36 results about "Node metastasis" patented technology

This invention provides a method, device, and equipment for preoperative risk stratification assessment of endometrial cancer. It involves segmenting and annotating preoperative ultrasound images to extract radiomics feature vectors, and simultaneously performing structured encoding and mapping of pathological biopsy data to generate pathological feature vectors. These two feature vectors are then input into a fusion network containing a cross-modal attention gating module. This gating module automatically generates dynamic weight vectors based on the consistency between the image and pathological features. The two feature vectors are then weighted and modulated separately before being concatenated to obtain a joint feature representation. This automatically reduces the weight of the lower-confidence modality and amplifies the weight of the higher-confidence modality when their assessment conclusions are inconsistent. Finally, the joint feature representation is input into a multi-task prediction network to output the probability of myometrial invasion depth and the probability of lymph node metastasis risk, thereby generating preoperative risk stratification results and surgical plan recommendations.

The invention discloses a tumor detection marker and application thereof in medicine preparation, and belongs to the technical field of biomedicine. Clinical sample qRT-PCR (quantitative real-time polymerase chain reaction) shows that ENST00000412203.1 is obviously up-regulated in breast cancer tissues, the area (AUC) under the ROC curve reaches 0.9822, and the diagnosis efficiency is excellent. In addition, the invention further finds that the expression of the polypeptide in lymph node metastasis tissues is obviously improved, and metastasis specificity is displayed. Meanwhile, the expression of ENST00000412203.1 is knocked down through siRNA, it is found that proliferation, migration and invasion of breast cancer cells are remarkably inhibited, and therefore the siRNA can be used for preparing targeted therapy drugs for breast cancer and has important clinical application value.

The invention discloses application of an IGF2BP2-m6A-VCAN-TLR2 (Insulin-like Growth Factor 2BP2-m6A-VCAN-TLR2) signal axis in preparation of a medicine for treating lymphatic metastasis of intrahepatic cholangiocarcinoma. Aiming at the problems of unknown lymphatic metastasis mechanism and lack of effective targets of intrahepatic cholangiocarcinoma, the invention discovers and verifies that mRNA binding protein 2 of insulin-like growth factor 2 is modified by m6A to regulate and control the expression of pluripotent proteoglycan so as to activate a Toll-like receptor 2 signal, promote macrophages to secrete vascular endothelial growth factor C and drive a brand new pathway of lymphatic metastasis. Based on this, the invention provides an inhibitor targeting the signal axis, especially a small molecule compound 8010-8498. Experiments show that the compound can significantly inhibit migration, invasion and epithelial-mesenchymal transition of bile duct cancer cells, reduce macrophage secretion of vascular endothelial growth factors C, effectively inhibit tumor growth and lymphatic metastasis in a nude mouse popliteal lymph node metastasis model and a spontaneous bile duct cancer model, and present a synergistic effect when combined with gemcitabine and cis-platinum.

The invention provides a breast cancer axillary lymph node metastasis load state prediction method and system based on cross-domain ultrasonic data, and relates to the technical field of image processing, and the method comprises the steps: obtaining a training set, training a constructed breast cancer axillary lymph node metastasis load state prediction model through the training set, and obtaining a breast cancer axillary lymph node metastasis load state prediction model; obtaining a trained breast cancer axillary lymph node metastasis load state prediction model; and inputting the ultrasonic images of the breast primary focus area and the axillary lymph node area of a patient to be predicted into the trained breast cancer axillary lymph node metastasis load state prediction model to obtain a breast cancer axillary lymph node metastasis load state fine prediction result. According to the method, the breast cancer axillary lymph node metastasis load state prediction model can be utilized to effectively extract and fuse the features of the cross-domain ultrasonic data, and the fused information is classified and corrected to obtain a fine prediction result of the breast cancer axillary lymph node metastasis load state.

The invention discloses application of a NOD1 truncated splicing isomer as a marker and a target spot in preparation of a thyroid papillary minimal cancer detection reagent, an immune targeting drug and a screening method, and belongs to the technical field of biological medicine. Aiming at the problem of clinical diagnosis and treatment difficulty caused by lack of an effective lymph node metastasis prediction marker for papillary thyroid carcinoma, the invention discovers that a truncated splicing isomer NOD1-S can be used as a key transcription marker. According to the method, the transcriptional instinct of NOD1-S is specifically amplified by a specific primer pair, and a detection reagent or a kit can be prepared on the basis of the transcriptional instinct of NOD1-S; meanwhile, the NOD1-S can be used as a drug target for screening or preparing immune targeting drugs. The kit is mainly used for auxiliary detection of thyroid papillary minimal carcinoma lymph node metastasis and research and development and screening of immune targeting drugs.

