Journal of Pathology and Translational Medicine

Gyeongsin Park

6 Articles

Response to comment on “A stepwise approach to fine needle aspiration cytology of lymph nodes”: Yosep Chong, Gyeongsin Park, Hee Jeong Cha, Hyun-Jung Kim, Chang Suk Kang, Jamshid Abdul-Ghafar, Seung-Sook Lee; J Pathol Transl Med. 2024;58(1):43-44. Published online January 10, 2024; DOI: https://doi.org/10.4132/jptm.2023.12.04

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A stepwise approach to fine needle aspiration cytology of lymph nodes: Yosep Chong, Gyeongsin Park, Hee Jeong Cha, Hyun-Jung Kim, Chang Suk Kang, Jamshid Abdul-Ghafar, Seung-Sook Lee; J Pathol Transl Med. 2023;57(4):196-207. Published online July 11, 2023; DOI: https://doi.org/10.4132/jptm.2023.06.12

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The cytological diagnosis of lymph node lesions is extremely challenging because of the diverse diseases that cause lymph node enlargement, including both benign and malignant or metastatic lymphoid lesions. Furthermore, the cytological findings of different lesions often resemble one another. A stepwise diagnostic approach is essential for a comprehensive diagnosis that combines: clinical findings, including age, sex, site, multiplicity, and ultrasonography findings; low-power reactive, metastatic, and lymphoma patterns; high-power population patterns, including two populations of continuous range, small monotonous pattern and large monotonous pattern; and disease-specific diagnostic clues including granulomas and lymphoglandular granules. It is also important to remember the histological features of each diagnostic category that are common in lymph node cytology and to compare them with cytological findings. It is also essential to identify a few categories of diagnostic pitfalls that often resemble lymphomas and easily lead to misdiagnosis, particularly in malignant small round cell tumors, poorly differentiated squamous cell carcinomas, and nasopharyngeal undifferentiated carcinoma. Herein, we review a stepwise approach for fine needle aspiration cytology of lymphoid diseases and suggest a diagnostic algorithm that uses this approach and the Sydney classification system.

Citations

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Development and Validation of Explainable Artificial Intelligence System for Automatic Diagnosis of Cervical Lymphadenopathy From Ultrasound Images
Ming Xu, Yubiao Yue, Zhenzhang Li, Yinhong Li, Guoying Li, Haihua Liang, Di Liu, Xiaohong Xu, Mohamadreza (Mohammad) Khosravi
International Journal of Intelligent Systems.2025;[Epub] CrossRef
Immunocytochemical markers, molecular testing and digital cytopathology for aspiration cytology of metastatic breast carcinoma
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Cytopathology.2024; 35(2): 218. CrossRef
Response to comment on “A stepwise approach to fine needle aspiration cytology of lymph nodes”
Yosep Chong, Gyeongsin Park, Hee Jeong Cha, Hyun-Jung Kim, Chang Suk Kang, Jamshid Abdul-Ghafar, Seung-Sook Lee
Journal of Pathology and Translational Medicine.2024; 58(1): 43. CrossRef
Comment on “A stepwise approach to fine needle aspiration cytology of lymph nodes”
Elisabetta Maffei, Valeria Ciliberti, Pio Zeppa, Alessandro Caputo
Journal of Pathology and Translational Medicine.2024; 58(1): 40. CrossRef
The Incidence of Thyroid Cancer in Bethesda III Thyroid Nodules: A Retrospective Analysis at a Single Endocrine Surgery Center
Iyad Hassan, Lina Hassan, Nahed Balalaa, Mohamad Askar, Hussa Alshehhi, Mohamad Almarzooqi
Diagnostics.2024; 14(10): 1026. CrossRef
Efficiency of Fine-Needle Aspiration (FNA) in Relation to Tru-Cut Biopsy of Lateral Neck Swellings
Mohammed S Al Olaimat, Fahad S Al Qooz, Zaid R Alzoubi, Elham M Alsharaiah, Ali S Al Murdif, Mohammad O Alanazi
Cureus.2024;[Epub] CrossRef
Pitfalls in the Cytological Diagnosis of Nodal Hodgkin Lymphoma
Uma Handa, Rasheeda Mohamedali, Rajpal Singh Punia, Simrandeep Singh, Ranjeev Bhagat, Phiza Aggarwal, Manveen Kaur
Diagnostic Cytopathology.2024; 52(12): 715. CrossRef
Rapid 3D imaging at cellular resolution for digital cytopathology with a multi-camera array scanner (MCAS)
Kanghyun Kim, Amey Chaware, Clare B. Cook, Shiqi Xu, Monica Abdelmalak, Colin Cooke, Kevin C. Zhou, Mark Harfouche, Paul Reamey, Veton Saliu, Jed Doman, Clay Dugo, Gregor Horstmeyer, Richard Davis, Ian Taylor-Cho, Wen-Chi Foo, Lucas Kreiss, Xiaoyin Sara J
npj Imaging.2024;[Epub] CrossRef

