Journal of Pathology and Translational Medicine

Original Articles

Primary Merkel cell carcinoma of the salivary gland: a clinicopathologic study of four cases with a review of literature: Gyuheon Choi, Joon Seon Song, Hee Jin Lee, Gi Hwan Kim, Young Ho Jung, Yoon Se Lee, Kyung-Ja Cho; Received November 18, 2024 Accepted March 25, 2025 Published online April 30, 2025; DOI: https://doi.org/10.4132/jptm.2025.03.25 [Epub ahead of print]

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Abstract PDF: Background
Primary Merkel cell carcinoma of the salivary gland is currently not listed in the World Health Organization classification. However, cases of Merkel cell type neuroendocrine carcinomas of the salivary gland with perinuclear cytokeratin 20 positivity have been intermittently reported. We here investigated the clinicopathologic features of additional cases.
Methods
Data of four cases of Merkel cell type small cell neuroendocrine carcinoma of the salivary gland were retrieved. To confirm the tumors’ primary nature, clinical records and pathologic materials were reviewed. Optimal immunohistochemical staining was performed to support the diagnosis.
Results
All tumors were located in the parotid gland. Possibilities of metastasis were excluded in all cases through a meticulous clinicopathological review. Tumor histology was consistent with the diagnosis of small cell neuroendocrine carcinoma. Tumors’ immunohistochemical phenotypes were consistent with Merkel cell carcinoma, including Merkel cell polyomavirus large T antigen positivity in two of the four cases.
Conclusions
Merkel cell carcinomas can originate in salivary glands and are partly associated with Merkel cell polyomavirus infection as in cutaneous Merkel cell carcinomas.

Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer: Jinahn Jeong, Deokhoon Kim, Yeon-Mi Ryu, Ja-Min Park, Sun Young Yoon, Bokyung Ahn, Gi Hwan Kim, Se Un Jeong, Hyun-Jung Sung, Yong Il Lee, Sang-Yeob Kim, Yong Mee Cho; J Pathol Transl Med. 2024;58(5):229-240. Published online August 9, 2024; DOI: https://doi.org/10.4132/jptm.2024.07.13

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Abstract PDF Supplementary Material: Background
Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable.
Methods
To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed.
Results
Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%.
Conclusions
These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

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