Clinical trial NCT05523245: Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
Official title: Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Rectal Cancer
Lead sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Sponsor class: OTHER
Collaborators: name: Fifth Affiliated Hospital, Sun Yat-Sen University
name: Second Affiliated Hospital of Guangzhou Medical University
name: First Affiliated Hospital of Jinan University
First posted: 2022-08-31
Enrollment: 1700
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and radical surgery

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.


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

Primary outcomes:
measure: The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response
timeFrame: baseline and pre-operation
Secondary outcomes:
measure: The specificity of models in prediction tumor response
timeFrame: baseline and pre-operation
measure: The sensitivity of models in prediction tumor response
timeFrame: baseline and pre-operation
measure: The positive predictive value of models in prediction tumor response
timeFrame: baseline and pre-operation
measure: The negative predictive value of models in prediction tumor response
timeFrame: baseline and pre-operation
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sixth Affiliated Hospital, Sun Yat-sen University
state: Guangdong
city: Guangzhou
country: China
facility: The First Affiliated Hospital of Jinan University
state: Guangdong
city: Guangzhou
country: China
facility: The Second Affiliated Hospital of Guangzhou Medical University
state: Guangdong
city: Zhuhai
country: China
facility: Fifth Affiliated Hospital, Sun Yat-sen University
state: Guangdong
Study officials:
References: