Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making : a Multicenter Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging.
研究概览
简要总结
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
详细描述
This is a retrospective, non-interventional, observational study evaluating the use of deep learning methods to improve preoperative CT-based TNM staging in patients with colon cancer. The study is conducted across multiple sites within the AP-HP hospital network (Paris, France) and uses data extracted from the institutional Health Data Warehouse.
Radiologic accuracy in assessing tumor stage (T) and lymph node status (N) remains limited, despite being critical for selecting neoadjuvant treatments. Artificial intelligence models trained on annotated imaging data may provide more consistent, reproducible, and accurate classification.
The study cohort includes adult patients who underwent colon resection between January 2017 and November 2024, with a preoperative CT scan and corresponding pathology report. Eligible cases are identified using standardized diagnostic (ICD-10) and procedural (CCAM) codes. Imaging and clinical data are de-identified prior to analysis.
Several AI model architectures will be tested, including 3D convolutional neural networks and transformer-based approaches. CT scans will be pre-processed using standard pipelines; pathology labels will be extracted using natural language processing (NLP) techniques or manual review when needed. Model performance will be assessed through cross-validation and evaluated using AUC, F1-score, sensitivity, and specificity.
Exploratory analyses will include fine-grained tumor staging and the potential prognostic value of image-based features for clinical outcomes such as survival.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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, HHFC
- •Confirmed diagnosis of colon tumor by ICD-10 code:
- •C18* (colonic neoplasms).
排除标准
- •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.
研究组 & 干预措施
Tran-validation group
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.
Test group
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.
结局指标
主要结局
Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging.
时间窗: Index preoperative CT through postoperative pathology report (within 90 days of surgery).
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.
次要结局
- Detection performance for T4 tumors on preoperative CT.(Index CT to pathology confirmation (≤90 days post-surgery).)
- Multiclass T-stage classification accuracy (T1, T2, T3, T4).(Index CT to pathology confirmation (≤90 days).)
- Prognostic value of CT-derived model features for clinical outcomes and survival.(From index CT to last follow-up (up to 5 years, or maximum available follow-up in EHR).)