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Background: Prostate cancer ranks as the most frequently diagnosed cancer in men in the USA, with significant mortality rates. Early detection is pivotal for optimal patient outcomes, providing increased treatment options and potentially less invasive interventions. There remain significant challenges in prostate cancer histopathology, including the potential for missed diagnoses due to pathologist variability and subjective interpretations. Methods: To address these challenges, this study investigates the ability of artificial intelligence (AI) to enhance diagnostic accuracy. The Galen™ Prostate AI algorithm was validated on a cohort of Puerto Rican men to demonstrate its efficacy in cancer detection and Gleason grading. Subsequently, the AI algorithm was integrated into routine clinical practice during a 3-year period at a CLIA certified precision pathology laboratory. Results: The Galen™ Prostate AI algorithm showed a 96.7% (95% CI 95.6-97.8) specificity and a 96.6% (95% CI 93.3-98.8) sensitivity for prostate cancer detection and 82.1% specificity (95% CI 73.9-88.5) and 81.1% sensitivity (95% CI 73.7-87.2) for distinction of Gleason Grade Group 1 from Grade Group 2+. The subsequent AI integration into routine clinical use examined prostate cancer diagnoses on >122,000 slides and 9200 cases over 3 years and had an overall AI Impact ™ factor of 1.8%. Conclusions: The potential of AI to be a powerful, reliable, and effective diagnostic tool for pathologists is highlighted, while the AI Impact™ in a real-world setting demonstrates the ability of AI to standardize prostate cancer diagnosis at a high level of performance across pathologists.
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Background: The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow. Methods: A systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability. Results: The Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical-legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculations. Conclusions: The digital pathology online cost calculator provides a comprehensive and reliable means of estimating the financial implications associated with implementing and maintaining a digital pathology system. By considering various cost factors and allowing customization based on institution-specific variables, the calculator empowers pathology laboratories, healthcare institutions, and administrators to make informed decisions and optimize resource allocation when adopting or expanding digital pathology technologies. The ROI calculator will enable healthcare institutions to assess the financial feasibility and potential return on investment on adopting digital pathology, facilitating informed decision-making and resource allocation.
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La muerte súbita es aquella que ocurre dentro de las 24 horas posteriores al inicio de los síntomas y se caracteriza por ser clínicamente inexplicable, inesperada y repentina. Debido a la naturaleza de la muerte súbita, no es posible llegar a un diagnóstico preciso sin una autopsia. En esta comunicación breve, evaluaremos el caso de un empleado de crucero de 33 años, sin historial médico/farmacológico previo, el cual falleció súbitamente mientras reposaba en su camarote. Debido a las sospechas iniciales de una posible muerte causada por una sobredosis de cocaína, se le realizó un panel toxicológico abarcador el cual resultó negativo. Empero, una tomografía computarizada (TC) craneal sin contraste revirtió la hipótesis inicial y la autopsia neuropatológica -sorpresivamente- confirmó que la verdadera causa de muerte fue la ruptura de un aneurisma sacular desconocido en el polígono de Willis.
Sudden death occurs within 24 hours after the onset of symptoms and is characterized by being clinically inexplicable, sudden, and unexpected. Due to the nature of sudden death, it is not possible an accurate diagnosis without performing an autopsy. In this brief communication, we will evaluate the case of a 33-year-old cruise employee, with no prior medical/pharmacological history, who suddenly died while resting in his cabin. Due to initial suspicions of a possible cocaine overdose death, a comprehensive toxicology panel was performed, although yielding a negative result. A cranial computed tomography without contrast reversed the initial hypothesis and the neuropathological autopsy -surprisingly- confirmed that the true cause of death was the rupture of an unknown saccular aneurysm in the Circle of Willis.
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Humanos , Masculino , Adulto , Círculo Arterial Cerebral/diagnóstico por imagen , Muerte Súbita/patología , Aneurisma/diagnóstico por imagen , Autopsia/métodosRESUMEN
The Human Papillomavirus (HPV) causes cervical cancer, the fourth most common cause of death in women in the United States (US). Several major screening clinical trials have demonstrated that high risk HPV (HR-HPV) DNA primary screen is more sensitive at determining the risk of cervical intraepithelial neoplasia level 3 or higher (CIN ≥ 3) than cytology alone and is similar to co-testing. In this cross-sectional study, we characterized a Hispanic population of 18,052 women ages 21-70 years with HR-HPV DNA testing and cytology to determine the prevalence of HR-HPV in the population and determine the likelihood of high grade squamous intraepithelial lesion (HSIL). We also compared cytology, HR-HPV DNA testing, and co-testing strategies to determine sensitivity, specificity, positive predictive value, and negative predictive value for HSIL in cervical biopsies. Results show that HR-HPV had a slightly higher sensitivity (94.2% vs 92.3%) compared to cytology for all high-grade disease (CIN2/3).