Virtual biopsy for diagnosis of steatohepatitis and chemotherapy-associated liver injuries. A combined radiomic and clinical model

Abstract

Purpose: Chemotherapy-associated liver injuries (CALI) have a major clinical impact, but their non-invasive diagnosis is still an unmet need. We aim to elucidate the contribution of radiomics to diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH) and non-alcoholic steatohepatitis (NASH). Methods: Patients undergoing liver resection for colorectal metastases after oxaliplatin- or irinotecan-based chemotherapy between January 2018 and February 2020 were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography (CT). A multivariate logistic regression model was performed to identify predictors of CALI. The model was internally validated. Results: Overall, 78 patients were analyzed. Of these, 25 (32%) had grade 2-3 sinusoidal dilatation, 27 (35%) NRH, and 14 (18%) NASH. Three fingerprints derived from radiomic features were independent predictors of grade 2-3 sinusoidal dilatation: GLRLM_f3 (OR=12.25), NGLDM_f1 (OR=7.77), and GLZLM_f2 (OR=0.53). The combined clinical/radiomic predictive model had 82% accuracy, 64% sensitivity, and 91% specificity (AUC=0.87 vs AUC=0.77 of the model without radiomics). Three radiomic parameters were independent predictors of NRH: conventional_HUQ2 (OR=0.76), GLZLM_f2 (OR=0.05), and GLZLM_f3 (OR=7.97). The combined clinical/radiomic model had 85% accuracy, 81% sensitivity, and 86% specificity (AUC=0.91 vs AUC=0.85 without radiomic features). One radiomic feature was associated with NASH: conventional_HUQ2 (OR=0.79). Steatohepatitis was predicted with 91% accuracy, 86% sensitivity, and 92% specificity (AUC=0.93 vs AUC=0.83 without radiomic features). In the validation set, accuracy was 72%, 71%, and 91% for sinusoidal dilatation, NRH, and NASH, respectively. Conclusions: Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves diagnosis of CALI.