Journal of Pathology and Translational Medicine

Mee Yon Cho

5 Articles

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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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
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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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Standardized Pathology Report for Colorectal Cancer, 2nd Edition: Baek-hui Kim, Joon Mee Kim, Gyeong Hoon Kang, Hee Jin Chang, Dong Wook Kang, Jung Ho Kim, Jeong Mo Bae, An Na Seo, Ho Sung Park, Yun Kyung Kang, Kyung-Hwa Lee, Mee Yon Cho, In-Gu Do, Hye Seung Lee, Hee Kyung Chang, Do Youn Park, Hyo Jeong Kang, Jin Hee Sohn, Mee Soo Chang, Eun Sun Jung, So-Young Jin, Eunsil Yu, Hye Seung Han, Youn Wha Kim; J Pathol Transl Med. 2020;54(1):1-19. Published online November 13, 2019; DOI: https://doi.org/10.4132/jptm.2019.09.28

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Abstract PDF Supplementary Material

The first edition of the ‘Standardized Pathology Report for Colorectal Cancer,’ which was developed by the Gastrointestinal Pathology Study Group (GIP) of the Korean Society of Pathologists, was published 13 years ago. Meanwhile, there have been many changes in the pathologic diagnosis of colorectal cancer (CRC), pathologic findings included in the pathology report, and immunohistochemical and molecular pathology required for the diagnosis and treatment of colorectal cancer. In order to reflect these changes, we (GIP) decided to make the second edition of the report. The purpose of this standardized pathology report is to provide a practical protocol for Korean pathologists, which could help diagnose and treat CRC patients. This report consists of “standard data elements” and “conditional data elements.” Basic pathologic findings and parts necessary for prognostication of CRC patients are classified as “standard data elements,” while other prognostic factors and factors related to adjuvant therapy are classified as “conditional data elements” so that each institution could select the contents according to the characteristics of the institution. The Korean version is also provided separately so that Korean pathologists can easily understand and use this report. We hope that this report will be helpful in the daily practice of CRC diagnosis.

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Fine needle aspiration cytology of malignant thymoma: two cases of invasive thymoma and thymic carcinoma.: Mee Yon Cho, Young Nyun Park, Kwang Gil Lee; Korean J Cytopathol. 1991;2(1):36-42.

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Abstract PDF: We report 4 cases of malignant thymoma which were composed of 2 cases of invasive thymoma and 2 cases of thymic carcinoma. The cytologic findings of invasive thymoma were similar to those of benign thymoma. The distinctive cytologic features of thymic carcinoma were necrotic background, irregular clusters and individually scattered arrangement of anaplastic epithelial cells, and some scattered mature small lymphocytes. These findings may be found in the Hodgkin'slymphoma, seminoma, and metastatic squamous cell carcinoma, undifferentiated carcinoma, and large cell carcinoma of the. lung. But, the feature of irregular clustering of anaplastic epithelial cell having scanty cytoplasm was different from Hodgkin'slymphoma and seminoma. Clinical and radiologic findings as well as cytologic finding were helpful in differential diagnosis of thymic carcinoma from metastatic carcinoma.

Fine Needle Aspiration Cytology of Solid and papillary Neoplasm of the Pancreas: Report of a Case.: Mee Yon Cho, Kwang Gil Lee, Kyi Beom Lee, Hyeun Joo Jeong, Woo Hee Jung; Korean J Cytopathol. 1990;1(1):85-92.

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Abstract PDF: We present the cytologic features of a case of solid and papillary neoplasm of the pancreas. Cytologically, the tumor was composed of a monotonous population of polygonal cells containing ecentrically located round nuclei with one or two distinct small nucleoli and a finely stippled chromatin pattern. The tumor cells were similar to those of the islet cell tumor and showed isolated loosety aggregated and solid sheedts or large cell clumps. The large cell clumps revealed a branching papillary structure containing fibrovascular central core, which is characteristic histologic feature of solid and papillary neoplasm of the pancreas. The case was confirmed by tissue examination including histochemical immunohistochemical and electron microscopical studies. Utrastructurally, the tumor cells contanined a few membrane-bound electron dense granules.

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