Rectal CancerMRAI-pCRNCT04273477
Radiomics-based Artificial Intelligence System to Predict Neoadjuvant Treatment Response in 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 utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Rectal Cancer
- How many people
- 100 participants
- Where
- 3 sitesChina
- Study dates
- 10 Jan 2020to Dec 2020
- 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: The population in the study are the patients with LARC, who are intended to receive or undergoing standard, concurrent neoadjuvant chemoradiotherapy with tumor response 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 by enhanced Magnetic Resonance Imaging (MRI)
- intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)
- intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy
- MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy
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)
- incomplete neoadjuvant chemoradiotherapy
- no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response
- tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy
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: Predicting Neoadjuvant Chemoradiotherapy Response by Radiomics-based Artificial Intelligence System in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
In this study, investigators utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Previously, the radiomics prediction model has been constructed based on the radiomics features extracted from pretreatment Magnetic Resonance Imaging (MRI) in the training set, and optimized in the external validation set. The predictive power of this radiomics prediction model to discriminate the pathologic complete response (pCR) patients from non-pCR individuals, will be further verified in this prospective, multicenter clinical study.
Read the detailed description
This is a multicenter, prospective, observational clinical study for validation of a radiomics-based artificial intelligence (AI) prediction model. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, the Third Affiliated Hospital of Kunming Medical College and Sir Run Run Shaw Hospital Affiliated by Zhejiang University School of Medicine. All participants should follow a standard treatment protocol, including concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Enhanced Magnetic Resonance Imaging (MRI) examination should be completed before the administration of nCRT treatment. The tumor volumes at high solution T2-weighted, contrast-enhanced T1-weighted and diffusion weighted images will be manually delineated, respectively. The outlined MRI images will be captured by the radiomics prediction model to generate a predicted response ("predicted pCR" vs. "predicted non-pCR") of each patient, whereas the true response ("confirmed pCR" vs. "confirmed non-pCR") is derived from pathologic reports after TME surgery serving as the gold standard for evaluation. The prediction accuracy, specificity, sensitivity and Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves will be calculated. This study is aimed to provide a reliable and accurate AI system to predict the pathologic tumor response to nCRT before its administration, which might facilitate the identification of pCR candidates for further precision therapy among patients with locally advanced rectal cancer.
- Conditions
- Rectal Cancer
- Keywords
- MRI Radiomics, Artificial intelligence, Locally advanced rectal cancer, Pathologic complete response, Neoadjuvant chemoradiotherapy
- Medical subject headings
- Rectal Neoplasms, Pathologic Complete Response
What is being measured
The primary outcome is the main question the study is designed to answer.
The prediction accuracy of the radiomics prediction model
The prediction accuracy of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Measured: baseline
Secondary outcomes (3)
The specificity of the radiomics prediction model
The specificity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Measured: baseline
The sensitivity of the radiomics prediction model
The sensitivity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Measured: baseline
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics prediction model
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Feb 2020
- Study start10 Jan 2020
- Primary completionJul 2020 (estimated)
- Study completionDec 2020 (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
- Other
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- The Third Affiliated Hospital of Kunming Medical College., Sir Run Run Shaw Hospital
- Responsible party
- Principal investigator, wanxiangbo, Sixth Affiliated Hospital, Sun Yat-sen University
- Investigators
- Xiangbo Wan, MD, PhD, Principal investigator, Sixth Affiliated Hospital, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04273477
- Sponsor's protocol ID
- MRILARC-pCR2020
- Acronym
- MRAI-pCR
- Status last verified
- Feb 2020
- First submitted
- 15 Feb 2020
- Last updated
- 18 Feb 2020
- 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 18 Feb 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Neoadjuvant Chemoradiotherapy Response by Radiomics-based Artificial Intelligence System in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
- ClinicalTrials.gov ID
- NCT04273477
- Acronym / sponsor ID
- MRAI-pCR / MRILARC-pCR2020
- Conditions
- Rectal Cancer
- Keywords
- MRI Radiomics, Artificial intelligence, Locally advanced rectal cancer, Pathologic complete response, Neoadjuvant chemoradiotherapy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- The Third Affiliated Hospital of Kunming Medical College., Sir Run Run Shaw Hospital
- Study start
- 10 Jan 2020 (actual)
- Primary completion
- Jul 2020 (estimated)
- Study completion
- Dec 2020 (estimated)
- First posted
- 18 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 utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Previously, the radiomics prediction model has been constructed based on the radiomics features extracted from pretreatment Magnetic Resonance Imaging (MRI) in the training set, and optimized in the external validation set. The predictive power of this radiomics prediction model to discriminate the pathologic complete response (pCR) patients from non-pCR individuals, will be further verified in this prospective, multicenter clinical study.
Detailed description
This is a multicenter, prospective, observational clinical study for validation of a radiomics-based artificial intelligence (AI) prediction model. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, the Third Affiliated Hospital of Kunming Medical College and Sir Run Run Shaw Hospital Affiliated by Zhejiang University School of Medicine. All participants should follow a standard treatment protocol, including concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Enhanced Magnetic Resonance Imaging (MRI) examination should be completed before the administration of nCRT treatment. The tumor volumes at high solution T2-weighted, contrast-enhanced T1-weighted and diffusion weighted images will be manually delineated, respectively. The outlined MRI images will be captured by the radiomics prediction model to generate a predicted response ("predicted pCR" vs. "predicted non-pCR") of each patient, whereas the true response ("confirmed pCR" vs. "confirmed non-pCR") is derived from pathologic reports after TME surgery serving as the gold standard for evaluation. The prediction accuracy, specificity, sensitivity and Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves will be calculated. This study is aimed to provide a reliable and accurate AI system to predict the pathologic tumor response to nCRT before its administration, which might facilitate the identification of pCR candidates for further precision therapy among patients with locally advanced rectal cancer.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The population in the study are the patients with LARC, who are intended to receive or undergoing standard, concurrent neoadjuvant chemoradiotherapy with tumor response 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 by enhanced Magnetic Resonance Imaging (MRI)
- intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)
- intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy
- MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy
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)
- incomplete neoadjuvant chemoradiotherapy
- no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response
- tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy
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 prediction accuracy of the radiomics prediction model
Time frame: baseline
The prediction accuracy of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated.
3 secondary outcomes
The specificity of the radiomics prediction model
Time frame: baseline
The sensitivity of the radiomics prediction model
Time frame: baseline
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics prediction model
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 18 Feb 2020.
- 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 principal investigator
Sixth Affiliated Hospital, Sun Yat-sen University
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 18 Feb 2020. 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 prediction accuracy of the radiomics prediction model (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 18 Feb 2020; AzTrials last refreshed its copy on 29 Oct 2026.