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

Rectal Cancer Clinical Trial

— RPAI-TRG  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04273451
• Other study ID #: RPAI-TRG2020
• Status: Recruiting
• Start date: January 10, 2020
• Est. completion date: December 2020

Study information

• Verified date: February 2020
• Source: Sixth Affiliated Hospital, Sun Yat-sen University
• Contact: Xiangbo Wan, MD, PhD
• Phone: +86 13826017157
• Email: wanxbo[@]mail.sysu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: December 2020
• Est. primary completion date: July 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Rectal Cancer
• Rectal Neoplasms

Locations

Country	Name	City	State
China	the Sixth Affiliated Hospital of Sun Yat-sen University	Guangzhou	Guangdong
China	Sir Run Run Shaw Hospital	Hangzhou	Zhejiang
China	The Third Affiliated Hospital of Kunming Medical College	Kunming	Yunnan

Sponsors (3)

Lead Sponsor	Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University	Sir Run Run Shaw Hospital, The Third Affiliated Hospital of Kunming Medical College.

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The prediction accuracy of the radiopathomics artificial intelligence model	The prediction accuracy of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.	baseline
Secondary	The specificity of the radiopathomics artificial intelligence model	The specificity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.	baseline
Secondary	The sensitivity of the radiopathomics artificial intelligence model	The sensitivity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.	baseline
Secondary	The F1 score of the radiopathomics artificial intelligence model	The F1 score of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.	baseline