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

NCT04273451 | Unknown

• 1 other identifier: RPAI-TRG2020
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 3

Brief 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.

Conditions

Rectal Cancer

Keywords

Radiopathomics features; Artificial intelligence; Locally advanced rectal cancer; Tumor regression grading; Neoadjuvant chemoradiotherapy

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• The specificity of the radiopathomics artificial intelligence model

  baseline

• The sensitivity of the radiopathomics artificial intelligence model

  baseline

• The F1 score of the radiopathomics artificial intelligence model

  baseline

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The population in the study are the patients with LARC, who are intended to receive or undergoing standard, neoadjuvant concurrent chemoradiotherapy with tumor pathologic response unknown.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Sixth Affiliated Hospital, Sun Yat-sen University: lead
• The Third Affiliated Hospital of Kunming Medical College.: collaborator
• Sir Run Run Shaw Hospital: collaborator

Study Sites (3)

the Sixth Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510655, China

RECRUITING

The Third Affiliated Hospital of Kunming Medical College

Kunming, Yunnan, 650000, China

RECRUITING

Sir Run Run Shaw Hospital

Hangzhou, Zhejiang, 310000, China

RECRUITING

MeSH Terms

Conditions

Rectal Neoplasms

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Diseases; Rectal Diseases

Study Officials

• Xiangbo Wan, MD, PhD

  Sixth Affiliated Hospital, Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

• Xinjuan Fan, MD, PhD

  Sixth Affiliated Hospital, Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiangbo Wan, MD, PhD

CONTACT

+86 13826017157; wanxbo@mail.sysu.edu.cn

Xinjuan Fan, MD, PhD

CONTACT

020-38254037; fanxjuan@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Radiation Oncology, Vice Director, Department of Radiation Oncology

Study Record Dates

• First Submitted: February 15, 2020
• First Posted: February 18, 2020
• Study Start: January 10, 2020
• Primary Completion: July 1, 2020
• Study Completion: December 1, 2020
• Last Updated: February 18, 2020

Record last verified: 2020-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (3)