CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma.

INTRODUCTION: Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumour characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort. METHODS: Patients who received first-line anti-PD1±anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT, and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict clinical benefit, defined as stable disease for more than 6 months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-centre-out cross validation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model. RESULTS: A total of 620 patients were included, of which 59.2% experienced clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95% CI, 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95% CI, 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95% CI, 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). DISCUSSION: The radiomics model achieved a moderate predictive value of clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT-derived radiomics, and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.

The clinical course and treatment of black mamba (Dendroaspis polylepis) envenomations: a narrative review.

CONTEXT: The black mamba (Dendroaspis polylepis) is, due to its extremely toxic venom, one of the most dangerous snake species in Sub-Saharan Africa. A D. polylepis bite is a medical emergency and requires adequate action to prevent severe complications. However, there are no comprehensive reviews available based on clinical cases, and no readily accessible guidelines for standardized treatment. Therefore, we aim to provide an overview regarding the currently available clinical literature on D. polylepis envenomations; in order to promote knowledge on symptomatology and treatment options. METHODS: We searched for cases reporting humans bitten by D. polylepis in PubMed, Embase, Scopus, and Sabinet. We searched the reference lists of all eligible articles for additional articles. After quality assessment, 29 cases were included in this review. We used descriptive analysis to create an overview of the collected parameters. DISCUSSION: Among the included case reports and case series, D. polylepis envenomations most frequently resulted in decreased respiratory function, sweating and paralysis. The onset of symptoms usually occurred within 60 minutes. Neurological symptoms occurred more often than symptoms of autonomic dysfunction. In the reported cases most patients (26/29) received antivenom and most survived (25/29). We recommend the reporting of additional structured case reports to improve future analyses on the clinical course of envenomations, in order to improve public health response to D. polylepis envenomations.