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.
Eligibility
Inclusion Criteria:
- Pathologically proved rectal adenocarcinoma
- The first MRI diagnosis was locally advanced rectal cancer (LARC)
- Age 18-70
- Underwent magnetic resonance examinations twice
- Preoperative neoadjuvant chemoradiotherapy was completed
- Complete total mesangial resection of rectal cancer and postoperative pathological examination
- Informed consent and signed informed consent form
Exclusion Criteria:
- Poor magnetic resonance image quality, such as severe artifacts
- Cases complicated with intestinal obstruction or perforation requiring emergency surgical treatment
- Previous treatment for rectal cancer
- A history of other malignant tumors
- A history of abdominal and pelvic surgery
- Patients were lost to follow-up and voluntarily withdrew from the study due to adverse reactions or other reasons