Presence of tumour capsule on contrast-enhanced CT is associated with improved outcomes of stereotactic body radiation therapy in hepatocellular carcinoma patients.

PURPOSE: Stereotactic body radiation therapy (SBRT) is a novel local therapy for the treatment of hepatocellular carcinoma (HCC). While effective, there is currently no reliable radiological marker to guide patient selection. In this study, we investigated the prognostic value of capsule appearance on contrast-enhanced computed tomography (CT) for patients undergoing SBRT. MATERIALS AND METHODS: Between 2006 and 2017, 156 consecutive patients with Child-Pugh score class A/B and HCC ≥ 5 cm who underwent SBRT were retrospectively analysed. Baseline triple-phase CTs of the abdomen were reviewed for the presence of capsule appearances and correlated with objective response rate (ORR), overall survival (OS) and pattern of treatment failure. RESULTS: Capsule appearance on CT was present in 83 (53.2%) patients. It was associated with improved ORR by Response Evaluation Criteria in Solid Tumours (RECIST) (60.2 vs. 24.7%, p < 0.001) and Modified Response Evaluation Criteria in Solid Tumours (mRECIST) (78.3 vs. 34.2%, p < 0.001). The presence of a capsule was also associated with superior 2­year local control (89.1 vs. 51.4%, p < 0.001) and 2­year OS (34.1 vs. 14.8%, p < 0.01). Hepatic out-field failure was the dominant mode of progression, which was less common in patients with intact capsule (54.2 vs. 60.3%, p = 0.01). CONCLUSION: Capsule appearance on CT could potentially be a non-invasive prognostic marker for selecting HCC patients to undergo SBRT. A larger cohort is warranted to validate our findings.

Cluster analysis and visualisation of electronic health records data to identify undiagnosed patients with rare genetic diseases.

Rare genetic diseases affect 5-8% of the population but are often undiagnosed or misdiagnosed. Electronic health records (EHR) contain large amounts of data, which provide opportunities for analysing and mining. Data mining, in the form of cluster analysis and visualisation, was performed on a database containing deidentified health records of 1.28 million patients across 3 major hospitals in Singapore, in a bid to improve the diagnostic process for patients who are living with an undiagnosed rare disease, specifically focusing on Fabry Disease and Familial Hypercholesterolaemia (FH). On a baseline of 4 patients, we identified 2 additional patients with potential diagnosis of Fabry disease, suggesting a potential 50% increase in diagnosis. Similarly, we identified > 12,000 individuals who fulfil the clinical and laboratory criteria for FH but had not been diagnosed previously. This proof-of-concept study showed that it is possible to perform mining on EHR data albeit with some challenges and limitations.