invention discloses an intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls, comprising: a positive control database module configured to establish a structured database of multiple categories of positive controls, generating structured fields for each category of control including control number, control category, tissue source, applicable antibody set, positive localization type, expression intensity level, slide date, storage conditions, effective storage duration, and inventory status; a slide information communication module configured to receive business slide application data from an external information system and perform standardized parsing, establish a binding relationship between the business slides and the optimal control slides output by the rule engine module, and issue staining tasks; and a rule engine module configured to retrieve a set of candidate controls from the positive control database module based on the antibody items of the business slides, and perform... Each candidate control is weighted and scored based on antibody suitability, positive localization matching, platform suitability, expiration date availability, and inventory availability. The candidate control with the highest score is selected as the optimal control slide for output. The early warning submodule is configured to calculate the remaining valid days based on the slide's slide date and valid storage duration, and output the expiration date status. When the expiration date status is expired, the control slide is prevented from participating in the binding process. The quality control data analysis and reporting module is configured to collect staining completion information and interpretation results to establish slide-level quality control records, and calculate quality control indicators according to a preset statistical period to generate quality control reports. The remote sharing module is configured to upload the quality control report and slide-level quality control records to the quality control center after desensitization and packaging in a unified data format, and receive multi-center cross-comparison results.

[0006] Furthermore, the rules engine module for the first Candidate controls Matching score Calculate using the following formula: ; in, Indicates the first The fit between a candidate control and the target antibody is set to 1 if the target antibody belongs to the set of applicable antibodies for that candidate control, and 0 otherwise. This indicates the positive localization matching degree. It is set to 1 when the preset positive localization type corresponding to the target antibody is consistent with the positive localization type of the candidate control, and 0 otherwise. This indicates the platform compatibility. It is set to 1 if the current staining platform belongs to the set of compatible platforms for the candidate control, and 0 otherwise. The validity period availability is represented by the smaller of the ratio of the remaining valid days of the candidate control to the valid storage time and 1. This indicates inventory availability, which is the smaller of the ratio of the currently available quantity of similar inventory to the preset reference inventory quantity and 1. to Represents the weighting coefficients, satisfying .

[0007] Furthermore, the slice information communication module extracts the case number during the standardized parsing phase. Slice number Wax Block Number Antibody Project Purpose of testing Application period Operators and staining platform The antibody name normalization submodule maps synonymous antibody names from different source systems to a unified standard name; when a business slice belongs to a hierarchical interpretation project, the interpretation rule version number is synchronously written. .

[0008] Furthermore, the binding record generated by the slice information communication module includes at least the business slice number. , control slide number Antibody Project , dyeing batch number Equipment number and issuance time ;in, The identifier of the target control slice for the best match. Indicates the dyeing batch number. Indicates the dyeing equipment number. This indicates the time when the task is issued to the staining equipment; for multiple business slides of the same batch and the same antibody project, a batch binding method is adopted, in which a group of business slides is bound to a shared control slide.

[0009] Furthermore, the early warning submodule calculates the expiration time in the following manner. and remaining valid days : ; ; in, Indicates the preparation time of the control section. Indicates the effective storage duration. Indicates the current time; validity period status. The following rules apply: When This is normal, when It was nearing its expiration date when It has expired. The threshold is set as the warning threshold. When the expiration date is nearing its expiration, the slice information communication module allows the use of the control slice but simultaneously pushes warning information to the operator.

[0010] Furthermore, the quality control data analysis and reporting module establishes a quality control record for each control slide that includes at least the preliminary judgment result. Re-judgment results Final result Exception markers and abnormal reasons Final result Determined according to the following rules: ; in, This refers to the arbitration result obtained after review by an arbitrator or a senior pathologist when the initial judgment and the retrial result are inconsistent.

[0011] Furthermore, the quality control data analysis and reporting module calculates quality control indicators including the repetition rate. Excellent rate pass rate Expiration rate Consistency rate of interpretation and batch drift rate Among them, the excellent rate Based on comprehensive quality score The overall quality score is calculated using the following formula: ; in, Indicates the staining intensity score. Indicates the background cleanliness score. The score indicates the accuracy of the positioning. Indicates organizational integrity score, to Indicates the scoring weight, satisfying ;when Not less than the excellent threshold It was judged to be excellent.

[0012] Furthermore, batch drift rate Calculate using the following formula: ; in, This represents the average intensity value of the current batch. Indicates the average strength value of the reference batch; when Exceeding the drift rate threshold At that time, it was determined that this batch had a risk of strength drift.

[0013] Furthermore, the remote sharing module receives the multi-center deviation analysis results returned by the quality control center, including the multi-center deviation index. Calculate using the following formula: ; in, Index indicating the laboratory. Indicates the first The target indicator values ​​for each laboratory This represents the average value of the corresponding indicator across all laboratories. Indicates the first The relative deviation of each laboratory from the overall mean; when When the threshold is exceeded, it is marked as deviating from the laboratory.

