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Abstract #2701

A Multisequence MRI-based Deep Learning Radiomics Nomogram for Predicting Ki-67 Expression and Prognosis in Locally Advanced Rectal Cancer

Zhiheng Li1, Yangyang Qin2, Xiaoqing Liao1, Enqi Wang1, Zengxin Lu3, Dandan Wang3, and Yan Lin1
1Radiology Department, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China, 2Radiology Department, The First Affiliated Hospital of Ningbo University, Ningbo, China, 3Radiology Department, The Shaoxing People's Hospital, Shaoxing, China

Synopsis

Keywords: Cancer, Cancer

Motivation: Accurately identifying Ki-67 expression and prognosis is crucial for guiding treatment decisions in locally advanced rectal cancer (LARC) patients who decline preoperative chemo-/radiotherapy.

Goal(s): To develop a deep learning (DL) radiomics nomogram using multisequence MRI for predicting Ki-67 expression and prognosis in LARC patients who decline preoperative chemo-/radiotherapy.

Approach: Radiomics and DL features from multisequence MRI were used to develop and evaluate six machine learning models. The optimal DL and radiomics models were combined into a nomogram to predict Ki-67 expression and disease-free survival (DFS).

Results: The nomogram can effectively predict Ki-67 expression and DFS in LARC who decline preoperative chemo-/radiotherapy.

Impact: This study highlights the potential of a nomogram that combines radiomics and DL to assess Ki-67 expression and prognosis in LARC patients who decline preoperative chemo-/radiotherapy, offering a promising tool for individualized treatment planning in this patient group.

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