TRUS-FNA For The Prediction Of pCR After Neoadjuvant Treatment In Rectal Cancer
NCT04939103
Colorectal Neoplasms, Digestive System Diseases
Guangzhou, Guangdong, China
View Trial DetailsNCT Number: NCT04271657
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.
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Notify Me18 year–75 year
All sexes
Observational
the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: baseline
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.
Time frame: baseline
The specificity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Time frame: baseline
The sensitivity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Sixth Affiliated Hospital, Sun Yat-sen University
Other
A RadioPathomics Integrated Artificial Intelligence System to Predict Neoadjuvant Chemoradiotherapy Response for Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
Acronym: RPAI-pCR
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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