Applying natural language processing to patient messages to identify depression concerns in cancer patients.

Objective: This study aims to explore and develop tools for early identification of depression concerns among cancer patients by leveraging the novel data source of messages sent through a secure patient portal.

Materials And Methods: We developed classifiers based on logistic regression (LR), support vector machines (SVMs), and 2 Bidirectional Encoder Representations from Transformers (BERT) models (original and Reddit-pretrained) on 6600 patient messages from a cancer center (2009-2022), annotated by a panel of healthcare professionals. Performance was compared using AUROC scores, and model fairness and explainability were examined.

Pancreatic Atrophy in Hepatocellular Carcinoma Patients Receiving Long-Term Treatment with Sorafenib.

Objective: To date, sorafenib is the only approved systemic therapy for advanced hepatocellular carcinoma (HCC). Pancreatic atrophy has recently been reported in 2 patients as a novel side effect after long-term sorafenib treatment.

Methods: We retrospectively analyzed clinical and radiological data of patients with advanced HCC with long-term treatment of sorafenib (median 279 days, range 153-826 days).