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

Predicting Neoadjuvant Chemoradiotherapy Response by Radiomics-based Artificial Intelligence System in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study

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.

Study Overview

• Status: Unknown
• Conditions: 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.

• Study Type: Observational
• Enrollment (Anticipated): 100

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510655
      • Recruiting
      • The Sixth Affiliated Hospital of Sun Yat-sen University
  • Yunnan
    • Kunming, Yunnan, China, 650000
      • Recruiting
      • The Third Affiliated Hospital of Kunming Medical College
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310000
      • Recruiting
      • Sir Run Run Shaw Hospital

Participation Criteria

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, concurrent neoadjuvant chemoradiotherapy with tumor 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

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The prediction accuracy of the radiomics prediction model	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.	baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The specificity of the radiomics prediction model	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.	baseline
The sensitivity of the radiomics prediction model	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.	baseline
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics prediction model	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.	baseline

Collaborators and Investigators

• Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
• Collaborators: Sir Run Run Shaw Hospital; The Third Affiliated Hospital of Kunming Medical College.

Study record dates

Study Major Dates

• Study Start (Actual): January 10, 2020
• Primary Completion (Anticipated): July 1, 2020
• Study Completion (Anticipated): December 1, 2020

Study Registration Dates

• First Submitted: February 15, 2020
• First Submitted That Met QC Criteria: February 15, 2020
• First Posted (Actual): February 18, 2020

Study Record Updates

• Last Update Posted (Actual): February 18, 2020
• Last Update Submitted That Met QC Criteria: February 15, 2020
• Last Verified: February 1, 2020

More Information

Terms related to this study

• Keywords: Locally advanced rectal cancer; Artificial intelligence; Neoadjuvant chemoradiotherapy; Pathologic complete response; MRI Radiomics
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Intestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Colorectal Neoplasms; Rectal Neoplasms
• Other Study ID Numbers: MRILARC-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