E-submission
Search
- Page Path
- HOME > Search
Original 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
- 4,584 View
- 126 Download
- 8 Web of Science
- 9 Crossref
-
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. -
Citations
Citations to this article as recorded by
- Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry
Diana Gina Poalelungi, Anca Iulia Neagu, Ana Fulga, Marius Neagu, Dana Tutunaru, Aurel Nechita, Iuliu Fulga
Journal of Personalized Medicine.2024; 14(7): 693. CrossRef - Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System
Anca Iulia Neagu, Diana Gina Poalelungi, Ana Fulga, Marius Neagu, Iuliu Fulga, Aurel Nechita
Diagnostics.2024; 14(17): 1853. CrossRef - Optimization of diagnosis and treatment of hematological diseases via artificial intelligence
Shi-Xuan Wang, Zou-Fang Huang, Jing Li, Yin Wu, Jun Du, Ting Li
Frontiers in Medicine.2024;[Epub] CrossRef - Real-Life Barriers to Diagnosis of Early Mycosis Fungoides: An International Expert Panel Discussion
Emmilia Hodak, Larisa Geskin, Emmanuella Guenova, Pablo L. Ortiz-Romero, Rein Willemze, Jie Zheng, Richard Cowan, Francine Foss, Cristina Mangas, Christiane Querfeld
American Journal of Clinical Dermatology.2023; 24(1): 5. CrossRef - 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 - Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives
Dai Chihara, Loretta J. Nastoupil, Christopher R. Flowers
British Journal of Haematology.2023; 202(2): 219. CrossRef - Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape
Muhammad Joan Ailia, Nishant Thakur, Jamshid Abdul-Ghafar, Chan Kwon Jung, Kwangil Yim, Yosep Chong
Cancers.2022; 14(10): 2400. CrossRef - Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review
Nishant Thakur, Mohammad Rizwan Alam, Jamshid Abdul-Ghafar, Yosep Chong
Cancers.2022; 14(14): 3529. CrossRef - Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
Yosep Chong, Nishant Thakur, Ji Young Lee, Gyoyeon Hwang, Myungjin Choi, Yejin Kim, Hwanjo Yu, Mee Yon Cho
Diagnostic Pathology.2021;[Epub] CrossRef
- Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry
- The Histone Acetyltransferase hMOF is Overexpressed in Non-small Cell Lung Carcinoma.
- Joon Seon Song, Sung Min Chun, Ji Young Lee, Dong Kwan Kim, Yong Hee Kim, Se Jin Jang
- Korean J Pathol. 2011;45(4):386-396.
- DOI: https://doi.org/10.4132/KoreanJPathol.2011.45.4.386
- 4,724 View
- 64 Download
- 13 Crossref
-
Abstract
PDF
- BACKGROUND
One of the histone acetyltransferases (HATs) family of proteins, human MOF (hMOF, MYST1), is involved in histone H4 acetylation, particularly at lysine 16 (H4K16Ac), an epigenetic mark of active genes. Dysregulation of the epigenetic mark influences cellular biology and possibly leads to oncogenesis. We examined the involvement of hMOF and H4K16Ac in primary non-small cell lung cancer (NSCLC).
METHODS
Reverse transcription polymerase chain reaction using fresh-frozen lung cancer tissues and lung cancer cell lines and immunohistochemistry for hMOF and H4K16Ac via tissue microarray of 551 formalin-fixed paraffin-embedded NSCLC tissue blocks were conducted.
RESULTS
hMOF mRNA was frequently overexpressed in lung cancer tissues, compared with normal lung tissues (10/20, 50%). NSCLC tissues were positive for hMOF in 37.6% (184/489) and H4K16Ac in 24.7% (122/493) of cases. hMOF protein expression was tightly correlated with the H4K16Ac level in tumors (p<0.001). Knockdown of hMOF mRNA with siRNA led to a significant inhibition of growth in the Calu-6 cell line.
