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Recruiting NCT05523245

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

— Observational Rectal Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
This is an observational study: the protocol does not assign a study treatment.
Who it may be relevant to
Registry conditions: Rectal Cancer. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Overview

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.

Primary outcome measures

  • The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response [Time frame: baseline and pre-operation]
Secondary outcome measures (4)
  • The specificity of models in prediction tumor response [Time frame: baseline and pre-operation]
  • The sensitivity of models in prediction tumor response [Time frame: baseline and pre-operation]
  • The positive predictive value of models in prediction tumor response [Time frame: baseline and pre-operation]
  • The negative predictive value of models in prediction tumor response [Time frame: baseline and pre-operation]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 4 centers
  • Sixth Affiliated Hospital, Sun Yat-sen University — Guangzhou
  • The First Affiliated Hospital of Jinan University — Guangzhou
  • The Second Affiliated Hospital of Guangzhou Medical University — Guangzhou
  • Fifth Affiliated Hospital, Sun Yat-sen University — Zhuhai

Identifiers

NCT: NCT05523245 · 2021ZSLYEC-478

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