[0014] This invention discloses an intelligent management and remote quality control method for multiple categories of immunohistochemical positive controls, used to execute the above-mentioned system, comprising: Step 1: Establish a structured database of multiple categories of positive controls through the positive control database module. For each type of control, generate structured fields containing control number, control category, tissue source, applicable antibody set, positive localization type, expression intensity level, slide date, storage conditions, effective storage duration and inventory status, and write them into the database record. Step 2: Receive business slide application data from external information systems through the slide information communication module, extract case number, slide number, paraffin block number, antibody item, testing purpose, application time, operator and staining platform information, and perform antibody name normalization processing to generate standardized application records; Step 3: The early warning submodule calculates the remaining valid days based on the slice date and valid storage duration of the reference slice, determines the validity period status, and outputs it to the slice information communication module and the rule engine module; Step 4: The rule engine module retrieves the candidate control set from the positive control database module based on the antibody items in the standardized application record. Each candidate control is weighted and scored according to antibody suitability, positive localization matching degree, platform suitability, shelf life availability and inventory availability. The candidate control with the highest score is selected as the optimal control slice output. Step 5: Establish a binding relationship between the business slice and the optimal control slice through the slice information communication module, generate a binding record and send the dyeing task to the dyeing equipment. When the validity period status is expired, prevent the control slice from participating in the binding process. Step 6: Collect staining completion information and interpretation results through the quality control data analysis and reporting module, establish slide-level quality control records, calculate repeatability rate, excellent rate, pass rate, expired usage rate, interpretation consistency rate and batch drift rate according to the preset statistical cycle, and generate a quality control report; Step 7: Upload the quality control report and slice-level quality control records to the quality control center via the remote sharing module after desensitizing and packaging them in a unified data format. Receive and display the multi-center cross-sectional comparison results and deviation analysis results.

[0015] The present invention has the following beneficial effects: This invention establishes a database record for multiple categories of positive controls through a positive control database module. This database record includes structured fields such as control number, control category, applicable antibody set, positive localization type, slide date, and effective storage duration. This allows multiple categories of control resources to be stored in a unified data format, providing a structured data foundation for subsequent automatic retrieval and matching. This overcomes the problems of scattered control information and inconsistent formats under manual management.

[0016] This invention uses a rule engine module to calculate a matching score based on a weighted score of five dimensions: antibody suitability, positive localization matching degree, platform suitability, shelf-life availability, and inventory availability. The optimal control slice is then selected according to the score ranking. This allows the matching process between business slices and control slices to be executed automatically based on a quantitative multi-dimensional scoring criterion, overcoming the problem of lacking a unified quantitative matching criterion when relying on manual experience to select control slices.

[0017] This invention uses an early warning submodule to dynamically calculate the remaining valid days based on the slice date and valid storage duration. When the remaining valid days are lower than the early warning threshold, an impending expiration warning is output. When the remaining valid days are less than or equal to zero, the control slice is prevented from participating in the binding process. This transforms the monitoring of the validity period of the control slice from manual recording to automated dynamic calculation and status determination, overcoming the problem of expired control slices being misused due to paper records or manual experience.

[0018] This invention establishes slice-level quality control records for staining and interpretation results through a quality control data analysis and reporting module. Based on these slice-level quality control records, it calculates quantitative indicators such as repeatability, excellent rate, pass rate, expired usage rate, interpretation consistency rate, and batch drift rate. This transforms quality control evaluation from qualitative description to quantitative analysis based on statistical indicators, overcoming the problem of insufficient quantification of quality control indicators caused by the scattered storage of interpretation results.

[0019] This invention uses a remote sharing module to encapsulate and anonymize quality control reports and slice-level quality control records in a unified format before uploading them to the quality control center. Based on relative deviation and standard score, it calculates multi-center deviation index, enabling cross-sectional comparison of quality control data between laboratories using a standardized deviation analysis method. This overcomes the problem of inconsistent multi-center quality control data formats leading to a lack of standardized analysis methods for cross-sectional comparison. Attached Figure Description

[0020] Figure 1 This is a flowchart of the intelligent management and remote quality control method for multiple categories of positive immunohistochemical controls provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the full-process data flow architecture for immunohistochemistry positive controls provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the standardized processing flow chart for HER2 antibody applications provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the five-dimensional scoring and matching flowchart of the rule engine module provided in an embodiment of the present invention. Detailed Implementation

[0021] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0022] Example 1

[0023] like Figure 1 and Figure 2 As shown, this embodiment provides an intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls. The intelligent management and remote quality control system includes a positive control database module, a slide information communication module, a rule engine module, an early warning sub-module, a quality control data analysis and reporting module, and a remote sharing module.

[0024] The positive control database module is configured to establish a structured database of multiple categories of positive controls and provide unified storage and retrieval services for control data; The positive control database module establishes a main table for positive controls, a table of control slide examples, a table of antibody items, a table of control-antibody mapping, a table of inventory status, and a table of interpretation rules, and enters multiple categories of positive controls into the database. These multiple categories of positive controls include at least amniotic membrane roll controls, lymphatic system controls, ER controls, PR controls, and HER2 controls.

[0025] For each type of control, the positive control database module generates structured fields and writes them to database records. The structured fields must include at least the control number. , comparison categories Organizational Source Applicable antibody sets Positive localization type Expression intensity level Slicing date Storage conditions Default effective save duration and current inventory status .in, A unique identifier for a control slice or control chip; This indicates the control category, used to distinguish between amniotic membrane roll control, lymphatic system control, and specific controls for each antibody. Indicates the source of the control tissue; This indicates the set of antibodies to which the control is applicable; This indicates the subcellular localization type of the positive signal, such as membrane type, cytoplasmic type, or nuclear type; Indicates the level of intensity of expression; Indicates the preparation time of the control section; Indicates the storage conditions for the control slides, such as refrigeration or room temperature; This indicates the effective preservation time of the control slide under the corresponding preservation conditions; This indicates the current inventory status.

[0026] In terms of specific storage content, the positive control database module processes different types of control data according to control category. For amnion roll controls, it stores positive expression information of various normal tissues and their corresponding commonly used antibodies. For lymphatic system controls, it stores the applicable range of antibodies and the distribution of positive cells for tonsillar and thymus tissues. For ER controls, it stores the applicable range of antibodies and nuclear type positive expression information for estrogen receptor-positive tissues. For PR controls, it stores the applicable range of antibodies and nuclear type positive expression information for progesterone receptor-positive tissues. For HER2 controls, a control chip sub-library is established according to four levels: 0, 1+, 2+, and 3+, to adapt to the specific quality control requirements of graded interpretation projects.

[0027] The slice information communication module is configured to collect business slice application information and perform standardized parsing, as well as establish the binding relationship between business slices and control slices and issue staining tasks.

[0028] The slice information communication module performs two-stage data processing: the application information collection and standardized parsing stage, and the binding relationship establishment and task issuance stage.

[0029] During the application information collection and standardized parsing phase, the slide information communication module receives business slide application data from the pathology department information system, LIS, or PIS, and performs standardized parsing on the received business slide application data.

[0030] The parsing process includes the processing of the following sub-modules: The data receiving submodule obtains the original application data from the external system and extracts the case number. Slice number Wax Block Number Antibody Project Purpose of testing Application period Operators and staining platform .in, Indicates a unique identifier for the case; This represents a unique identifier for a business slice; This indicates the unique identifier of the wax block; This indicates the target antibody project for the current business slice request; Indicate the purpose of the test; Indicates the application time; Indicates operator identification; Indicates the number of the staining equipment or platform to be used.

[0031] When different antibody naming methods exist in external systems, the antibody name normalization submodule performs normalization processing on antibody names, mapping synonymous antibody names from different source systems to a unified standard name to ensure consistency in subsequent rule matching.

[0032] When the business slice belongs to a hierarchical interpretation project, the interpretation rule association submodule synchronously writes the corresponding interpretation rule version number. . This indicates the version number of the interpretation rule, which is used to call the unified standard in subsequent interpretation and review processes.

[0033] After parsing, the slice information communication module writes the standardized application record into the queue of tasks to be matched. The standardized application record must include at least... , , , , , , , and .

[0034] During the binding relationship establishment and task issuance phases, the slice information communication module will slice the business. The best comparison slice output by the rules engine module Establish a binding relationship and generate a binding record. The binding record must include at least the business slice number. , control slide number Antibody Project , dyeing batch number Equipment number and issuance time .in, Indicates the target control slice identifier of the best match; Indicates the dyeing batch number; Indicates the dyeing equipment number; This indicates the time when the task is issued to the dyeing equipment.

[0035] The binding relationship can be represented as: ; In practice, business slices and control slices can be bound one-to-one. For multiple business slices from the same batch and the same antibody project, a batch binding method of one set of business slices and one shared control slice can also be used.

[0036] After the binding record is generated, the slide information communication module sends the binding record to the barcode printing device or immunohistochemistry staining instrument. After the staining instrument returns the confirmation message, the slide information communication module updates the task status to "on the machine".

[0037] Furthermore, the slice information communication module and the early warning submodule work together to perform binding control. This is when the early warning submodule outputs the validity period status. When the slice status is "expired," the slice information communication module prevents the control slice from participating in the binding process. (The last sentence appears to be incomplete and possibly refers to a different context.) When the slice is "nearing expiration", the slice information communication module allows the use of the control slice but simultaneously pushes a warning message to the operator.

[0038] The rules engine module is configured to perform intelligent matching of positive controls based on the antibody items in the business slice and the records in the control database; The rules engine module bases its data on antibody projects in the standardized application records. Retrieve candidate control sets from the positive control database module. ,in This represents the total number of candidate controls, and then for each candidate control... Calculate matching score ,in Index indicating candidate controls .

[0039] Among the various sub-indicators involved in the matching score calculation, , and A discrete index taking the value 0 or 1. and For the range of values ​​within The continuous ratio indicators within the range have consistent dimensions for each component, allowing for direct weighted summation.

