Pre-operative prediction of histopathological growth patterns of colorectal cancer liver metastasis using MRI-based radiomic models

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.

This retrospective study included 93 chemotherapy-naïve patients with CRLMs who underwent contrast-enhanced liver MRI and a partial hepatectomy between 2014 and 2022. Radiomic features were extracted from the tumor zone (R^Tumor), a 2-mm outer ring (R^T+2), a 2-mm inner ring (R^T−2), and a combined ring (R²⁺²) on late arterial phase MRI images. Analysis of variance method (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection. Logistic regression with five-fold cross-validation was used for model construction. Receiver operating characteristic curves, calibrated curves, and decision curve analyses were used to assess model performance. DeLong tests were used to compare different models.

Twenty-nine desmoplastic and sixty-four 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 R^Tumor, R^T−2, R^T+2, and R²⁺², respectively, in the training cohorts. The AUC values were 0.713, 0.876, 0.785, and 0.777 for R^Tumor, R^T−2, R^T+2, and R²⁺², respectively, in the validation cohort. R^T−2 exhibited the best performance.

The MRI-based radiomic models could predict HGPs in CRLMs pre-operatively.