Radiomics and deep learning computer vision algorithms can extract clinically relevant information from medical images, providing valuable insights for accurate diagnosis of cancerous lesions, tumor differentiation and molecular subtyping, prediction of treatment response, and prognostication of long-term outcomes. In head and neck squamous cell carcinoma (HNSCC), growing evidence supports the potential role of radiomics and deep learning models in predicting treatment response, long-term outcomes, and treatment complications following radiation therapy. This is especially important given the pivotal role of radiotherapy in early-stage and locally advanced HNSCC, as well as in post-operative and concomitant chemoradiotherapy. In this article, we summarize recent studies highlighting the role of radiomics in predicting early post-radiotherapy response, locoregional recurrence, survival outcomes, and treatment-related complications. Radiomics-guided tools have the potential to personalize HNSCC radiation treatment by identifying low-risk patients who may benefit from de-intensified therapy and high-risk individuals who require more aggressive treatment strategies.

The National Institutes of Health (NIH) Toolbox cognition battery and Trail Making Tests (TMT) are widely used to quantify cognitive aging and to detect early cognitive vulnerability in Alzheimer’s disease and related conditions. However, these tests are often treated as interchangeable markers of global cognition, despite likely differences in their dependence on specific brain systems, limiting interpretability across studies and clinical contexts. To address this gap, we examined associations between four commonly used cognitive measures—fluid cognition, crystallized cognition, TMT-A, and TMT-B—and multimodal MRI metrics in 725 healthy volunteers aged 36 to 100 years from the Human Connectome Project–Aging. Voxel-wise diffusion MRI and vertex-wise cortical thickness and volume analyses were adjusted for age, sex, and years of education. Higher crystallized and fluid cognition scores and faster TMT-A/B completion times were generally associated with greater white matter integrity. TMT-B showed the most extensive diffusion and cortical associations, involving major projection, commissural, and association pathways and frontoparietal and temporo-occipital cortices. TMT-A and crystallized cognition demonstrated intermediate, overlapping patterns, whereas fluid cognition showed only focal brainstem and limited cortical correlates. These findings demonstrate systematic differences in the neuroanatomical substrates underlying commonly used cognitive tests and provide normative structure–cognition reference maps that can improve test selection, mechanistic interpretation, and sensitivity to brain health in studies of aging, vascular risk, and preclinical neurodegenerative disease.