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Integration of Deep Learning and Sub-regional Radiomics Improves the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.

Acad Radiol

January 2025

Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China (X.W., C.C., W.C., Y.G., X.L., X.J.); Department of Pathology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou 325000, China (X.W., J.W., C.C., W.C., Y.G., K.G., Y.C., Y.S., J.C., X.L., X.J.). Electronic address:

Rationale And Objectives: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop and validate a model that integrates deep learning and sub-regional radiomics from MRI imaging to predict pathological complete response (pCR) in patients with LARC.

Materials And Methods: We retrospectively enrolled 768 eligible participants from three independent hospitals who had received neoadjuvant chemoradiotherapy followed by radical surgery.

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