Rectal CancerMR-AI-pCRNCT04278274
Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer
An observational study of Rectal Cancer, run by Sixth Affiliated Hospital, Sun Yat-sen University at 3 sites in China.
Trial summary
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Rectal Cancer
- What is being tested?
- artificial intelligence prediction system and the radiologists
- Type of treatment
- Surgery or procedure
- How many people
- 205 participants
- Where
- 3 sitesChina
- Study dates
- 8 Feb 2020to 31 Mar 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: In the study, the population are the patients with LARC, who receive neoadjuvant chemoradiotherapy or chemotherapy and TME surgery. The response of neoadjuvant treatment is unknown.
Who may be able to join
- pathologically diagnosed as rectal adenocarcinoma
- defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis
- receive neoadjuvant chemoradiotherapy or chemotherapy
- pre- and post-neoadjuvant treatment MRI data obtained
- receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response
Who cannot join
- with history of other cancer
- insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
- not completing neoadjuvant chemotherapy or chemoradiotherapy
- tumor recurrence or distant metastasis during neoadjuvant treatment
- not undergoing surgery resulting in lack of pathologic assessment of tumor response
Trial locations and contacts
3 sites in 1 country.
China3 sites, 3 recruiting
- Recruiting
the Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510655
Xiangbo Wan, MD, PhD, +86 13826017157, wanxbo@mail.sysu.edu.cn
- Recruiting
Sir Run Run Shaw Hospital
Hangzhou, Zhejiang, 310000
Weidong Han, MD, PhD, +86 13819124503, hanwd@zju.edu.cn
- Recruiting
The Third Affiliated Hospital of Kunming Medical College
Kunming, Yunnan, 650000
Zhenhui Li, MD, +86 13698736132, lizhenhui621@163.com
About this study
Official title: A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data.
Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study
Read the detailed description
This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III stage will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. All participants should follow a standard treatment protocol, including neoadjuvant treatment, total mesorectum excision (TME) surgery. Patients with LARC who received neoadjuvant treatment will be enrolled and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). The artificial intelligence prediction system and the expert radiologist will define the pathologic response as pCR or non-pCR, respectively. The pathologist will provide the final pathology report of TME surgery specimen (pCR or non-pCR) as a standard. The predictive efficacy of these two back-to-back approaches generated will be compared in this multicenter, prospective clinical study.
- Conditions
- Rectal Cancer
- Being tested
- artificial intelligence prediction system, the radiologists
- Keywords
- Radiomics features, Artificial intelligence model, Locally advanced rectal cancer, Pathologic complete response, Neoadjuvant treatment
- Medical subject headings
- Rectal Neoplasms, Pathologic Complete Response
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence prediction systemProcedure or surgery
The tumor ROI in the post- neoadjuvant treatment MRI images will be manually delineated, and further subjected to the AI prediction system arm to verify the predictive accuracy of this AI prediction system in identifying the pCR individuals from non-pCR patients with LARC.
the radiologistsProcedure or surgery
The enrolled patients will be assigned to the trained experienced radiologists to evaluate their predictive accuracy in identifying the pCR individuals from non-pCR patients
| Study group | Role | What they receive |
|---|---|---|
| patients will be evaluated by artificial intelligence system and expert radiologist | the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment, and not yet receive total mesorectum excision (TME) surgery will be enrolled. The post-neoadjuvant treatment MRI images features of each enrolled patients will be captured by the artificial intelligence system, and evaluated by experienced radiologists as well. Blind to the pathologic report of TME specimen, both approaches further respectively yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient, shown as pCR or non-pCR. Receives: Procedure: artificial intelligence prediction system; Procedure: the radiologists |
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 AI prediction system and expert radiologists in prediction tumor response
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
Measured: baseline
Secondary outcomes (2)
The specificity of AI prediction system and expert radiologists in prediction tumor response
The specificity of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
Measured: baseline
The sensitivity of AI prediction system and expert radiologists in prediction tumor response
The sensitivity of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Feb 2020
- Study start8 Feb 2020
- Primary completion10 Dec 2022 (estimated)
- Study completion31 Mar 2023 (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.
- 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
- 205 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Sir Run Run Shaw Hospital, The Third Affiliated Hospital of Kunming Medical College.
