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Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer (MR-AI-pCR)

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ClinicalTrials.gov Identifier: NCT04278274
Recruitment Status : Recruiting
First Posted : February 20, 2020
Last Update Posted : February 20, 2020
Sponsor:
Collaborators:
Sir Run Run Shaw Hospital
The Third Affiliated Hospital of Kunming Medical College.
Information provided by (Responsible Party):
wanxiangbo, Sixth Affiliated Hospital, Sun Yat-sen University

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. 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 randomized controlled, prospective, multicenter, observational clinical study.


Condition or disease Intervention/treatment
Rectal Cancer Procedure: the artificial intelligence Procedure: the radiologists

Detailed Description:
This is a randomized controlled, multicenter, prospective, observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) based on the post- neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. 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, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. All participants should follow a standard treatment protocol, including neoadjuvant treatment, total mesorectum excision (TME) surgery and adjuvant chemotherapy. Patients with LARC who received neoadjuvant treatment will be randomly assigned into two arms, and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). Patients in arm A assign to the artificial intelligence prediction system. While in group B, the patients assign to the expert radiologist prediction. The pathologist will provide the final pathology report of TME surgery specimen (pCR or non-pCR) as a standard. The predictive efficacy of these two arms will be compared in this randomized controlled, multicenter clinical study among patients with locally advanced rectal cancer.

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Study Type : Observational
Estimated Enrollment : 322 participants
Observational Model: Other
Time Perspective: Prospective
Official Title: A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Randomized Controlled, Multicenter, Prospective Clinical Study
Actual Study Start Date : January 8, 2020
Estimated Primary Completion Date : August 8, 2020
Estimated Study Completion Date : December 8, 2020

Group/Cohort Intervention/treatment
artificial intelligence system arm
the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment will be enrolled. In arm A, the post-neoadjuvant treatment MRI images features will be captured by the artificial intelligence system, which further generate a predicted pathologic response to neoadjuvant treatment for each enrolled patient (pCR or non-pCR).
Procedure: the artificial intelligence
Previously, the post-neoadjuvant treatment MRI radiomics based AI prediction model had been constructed, optimized and fixed at a training set, which recruited neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy treated LARC patients from different hospitals. The tumor region of interest (ROI) within the MRI images were manually outlined for radiomics features extraction and model construction. In this study, the tumor ROI in the post- neoadjuvant treatment MRI images will be manually delineated in the same way, 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.

radiologist group
the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment will be enrolled. In arm B, the post-neoadjuvant treatment MRI images will be reviewed by the experienced radiologists, who further yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient (pCR or non-pCR).
Procedure: the radiologists
In this arm, prior to this prospective study, the expert radiologists would receive a critical training to discriminate the pCR patients from non-pCR individuals by reviewing a huge amount of pCR and non-pCR patient's post-neoadjuvant treatment MRI. In this study, a group of patients will be assigned to the trained experienced radiologists arm to validate their predictive accuracy in identifying the pCR individuals from non-pCR patients with LARC.




Primary Outcome Measures :
  1. The prediction accuracy of AI prediction system and expert radiologists in prediction tumor response [ Time Frame: baseline ]
    The prediction accuracy 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.


Secondary Outcome Measures :
  1. The specificity of AI prediction system and expert radiologists in prediction tumor response [ Time Frame: baseline ]
    The specificity 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.

  2. The sensitivity of AI prediction system and expert radiologists in prediction tumor response [ Time Frame: baseline ]
    The sensitivity 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.

  3. The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response [ Time Frame: baseline ]
    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.



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
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.
Criteria

Inclusion Criteria:

  • 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

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)
  • 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

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): NCT04278274


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 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
Sir Run Run Shaw Hospital
The Third Affiliated Hospital of Kunming Medical College.
Investigators
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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.
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Responsible Party: wanxiangbo, professor of Radiation Oncology, Vice Director, Department of Radiation Oncology, Sixth Affiliated Hospital, Sun Yat-sen University
ClinicalTrials.gov Identifier: NCT04278274    
Other Study ID Numbers: MR-AI-pCR 2020
First Posted: February 20, 2020    Key Record Dates
Last Update Posted: February 20, 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:
Radiomics features
Artificial intelligence model
Locally advanced rectal cancer
Pathologic complete response
Neoadjuvant treatment
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