Staged Unimodal-to-Multimodal AI Analysis of Histopathology, CT/MRI, and Multiplex Tissue Imaging for Perioperative Risk Prediction in Colorectal Cancer (KIA-Korekt)

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Despite advances in surgical technique and perioperative care, short-term postoperative complications remain frequent and substantially impact patient quality of life, healthcare costs, and long-term prognosis. These complications include anastomotic leakage, wound infection, sepsis, thromboembolic events, and in-hospital mortality. Existing clinical risk scores (ASA, POSSUM) provide only limited individualised risk stratification and do not incorporate imaging-derived biological markers.

The KIA-Korekt study addresses this gap by developing and validating AI-based predictive models for perioperative complications in CRC, integrating three complementary imaging modalities:

Digital histopathology: Haematoxylin-eosin stained whole-slide images (H&E-WSIs) from surgical resection specimens and preoperative biopsies are analysed using attention-based multiple instance learning (MIL) and convolutional neural networks (CNNs), building on established pipelines from the Department of Computational Pathology, TU Dresden (AG Kather).

Radiology: Preoperative CT and MRI images are processed using automated segmentation (TotalSegmentator, nnU-Net) and radiomic feature extraction (PyRadiomics). Features are derived from the primary tumour, psoas muscle (sarcopenia), and visceral/subcutaneous fat compartments. A dedicated multi-metric quality control pipeline ensures stable imaging data representations across scanners and acquisition protocols.

Multiplex tissue imaging (mTI): Multiplex immunohistochemistry with multispectral imaging (mIHC-MSI) and imaging mass cytometry (IMC) are applied to formalin-fixed paraffin-embedded tumour tissue to characterise immune and stromal cell populations, marker expression intensities, and spatial distribution patterns within the tumour microenvironment.