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.
Clinical suspicion or colonoscopic pathology of rectal cancer
Age over 18 years
Informed consent and signed informed consent form
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)
Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and radical surgery
Cohort
Prospective
Not listed
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.
The specificity of models in prediction tumor response
The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
The sensitivity of models in prediction tumor response
The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
The positive predictive value of models in prediction tumor response
The positive predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
The negative predictive value of models in prediction tumor response
The negative predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
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Sixth Affiliated Hospital, Sun Yat-sen University
Lead sponsor
Fifth Affiliated Hospital, Sun Yat-Sen University
Collaborator
Second Affiliated Hospital of Guangzhou Medical University
Collaborator
First Affiliated Hospital of Jinan University
Collaborator