The invention discloses a mediastinal region lymph node metastasis identification method and system based on a double-control routing hybrid expert model, and belongs to the technical field of computer intelligent auxiliary diagnosis. The invention aims to solve the problems that the existing mediastinal region lymph node metastasis identification method is low in accuracy and poor in identification effect due to the fact that the mediastinal region lymph node metastasis identification task is complicated and includes multi-dimensional information identification. According to the technical key points, a mediastinal region lymph node metastasis identification task is split into two auxiliary sub-tasks of mediastinal region detection and lymph node metastasis prediction, and the three tasks jointly form a multi-task learning system. Knowledge transmission and complementation are realized through feature sharing between tasks and gradient-based collaborative optimization, so that the feature distinguishability of each task is improved. The method is characterized in that a double-control routing gating mechanism is constructed, similar features and unique features between different tasks are extracted through a feature routing branch and a task routing branch respectively, and correlation and difference between the tasks are balanced; meanwhile, a two-dimensional gradient balance algorithm is designed, multi-task gradient optimization is carried out from two angles of gradient direction alignment and amplitude dynamic balance, and the problems of gradient conflicts and task dominance are solved while gradient feature distribution between tasks is kept.

The application belongs to the technical field of biomedicine, and specifically discloses a gene marker of melanoma and application thereof. The gene marker is a PDP1 gene, and the application proves the correlation between the PDP1 gene and melanoma. The expression amount of the PDP1 gene in melanoma cells is significantly higher than that in normal melanocytes. The PDP1 expression is positively correlated with the pathological stage and the lymph node metastasis condition N value. The PDP1 gene influences the cell proliferation, cell apoptosis, cell migration and cell invasion of melanocytes. Therefore, the PDP1 gene can be used as a potential target for diagnosing and treating melanoma. By detecting the mRNA or protein expression level of the PDP1 gene, melanoma can be detected, and the application has good application value.

The application discloses an IGF2BP2-m6A-VCAN-TLR2 signal axis in the preparation of a drug for treating intrahepatic cholangiocarcinoma lymphatic metastasis. In view of the problems of unknown intrahepatic cholangiocarcinoma lymphatic metastasis mechanism and lack of effective target, the application finds and verifies a new pathway that from insulin-like growth factor 2 mRNA binding protein 2, m6A modification regulates the expression of multi-functional proteoglycan, and then activates Toll-like receptor 2 signal, promotes macrophage secretion of vascular endothelial growth factor C and drives lymphatic metastasis. Based on this, the application provides an inhibitor targeting the signal axis, in particular, a small molecule compound 8010-8498. Experiments show that the compound can significantly inhibit cholangiocarcinoma cell migration, invasion and epithelial mesenchymal transition, reduce macrophage secretion of vascular endothelial growth factor C, and effectively inhibit tumor growth and lymphatic metastasis in a naked mouse popliteal lymph node metastasis model and a spontaneous cholangiocarcinoma model. The compound presents a synergistic effect in combination with gemcitabine and cisplatin.

The invention discloses a deep learning-based method and system for predicting lymph node metastasis of esophageal squamous carcinoma at T1 stage. The method specifically comprises the following steps: collecting WSI data of an esophageal carcinoma patient; classifying the collected data; dividing each piece of WSI data into a plurality of image blocks with the same size, and preprocessing the image blocks; constructing the image blocks of each piece of WSI data into a graph structure; inputting the graph structure into a neural network model, and training the neural network model; the trained neural network model is used for evaluating the lymph node metastasis probability of the esophageal squamous carcinoma in the T1 stage. According to the invention, the AUC in an internal test reaches 0.949, and the accuracy rate of external verification reaches 90.1%.

