Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer
NCT ID: NCT04278274
Last Updated: 2022-10-26
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
205 participants
OBSERVATIONAL
2020-02-08
2023-03-31
Brief Summary
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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
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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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.
artificial intelligence prediction system
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 radiologists
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
Interventions
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artificial intelligence prediction system
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 radiologists
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
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Inclusion Criteria
* 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
Exclusion Criteria
* 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
18 Years
75 Years
ALL
No
Sponsors
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Sir Run Run Shaw Hospital
OTHER
The Third Affiliated Hospital of Kunming Medical College.
OTHER
Sixth Affiliated Hospital, Sun Yat-sen University
OTHER
Responsible Party
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wanxiangbo
professor of Radiation Oncology, Vice Director, Department of Radiation Oncology
Principal Investigators
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Xiangbo Wan, MD, PhD
Role: STUDY_CHAIR
Sixth Affiliated Hospital, Sun Yat-sen University
Weidong Han, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Sir Run Run Shaw Hospital
Zhenhui Li, MD
Role: PRINCIPAL_INVESTIGATOR
The Third Affiliated Hospital of Kunming Medical College.
Locations
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the Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
The Third Affiliated Hospital of Kunming Medical College
Kunming, Yunnan, China
Sir Run Run Shaw Hospital
Hangzhou, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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MR-AI-pCR 2020
Identifier Type: -
Identifier Source: org_study_id
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