Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
Trial Parameters
Brief Summary
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.
Eligibility Criteria
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