Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer

Study Overview
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Brief Summary
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In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study

Detailed Description
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This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III stage will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. All participants should follow a standard treatment protocol, including neoadjuvant treatment, total mesorectum excision (TME) surgery. Patients with LARC who received neoadjuvant treatment will be enrolled and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). The artificial intelligence prediction system and the expert radiologist will define the pathologic response as pCR or non-pCR, respectively. The pathologist will provide the final pathology report of TME surgery specimen (pCR or non-pCR) as a standard. The predictive efficacy of these two back-to-back approaches generated will be compared in this multicenter, prospective clinical study.

Official Title
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A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study

Conditions
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Rectal Cancer

Intervention / Treatment
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* Procedure: artificial intelligence prediction system
* Procedure: the radiologists


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: pathologically diagnosed as rectal adenocarcinoma defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis receive neoadjuvant chemoradiotherapy or chemotherapy pre- and post-neoadjuvant treatment MRI data obtained receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response Exclusion Criteria: with history of other cancer insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts) not completing neoadjuvant chemotherapy or chemoradiotherapy tumor recurrence or distant metastasis during neoadjuvant treatment not undergoing surgery resulting in lack of pathologic assessment of tumor response

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 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 |
| --- | --- |
| patients will be evaluated by artificial intelligence system and expert radiologist<br>the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment, and not yet receive total mesorectum excision (TME) surgery will be enrolled. The post-neoadjuvant treatment MRI images features of each enrolled patients will be captured by the artificial intelligence system, and evaluated by experienced radiologists as well. Blind to the pathologic report of TME specimen, both approaches further respectively yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient, shown as pCR or non-pCR. | Procedure: artificial intelligence prediction system<br>* The tumor ROI in the post- neoadjuvant treatment MRI images will be manually delineated, and further subjected to the AI prediction system arm to verify the predictive accuracy of this AI prediction system in identifying the pCR individuals from non-pCR patients with LARC.<br>Procedure: the radiologists<br>* The enrolled patients will be assigned to the trained experienced radiologists to evaluate their predictive accuracy in identifying the pCR individuals from non-pCR patients<br>|

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 AI prediction system and expert radiologists in prediction tumor response | The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively. | baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The specificity of AI prediction system and expert radiologists in prediction tumor response | The specificity of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively. | baseline | 
| The sensitivity of AI prediction system and expert radiologists in prediction tumor response | The sensitivity of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively. | baseline | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Neoadjuvant treatment, Radiomics features, Artificial intelligence model, Locally advanced rectal cancer, Pathologic complete response