A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database: Yosep Chong, Ji Young Lee, Yejin Kim, Jingyun Choi, Hwanjo Yu, Gyeongsin Park, Mee Yon Cho, Nishant Thakur; J Pathol Transl Med. 2020;54(6):462-470. Published online August 31, 2020; DOI: https://doi.org/10.4132/jptm.2020.07.11

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Abstract PDF Supplementary Material

Background
Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.
Methods
A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.
Results
IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.
Conclusions
Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease- specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

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Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms
Jamshid Abdul-Ghafar, Kyung Jin Seo, Hye-Ra Jung, Gyeongsin Park, Seung-Sook Lee, Yosep Chong
Diagnostics.2023; 13(7): 1308. CrossRef
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Cancers.2022; 14(10): 2400. CrossRef
Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review
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Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
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Diagnostic Pathology.2021;[Epub] CrossRef

Epstein-Barr Virus–Associated Lymphoproliferative Disorders: Review and Update on 2016 WHO Classification: Hyun-Jung Kim, Young Hyeh Ko, Ji Eun Kim, Seung-Sook Lee, Hyekyung Lee, Gyeongsin Park, Jin Ho Paik, Hee Jeong Cha, Yoo-Duk Choi, Jae Ho Han, Jooryung Huh; J Pathol Transl Med. 2017;51(4):352-358. Published online June 5, 2017; DOI: https://doi.org/10.4132/jptm.2017.03.15

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Abstract PDF

Epstein-Barr virus (human herpesvirus-4) is very common virus that can be detected in more than 95% of the human population. Most people are asymptomatic and live their entire lives in a chronically infected state (IgG positive). However, in some populations, the Epstein-Barr virus (EBV) has been involved in the occurrence of a wide range of B-cell lymphoproliferative disorders (LPDs), including Burkitt lymphoma, classic Hodgkin’s lymphoma, and immune–deficiency associated LPDs (post-transplant and human immunodeficiency virus–associated LPDs). T-cell LPDs have been reported to be associated with EBV with a subset of peripheral T-cell lymphomas, angioimmunoblastic T-cell lymphomas, extranodal nasal natural killer/T-cell lymphomas, and other rare histotypes. This article reviews the current evidence covering EBV-associated LPDs based on the 2016 classification of the World Health Organization. These LPD entities often pose diagnostic challenges, both clinically and pathologically, so it is important to understand their unique pathophysiology for correct diagnoses and optimal management.