The invention discloses a gene marker combination, a kit and a system for breast cancer lymph node metastasis risk assessment, and belongs to the field of precise medical big data analysis. The gene marker combination is composed of 18 genes, namely ACE2, BPIFB1, CLDN19, CT83, FABP7, FGFBP1, KLK7, KLK8, OCA2, PGLYRP4, PI3, PICSAR, PRSS33, ROPN1, ROPN1B, SCRG1, SERPINA11 and SRARP in total. Based on expression data of the combination, a brand new breast cancer lymph node metastasis risk assessment system (MRA18) is constructed, and breast cancer samples can be divided into eight molecular subtypes with different lymph node metastasis risks. Experiments show that the typing method has extremely strong category distinguishing ability (macro AUC score: 0.981 + / -0.004) and steady comprehensive classification performance (balance accuracy: 0.769 + / -0.027), and survival curves and lymph node metastasis risk grade distribution between different molecular subtypes have significant differences. The invention further provides a corresponding detection kit and an intelligent metastasis risk assessment system integrated with the random forest classification model, and an effective auxiliary diagnosis tool is provided for accurate assessment of the preoperative lymph node metastasis risk of the breast cancer patient and formulation of an individualized operation scheme.

The invention belongs to the technical field of biomedical engineering and near-infrared molecular imaging, and relates to a method for treating lung cancer based on a VEGFR3 targeted near-infrared diagnosis and treatment polypeptide skeleton platform and application of the method, a probe takes polypeptide with the sequence of SLIGHGRGRRVR-GSG-CGLARGRGC as a core structure, SLIGHGRGRRVR (TMVP1448) is used as a cell-penetrating peptide module, and the VEGFR3 targeted near-infrared diagnosis and treatment polypeptide skeleton platform is used as the core structure. The uptake and tissue penetration of the probe in lung cancer cells and lung tissues are remarkably improved; gSG serves as a flexible connecting peptide and is used for spatially separating functional fragments and weakening interference of upstream labeling groups on receptor binding conformation; the CGLARGRGC is a navigation peptide module with stable conformation through a disulfide bond, can recognize VEGFR3 in a high-affinity manner, and realizes active targeting enrichment of high-expression lung cancer lesions and lymph node metastasis lesions of VEGFR3. The preparation method comprises the following steps: further grafting a croconaine near-infrared dye Croc which is self-synthesized by the applicant at an N end of a polypeptide skeleton, so as to obtain a polypeptide probe with a structure Croc-SLIGHGRGRRVR-GSG-CGLARGRGC.

The present application relates to the medical health technical field, disclose a kind of lung cancer occult lymph node metastasis prediction method and device based on multimodal fusion, comprising: obtaining medical image data and clinical text data as original multimodal data;The original multimodal data is pre-trained with multimodal consistency, and robust multiscale fusion feature is obtained;Cross-modal prediction fine-tuning is carried out to the robust multiscale fusion feature, and cross-modal semantic alignment feature is obtained;The statistical morphological features and spatial geometric features of lung nodule are extracted by double-branch parallel, the statistical morphological features and spatial geometric features are converted into high-dimensional abstract features by depth feature mapping, and multidimensional supplementary features are constructed;The cross-modal semantic alignment feature is combined with the multidimensional supplementary feature, and the lung cancer occult lymph node metastasis prediction result is obtained by multiscale prediction decoding processing.

The invention discloses application of a substance for inhibiting polyamine synthesis in preparation of a medicine for treating or relieving endometrial cancer. According to experimental verification of an endometrial cancer cell model and an animal model, it is found that high-fat culture promotes rise of endometrial cancer cell ODC1 and accumulation of polyamine metabolites; mouse serum polyamine metabolites can be up-regulated by feeding the bilateral ovariectomy mouse with high fat; oA-induced polyamine metabolite accumulation and invasion and migration of endometrial cancer cells can be inhibited by knocking down the ODC1; in an animal model, ODC1 is knocked out, and transplantation tumor growth and lymph node metastasis promoted by high fat can also be inhibited by applying an ODC1 inhibitor DFMO. The DFMO can improve the sensitivity of endometrial cancer patients which are resistant to chemotherapeutic drugs. The invention finds that the serum polyamine can be used as a target for developing a medicine for treating or relieving endometrial cancer, and can also be used as a marker for grouping endometrial cancer patients of mammals.

This invention relates to the field of animal model construction technology, and in particular to a method and application for constructing a mouse model of cholangiocarcinoma lymph node metastasis based on SOX18 gene overexpression. This invention provides the application of the SOX18 gene in constructing a mouse model of cholangiocarcinoma lymph node metastasis, and based on this, provides a method for constructing a mouse model of cholangiocarcinoma lymph node metastasis based on SOX18 overexpression. The method provided by this invention can induce lymph node metastasis in cholangiocarcinoma cells with 100% success rate (n=5). Moreover, the model obtained using this method is stable and reliable, and can be used for research on the mechanism of cholangiocarcinoma lymph node metastasis and screening of potential therapeutic targets, demonstrating significant innovation and clinical application value.

