NCT07537491
KIA-Korekt: Staged Unimodal-to-Multimodal AI Evaluation for Perioperative Risk Prediction in Colorectal Cancer
Recruiting · Not specified · Rene Mantke · registry updated 2026-04-17
Inclusion and exclusion lines below are quoted from ClinicalTrials.gov. No match score is shown, because a score needs a person's age, biomarkers, and treatment dates. Confirm the record with the study team.
Perioperative complications following surgery for colorectal cancer (CRC) represent a major cause of postoperative morbidity and mortality. Existing risk stratification tools lack the precision to capture the complex biological and morphological factors that determine individual patient vulnerability. Artificial intelligence (AI)-based analysis of medical imaging data offers a promising approach to improve preoperative risk prediction. The KIA-Korekt study investigates whether perioperative complications in CRC patients can be predicted using multimodal AI-based image analysis. Three complementary imaging modalities are integrated: digital histopathology (haematoxylin-eosin whole-slide images, H\&E-WSIs), preoperative CT and MRI radiomics, and multiplex tissue imaging (mTI) including multiplex immunohistochemistry (mIHC) and imaging mass cytometry (IMC). The study includes a retrospective cohort of approximately 750 CRC patients treated between 2011 and 2021, and a prospective validation cohort of approximately 210 patients recruited from 2026 to 2028. Deep learning and radiomic feature extraction pipelines are applied to all modalities individually and in multimodal combination....
Inclusion
- Adult patients (≥18 years)
- Histologically confirmed colorectal adenocarcinoma
- Undergoing surgical resection (curative or palliative intent)
- Availability of H\&E-stained whole-slide images (WSIs) from the primary tumour
Exclusion
- Patients not undergoing surgical treatment
- Missing H\&E-stained tissue slides of the primary tumour
- Histopathological material of insufficient quality for analysis