Garcia, Chris

AbstractBreast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody‐drug conjugates (ADCs) like trastuzumab deruxtecan, which shows promise for breast cancers with low HER2 expression. Current HER2 testing methods, including immunohistochemistry (IHC) and in situ hybridization (ISH), have limitations. IHC, a semi‐quantitative assay, is prone to interobserver variability. While ISH provides higher precision than IHC, it remains more resource‐intensive in terms of cost and workflow. However, turnaround time is typically faster than that of other advanced molecular methods such as next‐generation sequencing. We adapted the clustering‐constrained‐attention multiple‐instance deep learning model to improve IHC testing and reduce dependence on reflex fluorescence ISH (FISH) tests. Using 5,731 HER2 IHC images, including 592 cases with FISH testing, we trained two models: one for predicting HER2 scores from IHC images and another for predicting FISH scores from equivocal cases. The HER2 IHC score prediction model achieved 91% ± 0.01 overall accuracy and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 ± 0.01. The FISH score prediction model had an ROC AUC of 0.84 ± 0.07, with sensitivity at 0.37 ± 0.13 and specificity at 0.96 ± 0.03. External validation on cases from 203 institutions showed similar performance. The HER2 IHC model maintained a 91% ± 0.01 accuracy and an ROC AUC of 0.98 ± 0.01, while the FISH model had an ROC AUC of 0.75 ± 0.03, with sensitivity at 0.28 ± 0.04 and specificity at 0.93 ± 0.01. Our model advances HER2 scoring by reducing subjectivity and variability in current scoring methods. Despite lower accuracy and sensitivity in the FISH prediction model, it may be a beneficial option for settings where reflex FISH testing is unavailable or prohibitive. With high specificity, our model can serve as an effective screening tool, enhancing breast cancer diagnosis and treatment selection.

Context.— Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. Objective.— To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. Design.— This article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. Results.— CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms’ performance compared to that of pathologists. Conclusions.— A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.

Abstract Background Empiric antibiotic selection in outpatient care often relies on inpatient antibiograms which may not accurately reflect ambulatory susceptibility rates. To address this, Novant Health (NH) and Labcorp analyzed urine culture results from North Carolina (NC) to create an ambulatory antibiogram and assess temporal and regional differences. Methods Cross-sectional study of urine culture data collected 1/1/20-12/31/21 from ambulatory sites in NC. Testing and antibiogram preparation were performed at Labcorp (Burlington, NC) based on CLSI guidelines (M100 S28). Pediatric and adult data were compiled separately. NH and state-wide data were analyzed using the chi-square or Fisher exact test with Bonferroni correction for multiple comparisons. Results In 2021, Escherichia coli was the most common isolate for NH (n=18937 adult, n=624 pediatric) and state-wide (n=87,226 adult; n=2696 pediatric). E. coli was ≥ 80% susceptible to all tested antibiotics except ampicillin; Klebsiella pneumoniae was ≥ 80% susceptible to all except nitrofurantoin; and Proteus mirabilis/penneri was ≥ 80% susceptible to all antibiotics tested. Susceptibility rates were similar between NH and state-wide data except for lower susceptibility to ceftriaxone (94% vs 95%; p< .001). Compared to 2020, state-wide adult E. coli isolates from 2021 had increased susceptibility for amoxicillin/clavulanic acid (86% vs 85%), ampicillin (60% vs 59%), cephalosporins (84-93% vs 82-92%), and quinolones (87% vs 86%) (p< .001 all). NH isolates did not show significant changes. Susceptibility rates by region were similar, except for lower susceptibility to cefuroxime among adult E. coli isolates in the Triad region relative to state-wide (82% vs 84%; p< .001). No significant temporal or regional differences in susceptibility were observed for K. pneumoniae, Proteus spp., P. aeruginosa, and among pediatric samples. Conclusion Susceptibility data were largely similar in NH vs. state-wide data, and between regions. Small but statistically significant temporal changes were found in state-wide data. Overall, our results support use of a state-wide antibiogram to inform treatment choices across all regions and within specific healthcare systems. Disclosures Dusica Curanovic, PhD, Labcorp: employed by labcorp during study|Labcorp: Stocks/Bonds Chris Garcia, MD, Labcorp: Prior employee|Labcorp: Stocks/Bonds Catherine Wright, PhD, Labcorp: Employee|Labcorp: Stocks/Bonds Rita Stainback, MS, MT (ASCP) SM, DLM, Labcorp: Employee|Labcorp: Stocks/Bonds Melissa Martin, BS, Labcorp: Employee Samia Naccache, PhD, D(ABMM), Labcorp: Employee|Labcorp: Stocks/Bonds Kristen Smith, PhD, bioMerieux: Employee|Labcorp: Stocks/Bonds Howard Engler, PhD, D(ABMM), Labcorp: Employee|Labcorp: Stocks/Bonds