- Responsible party
- Principal investigator, wanxiangbo, Sixth Affiliated Hospital, Sun Yat-sen University
- Investigators
- Xiangbo Wan, MD, PhD, Study chair, Sixth Affiliated Hospital, Sun Yat-sen UniversityWeidong Han, MD, PhD, Principal investigator, Sir Run Run Shaw HospitalZhenhui Li, MD, Principal investigator, The Third Affiliated Hospital of Kunming Medical College.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04278274
- Sponsor's protocol ID
- MR-AI-pCR 2020
- Acronym
- MR-AI-pCR
- Status last verified
- Oct 2022
- First submitted
- 19 Feb 2020
- Last updated
- 26 Oct 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Oct 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
- ClinicalTrials.gov ID
- NCT04278274
- Acronym / sponsor ID
- MR-AI-pCR / MR-AI-pCR 2020
- Conditions
- Rectal Cancer
- Keywords
- Radiomics features, Artificial intelligence model, Locally advanced rectal cancer, Pathologic complete response, Neoadjuvant treatment
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 205 participants (estimated)
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Sir Run Run Shaw Hospital, The Third Affiliated Hospital of Kunming Medical College.
- Study start
- 8 Feb 2020 (actual)
- Primary completion
- 10 Dec 2022 (estimated)
- Study completion
- 31 Mar 2023 (estimated)
- First posted
- 20 Feb 2020 (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.
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data.
Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study
Detailed description
This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III stage will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. All participants should follow a standard treatment protocol, including neoadjuvant treatment, total mesorectum excision (TME) surgery. Patients with LARC who received neoadjuvant treatment will be enrolled and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). The artificial intelligence prediction system and the expert radiologist will define the pathologic response as pCR or non-pCR, respectively. The pathologist will provide the final pathology report of TME surgery specimen (pCR or non-pCR) as a standard. The predictive efficacy of these two back-to-back approaches generated will be compared in this multicenter, prospective clinical study.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patients will be evaluated by artificial intelligence system and expert radiologist | – | Procedure: artificial intelligence prediction system, Procedure: the radiologiststhe patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment, and not yet receive total mesorectum excision (TME) surgery will be enrolled. The post-neoadjuvant treatment MRI images features of each enrolled patients will be captured by the artificial intelligence system, and evaluated by experienced radiologists as well. Blind to the pathologic report of TME specimen, both approaches further respectively yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient, shown as pCR or non-pCR. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- In the study, the population are the patients with LARC, who receive neoadjuvant chemoradiotherapy or chemotherapy and TME surgery. The response of neoadjuvant treatment is unknown.
Key inclusion criteria (5 listed)
- pathologically diagnosed as rectal adenocarcinoma
- defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis
- receive neoadjuvant chemoradiotherapy or chemotherapy
- pre- and post-neoadjuvant treatment MRI data obtained
- receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response
Key exclusion criteria (5 listed)
- with history of other cancer
- insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
- not completing neoadjuvant chemotherapy or chemoradiotherapy
- tumor recurrence or distant metastasis during neoadjuvant treatment
- not undergoing surgery resulting in lack of pathologic assessment of tumor response
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| artificial intelligence prediction systemThe tumor ROI in the post- neoadjuvant treatment MRI images will be manually delineated, and further subjected to the AI prediction system arm to verify the predictive accuracy of this AI prediction system in identifying the pCR individuals from non-pCR patients with LARC. | Procedure or surgery | patients will be evaluated by artificial intelligence system and expert radiologist |
| the radiologistsThe enrolled patients will be assigned to the trained experienced radiologists to evaluate their predictive accuracy in identifying the pCR individuals from non-pCR patients | Procedure or surgery | patients will be evaluated by artificial intelligence system and expert radiologist |
Primary outcome
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
Time frame: baseline
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
2 secondary outcomes
The specificity of AI prediction system and expert radiologists in prediction tumor response
Time frame: baseline
The sensitivity of AI prediction system and expert radiologists in prediction tumor response
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Oct 2022.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Xiangbo Wan, MD, PhD study chair
Sixth Affiliated Hospital, Sun Yat-sen University
Weidong Han, MD, PhD principal investigator
Sir Run Run Shaw Hospital
Zhenhui Li, MD principal investigator
The Third Affiliated Hospital of Kunming Medical College.
3 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 3 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 5 inclusion and 5 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: status not confirmed, as of the sponsor's last update on 26 Oct 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Xiangbo Wan, MD, PhD (+86 13826017157, wanxbo@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 AI prediction system and expert radiologists in prediction tumor response (time frame: baseline). 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 26 Oct 2022.