Kim GH, et al. J Gastric Cancer. 2024 Jul;24:e30. https://doi.org/10.5230/jgc.2024.24.e30

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.

Although SCNEC is based on its characteristic histology, immunohistochemistry (IHC) is commonly employed to confirm neuroendocrine differentiation (NED). The challenge here is that SCNEC may yield negative results for traditional neuroendocrine markers. To establish an IHC panel for NED, 17 neuronal, basal, and luminal markers were examined on a tissue microarray construct generated from 47 cases of 34 patients with SCNEC as a discovery cohort. A decision tree algorithm was employed to analyze the extent and intensity of immunoreactivity and to develop a diagnostic model. An external cohort of eight cases and transmission electron microscopy (TEM) were used to validate the model. Among the 17 markers, the decision tree diagnostic model selected 3 markers to classify NED with 98.4% accuracy in classification. The extent of synaptophysin (>5%) was selected as the initial parameter, the extent of CD117 (>20%) as the second, and then the intensity of GATA3 (≤1.5, negative or weak immunoreactivity) as the third for NED. The importance of each variable was 0.758, 0.213, and 0.029, respectively. The model was validated by the TEM and using the external cohort. The decision tree model using synaptophysin, CD117, and GATA3 may help confirm NED of traditional marker-negative SCNEC.

So Hye Nam, M.D., Gi Hwan Kim, M.D., Hong Ju Park, M.D., Ph.D., Yong-Gil Kim, M.D., Ph.D.. J Rheum Dis 2022;29:116-22. https://doi.org/10.4078/jrd.2022.29.2.116

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.