Author

A. Nassar

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Open access Aug 2026

Concordance of folate receptor expression in ovarian cancer over time: A single-institution retrospective study

Background Folate receptor alpha (FRα) is frequently expressed in ovarian high-grade serous carcinomas and is a clinically actionable biomarker targeted by FDA-approved mirvetuximab soravtansine (MIRV) for platinum-resistant disease with high expression (≥75% of tumor cells with ≥2+ staining). However, longitudinal variability in FRα expression remains poorly understood. We evaluated temporal FRα expression changes in paired ovarian cancer specimens. Methods We performed a retrospective study of epithelial ovarian malignancies at Mayo Clinic Florida (2013–2025) in patients with ≥2 tumor specimens available. Tumors were classified as FRα-positive when ≥75% of viable tumor cells showed moderate-to-strong (2+ and/or 3+) staining and categorized as concordant or discordant based on changes in FRα status. Associations between clinicopathologic variables and FRα variability were evaluated using Fisher exact and Kruskal–Wallis tests. Results Sixty-six patients (median age 66 years), most with advanced-stage high-grade serous carcinoma (65/66), had paired tumor specimens. FRα expression was concordant in 77.3% of cases and discordant in 22.7%. Negative-to-positive conversion occurred more frequently than positive-to-negative conversion (16.7% vs 6.1%). No significant associations were identified between FRα variability and clinicopathologic factors, including age, grade, stage, treatment setting, prior chemotherapy exposure, MIRV use, specimen type, sampling site, baseline FRα expression, or timing intervals. In patients treated with neoadjuvant chemotherapy, negative-to-positive conversion was numerically more frequent, although not statistically significant (25% vs 9%; p = 0.479). Conclusions FRα expression in ovarian carcinomas is generally stable over time, with concordance observed in most paired specimens. However, discordance in approximately one-quarter of cases suggests spatial and/or temporal tumor heterogeneity.

M. A. Neisani, Francis E. Marrero, Roberto Angeli-Morales et al. · Gynecologic Oncology Reports · 0 citations

Review Open access Aug 2026

Leveraging large language models to enhance cytopathology: Opportunities, challenges, and future directions; a practical review from the ASC Clinical Practice Committee.

Large language models (LLMs) and vision-language models represent a fundamentally different category of artificial intelligence (AI) compared to prior image analysis approaches in digital pathology, which have largely been based on convolutional neural network architectures. This review from the American Society of Cytopathology Clinical Practice Committee examines the current evidence for LLM and vision-language model applications in cytopathology, including structured reporting, diagnostic assistance, quality control, education, and workflow integration. The distinction between applications with preliminary evidence and those that remain hypothetical is described. A detailed assessment of the challenges that must be addressed before clinical deployment, including hallucination risk, limited explainability, bias, data privacy, validation gaps, and infrastructure barriers is discussed. A review of the regulatory landscape in the United States and European Union as it applies to AI-enabled software as a medical device is provided. Recommendations addressing cytopathology-specific benchmarks, multi-institutional validation, transparent governance, and incremental deployment beginning with low-risk applications are suggested. In the current environment, LLMs have the potential to augment cytopathology practice, but responsible adoption requires rigorous validation and sustained collaboration among cytopathologists, AI researchers, and regulatory bodies.

K. Bilal, Joanna A Gibson, David Kim et al. · Cancer Cytopathology · 0 citations