Rectal CancerDLARCNCT05523245
Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
An observational study of Rectal Cancer, run by Sixth Affiliated Hospital, Sun Yat-sen University at 4 sites in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Rectal Cancer
- How many people
- 1,700 participants
- Where
- 4 sitesChina
- Study dates
- 24 Jun 2022to Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and radical surgery
Who may be able to join
- Clinical suspicion or colonoscopic pathology of rectal cancer
- Age over 18 years
- Informed consent and signed informed consent form
Who cannot join
- 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
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites, 4 recruiting
- Recruiting
Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong
Xiaochun Meng, 13719166488, mengxch3@mail.sysu.edu.cn
- Not yet recruiting
The First Affiliated Hospital of Jinan University
Guangzhou, Guangdong
- Not yet recruiting
The Second Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong
- Not yet recruiting
Fifth Affiliated Hospital, Sun Yat-sen University
Zhuhai, Guangdong
About this study
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.
- Conditions
- Rectal Cancer
- Medical subject headings
- Rectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| complete response | Patients receiving neoadjuvant therapy achieved pathological complete response before LARC. | |
| non complete response | Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC. |
What is being measured
The primary outcome is the main question the study is designed to answer.
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response
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.
Measured: baseline and pre-operation
Secondary outcomes (4)
The specificity of models in prediction tumor response
The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
Measured: baseline and pre-operation
The sensitivity of models in prediction tumor response
The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
Measured: baseline and pre-operation
The positive predictive value of models in prediction tumor response
The positive predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
Measured: baseline and pre-operation
The negative predictive value of models in prediction tumor response
The negative predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
Measured: baseline and pre-operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Aug 2022
- Study start24 Jun 2022
- Primary completionDec 2026 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Rectal CancerStudies of the same condition, open and under way.
- More trials run by Sixth Affiliated Hospital, Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,700 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Fifth Affiliated Hospital, Sun Yat-Sen University, Second Affiliated Hospital of Guangzhou Medical University, First Affiliated Hospital of Jinan University
- Responsible party
- Principal investigator, xiaochun meng, Sixth Affiliated Hospital, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05523245
- Sponsor's protocol ID
- 2021ZSLYEC-478
- Acronym
- DLARC
- Status last verified
- Apr 2026
- First submitted
- 29 Aug 2022
- Last updated
- 23 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
- ClinicalTrials.gov ID
- NCT05523245
- Acronym / sponsor ID
- DLARC / 2021ZSLYEC-478
- Conditions
- Rectal Cancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,700 participants (estimated)
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Fifth Affiliated Hospital, Sun Yat-Sen University, Second Affiliated Hospital of Guangzhou Medical University, First Affiliated Hospital of Jinan University
- Study start
- 24 Jun 2022 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 31 Aug 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| complete response | – | –Patients receiving neoadjuvant therapy achieved pathological complete response before LARC. |
| non complete response | – | –Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and radical surgery
Key inclusion criteria (3 listed)
- Clinical suspicion or colonoscopic pathology of rectal cancer
- Age over 18 years
- Informed consent and signed informed consent form
Key exclusion criteria (8 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
4 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Apr 2026.
Related trials for Rectal Cancer
Not yet recruitingPhase 1
Hepatic Artery Infusion of Carfilzomib in Participants With Liver Metastatic Disease Previously Treated With Hepatic Artery Infusion Pump Therapy
Colorectal Neoplasms, Neoplasms
Active, not recruiting
Streamlined Geriatric and Oncological Evaluation Based On IC Technology
Breast Cancer, Comorbidities and Coexisting Conditions
RecruitingObservational study
Pre- and On-treatment Multi-omics Profiling of Locally Advanced Rectal Cancer
Rectal Neoplasms
Enrolling by invitationObservational study
Natural History Study of Early Life Exposures in Agriculture (ELEA)
Prostate Cancer, Leukemia
RecruitingPhase 2
Tislelizumab in People With Colorectal Cancer
Colorectal Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Rectal Cancer, run by Sixth Affiliated Hospital, Sun Yat-sen University at 4 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 23 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xiaochun Meng (13719166488, mengxch3@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response (time frame: baseline and pre-operation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Apr 2026.