[0040] The matching score is calculated using the following formula: ; in, Indicates the first Matching scores of candidate controls; Indicates the first The compatibility between candidate controls and target antibodies; The positive localization match is indicated by the preset positive localization type corresponding to the target antibody project and the first... Positive localization types of candidate controls The comparison yields the following results; Indicates the compatibility with the current staining platform, determined by the staining platform. With the The platform compatibility of the candidate controls was obtained; Indicates the validity period and availability; Indicates inventory availability; to Represents the weighting coefficients, satisfying .

[0041] 0.35 is acceptable. 0.20 is acceptable. 0.15 is acceptable. 0.15 is acceptable. 0.15 is acceptable.

[0042] Positive localization matching degree Defined as: ; in, This indicates the preset positive localization type corresponding to the target antibody item, and the value can be one of membrane type, cytoplasmic type or nuclear type; Indicates the first Positive localization type of candidate controls.

[0043] Platform compatibility Defined as: ; in, This indicates the staining platform number specified in the current business slice application. Indicates the first A set of candidate controls is recorded in the database for compatible staining platforms.

[0044] Antibody adaptability Defined as: ; in, Indicates the first The set of applicable antibodies corresponding to each candidate control.

[0045] Validity period and availability Defined as: ; in, Indicates the first The remaining effective days for each candidate control group Indicates the first The effective storage duration of each candidate control is in days, and the ratio is dimensionless. The early warning submodule is for the first The remaining effective days are provided by calculating the number of candidate controls.

[0046] Inventory availability Defined as: ; in, This indicates the number of currently available similar control groups. This indicates the preset reference inventory level. Both are in units of sheets, and the ratio is dimensionless.

[0047] After obtaining the matching scores for each candidate control, the rule engine module sorts them from highest to lowest matching score and selects the control slice with the highest matching score as the optimal control slice. It also outputs a unique identifier for the best control slice. When the optimal control slice is unavailable, the rule engine module automatically selects the second-best candidate control, or triggers a manual confirmation process.

[0048] The early warning submodule is configured to monitor the expiration date of control slides and output early warning information. The early warning submodule reads the slice date of the control slice. Storage conditions and default effective storage time and get the current time Calculate the expiration time of the control slides. and remaining valid days .

[0049] The expiration date is calculated using the following formula: ; The remaining valid days are calculated using the following formula: ; in, Indicates the preparation time of the control section. Indicates the effective storage duration. Indicates the current time. Indicates the expiration date. This indicates the remaining valid days; all the above quantities are in days.

[0050] Validity status The following rules apply: ; in, The warning threshold is set to 7 days. (Based on storage conditions) When stored at room temperature, Less than the effective storage time under refrigeration conditions.

[0051] The early warning submodule will expire at [time]. Remaining valid days and expiration status Output to the slice information communication module for binding near-expiration reminders or expiration blocking in the process. Also, the remaining valid days... and expiration status It is also output to the quality control data analysis and reporting module as the basic data for overdue usage rate statistics. In addition, the early warning submodule analyzes each candidate control retrieved by the rule engine module. Calculate separately ,in The index represents the candidate control, which is used by the rule engine module to calculate the validity period availability. Use when needed.

[0052] The quality control data analysis and reporting module is configured to collect staining results and interpretation results to establish quality control records, and calculate quality control indicators to generate quality control reports; The quality control data analysis and reporting module performs two-stage data processing: the quality control record collection stage and the quality control indicator calculation and report generation stage.

[0053] During the quality control record collection phase, the quality control data analysis and reporting module writes back the staining completion information from the staining equipment. The staining completion information includes at least the completion time. Equipment number reagent batch number and staining procedure number .in, Indicates the time it takes for the staining to complete; Indicates the reagent batch number; This indicates the staining procedure number. After receiving the results from the device, the quality control data analysis and reporting module receives the interpretation results entered by the pathologist or imported from the image system, and establishes a quality control record for each control slide.

[0054] Quality control records should include at least the initial assessment results. Re-judgment results Final result Exception markers and abnormal reasons .in, This indicates the preliminary judgment result; Indicates the result of the retrial; This indicates the final confirmation result; Indicates an anomaly flag; This indicates the reason for the abnormality.

[0055] For HER2 and other graded items, quality control records also include grade values. ,in This indicates the HER2 staining grade, which can be 0, 1+, 2+, or 3+.

[0056] Final result Determined according to the following rules: ; in, This indicates the arbitration result. The initial judgment. and the retrial results When the results are consistent, this result is taken as the final result. When the results are inconsistent, a third-party arbitration or senior pathologist review process is triggered, and the review decision is taken as the final result. Write it into the quality control record.

[0057] In addition, the quality control data analysis and reporting module records the staining intensity score for each control slide. Background cleanliness score Positioning accuracy score and organizational integrity score .in, Indicates the staining intensity score; Indicates the background cleanliness score; This indicates the accuracy score for positioning. This represents the organizational integrity score. The above four scores serve as the direct data source for subsequent calculations of the excellence rate.

