Abstract Aim Completeness of cytoreduction (CC) remains the strongest prognostic determinant after cytoreductive surgery (CRS) ± hyperthermic intraperitoneal chemotherapy (HIPEC) for colorectal peritoneal metastases (CPM) yet accurate pre‐operative prediction remains difficult. This study aimed to develop and validate a radiomic–clinical machine‐learning model to predict cytoreduction completeness. Methods 83 patients who underwent CRS ± HIPEC for CPM (2008–2025) were retrospectively analysed. Pre‐operative contrast‐enhanced CT scans were manually segmented in ITK‐SNAP, and radiomic features were extracted using PyRadiomics. Clinical variables were modelled alone and in combination with radiomics features using a nested five‐fold cross‐validated machine‐learning pipeline incorporating least absolute shrinkage and selection operator (LASSO) logistic regression, random forest (RF) and gradient‐boosted classifiers (GBC) algorithms. The primary endpoint was incomplete cytoreduction (iCC). Model discrimination (AUROC, AUPRC), Brier score and calibration were assessed. Results iCC occurred in 17 of 83 patients (20.5%). Independent predictors of iCC were high radiological PCI (≥15), upper‐abdominal disease, absence of pre‐operative chemotherapy and normal CEA (≤5 ng/mL). The radiomic–clinical model achieved the best performance (AUROC 0.90, AUPRC 0.69, Brier 0.077, sensitivity 0.83, specificity 0.92), outperforming clinical‐only (AUROC 0.82–0.86) and radiomic‐only (AUROC 0.69–0.75) models. Key radiomic predictors of iCC—low sphericity, high maximum 2D diameter and high zone entropy—reflected morphological irregularity and heterogeneity of CPM. Integrated models demonstrated superior calibration indicating stable and reliable probability estimates. Conclusion A CT‐based radiomic–clinical model accurately predicts CC pre‐operatively. This exploratory proof‐of‐concept model supports multicentre external validation to enhance decision‐making for CRS ± HIPEC in CPM.
Pau et al. (Sun,) studied this question.