ArticleJournal of the American Society of Cytopathology2026
Grading medullary thyroid carcinoma on fine needle aspiration biopsy specimens: a comparative study between manual counting and a deep learning platform.
Article in Journal of the American Society of Cytopathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionThe International Medullary Thyroid Carcinoma Grading System stratifies medullary thyroid carcinoma (MTC) using Ki-67 proliferation index, mitotic index, and tumor necrosis. However, the ability to predict MTC grade using fine needle aspiration (FNA) specimens is unclear. Artificial intelligence (AI)-based quantification tools are increasingly being adopted. We therefore evaluated the concordance of MTC grading on cytology using manual counting and an AI-based digital quantification platform. MATERIALS AND
methodsEighteen MTC cases with paired FNA and surgical resections (2013-2022) were retrospectively identified. MTC grading was assessed on FNAs and resections according to the International Medullary Thyroid Carcinoma Grading System criteria. Manual cytologic Ki-67 proliferation index (MCPI) and manual surgical proliferation index (MSPI) were compared. Digital cytologic Ki-67 proliferation index (DCPI) was generated using DeepLIIF. Concordance between Ki-67 proliferation indices and overall MTC grade was evaluated using Cohen's kappa.
resultsSubstantial agreement was observed between MCPI and MSPI (94% concordance; kappa = 0.77). Overall grade concordance between cytology and surgical specimens was 83% (kappa = 0.49), with discrepancies largely due to necrosis identified only in resections. Agreement between MCPI and DCPI was fair (61%; kappa = 0.22), with digital analysis frequently overestimating Ki-67 proliferation index. Concordance between DCPI and MSPI was 67% (kappa = 0.33), and overall grade agreement between digital cytology assessment and surgery was slight (56%; kappa = 0.11).
conclusionsGrading of MTC on cytology demonstrates substantial concordance for Ki-67 assessment but only modest agreement for overall grade, primarily because necrosis is often only focally present. The limited performance of the AI-based digital quantification platform emphasizes the need for specimen-specific development and validation prior to clinical implementation.
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