General SurgeryNCT07183124
3D Modeling for Detecting Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin
An observational study of General Surgery and Oncology, run by Taichung Veterans General Hospital at 1 site in Taiwan.
Trial summary
This retrospective study aims to develop an AI-assisted 3D modeling system to improve staging accuracy for stage II-III locally advanced rectal cancer (LARC). High-quality CT images from Taichung Veterans General Hospital will be used to reconstruct tumor boundaries and spatial relationships. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- General Surgery, Oncology, Medical Informatics
- What is being tested?
- AI-Assisted 3D Imaging Model for Tumor and CRM Assessmen
- How many people
- 1,500 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Oct 2025to 31 Jul 2026
- 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: The study population consists of adult patients (≥18 years) diagnosed with stage II-III locally advanced rectal cancer (LARC) without distant metastasis (M0), who received care at Taichung Veterans General Hospital (TVGH), Taiwan. Eligible participants have adequate physical status (ASA I-III) to undergo standard treatment and surgery, no history of other malignancies or major diseases affecting tumor assessment within the past three years, and complete medical records including CT and MRI imaging. Patients with stage I or IV disease, insufficient physical status, major comorbidities, or incomplete imaging/medical records are excluded.
Who may be able to join
- Diagnosed with rectal cancer, clinical stage II-III, with no distant metastasis (M0)
- Age over 18 years, with adequate physical status classified as American Society of Anesthesiologists (ASA) I-III, capable of receiving treatment and surgery
- No history of other malignancies or major diseases affecting study assessment within the past three years.
- Complete medical records, including available CT and MRI imaging.
Who cannot join
- Patients with clinical stage I or IV rectal cancer.
- Age under 18 years, or physical status not meeting American Society of Anesthesiologists (ASA) I-III criteria, unable to undergo surgery or related treatment.
- Presence of other major diseases or malignancies affecting tumor assessment (e.g., diagnosis of another malignancy within the past three years, uncontrolled cardiovascular disease).
- Incomplete medical records or imaging data, including missing required CT or MRI images.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Using 3D Modeling to Detect Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin
This retrospective study aims to develop an AI-assisted 3D modeling system to improve staging accuracy for stage II-III locally advanced rectal cancer (LARC). High-quality CT images from Taichung Veterans General Hospital will be used to reconstruct tumor boundaries and spatial relationships. The AI model will be trained and validated against MRI and pathology results to predict circumferential resection margin (CRM) status. Outcomes include sensitivity, specificity, accuracy, and agreement with standard imaging. This system seeks to support precise tumor staging and inform future clinical decision-making.
- Conditions
- General Surgery, Oncology, Medical Informatics
- Being tested
- AI-Assisted 3D Imaging Model for Tumor and CRM Assessmen
- Keywords
- rectal cancer, 3D Modeling, deep learning, Pelvic CT Imaging, Clinical Prediction Model
- Medical subject headings
- Neoplasms, Rectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted 3D Imaging Model for Tumor and CRM AssessmenDiagnostic test
This study uses an AI-assisted 3D imaging model to analyze existing CT and MRI images of stage II-III locally advanced rectal cancer patients. The system reconstructs tumor boundaries and spatial relationships, predicts circumferential resection margin (CRM) status, and supports staging assessment. No interventions are performed on participants, and all data are collected retrospectively from routine clinical care.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity
Model predictions are compared with pathology results (gold standard) to assess diagnostic accuracy.
Measured: Day 1 (At the time of retrospective imaging analysis)
Secondary outcomes (1)
Accuracy and agreement of AI model predictions with MRI interpretations
Agreement between AI model, MRI, and pathology results will be analyzed using Kappa statistics to evaluate consistency and reliability.
Measured: Day 1 (At the time of retrospective imaging analysis)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2025
- Study start1 Oct 2025 (estimated)
- Primary completion30 Jun 2026 (estimated)
- Study completion31 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for General SurgeryStudies of the same condition, open and under way.
- More trials run by Taichung Veterans General HospitalThe 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
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Taichung Veterans General Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Health Research Institutes, Taiwan
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07183124
- Sponsor's protocol ID
- CE25536A
- Other IDs
- TCVGH-NHRI1142008
- Status last verified
- Sep 2025
- First submitted
- 14 Sep 2025
- Last updated
- 19 Sep 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using 3D Modeling to Detect Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin
- ClinicalTrials.gov ID
- NCT07183124
- Acronym / sponsor ID
- CE25536A
- Conditions
- General Surgery, Oncology, Medical Informatics
- Keywords
- rectal cancer, 3D Modeling, deep learning, Pelvic CT Imaging, Clinical Prediction Model
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Taichung Veterans General HospitalUniversity, hospital or other
- Collaborators
- National Health Research Institutes, Taiwan
- Study start
- 1 Oct 2025 (estimated)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 31 Jul 2026 (estimated)
- First posted
- 19 Sep 2025 (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.
