KIA-Korekt: Staged Unimodal-to-Multimodal AI Evaluation for Perioperative Risk Prediction in Colorectal Cancer

Condition(s) studied

Study summary

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. Predicted outcomes include anastomotic leakage, wound infection, sepsis, ICU admission, and in-hospital mortality within 30 days of surgery.

The study is conducted at the University Hospital Brandenburg, Brandenburg Medical School Theodor Fontane, in collaboration with the Department of Computational Pathology, TU Dresden.

Eligibility

Primary outcome measure(s)

• Prediction accuracy of perioperative complications — 30 days postoperative
  Occurrence of postoperative complications including anastomotic leakage, sepsis, ICU admission, and in-hospital mortality. Outcomes are defined based on clinical documentation and assessed as binary variables (yes/no).

Trial sites (1)