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

(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it. It includes: 1.Automatically identifying the lymph nodes and the primary tumor. 2.Analyzing MRI images of the lymph node and surrounding area. 3.Using MRI scans before and after chemotherapy to track changes in the lymph node. (II) AI Model for Predicting Lymph Node Metastasis We created an AI model that predicts whether a lymph node in a specific area has cancer. This model uses a combination of the primary tumor's pathology and MRI images of both the tumor and lymph node. It also tracks changes in the lymph node over time. The model includes: 1.Analyzing the tumor's pathology to identify specific lymphatic structures. 2.Using MRI scans to predict the likelihood of metastasis in a single lymph node. 3.Examining MRI scans before and after chemotherapy to help determine if the lymph node has metastasized. (III) Verifying and Analyzing the Benefits of the AI Model We are testing the AI model to see how well it works and its potential benefits, including: 1.Checking if the AI can correct past diagnoses of recurrent lymph nodes in nasopharyngeal cancer, which could help guide treatment plans for radiotherapy. 2.Testing the model using biopsy results from head and neck cancer patients to see if it can accurately detect negative lymph nodes. 3.Running clinical trials to test the AI model's safety and effectiveness in guiding radiation treatment for upper neck and single lymph node areas in nasopharyngeal cancer. 4.Analyzing the economic benefits of using the AI model in radiation treatment for nasopharyngeal cancer.

Sex

All

Inclusion Criteria:

1. The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III);
2. 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.
3. PET/CT scan was performed at the initial diagnosis (before anti-tumor treatment)
4. 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:

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

• BACKGROUNDLiu Y, Lai F, Lin B, Gu Y, Chen L, Chen G, Xiao H, Luo S, Pang Y, Xiong D, Li B, Peng S, Lv W, Alexander EK, Xiao H. Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic study. EClinicalMedicine. 2023 May 18;60:102007. doi: 10.1016/j.eclinm.2023.102007. eCollection 2023 Jun. PMID 37251623
• BACKGROUNDWessels F, Schmitt M, Krieghoff-Henning E, Jutzi T, Worst TS, Waldbillig F, Neuberger M, Maron RC, Steeg M, Gaiser T, Hekler A, Utikal JS, von Kalle C, Frohling S, Michel MS, Nuhn P, Brinker TJ. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer. BJU Int. 2021 Sep;128(3):352-360. doi: 10.1111/bju.15386. Epub 2021 May 5. PMID 33706408
• BACKGROUNDChen C, Zhang Y, Wu X, Shen J. The role of tertiary lymphoid structure and B cells in nasopharyngeal carcinoma: Based on bioinformatics and experimental verification. Transl Oncol. 2024 Mar;41:101885. doi: 10.1016/j.tranon.2024.101885. Epub 2024 Jan 31. PMID 38295746
• BACKGROUNDWibawa MS, Zhou JY, Wang R, Huang YY, Zhan Z, Chen X, Lv X, Young LS, Rajpoot N. AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma. Cancers (Basel). 2023 Dec 10;15(24):5789. doi: 10.3390/cancers15245789. PMID 38136336
• BACKGROUNDHaggstrom I, Leithner D, Alven J, Campanella G, Abusamra M, Zhang H, Chhabra S, Beer L, Haug A, Salles G, Raderer M, Staber PB, Becker A, Hricak H, Fuchs TJ, Schoder H, Mayerhoefer ME. Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. Lancet Digit Health. 2024 Feb;6(2):e114-e125. doi: 10.1016/S2589-7500(23)00203-0. Epub 2023 Dec 21. PMID 38135556
• BACKGROUNDZhong Y, Cai C, Chen T, Gui H, Chen C, Deng J, Yang M, Yu B, Song Y, Wang T, Chen Y, Shi H, Xie D, Chen C, She Y. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study. Eur J Nucl Med Mol Imaging. 2024 Jan;51(2):521-534. doi: 10.1007/s00259-023-06434-7. Epub 2023 Sep 19.