Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study.

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.

Diagnostic features of tuberous sclerosis complex: case report and literature review.

Tuberous sclerosis complex (TSC) is a rare autosomal dominant genetic syndrome that is caused by mutations in the tumour suppressor genes TSC1 or TSC2 which causes multiorgan growths. TSC presents at any age as a wide range of clinical and phenotypic manifestations with varying severity. The main goal of this article was to state two cases of TSC and review the most commonly reported major and minor diagnostic clinical features and the most common features that led to an investigation of possible TSC diagnosis. Herein, we report two cases of TSC, which both presented with seizures during the first 6 months of life. Case 1 presented with multiple types of seizures from 6 months of age and was diagnosed by multiple calcified subependymal nodules (SENs) detected by computed tomography and magnetic resonance imaging (MRI). Case 2 presented with seizures from 3 months of age and was diagnosed prenatally when a tumour was seen in her heart during antenatal ultrasonography. In conclusion, the literature review revealed that neurological manifestations (mainly seizures) were the main feature that led to investigation and diagnosis of TSC followed by abdominal manifestations (mainly renal features) and antenatal follow-up imaging. Other manifestations in skin, chest, eyes, teeth and heart rarely led to TSC diagnosis. In some cases, TSC was incidentally discovered by medical imaging. The cortical tubers, SENs, and subependymal giant cell astrocytomas brain lesions were the most commonly reported major features. Skin features including angiofibromas, ungual fibromas and shagreen patch were the second most common major features reported in the literature. However, skin manifestations were not a common led to investigation and diagnosis of TSC. Renal features, mainly angiomyolipomas (AMLs), were the third most common major feature reported. Medical imaging plays an essential role in diagnosis of TSC, and clinical features are important clues that lead to investigation for the disease.

Liver Imaging Reporting and Data System Comprehensive Guide: MR Imaging Edition.

The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the lexicon, technique, interpretation, reporting, and data collection of liver imaging. Developed specifically for assessment of liver observations in patients at risk for hepatocellular carcinoma (HCC), LI-RADS classifies hepatic observations on the basis of the probability of their being HCC, from LR-1 (definitely benign) to LR-5 (definitely HCC). This article discusses the technical requirements, major features, and ancillary features of and a systematic approach for using the LI-RADS diagnostic algorithm, with special emphasis on MR imaging.

LI-RADS for CT diagnosis of hepatocellular carcinoma: performance of major and ancillary features.

PURPOSE: To evaluate the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) v2017 major features, the impact of ancillary features, and categories on contrast-enhanced computed tomography (CECT) for the diagnosis of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This retrospective study included 59 patients (104 observations including 72 HCCs) with clinical suspicion of HCC undergoing CECT between 2013 and 2016. Two radiologists independently assessed major and ancillary imaging features for each liver observation and assigned a LI-RADS category based on major features only and in combination with ancillary features. The composite reference standard included pathology or imaging. Per-lesion estimates of diagnostic performance of major features, ancillary features, and LI-RADS categories were assessed by generalized estimating equation models. RESULTS: Major features (arterial phase hyperenhancement, washout, capsule, and threshold growth) respectively had a sensitivity of 86.1%, 81.6%, 20.7%, and 26.1% and specificity of 39.3%, 67.9%, 89.9%, and 85.0% for HCC. Ancillary features (ultrasound visibility as discrete nodule, subthreshold growth, and fat in mass more than adjacent liver) respectively had a sensitivity of 42.6%, 50.8%, and 15.1% and a specificity of 79.2%, 66.9%, and 96.4% for HCC. Ancillary features modified the final category in 4 of 104 observations. For HCC diagnosis, categories LR-3, LR-4, LR-5, and LR-TIV (tumor in vein) had a sensitivity of 5.3%, 29.0%, 53.7%, and 10.7%; and a specificity of 49.1%, 84.4%, 97.3%, and 96.4%, respectively. CONCLUSION: On CT, LR-5 category has near-perfect specificity for the diagnosis of HCC and ancillary features modifies the final category in few observations.