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Radiomics-based Artificial Intelligence System to Predict Neoadjuvant Treatment Response in Rectal Cancer (MRAI-pCR)

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ClinicalTrials.gov Identifier: NCT04273477
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 utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Previously, the radiomics prediction model has been constructed based on the radiomics features extracted from pretreatment Magnetic Resonance Imaging (MRI) in the training set, and optimized in the external validation set. The predictive power of this radiomics prediction model to discriminate the pathologic complete response (pCR) patients from non-pCR individuals, will be further verified in this prospective, multicenter clinical study.

Condition or disease
Rectal Cancer

Detailed Description:
This is a multicenter, prospective, observational clinical study for validation of a radiomics-based artificial intelligence (AI) prediction model. 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 concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Enhanced Magnetic Resonance Imaging (MRI) examination should be completed before the administration of nCRT treatment. The tumor volumes at high solution T2-weighted, contrast-enhanced T1-weighted and diffusion weighted images will be manually delineated, respectively. The outlined MRI images will be captured by the radiomics prediction model to generate a predicted response ("predicted pCR" vs. "predicted non-pCR") of each patient, whereas the true response ("confirmed pCR" vs. "confirmed non-pCR") is derived from pathologic reports after TME surgery serving as the gold standard for evaluation. The prediction accuracy, specificity, sensitivity and Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves will be calculated. This study is aimed to provide a reliable and accurate AI system to predict the pathologic tumor response to nCRT before its administration, which might facilitate the identification of pCR candidates for further precision therapy among 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: Predicting Neoadjuvant Chemoradiotherapy Response by Radiomics-based Artificial Intelligence System 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 radiomics prediction model [ Time Frame: baseline ]
    The prediction accuracy of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.


Secondary Outcome Measures :
  1. The specificity of the radiomics prediction model [ Time Frame: baseline ]
    The specificity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.

  2. The sensitivity of the radiomics prediction model [ Time Frame: baseline ]
    The sensitivity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.

  3. The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics prediction model [ Time Frame: baseline ]
    The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.



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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, concurrent neoadjuvant chemoradiotherapy with tumor 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

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)
  • 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): NCT04273477


Contacts
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Contact: Xiangbo Wan, MD, PhD +86 13826017157 wanxbo@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 13826017157    wanxbo@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
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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: NCT04273477    
Other Study ID Numbers: MRILARC-pCR2020
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:
MRI Radiomics
Artificial intelligence
Locally advanced rectal cancer
Pathologic complete response
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