Impact of Clinical Factors on Accuracy of Ovarian Cancer Detection via Platelet RNA Profiling.

Ovarian cancer (OC) presents a diagnostic challenge, often resulting in poor patient outcomes. Platelet RNA sequencing, which reflects the host's response to disease, shows promise for earlier OC detection. This study examines the impact of sex, age, platelet count and the training on cancer types other than OC on classification accuracy achieved in the previous platelet-alone training dataset.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning.

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging.