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

Rectal Cancer Clinical Trial

— MR-AI-pCR  

Official title:

A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04278274
• Other study ID #: MR-AI-pCR 2020
• Status: Recruiting
• Start date: February 8, 2020
• Est. completion date: March 31, 2023

Study information

• Verified date: October 2022
• Source: Sixth Affiliated Hospital, Sun Yat-sen University
• Contact: Xiangbo Wan, MD, PhD
• Phone: +86 13826017157
• Email: wanxbo[@]mail.sysu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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. 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

Description:

This is a 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 stage 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. Patients with LARC who received neoadjuvant treatment will be enrolled and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). The artificial intelligence prediction system and the expert radiologist will define the pathologic response as pCR or non-pCR, respectively. 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 back-to-back approaches generated will be compared in this multicenter, prospective clinical study.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 205
• Est. completion date: March 31, 2023
• Est. primary completion date: December 10, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Rectal Cancer
• Rectal Neoplasms

Intervention

Procedure:
• 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

Locations

Country	Name	City	State
China	the Sixth Affiliated Hospital of Sun Yat-sen University	Guangzhou	Guangdong
China	Sir Run Run Shaw Hospital	Hangzhou	Zhejiang
China	The Third Affiliated Hospital of Kunming Medical College	Kunming	Yunnan

Sponsors (3)

Lead Sponsor	Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University	Sir Run Run Shaw Hospital, The Third Affiliated Hospital of Kunming Medical College.

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response	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.	baseline
Secondary	The specificity of AI prediction system and expert radiologists in prediction tumor response	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.	baseline
Secondary	The sensitivity of AI prediction system and expert radiologists in prediction tumor response	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.	baseline