Neoplasms

Digestive System Diseases

All Conditions

Rectal Neoplasms

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Intestinal Diseases

Rectal Diseases

Xiangbo Wan, MD, PhD

Xinjuan Fan, MD, PhD

Zhenhui Li, MD

Weidong Han, MD, PhD

In this study, investigators apply a radiopathomics artificial intelligence (AI) supportive model to predict neoadjuvant chemoradiotherapy (nCRT) response before the nCRT is delivered for the patients with locally advanced rectal cancer (LARC). The radiopathomics AI system predicts individual tumor regression grading (TRG) category based on each patient's radiopathomics features extracted from the Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to classify each patient into particular TRG category will be validated in this multicenter, prospective clinical study.

This is a multicenter, prospective, observational clinical study for validation of a radiopathomics integrated artificial intelligence (AI) system. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis 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 standard treatment protocol, including neoadjuvant concurrent chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Images of Magnetic Resonance Imaging (MRI) and biopsy hematoxylin \& eosin (H\&E) stained slides of each patient should be available before nCRT treatment. The tumor region within these images would be delineated manually by experienced radiologists and pathologists. Further, the outlined images will be presented to the radiopathomics AI system to classify each participant into particular tumor regression grading (TRG) category. Here, the American Joint Committee on Cancer and College of American Pathologist (AJCC/CAP) 4-category TRG system is served as the standard. The actual TRG category of each participant will be confirmed based on pathologic assessment after TME surgery. Through comparisons of the predicted TRG and actual TRG category, investigators calculate the prediction accuracy, specificity and sensitivity as well as the F1 score. This study is aimed to develop a reliable and robust AI system to predict pathologic TRG prior to nCRT administration, facilitating response-guided precision therapy for patients with locally advanced rectal cancer.

RadioPathomics Artificial Intelligence Model to Predict Tumor Regression Grading in Locally Advanced Rectal Cancer

A RadioPathomics Integrated Artificial Intelligence System to Predict Tumor Regression Grading of Neoadjuvant Treatment in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study

The prediction accuracy of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.

The specificity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.

The sensitivity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.

The F1 score of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.

The Third Affiliated Hospital of Kunming Medical College.

Sir Run Run Shaw Hospital

Sixth Affiliated Hospital, Sun Yat-sen University

Associate Professor of Radiation Oncology, Vice Director, Department of Radiation Oncology

Symptoms and General Pathology

Pathologic Complete Response

In this study, investigators utilize a radiopathomics integrated Artificial Intelligence (AI) supportive system to predict tumor response to neoadjuvant chemoradiotherapy (nCRT) before its administration for patients with locally advanced rectal cancer (LARC). By the system, whether the participants achieve the pathologic complete response (pCR) will be identified based on the radiopathomics features extracted from the pre-nCRT Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to discriminate the pCR individuals from non-pCR patients, will be validated in this multicenter, prospective 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.

RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer

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

The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.

The specificity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.

The sensitivity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
RadioPathomics Artificial Intelligence Model to Predict Tumor Regression Grading in Locally Advanced Rectal Cancer	NCT04273451	Rectal Cancer		18 Years - 75 Years	Sixth Affiliated Hospital, Sun Yat-sen University
RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer	NCT04271657	Rectal Cancer		18 Years - 75 Years	Sixth Affiliated Hospital, Sun Yat-sen University

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