Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.

Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.

Racial Discrimination-related Interoceptive Network Disruptions: A Pathway to Disconnection.

Background: Racial discrimination (RD) disrupts regulatory systems in minoritized individuals, particularly those that govern attention, including attention to visceral signals (interoception). RD frequency is linked to physiological "shut down" responses, characterized clinically by dissociation. We examined associations between RD frequency and functional connectivity of attention and interoceptive networks in sample of trauma-exposed Black women, investigating how connectivity alterations associate with dissociation severity.

Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.

Purpose: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints.