Clinical trial · Observational
ArTificial inTelligence-based RAdiogenomics in Colon Tumors
ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors
- 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)
The goal of this clinical trial is to develop an artificial intelligence-based model to assess radiogenomics signature of colon tumor in patients with stage II-III colon cancer. The main question it aims to answer is: • Can artificial intelligence-based algorithm of radiomics features combined with clinical factors, biochemical biomarkers, and genomic data recognise tumor behaviour, aggressiveness, and prognosis, identifying a radiogenomics signature of the tumor? Participants will * undergo a preoperative contrast-enhanced CT examination; * undergo surgical excision of colon cancer * undergo adjuvant therapy if deemed necessary based on current guidelines
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colon Cancer Stage II | Malignant Colon Neoplasm | CURATED_BROADER | 0.78 |
| Colon Cancer Stage III | Malignant Colon Neoplasm | CURATED_BROADER | 0.78 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors
- timeFrame
- From January 2021 to December 2023
Secondary outcomes (1)
- measure
- Correlation of radiogenomics signature (ATTRACT AI-model) of colon cancer with clinical outcomes (DFS and RFS)
- timeFrame
- From January 2024 to December 2025
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * patients with pathologically proven stage II and stage III colon cancer; * availability of a CT scan with portal-venous phase at the time of diagnosis; * availability of immunohistochemical panel Exclusion Criteria: * patients with no CT images prior to surgical resection; * patients with CT scans characterized by motion artifacts preventing radiomics analysis
References
Publications (41)
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- BACKGROUNDHuang L, Brunell D, Stephan C, Mancuso J, Yu X, He B, Thompson TC, Zinner R, Kim J, Davies P, Wong STC. Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics. 2019 Oct 1;35(19):3709-3717. doi: 10.1093/bioinformatics/btz109. PMID 30768150
- BACKGROUNDLong NP, Jung KH, Anh NH, Yan HH, Nghi TD, Park S, Yoon SJ, Min JE, Kim HM, Lim JH, Kim JM, Lim J, Lee S, Hong SS, Kwon SW. An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers (Basel). 2019 Jan 29;11(2):155. doi: 10.3390/cancers11020155. PMID 30700038