Sophia Z. Shalhout

Jan 8, 2025 | nature.com | Tom Andrew |Ruth Plummer |Nick Reynolds |Isaac Brownell |Penny E Lovat |Sophia Z. Shalhout | +3 more

Accurate prognostication guides optimal clinical management in skin cancer. Merkel cell carcinoma (MCC) is the most aggressive form of skin cancer that often presents in advanced stages and is associated with poor survival rates. There are no personalized prognostic tools in use in MCC. We employed explainability analysis to reveal new insights into mortality risk factors for this highly aggressive cancer. We then combined deep learning feature selection with a modified XGBoost framework, to develop a web-based prognostic tool for MCC termed ‘DeepMerkel’. DeepMerkel can make accurate personalised, time-dependent survival predictions for MCC from readily available clinical information. It demonstrated generalizability through high predictive performance in an international clinical cohort, out-performing current population-based prognostic staging systems. MCC and DeepMerkel provide the exemplar model of personalised machine learning prognostic tools in aggressive skin cancers.

Aug 26, 2024 | nature.com | Meghan J. Mooradian |Florian J. Fintelmann |Thomas LaSalle |Alexander Graur |Sophia Z. Shalhout |Howard L. Kaufman | +4 more

AbstractImage-guided percutaneous cryoablation is an established minimally invasive oncologic treatment. We hypothesized that cryoablation may modify the immune microenvironment through direct modulation of the tumor, thereby generating an anti-tumor response in tumors refractory to immune checkpoint inhibition (ICI).