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Pulmonary nodule database and prediction model construction method and system based on column diagram

A technology of prediction model and construction method, applied in the field of data analysis, which can solve the problems of patient physical injury, complex and diverse causes of nodules, and economic burden on patients, so as to reduce overdiagnosis and treatment, rapid and reasonable clinical management, and improve treatment status. Effect

Pending Publication Date: 2021-08-06
ZHENGZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, LDCT has some unavoidable shortcomings. LDCT can find nodules in 25% of high-risk groups. After further inspection and tracking, only 4% of them are finally diagnosed as lung cancer, and the false positive rate is as high as 96%. The screening indicators for high-risk groups are strict. Only 30% of all lung cancer patients meet the high-risk group screening criteria of LDCT, and most lung cancer patients cannot be found by LDCT screening
[0003] Through the above analysis, the problems and defects of the existing technology are: the false positive rate of LDCT in lung cancer screening is high, and the sensitivity and specificity of tumor markers alone are not enough; , do not meet the indications for puncture and bronchoscopic biopsy, surgical exploration may be required to confirm the diagnosis, which brings a huge economic burden to patients; for patients with pulmonary nodules that cannot be judged as benign or malignant, regular LDCT testing is required , long-term follow-up, the accumulation of radiation may cause damage to the patient's body, and it will also cause a great psychological burden on the patient
[0004] The difficulty in solving the above problems and defects is: patients have different physiques, and the causes of nodules are complex and diverse. At present, no diagnostic methods and measures that can effectively distinguish benign and malignant pulmonary nodules and cause less damage to patients have been found; although there are many tumor markers It has been used clinically, but for the screening of pulmonary nodules, there is no effective tumor marker to clearly distinguish benign and malignant pulmonary nodules; for the follow-up of indeterminate pulmonary nodules, due to the long follow-up period, patients face economic and psychological burdens , pulmonary nodules are not effectively monitored and actively treated

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  • Pulmonary nodule database and prediction model construction method and system based on column diagram
  • Pulmonary nodule database and prediction model construction method and system based on column diagram
  • Pulmonary nodule database and prediction model construction method and system based on column diagram

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Embodiment 1

[0060] The flow chart of the present invention is figure 1 As shown, by collecting 363 cases of LDCT feature information (containing 14 variable information) and serum CEA level detection results, the collected 15 variable information is analyzed for the collection of 15 variable information. And perform model build. In addition, 129 patients with 129 patients with malignant pulmonary tuberculosis were collected by LDCT information and serum CEA level results, and the robustness of model was further verified.

[0061] 1) Collect the medical record information and screening model indicators. Collect the clinical information, LDCT feature information and serum CEA level information of 363 patients with malignant pulmonary nodules of a triply hospital, and construct a good malignant lung nodubit database. The LDCT detection information includes 14 characteristic indicators, namely the number of nodules, nodules, nodules, nodules, nodule edges, empty prizes, glitch, angiogenesis, divi...

Embodiment 2

[0075] 1. Collection of serum specimens clinical information

[0076] The present invention is included in 492 cases of studies, divided into test groups and verification groups, and test groups for screening LDCT indicators with high value of good malignant tuberculosis, and construct a Logistic regression model. The verification group verifies the Logistic regression model.

[0077] The 363 patients with malignant tuberculosis included in the test group came from the lung nailing patients in Henan Province, 2016-2018. The 129 patients with lung nodules included in the verification group came from 2019-2020, diagnosis of lung nodules in the same hospital. All cases were diagnosed by histopathology, and patients with malignant lung nodules did not pass any surgical and chemotherapy. The specific medical record information is shown in Table 1.

[0078] 2. Screening and identification of LDCT indicators

[0079] A total of 14 LDCT variables (Number of nodules, nodules, nodules, nodu...

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Abstract

The invention belongs to the technical field of data analysis, and discloses a method and a system for constructing a pulmonary nodule database and a prediction model based on a column diagram. The method comprises the steps of collecting LDCT features and clinical related information of benign and malignant pulmonary nodule patients with clear pathological diagnosis to construct a benign and malignant pulmonary nodule database; incorporating a plurality of LDCT indexes and serum CEA levels into a Logistic regression model by adopting an entry method, and forming a pulmonary nodule malignant prediction model; and performing external verification on the constructed pulmonary nodule malignant prediction model. The invention provides an effective and practical malignant pulmonary nodule prediction model jointly applying the LDCT index and the serum CEA level, the malignant probability of the pulmonary nodule can be accurately predicted, and a basis is provided for rapid and reasonable clinical management and effective clinical treatment of a pulmonary nodule patient. The method overcomes the problems of high LDCT false positive rate in lung cancer screening and insufficient sensitivity and specificity in individual application of tumor markers.

Description

Technical field [0001] The present invention belongs to the field of data analysis, and in particular, the present invention relates to a line of lung nodules database and a predictive model, a system, terminal, and storage medium. Background technique [0002] At present, lung cancer is a malignant tumor having a global morbidity and mortality. The incidence and mortality rate of lung cancer are also first. Due to the lack of typical clinical symptoms, only non-specific symptoms such as cough, sputum, chest pain, fever, physical strength. Most lung cancer patients have diagnosed more in the late stage and lose their best time. Early lung cancer is hidden and diversified, clinical characteristics and signs are not obvious, and nearly 60% of patients have been in advanced period of lung cancer. Studies have shown that the five-year survival rate of early lung cancer reached 70%, while the five-year survival rate of late lung cancer was only 16%. With the improvement of people's li...

Claims

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Application Information

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IPC IPC(8): G16H50/50G16H50/70G16H50/30G06F17/18
CPCG16H50/50G16H50/70G16H50/30G06F17/18Y02A90/10
Inventor 代丽萍刘曼王猛周志刚孙慧芳欧阳松云赵春玲
Owner ZHENGZHOU UNIV
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