Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI

Sixth Affiliated Hospital, Sun Yat-sen University1,700 enrolled

Overview

Establish a deep learning model based on multi-parameter magnetic resonance imaging to predict the efficacy of neoadjuvant therapy for locally advanced rectal cancer.This study intends to combine DCE with conventional MRI images for DL, establish a multi-parameter MRI model for predicting the efficacy of CRT, and compare it with the DL and non-artificial quantitative MRI diagnostic model constructed by conventional MRI to evaluate the role of DL in MRI predicting CRT. And this study also tries to build a DL platform to assess the efficacy of LARC neoadjuvant radiotherapy and chemotherapy, accurately assess patients' complete respose (pCR) after CRT, and provide an important basis for guiding clinical decision-making.

Study Type

OBSERVATIONAL

Enrollment

1,700

Conditions

Rectal Cancer

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Clinical suspicion or colonoscopic pathology of rectal cancer * Age over 18 years * Informed consent and signed informed consent form Exclusion Criteria: * Poor magnetic resonance image quality, such as severe artifacts * Previous treatment for rectal cancer * History or combination of other malignant tumours * Not Locally Advanced Rectal Cancer (LARC) * Not received neoadjuvant therapy or not completed neoadjuvant therapy * No surgery * Time interval between MRI and surgery was more than 2 weeks * Patients were lost to follow-up and voluntarily withdrew from the study due to adverse reactions or other reasons

Locations (4)

Sixth Affiliated Hospital, Sun Yat-sen University

Guangzhou, Guangdong, China

RECRUITING

The First Affiliated Hospital of Jinan University

Guangzhou, Guangdong, China

NOT_YET_RECRUITING

The Second Affiliated Hospital of Guangzhou Medical University

Guangzhou, Guangdong, China

NOT_YET_RECRUITING

Fifth Affiliated Hospital, Sun Yat-sen University

Zhuhai, Guangdong, China

NOT_YET_RECRUITING

Outcomes

Primary Outcomes

The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response

The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.