The application belongs to the field of biomedicine and relates to application of reagents and kits for detecting tsRNA for colon cancer diagnosis. The application relates to application of reagents for detecting tsRNA for colon cancer diagnosis in preparation of products for detecting or predicting colon cancer, wherein the sequence of the tsRNA for colon cancer diagnosis is GTAGAATTCTCGCCTGCCACG. The application separates a tsRNA through a large number of experiments, and through verification, the expression level of the tsRNA in colon cancer patients is obviously higher than that in healthy people, and a higher level of exosome tRF-21-S2I7L7P50 is significantly related to patient T stage, lymph node metastasis and TNM stage characteristics, the AUC value of the ROC curve of serum tRF-21-S2I7L7P50 in colon cancer diagnosis is 0.870, and the tRF-21-S2I7L7P50 is expected to become a new type of biological marker for colon cancer screening and individualized treatment, and provides a new direction for colon cancer treatment.

This invention discloses the use of FXR inhibitors in the preparation of drugs for the prevention and / or treatment of lung cancer lymph node metastasis, wherein the FXR inhibitor is Z-Guggulsterone or DY268. The drug comprises a pharmaceutically acceptable carrier and an FXR inhibitor. This invention corrects the pathological remodeling of high endothelial venules (HEVs) within draining lymph nodes by blocking the “FXR-HEV-MDSC” signaling axis with an FXR inhibitor, thereby reducing myeloid-derived suppressor cell (MDSC) recruitment and inhibiting lung cancer lymph node metastasis.

The application belongs to the field of biological medicine, and discloses application of YTHDF3 in preparation of products for diagnosing and treating cervical cancer. Specifically disclosed are: application of YTHDF3 as a biomarker in preparation of reagents for diagnosing cervical cancer and lymph node metastasis of cervical cancer; application of YTHDF3 protein in preparation of a prognostic evaluation kit for cervical cancer, the kit taking the above protein as a prognostic evaluation marker; a drug for treating cervical cancer, including any one of a drug capable of specifically inhibiting SREBF1 gene or protein, a drug capable of specifically inhibiting YTHDF3 gene or protein, a drug for inhibiting translation of an m6A modified transcript of LRP6, and a drug for inhibiting proliferation, migration and invasion of cervical cancer cells. The above products are used for diagnosing and treating cervical cancer, and have good specificity. In addition, YTHDF3 can promote lymph node metastasis of cervical cancer, so products taking the molecule as a marker can effectively treat cervical cancer with lymph node metastasis.

The invention belongs to the technical field of biology, and provides a melanoma biomarker METTL9 gene and application thereof. The result of the embodiment of the invention shows that the METTL9 gene is highly expressed in melanoma cells and patient tissues, and the expression level of the METTL9 is in positive correlation with lymph node metastasis of melanoma patients. By knocking down the METTL9, the progress of melanoma cells can be regulated and controlled through BCKDHA, proliferation, migration and invasion of the cells are inhibited, and the anti-tumor effect is achieved. Furthermore, the METTL9 gene, as a potential target of melanoma, can be applied to tumor treatment, tumor diagnosis and tumor prognosis evaluation, and provides a new thought and a new strategy for treatment-related drugs and diagnosis products.

The present invention relates to a use of a biomarker in the metastasis risk diagnosis and treatment of a tumor lymph node, and specifically relates to a use of specific recognition of a biomarker SPP1+TAM in combination with an existing N0-N3 lymph node metastasis classification system in the clinical metastasis risk diagnosis and treatment of a tumor lymph node. By means of systematical analysis on metastatic lymph nodes and non-metastatic lymph nodes of a variety of tumor patients, it is discovered that SPP1+TAM is associated with a N0-N3 staging system for lymph node metastasis in the tumor lymph node metastasis risk diagnosis. Provided is a clinical application of SPP1+TAM in combination with an N0-N3 staging system in the assisted or alternative metastasis risk diagnosis and guided treatment of a tumor lymph node. Also provided is a corresponding kit for the tumor lymph node metastasis risk diagnosis.

