Colonic NeoplasmDeepColScanNCT07406958
Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making.
An observational study of Colonic Neoplasm, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colonic Neoplasm
- How many people
- 1,000 participants
- Where
- 1 siteFrance
- Study dates
- Feb 2026to Feb 2028
- 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: Adults (≥18 years) with pathologically confirmed colon cancer who underwent surgical resection at participating hospitals and had an available preoperative thoraco-abdominopelvic CT performed within the institution and linked to a pathology report containing TNM staging within 90 days of surgery. Imaging and reports extracted from institutional data warehouse; ambiguous or missing labels resolved by manual medical review.
Who may be able to join
- 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).
Who cannot join
- 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.
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: Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making : a Multicenter Study
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
Read the detailed description
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.
No study-related procedures are performed. All analyses are conducted on existing data, in compliance with French data protection and ethical regulations.
- Conditions
- Colonic Neoplasm
- Keywords
- Colon cancer, deep learning, CT scan, TNM classification, radiology, AI, machine learning, tumor staging, colorectal neoplasms
- Medical subject headings
- Colonic Neoplasms, Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging.
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.
Measured: Index preoperative CT through postoperative pathology report (within 90 days of surgery).
Secondary outcomes (3)
Detection performance for T4 tumors on preoperative CT.
AUC, F1, sensitivity, specificity for binary classification T4 vs non-T4 (T1-3). Analyses stratified by tumor location and contrast phase when data permit.
Measured: Index CT to pathology confirmation (≤90 days post-surgery).
Multiclass T-stage classification accuracy (T1, T2, T3, T4).
Macro-averaged F1, per-class sensitivity/specificity, confusion matrix, and Cohen's kappa comparing model-predicted 4-class T stage with pathology.
Measured: Index CT to pathology confirmation (≤90 days).
Prognostic value of CT-derived model features for clinical outcomes and survival.
Association between model outputs (probabilities, embeddings) and (a)coverall survival, (b) disease-free survival. Evaluated using Kaplan-Meier analysis, log-rank tests, and multivariable Cox models adjusted for clinical covariates where available.
Measured: From index CT to last follow-up (up to 5 years, or maximum available follow-up in EHR).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Feb 2026
- Study startFeb 2026 (estimated)
- Primary completionFeb 2028 (estimated)
- Study completionFeb 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colonic NeoplasmStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe 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
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- Institut National de Recherche en Informatique et en Automatique
- Responsible party
- Sponsor
- Investigators
- Quentin Vanderbecq, MD, Principal investigator, Assistance Publique - Hôpitaux de Paris
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07406958
- Sponsor's protocol ID
- APHP251408
- Acronym
- DeepColScan
- Status last verified
- Feb 2025
- First submitted
- 6 Feb 2026
- Last updated
- 12 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Advanced Classification of Colon Tumors From CT Scans Using Deep Learning for Optimized Treatment Decision-making : a Multicenter Study
- ClinicalTrials.gov ID
- NCT07406958
- Acronym / sponsor ID
- DeepColScan / APHP251408
- Conditions
- Colonic Neoplasm
- Keywords
- Colon cancer, deep learning, CT scan, TNM classification, radiology, AI, machine learning, tumor staging
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- Institut National de Recherche en Informatique et en Automatique
- Study start
- Feb 2026 (estimated)
- Primary completion
- Feb 2028 (estimated)
- Study completion
- Feb 2028 (estimated)
- First posted
- 12 Feb 2026 (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 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
Detailed description
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.
No study-related procedures are performed. All analyses are conducted on existing data, in compliance with French data protection and ethical regulations.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults (≥18 years) with pathologically confirmed colon cancer who underwent surgical resection at participating hospitals and had an available preoperative thoraco-abdominopelvic CT performed within the institution and linked to a pathology report containing TNM staging within 90 days of surgery. Imaging and reports extracted from institutional data warehouse; ambiguous or missing labels resolved by manual medical review.
Key inclusion criteria (7 listed)
- 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.
- HHFA002, HHFA004, HHFA005, HHFA006, HHFA008, HHFA009, HHFA010, HHFA014, HHFA017, HHFA018, HHFA021, HHFA022, HHFA023, HHFA024, HHFA026, HHFA028, HHFA029, HHFA030, HHFA031, HHFC040, HHFC296.
- C18 (colonic neoplasms).
Key exclusion criteria (3 listed)
- 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.
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
Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging.
Time frame: 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.
3 secondary outcomes
Detection performance for T4 tumors on preoperative CT.
Time frame: Index CT to pathology confirmation (≤90 days post-surgery).
Multiclass T-stage classification accuracy (T1, T2, T3, T4).
Time frame: Index CT to pathology confirmation (≤90 days).
Prognostic value of CT-derived model features for clinical outcomes and survival.
Time frame: From index CT to last follow-up (up to 5 years, or maximum available follow-up in EHR).
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 12 Feb 2026.
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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.
Quentin Vanderbecq, MD principal investigator
Assistance Publique - Hôpitaux de Paris
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 Colonic Neoplasm, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
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 7 inclusion and 3 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 12 Feb 2026. 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 France. 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 Quentin Vanderbecq, MD (00 33 1 49 28 20 00, quentin.vanderbecq@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of CT-based deep learning models for T (T1-2 vs T3-4) and N (N- vs N+) staging. (time frame: Index preoperative CT through postoperative pathology report (within 90 days of surgery).). 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 12 Feb 2026.