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

Study Overview
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Brief Summary
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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.

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

Conditions
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Rectal Cancer

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| complete response<br>Patients receiving neoadjuvant therapy achieved pathological complete response before LARC. | |
| non complete response<br>Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | baseline and pre-operation | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | baseline and pre-operation | 
| 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. | baseline and pre-operation | 
| 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. | baseline and pre-operation | 
| 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. | baseline and pre-operation |