Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI (DLARC)
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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.
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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
Trial design
1,700 participants in 2 patient groups
Trial contacts and locations
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Central trial contact
Xiaochun Meng; Peiyi Xie
Data sourced from clinicaltrials.gov
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