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RadioPathomics Artificial Intelligence Model to Predict Tumor Regression Grading in Locally Advanced Rectal Cancer (RPAI-TRG)

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ClinicalTrials.gov Identifier: NCT04273451
Recruitment Status : Recruiting
First Posted : February 18, 2020
Last Update Posted : February 18, 2020
Sponsor:
Collaborators:
The Third Affiliated Hospital of Kunming Medical College.
Sir Run Run Shaw Hospital
Information provided by (Responsible Party):
wanxiangbo, Sixth Affiliated Hospital, Sun Yat-sen University

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.

Condition or disease
Rectal Cancer

Detailed Description:
This is a multicenter, prospective, observational clinical study for validation of a radiopathomics integrated artificial intelligence (AI) system. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis 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 standard treatment protocol, including neoadjuvant concurrent chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Images of Magnetic Resonance Imaging (MRI) and biopsy hematoxylin & eosin (H&E) stained slides of each patient should be available before nCRT treatment. The tumor region within these images would be delineated manually by experienced radiologists and pathologists. Further, the outlined images will be presented to the radiopathomics AI system to classify each participant into particular tumor regression grading (TRG) category. Here, the American Joint Committee on Cancer and College of American Pathologist (AJCC/CAP) 4-category TRG system is served as the standard. The actual TRG category of each participant will be confirmed based on pathologic assessment after TME surgery. Through comparisons of the predicted TRG and actual TRG category, investigators calculate the prediction accuracy, specificity and sensitivity as well as the F1 score. This study is aimed to develop a reliable and robust AI system to predict pathologic TRG prior to nCRT administration, facilitating response-guided precision therapy for patients with locally advanced rectal cancer.

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Study Type : Observational
Estimated Enrollment : 100 participants
Observational Model: Other
Time Perspective: Prospective
Official Title: A RadioPathomics Integrated Artificial Intelligence System to Predict Tumor Regression Grading of Neoadjuvant Treatment in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
Actual Study Start Date : January 10, 2020
Estimated Primary Completion Date : July 2020
Estimated Study Completion Date : December 2020



Primary Outcome Measures :
  1. The prediction accuracy of the radiopathomics artificial intelligence model [ Time Frame: baseline ]
    The prediction accuracy of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.


Secondary Outcome Measures :
  1. The specificity of the radiopathomics artificial intelligence model [ Time Frame: baseline ]
    The specificity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.

  2. The sensitivity of the radiopathomics artificial intelligence model [ Time Frame: baseline ]
    The sensitivity of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.

  3. The F1 score of the radiopathomics artificial intelligence model [ Time Frame: baseline ]
    The F1 score of the radiopathomics artificial intelligence model for classifying each individual into particular AJCC/CAP TRG category will be calculated.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 75 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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.
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04273451


Contacts
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Contact: Xiangbo Wan, MD, PhD +86 13826017157 wanxbo@mail.sysu.edu.cn
Contact: Xinjuan Fan, MD, PhD 020-38254037 fanxjuan@mail.sysu.edu.cn

Locations
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China, Guangdong
the Sixth Affiliated Hospital of Sun Yat-sen University Recruiting
Guangzhou, Guangdong, China, 510655
Contact: Xiangbo Wan, MD, PhD    86-20-85655905    wanxbo@mail.sysu.edu.cn   
Contact: Xinjuan Fan, MD, PhD    020-38254037    fanxjuan@mail.sysu.edu.cn   
China, Yunnan
The Third Affiliated Hospital of Kunming Medical College Recruiting
Kunming, Yunnan, China, 650000
Contact: Zhenhui Li, MD    +86 13698736132    lizhenhui621@163.com   
China, Zhejiang
Sir Run Run Shaw Hospital Recruiting
Hangzhou, Zhejiang, China, 310000
Contact: Weidong Han, MD, PhD    +86 13819124503    hanwd@zju.edu.cn   
Sponsors and Collaborators
Sixth Affiliated Hospital, Sun Yat-sen University
The Third Affiliated Hospital of Kunming Medical College.
Sir Run Run Shaw Hospital
Investigators
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Principal Investigator: Xiangbo Wan, MD, PhD Sixth Affiliated Hospital, Sun Yat-sen University
Principal Investigator: Xinjuan Fan, MD, PhD Sixth Affiliated Hospital, Sun Yat-sen University
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Responsible Party: wanxiangbo, Associate Professor of Radiation Oncology, Vice Director, Department of Radiation Oncology, Sixth Affiliated Hospital, Sun Yat-sen University
ClinicalTrials.gov Identifier: NCT04273451    
Other Study ID Numbers: RPAI-TRG2020
First Posted: February 18, 2020    Key Record Dates
Last Update Posted: February 18, 2020
Last Verified: February 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by wanxiangbo, Sixth Affiliated Hospital, Sun Yat-sen University:
Radiopathomics features
Artificial intelligence
Locally advanced rectal cancer
Tumor regression grading
Neoadjuvant chemoradiotherapy
Additional relevant MeSH terms:
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Rectal Neoplasms
Colorectal Neoplasms
Intestinal Neoplasms
Gastrointestinal Neoplasms
Digestive System Neoplasms
Neoplasms by Site
Neoplasms
Digestive System Diseases
Gastrointestinal Diseases
Intestinal Diseases
Rectal Diseases