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). Pancreata were semi-manually segmented section by section to calculate the pancreas volumes before and under sorafenib treatment. RESULTS: Sorafenib reduced pancreatic volume in 18/19 (95%) HCC patients with a mean pancreatic volume loss of 25% (p = 0.002). Pancreatic volume loss depended on the dose (r = 0.36) and exposure time of sorafenib (r = 0.35) and was detectable as early as after 3 months of sorafenib treatment and already after a cumulative sorafenib dose of <100 g. Median overall survival was 13.2 months (range 7.8-31.3 months) but did not correlate with sorafenib-induced pancreatic volume reduction (hazard ratio 1.002, 95% confidence interval 0.981-1.060, p = 0.24). CONCLUSION: We could confirm pancreatic atrophy as a novel adverse event of sorafenib therapy in HCC patients, correlating with sorafenib dose and exposure time.

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. We also examined correlations between model predictions and depression diagnosis and treatment. RESULTS: BERT and RedditBERT attained AUROC scores of 0.88 and 0.86, respectively, compared to 0.79 for LR and 0.83 for SVM. BERT showed bigger differences in performance across sex, race, and ethnicity than RedditBERT. Patients who sent messages classified as concerning had a higher chance of receiving a depression diagnosis, a prescription for antidepressants, or a referral to the psycho-oncologist. Explanations from BERT and RedditBERT differed, with no clear preference from annotators. DISCUSSION: We show the potential of BERT and RedditBERT in identifying depression concerns in messages from cancer patients. Performance disparities across demographic groups highlight the need for careful consideration of potential biases. Further research is needed to address biases, evaluate real-world impacts, and ensure responsible integration into clinical settings. CONCLUSION: This work represents a significant methodological advancement in the early identification of depression concerns among cancer patients. Our work contributes to a route to reduce clinical burden while enhancing overall patient care, leveraging BERT-based models.