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

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

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

Conditions2

Browse More Trials