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

ConditionRectal Cancer
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorSixth Affiliated Hospital, Sun Yat-sen University

About this trial

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 criteria

Qualifiers

Clinical suspicion or colonoscopic pathology of rectal cancer

Age over 18 years

Informed consent and signed informed consent form

Disqualifiers

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)

Trial design

Treatments tested in this trial

  • Not listed

Trial groups

1,700 Participants
are grouped into 2 trial groups

Sponsors and collaborators

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