[0058] During the quality control indicator calculation and report generation phase, the quality control data analysis and reporting module uses a preset statistical period. Summarize all batch quality control records and binding records, and calculate the repeatability rate, excellent rate, pass rate, expired usage rate, interpretation consistency rate, and batch drift rate. Among these, This indicates the preset statistical period.

[0059] Repetition rate Calculate using the following formula: ; in, This indicates the total number of repeated tests. This indicates the number of times the repeated tests yielded the same result.

[0060] Excellent rate Calculate using the following formula: ; in, This indicates the number of slices that meet the excellent standard. This indicates the total number of slices within the statistical period.

[0061] Excellent standards are determined through a comprehensive quality score. (Comprehensive quality score) Calculate using the following formula: ; in, to Indicates the scoring weight, satisfying ; , , , All scores are given using a standardized percentage system, and no additional normalization is required before weighted summation. At that time, it was judged to be excellent. The threshold for judging excellence is 80 to 90 points.

[0062] pass rate Calculate using the following formula: ; in, This indicates the number of slices that meet the minimum quality control standards.

[0063] Expiration rate Calculate using the following formula: ; in, This indicates the number of times the binding attempt was made after the expiration date, derived from the binding attempt records when the expiration status was "expired" output by the early warning submodule; This represents the total number of bindings, derived from the count of binding records.

[0064] Read consistency rate Calculate using the following formula: ; in, This indicates the total number of cases that entered the review process. This indicates the number of cases where the initial judgment and the re-judgment were consistent. Both are derived from quality control records. and Comparative statistics.

[0065] In a further implementation, the Kappa coefficient can also be used for consistency evaluation: ; in, Represents the Kappa coefficient. Indicates the observational consistency rate. This represents the random consistency rate.

[0066] For graded projects such as HER2, ER, and PR, the quality control data analysis and reporting module calculates the batch drift rate. : ; in, This represents the average intensity value of the current batch. This represents the average strength value of the reference batch; both have the same unit, and the ratio is dimensionless. When At that time, it was determined that this batch had a risk of strength drift. This represents the threshold for determining the drift rate, which can be set to 0.15.

[0067] After calculating the above quality control indicators, the quality control data analysis and reporting module generates a quality control report. The quality control report must include at least the statistical period. Antibody item distribution, control category usage frequency, expiration warning records, overdue interception records, and duplication rate. Excellent rate pass rate Consistency rate of interpretation or Kappa value Batch drift rate A list of abnormal batches and improvement suggestions. Quality control reports are generated separately at the department, project, and equipment levels.

[0068] In the specific processing of the HER2 project, after receiving the HER2 immunohistochemistry request, the quality control data analysis and reporting module retrieves control slide information of different intensity levels (0, 1+, 2+, 3+) from the HER2 control sub-library. After matching by the rule engine module, the binding and staining tasks are completed and distributed. After staining is completed, the quality control data analysis and reporting module records the HER2 staining level. Preliminary judgment results Re-judgment results and final results And statistically analyze the distribution of different levels and the consistency rate of interpretation. and batch drift rate .

[0069] The remote sharing module is configured to perform desensitization and remote transmission of quality control data, and to receive the results of multi-center cross-comparison. The remote sharing module encapsulates quality control reports and slide-level quality control records in a unified data format and performs anonymization processing on the uploaded data to remove sensitive data such as patient identification information. Subsequently, the remote sharing module uploads the encapsulated data to the superior quality control center through a secure channel such as the hospital's intranet, regional intranet, or the Internet.

[0070] The remote sharing module receives the cross-sectional comparison results and deviation analysis results returned by the quality control center. The multi-center deviation index is calculated according to the following formula: ; in, Index indicating the laboratory. Indicates the first The target indicator values ​​for each laboratory This represents the average value of the corresponding indicator across all laboratories. Indicates the first The relative deviation of each laboratory from the overall mean; and The dimensions are the same, but the ratio is dimensionless.

[0071] If a standardized score comparison is required, Z-score standardization can also be used, with the regional average as the benchmark. and regional standard deviation For the target index values ​​of each laboratory Standardization is performed to obtain the standard score deviation value. .in, This represents the average value of the corresponding indicator across all laboratories within the region. This represents the standard deviation of the corresponding indicator across all laboratories within the region. Indicates the first The standard deviation value of each laboratory. When When the threshold is exceeded, it is marked as deviating from the laboratory. or At that time, it was determined to be a significant deviation from the central level.

[0072] The remote sharing module displays the results of multicenter deviation analysis and the feedback results from the central end, and outputs the remotely uploaded data packet, the results of multicenter deviation analysis, and the feedback results from the central end.

[0073] This implementation uses a positive control database module to establish a database record containing structured fields such as control number, control category, applicable antibody set, positive localization type, slide date, and effective storage duration for amniotic membrane roll control, lymphatic system control, ER control, PR control, and HER2 control. This allows multi-category control resources to be stored in a unified data format, thus providing a structured data foundation for subsequent automatic retrieval and matching, overcoming the factors of scattered control information and inconsistent formats under manual management.

