Abstract #3982

Predicting Disease-Free Survival in Rectal Cancer Patients via Radiomics Signatures from Multi-Modality MRI Images: a multi-center study

Dexuan Li¹, Chenglong Wang¹, Qiong Ma², Yiqun Sun², Tong Tong², Yang Song³, Haijie Wang⁴, and Guang Yang¹

¹Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ²Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China, ³MR Research Collaboration, Siemens Healthineers, Shanghai, China, ⁴Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing, China

Synopsis

Keywords: Cancer, Data Analysis

Motivation: Accurate prediction of disease-free survival (DFS) of rectal cancer patients has substantial influences on treatment planning.

Goal(s): To explore the value of ADC and T2W images in predicting DFS for rectal cancer patients.

Approach: Radiomics models using features extracted from ADC and T2W were built and evaluated.

Results: Models built with ADC features and clinical variables achieved C-Index values of 0.675 and 0.768 over the internal and external test cohorts, respectively. Models built with T2W features and clinical variables achieved C-Index values of 0.724 and 0.747 over the internal and external test cohorts, respectively.

Impact: Both the shape features extracted from ADC maps and the first-order features extracted from T2W images demonstrated strong predictive power for DFS estimation, implying the potential of combination anatomical images with diffusion models to predict DFS in rectal cancer patients.

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