Marybeth A. Nedrud

Purpose The Liver Imaging Reporting and Data System (LI-RADS, LR) provides a framework for diagnosing hepatocellular carcinoma (HCC, LR-5). However, not all HCCs meet LR-5 criteria and are instead categorized as LR-M, probably or definitely malignant but not specific for HCC, necessitating biopsy for diagnosis. The purpose is to identify factors associated with HCC in LR-M observations. Methods This is an IRB-approved, retrospective analysis of participants from 8 institutions that had a LR-M observation on CT or MRI with corresponding histopathologic diagnosis. Demographics and biochemical data were examined. Central review using the LI-RADS v2018 algorithm was performed. Kappa statistics defined inter-reader agreement. Random forest and logistic regression analyses generated a model for HCC diagnosis. Results 162 participants with 162 LR-M observations were included. 46% of observations (74/162) were HCC and 37% were cholangiocarcinoma (60/162). Two of 34 imaging features– observation size and intra-lesion iron– showed moderate to strong inter-reader agreement (Kappa ≥ 0.60) while the remainder showed weak or no agreement (Kappa < 0.60). Random forest analysis showed biochemical features to be more predictive of HCC than imaging features. Logistic regression analysis of a model based on INR and AFP provided a 72% sensitivity and 61% specificity for HCC by Youden’s index and a 90% specificity threshold yielded 38% sensitivity, 75% positive predictive value, and 66% negative predictive value. Conclusions Our results show INR and AFP are associated with HCC in LR-M observations. A high-specificity threshold may assist in the non-invasive diagnosis of HCC in the appropriate setting. Summary statement In certain at-risk patients with a LR-M observation on diagnostic imaging, serum AFP and INR maybe useful tools for the non-invasive diagnosis of HCC.

In nonalcoholic steatohepatitis clinical trials, placebo treatment resulted in decreased liver fat as assessed using the MRI-derived proton density fat fraction. Background Several early-phase clinical trials for the treatment of nonalcoholic steatohepatitis (NASH) use liver fat content as measured with the MRI-derived proton density fat fraction (PDFF) for...

Purpose Treatment for gastroesophageal adenocarcinomas can result in significant morbidity and mortality. The purpose of this study is to supplement methods for choosing treatment strategy by assessing the relationship between CT-derived body composition, patient, and tumor features, and clinical outcomes in this population. Methods Patients with neoadjuvant treatment, biopsy-proven gastroesophageal adenocarcinoma, and initial staging CTs were retrospectively identified from institutional clinic encounters between 2000 and 2019. Details about patient, disease, treatment, and outcomes (including therapy tolerance and survival) were extracted from electronic medical records. A deep learning semantic segmentation algorithm was utilized to measure cross-sectional areas of skeletal muscle (SM), visceral fat (VF), and subcutaneous fat (SF) at the L3 vertebra level on staging CTs. Univariate and multivariate analyses were performed to assess the relationships between predictors and outcomes. Results 142 patients were evaluated. Median survival was 52 months. Univariate and multivariate analysis showed significant associations between treatment tolerance and SM and VF area, SM to fat and VF to SF ratios, and skeletal muscle index (SMI) (p = 0.004–0.04). Increased survival was associated with increased body mass index (BMI) (p = 0.01) and increased SMI (p = 0.004). A multivariate Cox model consisting of BMI, SMI, age, gender, and stage demonstrated that patients in the high-risk group had significantly lower survival (HR = 1.77, 95% CI = 1.13–2.78, p = 0.008). Conclusion CT-based measures of body composition in patients with gastroesophageal adenocarcinoma may be independent predictors of treatment complications and survival and can supplement methods for assessing functional status during treatment planning. Graphical abstract