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

Completed

• Conditions: Rectal Cancer

• Registration Number: NCT04271657
• Lead Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University

Brief Summary

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.

Detailed Description

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.

Study Timeline

• Study Start: 2020-01-10
• Study First Submitted: 2020-02-13
• Study First Submitted QC: 2020-02-13
• Study First Posted: 2020-02-17
• Primary Completion: 2020-11-09
• Study Completion: 2020-12-30
• Status Verified: 2021-05
• Last Update Submitted: 2021-05-01
• Last Update Posted: 2021-05-06

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 100

Inclusion Criteria

• pathologically diagnosed as rectal adenocarcinoma
• defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis by enhanced Magnetic Resonance Imaging (MRI)
• intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)
• intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy
• MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy
• biopsy H&E stained slides are available and scanned with high resolution before the neoadjuvant chemoradiotherapy

Exclusion Criteria

• with history of other cancer
• insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
• insufficient imaging quality of biopsy slides imaging to delineate tumor volume or obtain measurements (e.g., tissue dissection, color anomaly)
• incomplete neoadjuvant chemoradiotherapy
• no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response
• tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model	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.

Secondary Outcome Measures

Name	Time	Method
The specificity of the radiopathomics artificial intelligence model	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.
The sensitivity of the radiopathomics artificial intelligence model	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.

Trial Locations

Locations (3)

The Third Affiliated Hospital of Kunming Medical College; 🇨🇳 Kunming, Yunnan, China

the Sixth Affiliated Hospital of Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China

Sir Run Run Shaw Hospital; 🇨🇳 Hangzhou, Zhejiang, China