[0074] This implementation uses a rule engine module to calculate a matching score based on a weighted score of five dimensions: antibody suitability, positive localization matching degree, platform suitability, shelf-life availability, and inventory availability. The optimal control slice is then selected by sorting the matching scores. This allows the matching process between business slices and control slices to be executed automatically based on a quantitative multi-dimensional scoring criterion, overcoming the lack of a unified quantitative matching criterion when relying on manual experience to select control slices.

[0075] This implementation uses an early warning submodule to calculate the remaining valid days based on the slice date and valid storage duration. When the remaining valid days are less than the early warning threshold, an impending expiration warning is output. When the remaining valid days are less than or equal to zero, the control slice is prevented from participating in the binding process. This transforms the monitoring of the validity period of the control slice from manual recording to automated dynamic calculation and status determination, overcoming the factors that lead to the misuse of expired control slices due to paper records or manual experience.

[0076] This implementation method establishes slice-level quality control records for staining and interpretation results through a quality control data analysis and reporting module. Based on the slice-level quality control records, it calculates quantitative indicators such as repeatability, excellent rate, pass rate, expired usage rate, interpretation consistency rate, and batch drift rate. This transforms quality control evaluation from qualitative description to quantitative analysis based on statistical indicators, overcoming the factor that the scattered storage of staining interpretation results leads to insufficient quantification of quality control indicators.

[0077] This implementation method uses a remote sharing module to encapsulate and anonymize quality control reports and slice-level quality control records in a unified format before uploading them to the quality control center. Based on relative deviation and standard score, a multi-center deviation index is calculated, enabling cross-sectional comparison of quality control data between laboratories using a standardized deviation analysis method. This overcomes the lack of standardized analysis methods for cross-sectional comparison caused by inconsistent formats of multi-center quality control data.

[0078] In one embodiment of the present invention, the above-described system is applied to the following example, in which the background is as follows: The immunohistochemistry laboratory of the pathology department of a tertiary-level Class A hospital (hereinafter referred to as "Laboratory A") undertakes the daily pathological diagnosis of breast cancer. In month A of a certain year, the laboratory received a batch of immunohistochemistry requests for breast cancer surgical specimens, which included multiple antibody projects such as HER2, ER, and PR.

[0079] like Figure 3 and Figure 4As shown, the laboratory currently has a stock of positive control resources including HER2 control chips (containing four levels of sub-libraries from 0 to 3+), ER control slides, and PR control slides, among others. The preparation time and stock quantity of each control slide vary. This application example uses one HER2 antibody project application as the main thread to fully demonstrate the entire data flow from control database retrieval, rule engine matching, expiration date warning, quality control record establishment to multi-center deviation analysis.

[0080] The following actions were performed in the positive control database module: The positive control database module pre-enters the laboratory's existing HER2 control chips into the database, creating structured field records for four levels: 0, 1+, 2+, and 3+. The following example uses three candidate controls matched for this HER2 application to demonstrate the database's stored content.

[0081] Table 1. HER2 candidate control database records

[0082] The following content is executed in the slice information communication module: On the morning of the 22nd of month A, the pathology department information system of Laboratory A initiated a HER2 antibody project application. The data receiving submodule of the slide information communication module extracted the original application fields from the external system, the antibody name normalization submodule mapped the original name "c-erbB-2" from the source system to the standard name "HER2," and the interpretation rule association submodule synchronously wrote the HER2 interpretation rule version number. After standardization and parsing were completed, the application record was written to the pending matching task queue.

[0083] Table 2 Business Slice Standardization Application Record

[0084] After the rule engine module completes the matching, the slice information communication module establishes a binding relationship between the business slice SL-0891 and the optimal control slice HER2-C034, generates a binding record, and sends it to the staining instrument PLT-A.

[0085] ; The following content is executed in the early warning submodule: The early warning submodule uses the 22nd day of month A as the current time. The expiration dates and remaining valid days were calculated for the three candidate controls, and based on... The daily warning threshold determines the validity period.

[0086] The calculation of the expiration date is based on Reference 1 (HER2-C021) as an example: That is, add 30 days to the preparation date of the 3rd day of month A to get the expiration date of the 33rd day of month A (i.e., the 2nd day of month B); remaining valid days sky.

[0087] Table 3 Calculation Results of Candidate Control Validity Period Warning

[0088] Compared with HER2-C047 Heaven, satisfied. The status was determined to be "near expiration". After receiving this status, the slice information communication module allowed HER2-C047 to participate in the binding but sent a warning message to operator OP-017. None of the three comparison slices had an "expired" status, and the binding process did not trigger a block.

[0089] The following content is executed in the rules engine module: The rules engine module standardizes the application records. , As input, retrieve a set of candidate controls from the database. A matching score is calculated for each candidate control. The weights are set as follows: , , , , .

[0090] The calculation explanations for each sub-indicator are as follows. (Three comparison sheets) All contain HER2, therefore The target antibody HER2 is pre-defined as a positive localization type. Cell membrane type, three control images All are of the cell membrane type, therefore Current platform Compare C021 and C047 Includes PLT-A, ; Compare with C034 Contains only PLT-A, also The validity period and availability are provided by the early warning submodule. Calculate and refer to inventory levels open.

