Clinical trial · Observational
Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making.
Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making : a Multicenter Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study aims to improve the classification of colon tumors using deep learning models trained on CT scans, specifically to distinguish between T1-T2 vs. T3-T4 stages and N- vs. N+ lymph node involvement. This classification is critical to guide preoperative treatment such as chemotherapy or immunotherapy. Given the limited accuracy of radiologists in current staging practice, automated image-based AI tools could enhance diagnostic precision and reproducibility, leading to more personalized and effective treatment planning. The investigator will develop and validate convolutional and transformer-based deep learning models using a large annotated dataset from multiple centers. Secondary objectives include fine-grained staging (T1 to T4), subgroup-specific models (MSS vs MSI), and predictive models for surgical
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colonic Neoplasm | Colon Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Tran-validation group
- description
- Adults with pathologically confirmed colon cancer who underwent preoperative thoraco-abdominopelvic CT and subsequent colectomy at participating centers; cases meeting imaging and pathology quality criteria used for model training and internal cross-validation. AI / Deep Learning CT Analysis: Retrospective analysis of existing preoperative CT scans and linked pathology/clinical data to develop and evaluate automated staging models. No experimental drug, device, or procedure is administered.
- label
- Test group
- description
- Distinct subset (aleatory taken at the beginning of model training) of eligible colon cancer cases withheld from model development to provide independent performance validation for T and N classification models and exploratory secondary analyses.
Primary outcomes (1)
- measure
- Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging.
- timeFrame
- Index preoperative CT through postoperative pathology report (within 90 days of surgery).
- description
- Area under the ROC curve (AUC) and F1-score of deep learning models in (a) classifying early vs advanced T stage (T1-2 vs T3-4) and (b) nodal status (N- vs N+) compared with pathology gold standard. Sensitivity, specificity, PPV, NPV reported as supportive metrics; model performance compared to historical radiologist benchmarks when available.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: Adults who underwent colon resection surgery at an AP-HP hospital between 01/01/2017 and 01/11/2024, with: A preoperative abdominopelvic CT scan available within 60 days prior to surgery. A corresponding pathology report (anatomopathological results) available within 90 days post-surgery. Colon resection identified by CCAM procedure codes: HHFA002, HHFA004, HHFA005, HHFA006, HHFA008, HHFA009, HHFA010, HHFA014, HHFA017, HHFA018, HHFA021, HHFA022, HHFA023, HHFA024, HHFA026, HHFA028, HHFA029, HHFA030, HHFA031, HHFC040, HHFC296. Confirmed diagnosis of colon tumor by ICD-10 code: C18\* (colonic neoplasms). Exclusion Criteria: Patients who received neoadjuvant chemotherapy prior to surgery, identified by ICD-10 codes Z511 or Z512 recorded before the surgical act. These exclusions will be refined and confirmed through manual medical record review to ensure accuracy. Absence of usable CT imaging or anatomical pathology data linked to the surgical event.
References
Publications (0)
Data not yet available