The invention discloses an hsa-miR-185-5p primer group, a kit and a method for PTC (Positive Temperature Coefficient) metastasis risk detection in a high-iodine environment, and belongs to the technical field of in-vitro auxiliary evaluation of tumor molecules. The primer group comprises hsa-miR-185-5p, a nucleotide sequence of the hsa-miR-185-5p is as shown in SEQ ID NO. 1; the nucleotide sequence of the U6 upstream primer is as shown in SEQ ID NO. 2; the nucleotide sequence of the U6 downstream primer is as shown in SEQ ID NO. 3; and the nucleotide sequence of the miRNA reverse universal downstream primer is as shown in SEQ ID NO. 4. The invention proves that high iodine is an independent risk factor of PTC lymph node metastasis; it is found that the serum exosome miR-185-5p under high iodine is a PTC LNM specific marker; a detection kit containing an autonomously designed primer group and a standardized detection process are constructed, the kit is clinically adapted, and a non-invasive and accurate auxiliary detection tool is provided for metastasis risk stratification of PTC subjects in high-iodine regions.

The invention relates to the technical field of medical biological detection, and provides an ARHGDIB lactylation detection kit applied to diagnosis of bladder cancer lymph node metastasis and prediction of chemotherapy curative effect, and the ARHGDIB lactylation detection kit comprises a first antibody, a second antibody, a third antibody, a fourth antibody and a fifth antibody, the second antibody is goat anti-rabbit IgG marked by TYR-570; an antigen repair liquid; a confining liquid; a cell nucleus counterstaining agent; an anti-fluorescence quenching mounting medium; a positive reference; a negative reference; and 10 * phosphate buffer (10 * PBST). The invention also provides a use method of the ARHGDIB lactylation detection kit for diagnosing bladder cancer lymph node metastasis and predicting the curative effect of chemotherapy, lymph node metastasis and chemotherapy prognosis can be evaluated by using a bladder cancer first operation specimen, imaging lymph node staging is assisted before a whole bladder radical operation, and guidance is provided for subsequent neoadjuvant chemotherapy.

This invention belongs to the field of biotechnology and molecular diagnostics. Addressing the limitations of existing technologies in gastric cancer diagnosis, such as complex procedures, low patient compliance, and high costs, this invention proposes a circRNA biomarker for gastric cancer diagnosis and its application. The circRNA biomarker of this invention is hsa_circ_0001185. Quantitative analysis of hsa_circ_0001185 confirms its significant high expression in the serum of gastric cancer patients and its close correlation with clinical parameters such as tumor differentiation degree, lymph node metastasis, and TNM stage. It can significantly distinguish between gastric cancer patients, gastritis patients, and healthy individuals, exhibiting high sensitivity and specificity, thus providing a new solution for gastric cancer screening and auxiliary diagnosis.

A method is provided for predicting whether a melanoma patient has a single lymph node metastasis or multiple metastases. RNA data can be received from tumor-involved lymph nodes of patients confirmed to have single or multiple metastases. The data between these groups can be compared to identify a set of differentially expressed genetic features independently associated with either single or multiple metastatic nodes. A machine learning model can be trained on the identified features to predict whether a new lymph node specimen is from a patient with single metastasis. The model can be used for generating personalized lymphadenectomy treatment recommendations.

The invention discloses a breast cancer axillary lymph node metastasis risk prediction method and system, equipment and a medium, and relates to the technical field of deep learning and medical image processing. The method comprises the following steps: acquiring structured clinical pathology data and multi-modal ultrasonic image data of a patient; carrying out missing state coding on the structured clinical pathology data to generate structured representation; inputting the structured representation into the super network to generate a dynamic parameter set; the method comprises the following steps of: generating basic image representation from multi-modal ultrasonic image data through a dynamic self-adaptive aggregation mechanism, extracting shallow layer characteristics for existing modals by utilizing a real image, and occupying positions for missing modals by adopting learnable baseline characteristics; and performing deep feature modulation on the basic image representation according to the dynamic parameter set, and outputting a transfer risk prediction probability. The method can be stably operated under the condition that the ultrasonic mode is incomplete, deep modulation of image feature extraction by structured clinical prior is realized, and prediction accuracy and clinical applicability are improved.

