Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer
MR-AI-pCR
A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
1 other identifier
observational
205
1 country
3
Brief Summary
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Feb 2020
Typical duration for all trials
3 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
February 8, 2020
CompletedFirst Submitted
Initial submission to the registry
February 19, 2020
CompletedFirst Posted
Study publicly available on registry
February 20, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 10, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
March 31, 2023
CompletedOctober 26, 2022
October 1, 2022
2.8 years
February 19, 2020
October 25, 2022
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
baseline
Secondary Outcomes (2)
The specificity of AI prediction system and expert radiologists in prediction tumor response
baseline
The sensitivity of AI prediction system and expert radiologists in prediction tumor response
baseline
Study Arms (1)
patients will be evaluated by artificial intelligence system and expert radiologist
the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment, and not yet receive total mesorectum excision (TME) surgery will be enrolled. The post-neoadjuvant treatment MRI images features of each enrolled patients will be captured by the artificial intelligence system, and evaluated by experienced radiologists as well. Blind to the pathologic report of TME specimen, both approaches further respectively yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient, shown as pCR or non-pCR.
Interventions
The tumor ROI in the post- neoadjuvant treatment MRI images will be manually delineated, and further subjected to the AI prediction system arm to verify the predictive accuracy of this AI prediction system in identifying the pCR individuals from non-pCR patients with LARC.
The enrolled patients will be assigned to the trained experienced radiologists to evaluate their predictive accuracy in identifying the pCR individuals from non-pCR patients
Eligibility Criteria
In the study, the population are the patients with LARC, who receive neoadjuvant chemoradiotherapy or chemotherapy and TME surgery. The response of neoadjuvant treatment is 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
- receive neoadjuvant chemoradiotherapy or chemotherapy
- pre- and post-neoadjuvant treatment MRI data obtained
- receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response
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)
- not completing neoadjuvant chemotherapy or chemoradiotherapy
- tumor recurrence or distant metastasis during neoadjuvant treatment
- not undergoing surgery resulting in lack of pathologic assessment of tumor response
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (3)
the Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510655, China
The Third Affiliated Hospital of Kunming Medical College
Kunming, Yunnan, 650000, China
Sir Run Run Shaw Hospital
Hangzhou, Zhejiang, 310000, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY CHAIR
Xiangbo Wan, MD, PhD
Sixth Affiliated Hospital, Sun Yat-sen University
- PRINCIPAL INVESTIGATOR
Weidong Han, MD, PhD
Sir Run Run Shaw Hospital
- PRINCIPAL INVESTIGATOR
Zhenhui Li, MD
The Third Affiliated Hospital of Kunming Medical College.
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- professor of Radiation Oncology, Vice Director, Department of Radiation Oncology
Study Record Dates
First Submitted
February 19, 2020
First Posted
February 20, 2020
Study Start
February 8, 2020
Primary Completion
December 10, 2022
Study Completion
March 31, 2023
Last Updated
October 26, 2022
Record last verified: 2022-10
Data Sharing
- IPD Sharing
- Will not share