A RadioPathomics Integrated Artificial Intelligence System to Predict Neoadjuvant Chemoradiotherapy Response for Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study

This is a multicenter, prospective, observational clinical study for validation of a radiopathomics artificial intelligence (AI) system. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis by enhanced Magnetic Resonance Imaging (MRI) will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, the Third Affiliated Hospital of Kunming Medical College and Sir Run Run Shaw Hospital Affiliated by Zhejiang University School of Medicine. All participants should follow a very standard treatment protocol, including of concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. The MRI and biopsy examination should be completed before the nCRT and the images will be subjected to the manual delineation of the tumor regions of interest (ROI) by experienced radiologists and pathologists. Subsequently, the outlined MRI and biopsy slides images will be employed to the radiopathomics AI system to generate the predicted response ("predicted pathologic complete response (pCR)" vs. "predicted non-pCR") of individual patient, whereas the actual response ("pathologic confirmed as pCR" vs. "pathologic confirmed as non-pCR") will be diagnosed at surgery excised specimen. Through comparisons of the predicted responses and true pathologic responses, investigators calculate the prediction accuracy, specificity, sensitivity as well as the Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves. This study is aimed to validate the high accuracy and robustness of the radiopathomics AI system for identifying pCR candidates from non-pCR individuals before nCRT which will facilitate further precision therapy for patients with locally advanced rectal cancer.