This retrospective study aims to develop an AI-assisted 3D modeling system to improve staging accuracy for stage II-III locally advanced rectal cancer (LARC). High-quality CT images from Taichung Veterans General Hospital will be used to reconstruct tumor boundaries and spatial relationships. The AI model will be trained and validated against MRI and pathology results to predict circumferential resection margin (CRM) status. Outcomes include sensitivity, specificity, accuracy, and agreement with standard imaging. This system seeks to support precise tumor staging and inform future clinical decision-making.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients (≥18 years) diagnosed with stage II-III locally advanced rectal cancer (LARC) without distant metastasis (M0), who received care at Taichung Veterans General Hospital (TVGH), Taiwan. Eligible participants have adequate physical status (ASA I-III) to undergo standard treatment and surgery, no history of other malignancies or major diseases affecting tumor assessment within the past three years, and complete medical records including CT and MRI imaging. Patients with stage I or IV disease, insufficient physical status, major comorbidities, or incomplete imaging/medical records are excluded.
Key inclusion criteria (4 listed)
- Diagnosed with rectal cancer, clinical stage II-III, with no distant metastasis (M0)
- Age over 18 years, with adequate physical status classified as American Society of Anesthesiologists (ASA) I-III, capable of receiving treatment and surgery
- No history of other malignancies or major diseases affecting study assessment within the past three years.
- Complete medical records, including available CT and MRI imaging.
Key exclusion criteria (4 listed)
- Patients with clinical stage I or IV rectal cancer.
- Age under 18 years, or physical status not meeting American Society of Anesthesiologists (ASA) I-III criteria, unable to undergo surgery or related treatment.
- Presence of other major diseases or malignancies affecting tumor assessment (e.g., diagnosis of another malignancy within the past three years, uncontrolled cardiovascular disease).
- Incomplete medical records or imaging data, including missing required CT or MRI images.
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 |
|---|---|---|
| AI-Assisted 3D Imaging Model for Tumor and CRM AssessmenThis study uses an AI-assisted 3D imaging model to analyze existing CT and MRI images of stage II-III locally advanced rectal cancer patients. The system reconstructs tumor boundaries and spatial relationships, predicts circumferential resection margin (CRM) status, and supports staging assessment. No interventions are performed on participants, and all data are collected retrospectively from routine clinical care. | Diagnostic test | – |
Primary outcome
Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity
Time frame: Day 1 (At the time of retrospective imaging analysis)
Model predictions are compared with pathology results (gold standard) to assess diagnostic accuracy.
1 secondary outcome
Accuracy and agreement of AI model predictions with MRI interpretations
Time frame: Day 1 (At the time of retrospective imaging analysis)
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 19 Sep 2025.
Related trials for General Surgery
Not yet recruiting
High-Flow Nasal Oxygen-Assisted Deep Extubation Versus Awake Extubation in Morbidly Obese Patients
Deep Tracheal Extubation
Not yet recruitingObservational study
Assessment of Transnational Performance of the Universal Cardiac Surgical Risk Score Across International Sites
Heart Valve Diseases, Cardiac Surgical Procedures
RecruitingEarly Phase 1
Testing the Safety of Dapagliflozin Prior to Surgery for the Treatment of Patients With Stage IA Lung Adenocarcinoma
Lung Adenocarcinoma, Stage IA1 Lung Cancer American Joint Committee on Cancer (AJCC) v8
RecruitingObservational study
Evaluation for NCI Surgery Branch Clinical Research Protocols
Synovial Cell Cancer, Melanoma
Not yet recruiting
Individualized Inspired Oxygen Fraction Titration During Lung Surgery (iOXY-LUNG)
Postoperative Complications
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of General Surgery and Oncology, run by Taichung Veterans General Hospital at 1 site in Taiwan.
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 4 inclusion and 4 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: opening soon, as of the sponsor's last update on 19 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. 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 Chun-Yu Lin, PhD (886-4-23592525 ext. 5161, classicpiano2003@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity (time frame: Day 1 (At the time of retrospective imaging analysis)). 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 19 Sep 2025.