Mustafa Bashir

The focus of hepatocellular carcinoma (HCC) research for many years has been on noninvasive diagnosis. Standardized systematic algorithms composed of combinations of precise features now serve as diagnostic imaging markers of HCC and constitute a major innovation for liver imaging. In clinical practice, the diagnosis of HCC is based primarily on imaging and secondarily on pathologic analysis if the imaging features are not specific. Whereas accurate diagnosis is essential, the next phase of innovation for HCC will likely encompass predictive and prognostic markers. HCC is a biologically heterogeneous malignancy because of complex molecular, pathologic, and patient-level factors that impact the outcomes of treatment. In recent years, there have been many advances in systemic therapy to augment and extend the existing large cache of local and regional options. However, the guideposts for treatment decisions are neither sophisticated nor individualized. This review provides an overview of prognosis in HCC from the patient to the imaging feature level with a focus on future directions toward more individualized treatment guidance.

Background Peritumoral hepatobiliary phase (HBP) hypointensity is an established prognostic imaging feature in hepatocellular carcinoma (HCC), often associated with microvascular invasion (MVI). Similar prognostic features are needed for non-HBP MRI. Purpose To propose a non-hepatobiliary-specific MRI tool with similar prognostic value to peritumoral HBP hypointensity. Materials and Methods From December 2011 to November 2021, consecutive patients with HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled and followed up until recurrence. All MRI scans were reviewed by two blinded radiologists with 7 and 10 years of experiences with liver MRI. A scoring system based on non-hepatobiliary-specific features that highly correlated with peritumoral HBP hypointensity was identified in a stratified sampling-derived training set of the gadoxetate disodium (EOB) group by means of multivariable logistic regression, and its values to predict MVI and recurrence-free survival (RFS) were assessed. Results There were 660 patients (551 men; median age, 53 years; IQR, 45-61 years) enrolled. Peritumoral portal venous phase hypoenhancement (odds ratio [OR] = 8.8), incomplete "capsule" (OR = 3.3), corona enhancement (OR, 2.6), and peritumoral mild-moderate T2 hyperintensity (OR, 2.2) (all P < .001) were associated with peritumoral HBP hypointensity and constituted the "VICT2 trait" (test set area under the receiver operating characteristic curve = 0.84; 95% CI: 0.78, 0.90). For the EOB group, both peritumoral HBP hypointensity (OR for MVI = 2.5, P = .02; hazard ratio for RFS = 2.5, P < .001) and the VICT2 trait (OR for MVI = 5.1, P < .001; hazard ratio for RFS = 2.3, P < .001) were associated with MVI and RFS, despite a higher specificity of the VICT2 trait for MVI (89% vs 80%, P = .01). These values of the VICT2 trait were confirmed in the extracellular contrast agent group (OR for MVI = 4.0; hazard ratio for RFS = 1.7; both P < .001). Conclusion Based on four non-hepatobiliary-specific MRI features, the VICT2 trait was comparable to peritumoral hepatobiliary phase hypointensity in predicting microvascular invasion and postoperative recurrence of hepatocellular carcinoma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Harmath in this issue.

PURPOSE: To compare liver fat quantification between MRI and photon-counting CT (PCCT). METHOD: A cylindrical phantom with inserts containing six concentrations of oil (0, 10, 20, 30, 50 and 100%) and oil-iodine mixtures (0, 10, 20, 30 and 50% fat +3 mg/mL iodine) was imaged with a PCCT (NAEOTOM Alpha) and a 1.5 T MRI system (MR 450w, IDEAL-IQ sequence), using clinical parameters. An IRB-approved prospective clinical evaluation included 12 obese adult patients with known fatty liver disease (seven women, mean age: 61.5 ± 13 years, mean BMI: 30.3 ± 4.7 kg/m2). Patients underwent a same-day clinical MRI and PCCT of the abdomen. Liver fat fractions were calculated for four segments (I, II, IVa and VII) using in- and opposed-phase on MRI ((Meanin - Meanopp)/2*Meanin) and iodine-fat, tissue decomposition analysis in PCCT (Syngo.Via VB60A). CT and MRI Fat fractions were compared using two-sample t-tests with equal variance. Statistical analysis was performed using RStudio (Version1.4.1717). RESULTS: Phantom results showed no significant differences between the known fat fractions (P = 0.32) or iodine (P = 0.6) in comparison to PCCT-measured concentrations, and no statistically significant difference between known and MRI-measured fat fractions (P = 0.363). In patients, the mean fat signal fraction measured on MRI and PCCT was 13.1 ± 9.9% and 12.0 ± 9.0%, respectively, with an average difference of 1.1 ± 1.9% between the modalities (P = 0.138). CONCLUSION: First experience shows promising accuracy of liver fat fraction quantification for PCCT in obese patients. This method may improve opportunistic screening for CT in the future.