Shafi, Nimra

e18624 Background: Invasive fungal infections (IFIs) are a major cause of morbidity and mortality in patients with hematologic malignancies. Although meta-analyses identify systemic corticosteroid exposure as a strong relative risk factor for IFI, the absolute burden attributable to corticosteroids across heterogeneous baseline risk settings remains poorly defined. We applied meta-analysis-based projection modeling to translate relative risk estimates into absolute, population-level IFI outcomes. Methods: Using pooled odds ratios for IFI associated with systemic corticosteroid exposure from a published meta-analysis (OR 2.84; 95% CI 1.42–5.70), we projected IFI incidence under a proportional odds framework. The primary analysis assumed a baseline IFI incidence of 15% in patients not receiving corticosteroids, with sensitivity analyses conducted across baseline incidences ranging from 10% to 25%. IFI-related mortality was estimated using a case-fatality rate of 45%. Outcomes were expressed as projected IFI incidence, excess IFIs and IFI-attributable deaths per 1,000 patients, population attributable fractions, and one-way sensitivity analyses. Results: At a baseline IFI incidence of 15%, systemic corticosteroid exposure increased projected IFI incidence to approximately 34%, corresponding to an excess of ~190 IFIs per 1,000 patients. This translated into approximately 80 excess IFI-attributable deaths per 1,000 patients, with a plausible range of 25–160 based on uncertainty in the pooled effect estimate. Sensitivity analyses demonstrated a monotonic increase in projected IFI incidence with rising baseline risk, with corticosteroid-associated IFI incidence approaching 50% at higher baseline levels. Population-attributable fraction modeling indicated that when corticosteroid use prevalence exceeded 40%, nearly half of IFIs in the population were attributable to corticosteroid exposure. One-way sensitivity analyses identified the corticosteroid effect size as the dominant driver of excess IFI mortality, exceeding the influence of baseline incidence or IFI case-fatality assumptions. Conclusions: Projection modeling based on meta-analytic risk estimates demonstrates that systemic corticosteroid exposure is associated with a substantial and clinically meaningful absolute increase in IFIs and IFI-related mortality. Translating relative risk estimates into absolute population-level outcomes highlights the potential magnitude of preventable infectious harm associated with corticosteroid use and provides actionable insight to inform risk-mitigation strategies in high-risk hematologic populations.

e18602 Background: Chimeric antigen receptor T-cell (CAR-T) therapy has transformed the treatment of hematologic malignancies but is frequently complicated by cytokine release syndrome (CRS) and immune-mediated neurotoxicity (NS). As CAR-T programs expand, population-level projections of treatment-related toxicities are essential for program planning and risk mitigation, yet remain poorly characterized. Methods: We performed a cohort-based forecasting analysis using pooled incidence rates of CRS and NS derived from published meta-analyses of CAR-T clinical trials. Expected numbers of toxicity events were projected for hypothetical cohorts of 100, 500, and 1,000 CAR-T–treated patients. Uncertainty was quantified using binomial 95% confidence intervals. Disease-specific projections were generated by applying stratified pooled toxicity rates for hematologic malignancies and solid tumors. Results: For a projected cohort of 1,000 CAR-T–treated patients, forecasts estimated approximately 770 patients would experience CRS of any grade, 290 would develop grade ≥3 CRS, 400 would experience NS of any grade, and 280 would develop grade ≥3 NS. Toxicity counts increased proportionally with cohort size and demonstrated predictable uncertainty bounds. When stratified by disease type, hematologic malignancies exhibited substantially higher toxicity burdens than solid tumors across all endpoints. At a cohort size of 1,000 patients, hematologic malignancies were associated with markedly higher rates of both CRS and severe neurotoxicity, whereas solid tumors demonstrated consistently lower toxicity burdens. Conclusions: Using pooled clinical trial data, this forecasting model provides quantitative estimates of CAR-T–associated toxicity burdens at the population level. Severe CRS and neurotoxicity remain common and scale predictably with program size, particularly in hematologic malignancies. These findings offer a pragmatic framework for anticipating resource utilization, toxicity management needs, and safety planning as CAR-T therapy continues to expand.

8021 Background: Lung cancer is the leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and diagnostic variability in histopathological assessment. Advances in deep learning applied to medical imaging offer the potential to enhance early detection accuracy, reduce interobserver variability, and improve diagnostic efficiency in lung cancer. Methods: In this retrospective study, we evaluated DAVINCI using a dataset of 8,962 patients with confirmed lung malignancies. The model utilizes a specialized architecture combining convolutional neural networks (CNNs) and residual blocks to optimize feature extraction from high-resolution pathological slides. Performance was measured via detection accuracy, recall, specificity, and Area Under the Curve (AUC). Results: A total of 8,962 patients were included in the analysis. The DAVINCI model correctly identified lung cancer in 93% of cases, corresponding to 8,334 of 8,962 patients, with a 95% confidence interval (CI) of 86%–100% (7,707–8,962 patients). Model specificity was also 93% (95% CI, 86%–100%), indicating a high true-negative rate. Sensitivity (recall) was 87%, with correct detection in 7,797 patients and a 95% CI of 80%–94% (7,170–8,425 patients). The model demonstrated strong discriminatory performance, achieving an area under the curve (AUC) of 0.91 (95% CI, 0.84–0.98). In comparison, active pathologists achieved a mean diagnostic accuracy of 83%, corresponding to 7,438 of 8,962 cases, with a 95% CI of 76%–90% (6,811–8,066 cases). In terms of efficiency, DAVINCI processed whole-slide images in 16–19 seconds, substantially faster than conventional manual review, which typically requires several minutes per slide. Conclusions: DAVINCI demonstrated superior accuracy, robust discriminatory performance, and markedly faster processing compared with human review, supporting its potential role in early lung cancer detection at scale.