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Abstract #1398

Radiomics Combined with Dosiomics and Clinical Omics for Predicting Response to Neoadjuvant Therapy in Rectal Cancer

Sha Li1, Zhengxian Li2, Yibao Zhang3, Fei Wang4, Chen Zhang5, and Yanye Lu1
1Peking University Health Science Center, Beijing, China, 2Guowen Hospital, jilin, China, 3Beijing cancer hospital, beijing, China, 4Beijing cancer hospital, Beijing, China, 5Digital Imaging, Siemens Healthineers Ltd, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Radiomics, , Dosiomics, Neoadjuvant therapy, Rectal cancer, Predict therapy response

Motivation: Approximately 15-27% of patients with LARC achieve pCR. However, determining whether these patients should undergo surgery or follow "watch-and-wait" approach requires accurately predicting preoperatively if they have achieved pCR.

Goal(s): To developed a comprehensive multi-omics model to predict pCR.

Approach: We used logistic regression to generate a non-imaging model, three radiomics-based models, and a dosiomics-based model and were then combined into a comprehensive model, its performance was tested on multi-center datasets.

Results: The radiomic model exhibits relatively comparable performance, and the final comprehensive multi-omics model exhibits good performance after combining clinical characteristics and dositomics.

Impact: Predicting patients who achieve pCR after nCRT can assist doctors in formulating personalized treatment strategies. This helps determine whether patients require surgery or if the "watch-and-wait" approach can be adopted.

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