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Gammaherpesviral infections in patients with immunological disorders
Anna Żuk-Wasek, Maciej Przybylski, Natalia Żeber, Grażyna Młynarczyk, Tomasz Dzieciątkowski
Postępy Mikrobiologii - Advancements of Microbiology.2018; 57(2): 145. CrossRef
COMPARATIVE ANALYSIS OF SEROLOGICAL MARKERS OF HERPES VIRUSES AND QUANTITATIVE IMMUNOGLOBULINOPATHIES IN PRIMARY PATIENTS WITH ANGIOIMMUNOBLASTIC T-CELL LYMPHOMA
N. G. Chernova, D. S. Tihomirov, N. P. Soboleva, S. A. Mariina, Y. V. Sidorova, M. N. Sinitsyna, V. N. Dvirnyk, S. M. Kulikov, T. A. Tupoleva, E. E. Zvonkov
Problems of Virology.2018; 63(4): 171. CrossRef

Histologic Disorderliness in the Arrangement of Tumor Cells as an Objective Measure of Tumor Differentiation: Sungwook Suh, Gyeongsin Park, Young Sub Lee, Yosep Chong, Youn Soo Lee, Yeong Jin Choi; Korean J Pathol. 2014;48(5):339-345. Published online October 27, 2014; DOI: https://doi.org/10.4132/KoreanJPathol.2014.48.5.339

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Abstract PDF: Background: Inter-observer and intra-observer variation in histologic tumor grading are well documented. To determine whether histologic disorderliness in the arrangement of tumor cells may serve as an objective criterion for grading, we tested the hypothesis the degree of disorderliness is related to the degree of tumor differentiation on which tumor grading is primarily based. Methods: Borrowing from the statistical thermodynamic definition of entropy, we defined a novel mathematical formula to compute the relative degree of histologic disorderliness of tumor cells. We then analyzed a total of 51 photomicrographs of normal colorectal mucosa and colorectal adenocarcinoma with varying degrees of differentiation using our formula. Results: A one-way analysis of variance followed by post hoc pairwise comparisons using Bonferroni correction indicated that the mean disorderliness score was the lowest for the normal colorectal mucosa and increased with decreasing tumor differentiation. Conclusions: Disorderliness, a pathologic feature of malignant tumors that originate from highly organized structures is useful as an objective tumor grading proxy in the field of digital pathology.

Mesenchymal Stromal Cells Promote Tumor Progression in Fibrosarcoma and Gastric Cancer Cells: Byunghoo Song, Bokyung Kim, Se-Ha Choi, Kyo Young Song, Yang-Guk Chung, Youn-Soo Lee, Gyeongsin Park; Korean J Pathol. 2014;48(3):217-224. Published online June 26, 2014; DOI: https://doi.org/10.4132/KoreanJPathol.2014.48.3.217

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Abstract PDF

Background

Extensive evidence has accumulated regarding the role of mesenchymal stromal cells (MSCs) in tumor progression, but the exact effects and mechanisms underlying this role remain unclear. We investigated the effects of MSC-associated tumor progression in MSC-sarcoma models and a gastric cancer metastatic model.

Methods

We conducted an in vitro growth kinetics assay and an in vivo tumor progression assay for sarcoma cells and gastric cancer cells in the presence or absence of MSCs.

Results

MSC-cocultured human fibrosarcoma cells (HT1080) showed accelerated growth compared with HT1080 alone (79- vs 37-fold change, p<.050). For HT1080, human MSC-coinjected tumors showed significantly greater and highly infiltrative growth compared to those of HT1080 alone (p=.035). For mouse fibrosarcoma cells (WEHI164), mouse MSC-coinjected tumors had greater volume than those of WEHI164 alone (p=.141). For rat sarcoma cells (RR1022), rat MSC-coinjected tumors exhibited greater volume and infiltrative growth than those of RR1022 alone (p=.050). For human gastric cancer cells (5FU), tumors of 5FU alone were compact, nodular in shape, and expansile with good demarcation and no definite lung metastatic nodules, whereas tumors grown in the presence of human MSCs showed highly desmoplastic and infiltrative growth and multiple lung metastasis.

Conclusions

We observed morphological evidence for MSC-associated tumor progression of fibrosarcomas and gastric cancer cells.

Citations

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