A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)

(I) AI Model for Assisting Diagnosis of Lymph Node Metastasis in Nasopharyngeal Carcinoma Based on MRI Features of the Lymph Node and Surrounding 3mm Area.

An AI model is developed to assist in diagnosing whether a single lymph node in nasopharyngeal carcinoma has metastasized, based on the MRI features of the lymph node itself and its surrounding 3mm area. Specifically, the model includes: 1.Automatic segmentation of the lymph node and primary lesion using a semi-supervised AI model. 2.Construction of a single dual-view AI model based on baseline MRI features of the lymph node itself and its surrounding 3mm area. 3.Development of a dual-time series dual-view AI model based on MRI features of the lymph nodes before and after induction chemotherapy.

(II) Multimodal AI Model for Predicting Lymph Node Metastasis. A multimodal AI model is constructed to predict metastasis of a single station lymph node and diagnose lymph node metastasis "from surface to point." This is based on pathological features of the nasopharyngeal primary lesion, MRI images, lymph node location, and other factors. Specifically, the model includes: 1.Construction of an AI model based on H&E stained digital pathology of the primary lesion to extract features of the tertiary lymphatic structure. 2.Development of a multimodal AI model integrating pathological features of the nasopharyngeal primary lesion and baseline MRI images of both the primary lesion and lymph node to predict the probability of lymph node metastasis in a single station. 3.Construction of a single or dual-time series multimodal AI model using the probability of lymph node metastasis and baseline MRI or two MRI scans before and after induction chemotherapy to diagnose whether a single lymph node in the station has metastasized.

(III) Verification and Economic Benefit Analysis of AI Models. The AI models are subjected to thorough verification and economic benefit analysis. Specifically, the process includes: 1.Retrospective correction of historical diagnoses of in situ recurrent lymph nodes in nasopharyngeal carcinoma patients using the AI model to verify its potential benefits in guiding prescription doses for single lymph node radiotherapy. 2.Validation of the AI model using pathological results from lymph node dissection in head and neck squamous cell carcinoma patients to assess the detection rate of clinically diagnosed negative lymph nodes. 3.Prospective clinical trials to evaluate the effectiveness and safety of the AI model in guiding prescription doses for upper neck radiotherapy and single lymph node radiotherapy in nasopharyngeal carcinoma patients. 4.Economic benefit analysis to illustrate the economic value of the AI model in guiding upper neck radiotherapy for nasopharyngeal carcinoma.

Inclusion criteria

• The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III);
• MRI scan was performed at the initial diagnosis (before anti-tumor treatment), and transverse and coronal MRI images before treatment were available, including T1-weighted, T2-weighted and T1-enhanced scanning sequences.
• PET/CT scan was performed at the initial diagnosis (before anti-tumor treatment)
• When MRI and PET/CT were inconsistent in judging the benign or malignant nature of lymph nodes, the patient agreed to undergo cervical lymph node puncture and pathological examination.

Exclusion criteria

• The patient has undergone cervical lymph node radiotherapy for any reason
• Combined with other malignant tumors