Pre-operative Prediction of Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis Using an MRI-based Radiomic Models

Abstract

Abstract Purpose Histopathological growth patterns (HGPs) of colorectal liver metastases (CRLMs) have prognostic value. However, the differentiation of HGPs relies on postoperative pathology. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomic model to predict HGP pre-operatively, following the latest guidelines. Methods This retrospective study included 93 chemotherapy-naïve patients with CRLMs who underwent contrast-enhanced liver MRI and partial hepatectomy between 2014 and 2022. Radiomic features were extracted from the tumor zone (RTumor), a 2-mm inner ring (RT−2), a 2-mm outer ring (RT+2), and a combined ring (R2 + 2) on late arterial phase MR images. ANOVA and LASSO algorithms were used for feature selection. Logistic regression with five-fold cross-validation was used for model con-struction. Receiver operating characteristic curves, calibrated curves, and decision curve analyses were used to assess model performance. Delong’s test was used to compare different models. Results Twenty-nine desmoplastic and 64 non-desmoplastic CRLMs were included. The radiomic models achieved area under the curve (AUC) values of 0.736, 0.906, 0.804, and 0.794 for RTumor, RT−2, RT+2, and R2 + 2, respectively, in the training cohorts. The AUC values were 0.713, 0.876, 0.785, and 0.777 for RTumor, RT−2, RT+2, and R2 + 2, respectively, in the validation cohort. RT−2 exhibited the best performance. Conclusion The MRI-based radiomic models could predict HGPs in CRLMs pre-operatively. The differences between dHGP and non-dHGP are primarily observed near the tumor margins, particularly the internal edges.