[0091] Taking HER2-C034 as an example, its sky, Currently available quantity of the same type. open: ; ; ; Table 4. Calculation results of candidate control matching scores

[0092] The rules engine modules are sorted by score from highest to lowest, HER2-C034. The highest output is the optimal control slice. Its unique identifier The information is transmitted to the slice communication module to complete the binding.

[0093] The following actions are performed in the quality control data analysis and reporting module: After the PLT-A staining instrument completes staining for this batch, it writes back the completion information. The quality control data analysis and reporting module collects the staining completion information and receives the interpretation results entered by the pathologist. A quality control record is established for the control slide HER2-C034. Preliminary judgment results. The result of the retrial Since the two are consistent, the final result is... No arbitration process needs to be triggered.

[0094] After the four scoring items are collected, the overall quality score is calculated. Weighting , , , : ; It was rated as excellent.

[0095] Table 5. Quality control records for control slide HER2-C034

[0096] At the end of the statistical period, the quality control data analysis and reporting module summarizes the quality control records of all batches within this period and calculates the batch drift rate. The current batch average intensity value is also included. Reference batch average strength value : ; This batch was determined to have no intensity drift risk. Quality control reports are generated at three levels: departmental, project, and equipment. The project-level report shows the consistency rate of interpretation for the HER2 item. Excellent rate pass rate Overdue usage rate .

[0097] The following content is executed in the remote sharing module: After the quality control report is generated, the remote sharing module encapsulates the quality control report and slice-level quality control records for the current statistical period in a unified format, removes sensitive data such as patient identification information, and uploads them to the superior quality control center via the regional private network. The quality control center then returns the cross-sectional comparison results of the three participating laboratories within the region. Using the HER2 project interpretation consistency rate as the target indicator, the multicenter bias index of Laboratory A is calculated.

[0098] The HER2 interpretation concordance rates of the three laboratories (Laboratory A, Laboratory B, and Laboratory C) in the region were 94%, 89%, and 91%, respectively, with the regional average being [missing data]. : ; Table 6. Results of Multicenter HER2 Reading Consistency Analysis

[0099] Three laboratories None of the results exceeded the preset threshold. The remote sharing module displayed the deviation analysis results and fed them back to Laboratory A. The multi-center quality control collaborative process for this cycle was completed.

[0100] The entire data flow process exhibits a clear chain logic: the positive control database module provides structured control attribute data, serving as the basic input for the rule engine module's matching calculations; the early warning submodule, based on the data in the database... and Fields calculate each candidate control The system synchronously provides the validity period and availability component to the rule engine module and outputs status control signals to the slice information communication module; the rule engine module outputs the optimal comparison based on the comprehensive five-dimensional score. The slice information communication module generates binding records and drives the staining task to be issued accordingly; the quality control data analysis and reporting module takes the binding records and staining write-back data as input, establishes slice-level quality control records and completes the statistics of various quantitative indicators; the remote sharing module uploads the standardized quality control report to the quality control center to realize the closed-loop output of multi-center deviation analysis.

[0101] Data fields between modules (such as...) , , , , (etc.) are consistently referenced throughout the entire process, ensuring full traceability from control selection to quality control evaluation.

[0102] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. An intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls, characterized in that, include: The positive control database module is configured to create a structured database of multiple categories of positive controls. For each category of control, it generates structured fields including control number, control category, tissue source, applicable antibody set, positive localization type, expression intensity level, slide date, storage conditions, effective storage duration, and inventory status. The slice information communication module is configured to receive business slice application data from external information systems and perform standardized parsing, establish a binding relationship between the business slice and the optimal comparison slice output by the rule engine module, and issue coloring tasks. The rules engine module is configured to retrieve a set of candidate controls from the positive control database module based on the antibody items of the business slice, and to give each candidate control a weighted score based on antibody suitability, positive localization matching degree, platform suitability, shelf life availability and inventory availability, and select the candidate control with the highest score as the optimal control slice output. The early warning submodule is configured to calculate the remaining valid days based on the slice date and valid storage duration of the reference slice, and output the validity status. When the validity status is expired, the reference slice is prevented from participating in the binding process. The quality control data analysis and reporting module is configured to collect staining completion information and interpretation results to establish slide-level quality control records, and calculate quality control indicators according to a preset statistical period to generate quality control reports; The remote sharing module is configured to upload quality control reports and slice-level quality control records to the quality control center after being desensitized and packaged in a unified data format, and to receive the results of cross-center comparisons.

2. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The rule engine module for the first Candidate controls Matching score Calculate using the following formula: ； in, Indicates the first The fit between a candidate control and the target antibody is set to 1 if the target antibody belongs to the set of applicable antibodies for that candidate control, and 0 otherwise. This indicates the positive localization matching degree. It is set to 1 when the preset positive localization type corresponding to the target antibody is consistent with the positive localization type of the candidate control, and 0 otherwise. This indicates the platform compatibility. It is set to 1 if the current staining platform belongs to the set of compatible platforms for the candidate control, and 0 otherwise. The validity period availability is represented by the smaller of the ratio of the remaining valid days of the candidate control to the valid storage time and 1. This indicates inventory availability, which is the smaller of the ratio of the currently available quantity of similar inventory to the preset reference inventory quantity and 1. to Represents the weighting coefficients, satisfying .

3. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The slice information communication module extracts the case number during the standardized parsing phase. Slice number Wax Block Number Antibody Project Purpose of testing Application period Operators and staining platform The antibody name normalization submodule maps synonymous antibody names from different source systems to a unified standard name; when a business slice belongs to a hierarchical interpretation project, the interpretation rule version number is synchronously written. .

4. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The binding record generated by the slice information communication module includes at least the business slice number. , control slide number Antibody Project , dyeing batch number Equipment number and issuance time ;in, The identifier of the target control slice for the best match. Indicates the dyeing batch number. Indicates the dyeing equipment number. This indicates the time when the task is issued to the staining equipment; for multiple business slides of the same batch and the same antibody project, a batch binding method is adopted, in which a group of business slides is bound to a shared control slide.

5. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The early warning submodule calculates the expiration time in the following manner. and remaining valid days : ； ； in, Indicates the preparation time of the control section. Indicates the effective storage duration. Indicates the current time; validity period status. The following rules apply: When This is normal, when It was nearing its expiration date when It has expired. The threshold is set as the warning threshold. When the expiration date is nearing expiration, the slice information communication module allows the use of the control slice but simultaneously pushes warning information to the operator.

6. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The quality control data analysis and reporting module establishes a quality control record for each control slide that includes at least the preliminary judgment result. Retrial results Final result Exception markers and abnormal reasons Final result Determined according to the following rules: ； in, This refers to the arbitration result obtained after review by an arbitrator or a senior pathologist when the initial judgment and the retrial result are inconsistent.

7. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The quality control indicators calculated by the quality control data analysis and reporting module include the repetition rate. Excellent rate pass rate Expiration rate Consistency rate of interpretation and batch drift rate Among them, the excellent rate Based on comprehensive quality score The overall quality score is calculated using the following formula: ； in, Indicates the staining intensity score. Indicates the background cleanliness score. The score indicates the accuracy of the positioning. Indicates organizational integrity score, to Indicates the scoring weight, satisfying ;when Not less than the excellent threshold It was judged to be excellent.

8. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 7, characterized in that, The batch drift rate Calculate using the following formula: ； in, This represents the average intensity value of the current batch. Indicates the average strength value of the reference batch; when Exceeding the drift rate threshold At that time, it was determined that this batch had a risk of strength drift.

9. The intelligent management and remote quality control system for multiple categories of immunohistochemical positive controls according to claim 1, characterized in that, The remote sharing module receives the multi-center deviation analysis results returned by the quality control center, including the multi-center deviation index. Calculate using the following formula: ； in, Index indicating the laboratory. Indicates the first The target indicator values ​​for each laboratory This represents the average value of the corresponding indicator across all laboratories. Indicates the first The relative deviation of each laboratory from the overall mean; when When the threshold is exceeded, it is marked as deviating from the laboratory.

10. A method for intelligent management and remote quality control of immunohistochemical multi-class positive controls, executed by the intelligent management and remote quality control system for immunohistochemical multi-class positive controls as described in any one of claims 1 to 9, characterized in that, Includes the following steps: Step 1: Establish a structured database of multiple categories of positive controls through the positive control database module. For each type of control, generate structured fields containing control number, control category, tissue source, applicable antibody set, positive localization type, expression intensity level, slide date, storage conditions, effective storage duration and inventory status, and write them into the database record. Step 2: Receive business slide application data from external information systems through the slide information communication module, extract case number, slide number, paraffin block number, antibody item, testing purpose, application time, operator and staining platform information, and perform antibody name normalization processing to generate standardized application records; Step 3: The early warning submodule calculates the remaining valid days based on the slice date and valid storage duration of the reference slice, determines the validity period status, and outputs it to the slice information communication module and the rule engine module; Step 4: The rule engine module retrieves the candidate control set from the positive control database module based on the antibody items in the standardized application record. Each candidate control is weighted and scored according to antibody suitability, positive localization matching degree, platform suitability, shelf life availability and inventory availability. The candidate control with the highest score is selected as the optimal control slice output. Step 5: Establish a binding relationship between the business slice and the optimal control slice through the slice information communication module, generate a binding record and send the dyeing task to the dyeing equipment. When the validity period status is expired, prevent the control slice from participating in the binding process. Step 6: Collect staining completion information and interpretation results through the quality control data analysis and reporting module, establish slide-level quality control records, calculate repeatability rate, excellent rate, pass rate, expired usage rate, interpretation consistency rate and batch drift rate according to the preset statistical cycle, and generate a quality control report; Step 7: Upload the quality control report and slice-level quality control records to the quality control center via the remote sharing module after desensitizing and packaging them in a unified data format. Receive and display the multi-center cross-sectional comparison results and deviation analysis results.

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