Journal of Pathology and Translational Medicine

Ja-Min Park

2 Articles

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.

Diverse Immunoprofile of Ductal Adenocarcinoma of the Prostate with an Emphasis on the Prognostic Factors: Se Un Jeong, Anuja Kashikar Kekatpure, Ja-Min Park, Minkyu Han, Hee Sang Hwang, Hui Jeong Jeong, Heounjeong Go, Yong Mee Cho; J Pathol Transl Med. 2017;51(5):471-481. Published online August 9, 2017; DOI: https://doi.org/10.4132/jptm.2017.06.02

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14 Web of Science
14 Crossref

Background
Ductal adenocarcinoma (DAC) of the prostate is an uncommon histologic subtype whose prognostic factors and immunoprofile have not been fully defined. Methods: To define its prognostic factors and immunoprofile, the clinicopathological features, including biochemical recurrence (BCR), of 61 cases of DAC were analyzed. Immunohistochemistry was performed on tissue microarray constructs to assess the expression of prostate cancer-related and mammalian target of rapamycin (mTOR) signaling-related proteins. Results: During the median follow-up period of 19.3 months, BCR occurred in 26 cases (42.6%). DAC demonstrated a wide expression range of prostate cancer-related proteins, including nine cases (14.8%) that were totally negative for pan-cytokeratin (PanCK) immunostaining. The mTOR signaling-related proteins also showed diverse expression. On univariate analysis, BCR was associated with high preoperative serum levels of prostate-specific antigen (PSA), large tumor volume, predominant ductal component, high Gleason score (GS), comedo-necrosis, high tumor stage (pT), lymphovascular invasion, and positive surgical margin. High expressions of phospho-mTOR (p-mTOR) as well as low expressions of PSA, phospho-S6 ribosomal protein (pS6) and PanCK were associated with BCR. On multivariable analysis, GS, pT, and immunohistochemical expressions of PanCK and p-mTOR remained independent prognostic factors for BCR. Conclusions: These results suggest GS, pT, and immunohistochemical expressions of PanCK and p-mTOR as independent prognostic factors for BCR in DAC. Since DAC showed diverse expression of prostate cancer–related proteins, this should be recognized in interpreting the immunoprofile of DAC. The diverse expression of mTOR-related proteins implicates their potential utility as predictive markers for mTOR targeted therapy.

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