- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04271657
RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer (RPAI-pCR)
May 1, 2021 updated by: wanxiangbo, Sixth Affiliated Hospital, Sun Yat-sen University
A RadioPathomics Integrated Artificial Intelligence System to Predict Neoadjuvant Chemoradiotherapy Response for Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
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.
Study Overview
Status
Completed
Conditions
Detailed Description
This is a multicenter, prospective, observational clinical study for validation of a radiopathomics artificial intelligence (AI) system.
Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis by enhanced Magnetic Resonance Imaging (MRI) 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 very standard treatment protocol, including of concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy.
The MRI and biopsy examination should be completed before the nCRT and the images will be subjected to the manual delineation of the tumor regions of interest (ROI) by experienced radiologists and pathologists.
Subsequently, the outlined MRI and biopsy slides images will be employed to the radiopathomics AI system to generate the predicted response ("predicted pathologic complete response (pCR)" vs. "predicted non-pCR") of individual patient, whereas the actual response ("pathologic confirmed as pCR" vs. "pathologic confirmed as non-pCR") will be diagnosed at surgery excised specimen.
Through comparisons of the predicted responses and true pathologic responses, investigators calculate the prediction accuracy, specificity, sensitivity as well as the Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves.
This study is aimed to validate the high accuracy and robustness of the radiopathomics AI system for identifying pCR candidates from non-pCR individuals before nCRT which will facilitate further precision therapy for patients with locally advanced rectal cancer.
Study Type
Observational
Enrollment (Actual)
100
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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Guangdong
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Guangzhou, Guangdong, China, 510655
- The Sixth Affiliated Hospital of Sun Yat-sen University
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Yunnan
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Kunming, Yunnan, China, 650000
- The Third Affiliated Hospital of Kunming Medical College
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Zhejiang
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Hangzhou, Zhejiang, China, 310000
- Sir Run Run Shaw Hospital
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-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years to 75 years (Adult, Older Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
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.
Description
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
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Other
- Time Perspectives: Prospective
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathomics artificial intelligence model
Time Frame: baseline
|
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 Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
The specificity of the radiopathomics artificial intelligence model
Time Frame: baseline
|
The specificity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
|
baseline
|
The sensitivity of the radiopathomics artificial intelligence model
Time Frame: baseline
|
The sensitivity of the radiopathomics artificial intelligence model for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
|
baseline
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
January 10, 2020
Primary Completion (Actual)
November 9, 2020
Study Completion (Actual)
December 30, 2020
Study Registration Dates
First Submitted
February 13, 2020
First Submitted That Met QC Criteria
February 13, 2020
First Posted (Actual)
February 17, 2020
Study Record Updates
Last Update Posted (Actual)
May 6, 2021
Last Update Submitted That Met QC Criteria
May 1, 2021
Last Verified
May 1, 2021
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- RPAI-pCR2020
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
No
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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