A Bidirectional Study of Individualized Postoperative Adjuvant Treatment Decision Model for Locally Advanced Head and Neck Squamous Cell Carcinoma Based on Multimodal Dynamic Data
A Bidirectional Study of Individualized Postoperative Adjuvant Treatment Decision Model for Locally Advanced Head and Neck Squamous Cell Carcinoma Based on Multimodal Dynamic Data
Based on multimodal data, the investigators establish a dynamic deep learning model to conduct prognostic risk assessment for patients and recommend the 'most suitable' treatment/follow-up regimen after radical treatment, assisting clinicians in improving homogenized evaluation levels and achieving individualized precision therapy, thereby providing scientific evidence for the currently widely debated selection of postoperative adjuvant therapy in locally advanced head and neck squamous cell carcinoma patients.
This clinical study is an observational bidirectional study. A retrospective cohort is used to train the model for recurrent risk prediction and adjuvant treatment decision-making, while a prospective cohort serves as an external validation set to verify the model performance.
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