For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications.

The use of armed drones in post-9/11 US military conflicts has increasingly been the subject of academic writings; few, however, examine its collateral effects from a biopolitically-framed, phenomenological lens. This article examines how the indeterminate field of threat produced and sustained by the preventive military paradigm of drone warfare transforms potential threats into determinate targets of military violence. The spatial disruption experienced by inhabitants of the "space of death" generated by the "drone zone" thus transforms their existential comportment of living under drones into that of 'walking corpses' whose relationships to space expose the devastating phenomenological consequences of drone warfare.

Background: Muscle loss influences pancreatic ductal adenocarcinoma (PDAC) outcomes, but treatment-related edema may cause overestimation of total skeletal muscle area (tSMA), confounding our understanding of muscle changes. However, no studies have quantified the impact of edema on tSMA and psoas skeletal muscle area (pSMA) changes. Thus, we sought to (i) assess the impact of edema on tSMA and pSMA changes between diagnosis and follow-up and (ii) explore the utility of pSMA as a clinically relevant measure of muscle and muscle loss among patients with PDAC. Methods: Body composition was measured using CT scans at diagnosis and follow-up from 95 patients enrolled in the Florida Pancreas Collaborative cohort study. Edema was assessed by opacifications in subcutaneous fat, and tSMA and pSMA changes were expressed as percent change between diagnosis and follow-up. We used multivariable generalized linear models to estimate mean tSMA and pSMA changes overall and by edema status. Spearman correlation was used to measure interrelationships of tSMA and pSMA. Results: tSMA increased between diagnosis and follow-up (Δ = 0.66) but only in patients with edema (Δ = 3.35) whereas non-edematous patients lost tSMA (Δ = −2.03). Conversely, pSMA decreased regardless of edema status. Furthermore, tSMA and pSMA were strongly correlated overall (r = 0.75) and in non-edematous patients (r = 0.83). Conclusions: Edema inflated estimates of tSMA at follow-up in patients with PDAC, but pSMA was impervious to edema and may represent a suitable proxy for tSMA. Impact: pSMA is a reliable measure of muscle and muscle loss and should be considered in future studies assessing muscle loss in patients with PDAC.

Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. To illustrate translational potential, we describe representative applications, including: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Collectively, these applications highlight the importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice.