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NCT Number: NCT05523245

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

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.

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Key information

Conditions

Rectal Cancer Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Neoplasms Neoplasms by Site Rectal Diseases Rectal Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China

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Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response

    Time frame: 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 outcomes

  1. The specificity of models in prediction tumor response

    Time frame: 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.

  2. The sensitivity of models in prediction tumor response

    Time frame: 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.

  3. The positive predictive value of models in prediction tumor response

    Time frame: 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.

  4. The negative predictive value of models in prediction tumor response

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Peiyi Xie

CONTACT

[email protected]

13724071514

Xiaochun Meng

CONTACT

[email protected]

13719166488

Sponsors and collaborators

Lead sponsor

Sixth Affiliated Hospital, Sun Yat-sen University

Other

Collaborators

  • Fifth Affiliated Hospital, Sun Yat-Sen University
  • First Affiliated Hospital of Jinan University
  • Second Affiliated Hospital of Guangzhou Medical University

Registry information

Acronym: DLARC

Important dates

Study start
2022
Primary completion
2026
Study completion
2027
First posted
Aug 31, 2022
Registry last updated
Apr 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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