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Background & Aims: Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists. Methods: High-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n = 84 lesions) and a test set (n = 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard. Results: A total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62-0.72) and 0.91 (95% CI, 0.87-0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers). Conclusions: Quantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist's visual analysis in patients at high-risk for HCC. Impact and implications: Assessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist-artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist's visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions.
RESUMO
OBJECTIVES: To determine the degree of relationship between iodine concentrations derived from dual-energy CT (DECT) and perfusion CT parameters in patients with advanced HCC under treatment. METHODS: In this single-centre IRB approved study, 16 patients with advanced HCC treated with sorafenib or radioembolization who underwent concurrent dynamic perfusion CT and multiphase DECT using a single source, fast kV switching DECT scanner were included. Written informed consent was obtained for all patients. HCC late-arterial and portal iodine concentrations, blood flow (BF)-related and blood volume (BV)-related perfusion parameters maps were calculated. Mixed-effects models of the relationship between iodine concentrations and perfusion parameters were computed. An adjusted p value (Bonferroni method) < 0.05 was considered significant. RESULTS: Mean HCC late-arterial and portal iodine concentrations were 22.7±12.7 mg/mL and 18.7±8.3 mg/mL, respectively. Late-arterial iodine concentration was significantly related to BV (mixed-effects model F statistic (F)=28.52, p<0.0001), arterial BF (aBF, F=17.62, p<0.0001), hepatic perfusion index (F=28.24, p<0.0001), positive enhancement integral (PEI, F=66.75, p<0.0001) and mean slope of increase (F=32.96, p<0.0001), while portal-venous iodine concentration was mainly related to BV (F=29.68, p<0.0001) and PEI (F=66.75, p<0.0001). CONCLUSIONS: In advanced HCC lesions, DECT-derived late-arterial iodine concentration is strongly related to both aBF and BV, while portal iodine concentration mainly reflects BV, offering DECT the ability to evaluate both morphological and perfusion changes. KEY POINTS: ⢠Late-arterial iodine concentration is highly related to arterial BF and BV. ⢠Portal iodine concentration mainly reflects tumour blood volume. ⢠Dual-energy CT offers significantly decreased radiation dose compared with perfusion CT.
