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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05523245

Acronym

DLARC

Enrollment

1700

Registered

2022-08-31

Start date

2022-06-24

Completion date

2027-12-01

Last updated

2026-04-23

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Rectal Cancer

Brief summary

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response	baseline and pre-operation	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.

Secondary

Measure	Time frame	Description
The specificity of models in prediction tumor response	baseline and pre-operation	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	baseline and pre-operation	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	baseline and pre-operation	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	baseline and pre-operation	The negative predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.

Countries

China

Contacts

CONTACTXiaochun Meng

mengxch3@mail.sysu.edu.cn13719166488

CONTACTPeiyi Xie

xiepy6@mail.sysu.edu.cn13724071514

Outcome results