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

NCT04271657 | Completed

• 1 other identifier: RPAI-pCR2020
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 3

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.

Conditions

Rectal Cancer

Keywords

Radiopathomics features; Artificial intelligence; Locally advanced rectal cancer; Pathologic complete response; Neoadjuvant chemoradiotherapy

Outcome Measures

Primary Outcomes (1)

• The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model

  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.

  baseline

Secondary Outcomes (2)

• The specificity of the radiopathomics artificial intelligence model

  baseline

• The sensitivity 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

Location

The Third Affiliated Hospital of Kunming Medical College

Kunming, Yunnan, 650000, China

Location

Sir Run Run Shaw Hospital

Hangzhou, Zhejiang, 310000, China

Location

Related Publications (1)

• Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, Liu S, He F, Zheng J, Meng X, Xie P, Yang G, Ding Y, Wei M, Yun J, Hung MC, Zhou W, Wahl DR, Lan P, Tian J, Wan X. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health. 2022 Jan;4(1):e8-e17. doi: 10.1016/S2589-7500(21)00215-6.

  PMID: 34952679 DERIVED

MeSH Terms

Conditions

Rectal Neoplasms; Pathologic Complete Response

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; Disease Progression; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Xinjuan Fan, MD, PhD

  Sixth Affiliated Hospital, Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

• Xiangbo Wan, MD, PhD

  Sixth Affiliated Hospital, Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

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 13, 2020
• First Posted: February 17, 2020
• Study Start: January 10, 2020
• Primary Completion: November 9, 2020
• Study Completion: December 30, 2020
• Last Updated: May 6, 2021

Record last verified: 2021-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (3)