Bodduluri, Sandeep

e13656 Background: Effective multidisciplinary cancer care is bottlenecked by the cognitive burden of synthesizing fragmented electronic health records across different specialties. Current AI tools often function as isolated chatbots rather than integrated workflow assistants. We developed and validated "OncoSphere AI," an Agentic AI multidisciplinary clinical workflow support tool designed to autonomously orchestrate the multidisciplinary review process by deconstructing complex cases into domain-specific insights. Methods: OncoSphere AI is a novel, multi-agent large language model–driven platform that simulates a multidisciplinary tumor board for cancer care. The architecture uses a parallel reasoning framework in which eight virtual specialty agents—Medical Oncology, Radiation Oncology, Surgical Oncology, Pathology, Radiology, Pharmacy, Clinical Research, and Nurse Navigation—independently analyze structured and unstructured patient data against specialty specific guidelines and contemporary peer-reviewed literature. System performance was assessed via built-in telemetry (task completion, latency, inter-agent agreement) and blinded review by eight expert oncologists using a structured survey to rate data extraction fidelity, guideline concordance, safety, and clinical utility compared with standard manual review. Results: The study cohort (n=20, median age 59.5 years; 55% female) had a median of 2.5 (range 0-7) prior lines of therapy and multiorgan metastatic disease (median: 2, range 1–4), including lung, breast, gastrointestinal (CRC, pancreatic, hepatobiliary), and genitourinary (prostate, renal) malignancies. OncoSphere AI demonstrated high workflow utility (mean rating: 4/5) and strong extraction fidelity, with more than 90% of biomarker extractions rated as a “perfect match” by expert reviewers. Operational efficiency gains were substantial with AI processing averaged 4.7 minutes per case versus 29.3 minutes for manual review, corresponding to an 84% reduction in time (p<0.001). System telemetry revealed adaptive compute behavior, with the platform autonomously allocating 1.73-fold more reasoning tokens to complex, refractory cases than to standard scenarios. Safety review identified only one critical contraindication issue in a heavily pretreated, multi-line setting. Conclusions: OncoSphere AI effectively automates the clinical “perception layer,” consistently transforming heterogeneous longitudinal data into decision-grade insights with high fidelity and marked time savings. By operating as a coordinated ensemble of domain-specialized AI agents rather than a single generic model, it offers a scalable approach to reduce physician cognitive burden and to standardize guideline-concordant care planning in diverse, heavily pretreated oncology populations.

Abstract Rationale Ground-glass opacities (GGOs) in the absence of interstitial lung disease are understudied. Objectives To assess the association of GGOs with white blood cells (WBCs) and progression of quantified chest computed tomography emphysema. Methods We analyzed data of participants in the SPIROMICS study (Subpopulations and Intermediate Outcome Measures in COPD Study). Chest radiologists and pulmonologists labeled regions of the lung as GGOs, and the adaptive multiple feature method (AMFM) trained the computer to assign those labels to image voxels and quantify the volume of the lung with GGOs (%GGOAMFM). We used multivariable linear regression, zero-inflated negative binomial, and proportional hazards regression models to assess the association of %GGOAMFM with WBCs, changes in percentage emphysema, and clinical outcomes. Measurements and Main Results Among 2,714 participants, 1,680 had chronic obstructive pulmonary disease (COPD) and 1,034 had normal spirometry. Among participants with COPD, on the basis of multivariable analysis, current smoking and chronic productive cough were associated with higher %GGOAMFM. Higher %GGOAMFM was cross-sectionally associated with higher WBC and neutrophil concentrations. Higher %GGOAMFM per interquartile range at visit 1 (baseline) was associated with an increase in emphysema at 1-year follow-up visit by 11.7% (relative increase; 95% confidence interval, 7.5–16.1%; P &lt; 0.001). We found no association between %GGOAMFM and 1-year FEV1 decline, but %GGOAMFM was associated with exacerbations and all-cause mortality during a median follow-up of 1,544 days (interquartile interval, 1,118–2,059). Among normal spirometry participants, we found similar results, except that %GGOAMFM was associated with progression to COPD at 1-year follow-up. Conclusions Our findings suggest that GGOAMFM is associated with increased systemic inflammation and emphysema progression.