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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