KIA-Korekt: Staged Unimodal-to-Multimodal AI Evaluation for Perioperative Risk Prediction in Colorectal Cancer
Summary
NIH ClinicalTrials.gov registered NCT07537491 (KIA-Korekt study), an observational study conducted at University Hospital Brandenburg investigating whether multimodal AI analysis of medical imaging can predict perioperative complications in colorectal cancer patients. The study integrates digital histopathology (H&E-WSIs), CT/MRI radiomics, and multiplex tissue imaging across a retrospective cohort of approximately 750 CRC patients (2011–2021) and a prospective validation cohort of approximately 210 patients (2026–2028).
What changed
NIH ClinicalTrials.gov registered NCT07537491 (KIA-Korekt), an observational study at University Hospital Brandenburg, Brandenburg Medical School Theodor Fontane, investigating multimodal AI-based image analysis for perioperative risk prediction in colorectal cancer surgery patients. The study integrates three imaging modalities—digital histopathology (H&E whole-slide images), preoperative CT/MRI radiomics, and multiplex tissue imaging (mIHC and imaging mass cytometry)—applied to approximately 750 retrospective patients (2011–2021) and 210 prospective patients (2026–2028). Predicted outcomes include anastomotic leakage, wound infection, sepsis, ICU admission, and 30-day in-hospital mortality.
Healthcare providers and clinical researchers should note this study as an emerging application of AI-based medical imaging analysis for surgical risk stratification. Institutions developing or deploying similar AI imaging tools for perioperative assessment should track this study's outcomes, particularly the prospective validation phase. The study's use of multimodal imaging integration may inform future regulatory considerations for AI-based diagnostic and prognostic medical devices.
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Apr 18, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
KIA-Korekt: Staged Unimodal-to-Multimodal AI Evaluation for Perioperative Risk Prediction in Colorectal Cancer
Observational NCT07537491 Kind: OBSERVATIONAL Apr 17, 2026
Abstract
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 Computa...
Conditions: Colorectal Cancer Postoperative Complications
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