The application discloses application of a substance for inhibiting FAM111B protein activity and / or expression quantity in prevention of non-small cell lung cancer. The substance for inhibiting FAM111B protein activity and / or expression quantity is z1) or z2): z1) an RNAi molecule shown in SEQ ID NO:1; and z2) an shRNA synthesized by an shRNA expression system and taking the RNAi molecule as a target. Experiments prove that FAM111B is significantly highly expressed in non-small cell lung cancer tissues, and is obviously related to gender, histological type, tumor size, stage, and lymph node metastasis; FAM111B enhances the anti-apoptosis ability of non-small cell lung cancer cells by participating in a p53-regulated mitochondrial apoptosis pathway; and inhibition of the activity and / or expression quantity of FAM111B in non-small cell lung cancer cells can promote apoptosis of the non-small cell lung cancer cells, thereby preventing and / or treating non-small cell lung cancer. The application has important application value.

The present disclosure provides a fluorescent contrast agent with a targeting function, and a preparation method and a use thereof, and belongs to the technical fields of nanomaterials and biomedical materials. The fluorescent contrast agent (MR780 NPs) of the present disclosure can specifically bind to CD206 on a surface of tumor-associated macrophages (TAMs). MR780 NPs accumulate in lymph nodes invaded by tumor cells and undergo an oxidation-reduction reaction with reduced glutathione in a tumor microenvironment, which triggers a fluorescence signal of MR780 NPs; and MR780 NPs do not accumulate and do not show fluorescence in normal lymph nodes. Therefore, the fluorescent contrast agent of the present disclosure can be used to diagnose lymph node metastasis (LNM) of breast cancer, realize the preoperative evaluation of LNM, assist in the clinical determination of tumor staging and the formulation of a surgical plan, and achieve the accurate resection under intraoperative fluorescence navigation.

The invention discloses a colorectal cancer lymph node metastasis condition prediction method and device, a storage medium and equipment. A full-slice image and an enhanced CT image of a patient are acquired; extracting WSI original features from the full-slice image; extracting radiomics original features from the enhanced CT image; mapping the WSI original features and the radiomics original features to the same feature dimension; guiding and updating the mapped WSI features through the mapped radiomics features by using a collaborative attention mechanism to obtain new WSI features fused with radiomics information, and aggregating the new WSI features into feature embedding representation; aggregating the original WSI features to obtain WSI aggregated features; splicing the feature embedding representation and the WSI aggregation feature to obtain a composite feature; and inputting the composite features into a classifier to obtain a prediction result of the lymph node metastasis condition. The colorectal cancer lymph node metastasis condition can be accurately predicted by fully utilizing the pathological graph and enhancing the characteristics in the CT image.

The application provides a Chinese lung large cell neuroendocrine carcinoma cell line with high lymph node metastasis potential and an application, and the preservation number of the cell line is CCTCC NO: C2024277. The cell line of the application is a lung large cell neuroendocrine carcinoma cell line which is rare in the histopathological type of lung cancer, has no homology with any human lung cancer cell line of other histopathological types, and has an abnormal karyotype. The cell line of the application can be continuously subcultured in vitro while keeping the biological characteristics of lung large cell neuroendocrine carcinoma tumor cells unchanged; has an in-vitro unlimited proliferation capacity, a good invasion and migration capacity, can induce subcutaneous tumor formation in mice, has a strong popliteal lymph node metastasis capacity in mice, and is suitable for being used as an in-vitro cell platform and an animal model cell line for researching the molecular mechanism of the occurrence, development and metastasis of the rare lung large cell neuroendocrine carcinoma and related diagnosis, treatment and drug research.

This invention relates to the field of medical data processing technology, specifically to a method and system for constructing a cervical metastasis prediction model for T1 stage oral squamous cell carcinoma. Addressing the problems of existing technologies, such as the difficulty in accurately assessing the risk of occult lymph node metastasis in T1 stage patients preoperatively, the decreased discrimination and calibration of existing models due to mixed staging, and the lack of intuitive and usable tools, this application constructs a cervical metastasis prediction model for T1 stage oral squamous cell carcinoma. The specific construction method is as follows: Obtaining the subject's characteristic data; using the occurrence of cervical lymph node metastasis as the dependent variable, performing univariate logistic regression analysis on the independent variables of the characteristic data to determine candidate risk factors; performing multivariate logistic regression analysis on the obtained candidate risk factors to screen independent predictive factors; constructing a nomogram based on the independent predictive factors, and predicting the risk of cervical lymph node metastasis in T1 stage oral squamous cell carcinoma patients based on the nomogram.