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Introduction The research field of Artificial intelligence (AI) in medicine and especially in gastroenterology is rapidly progressing with the first AI tools entering routine clinical practice, for example in colorectal cancer screening. Contrast-enhanced ultrasound (CEUS) is a highly reliable, low-risk and low-cost diagnostic modality for the examination of the liver. However, doctors need many years of training and experience to master this technique and, despite all efforts to standardize CEUS, it is often believed to contain significant interrater variability. As has been shown for endoscopy, AI holds promise to support examiners at all training levels in their decision-making and efficiency. Methods In this systematic review, we analyzed and compared original research studies applying AI methods to CEUS examinations of the liver published between January 2010 and February 2024. We performed a structured literature search on PubMed, Web of Science and IEEE. Two independent reviewers screened the articles and subsequently extracted relevant methodological features, e.g. cohort size, validation process, machine learning algorithm used, as well as indicative performance measures from the included articles. Results We included 41 studies with most applying AI methods for classification tasks related to focal liver lesions. These included distinguishing benign vs. malignant or classifying the entity itself, while a few studies tried to classify tumor grading, microvascular invasion status or response to transcatheter arterial chemoembolization directly from CEUS. Some articles tried to segment or detect focal liver lesions, while others aimed to predict survival and recurrence after ablation. The majority (25/41) of studies used hand-picked and/or annotated images as data input to their models. We observed mostly good to high reported model performances with accuracies ranging between 58.6% and 98.9%, while noticing a general lack of external validation. Conclusion Even though multiple proof-of-concept studies for the application of AI methods to CEUS examinations of the liver exist and report high performance, more prospective, externally validated and multicenter research is needed to bring such algorithms from desk to bedside.
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BACKGROUND AND AIM: Treatment with sorafenib causes diverse side effects, which limits adherence. This work assesses whether Home Care, a psychosocial nursing intervention, prolongs the duration of treatment in patients with advanced hepatocellular carcinoma (HCC) and if it influences health-related quality of life (HRQL). METHODS AND RESULTS: This is a cohort study using data from patients receiving sorafenib in the prospective Bern HCC Cohort at the University Hospital. Duration of treatment, overall survival, and HRQL using the Functional Assessment of Cancer Therapy-Hepatobiliary questionnaire were compared in the two groups. A total of 173 patients were eligible for the analysis. Among them, 141 were in the Home Care program, and 32 were not. Patients with Home Care had a significantly longer duration of treatment (265 days vs 152 days, P = 0.003) and a better functional well-being (17.7 vs 12.5, P = 0.015). CONCLUSION: Psychosocial interventions such as Home Care are a valid method in improving adherence to sorafenib and can therefore be recommended.