CONCLUSIONS
hMOF was frequently expressed in NSCLC and was correlated with H4K16Ac. To our knowledge, this is the first study that has focused on the expression status of HATs and hMOF in NSCLC. Our results clearly suggest a potential oncogenic role of the gene and support its utility as a potential therapeutic target. -
Citations
Citations to this article as recorded by- Stabilization of MOF (KAT8) by USP10 promotes esophageal squamous cell carcinoma proliferation and metastasis through epigenetic activation of ANXA2/Wnt signaling
Peichao Li, Lingxiao Yang, Sun Young Park, Fanrong Liu, Alex H. Li, Yilin Zhu, Huacong Sui, Fengyuan Gao, Lingbing Li, Lan Ye, Yongxin Zou, Zhongxian Tian, Yunpeng Zhao, Max Costa, Hong Sun, Xiaogang Zhao
Oncogene.2024; 43(12): 899. CrossRef - The Biological Significance of Targeting Acetylation-Mediated Gene Regulation for Designing New Mechanistic Tools and Potential Therapeutics
Chenise O’Garro, Loveth Igbineweka, Zonaira Ali, Mihaly Mezei, Shiraz Mujtaba
Biomolecules.2021; 11(3): 455. CrossRef - Histone Acetyltransferase MOF Orchestrates Outcomes at the Crossroad of Oncogenesis, DNA Damage Response, Proliferation, and Stem Cell Development
Mayank Singh, Albino Bacolla, Shilpi Chaudhary, Clayton R. Hunt, Shruti Pandita, Ravi Chauhan, Ashna Gupta, John A. Tainer, Tej K. Pandita
Molecular and Cellular Biology.2020;[Epub] CrossRef - The Functional Analysis of Histone Acetyltransferase MOF in Tumorigenesis
Jiaming Su, Fei Wang, Yong Cai, Jingji Jin
International Journal of Molecular Sciences.2016; 17(1): 99. CrossRef - Expression of hMOF, but not HDAC4, is responsible for the global histone H4K16 acetylation in gastric carcinoma
LIN ZHU, JIAXING YANG, LINHONG ZHAO, XUE YU, LINGYAO WANG, FEI WANG, YONG CAI, JINGJI JIN
International Journal of Oncology.2015; 46(6): 2535. CrossRef - Arsenic Trioxide Reduces Global Histone H4 Acetylation at Lysine 16 through Direct Binding to Histone Acetyltransferase hMOF in Human Cells
Da Liu, Donglu Wu, Linhong Zhao, Yang Yang, Jian Ding, Liguo Dong, Lianghai Hu, Fei Wang, Xiaoming Zhao, Yong Cai, Jingji Jin, Tim Thomas
PLOS ONE.2015; 10(10): e0141014. CrossRef - The histone acetyltransferase hMOF suppresses hepatocellular carcinoma growth
Jin Zhang, Hui Liu, Hao Pan, Yuan Yang, Gang Huang, Yun Yang, Wei-Ping Zhou, Ze-Ya Pan
Biochemical and Biophysical Research Communications.2014; 452(3): 575. CrossRef - Regulation and function of histone acetyltransferase MOF
Yang Yang, Xiaofei Han, Jingyun Guan, Xiangzhi Li
Frontiers of Medicine.2014; 8(1): 79. CrossRef - The histone acetylranseferase hMOF acetylates Nrf2 and regulates anti‐drug responses in human non‐small cell lung cancer
Zhiwei Chen, Xiangyun Ye, Naiwang Tang, Shengping Shen, Ziming Li, Xiaomin Niu, Shun Lu, Ling Xu
British Journal of Pharmacology.2014; 171(13): 3196. CrossRef - Correlation of low expression of hMOF with clinicopathological features of colorectal carcinoma, gastric cancer and renal cell carcinoma
LINGLING CAO, LIN ZHU, JIAXING YANG, JIAMING SU, JINSONG NI, YUJUN DU, DA LIU, YANFANG WANG, FEI WANG, JINGJI JIN, YONG CAI
International Journal of Oncology.2014; 44(4): 1207. CrossRef - Coactivator MYST1 Regulates Nuclear Factor-κB and Androgen Receptor Functions During Proliferation of Prostate Cancer Cells
Anbalagan Jaganathan, Pratima Chaurasia, Guang-Qian Xiao, Marc Philizaire, Xiang Lv, Shen Yao, Kerry L. Burnstein, De-Pei Liu, Alice C. Levine, Shiraz Mujtaba
Molecular Endocrinology.2014; 28(6): 872. CrossRef - A potential diagnostic marker for ovarian cancer: Involvement of the histone acetyltransferase, human males absent on the first
NING LIU, RUI ZHANG, XIAOMING ZHAO, JIAMING SU, XIAOLEI BIAN, JINSONG NI, YING YUE, YONG CAI, JINGJI JIN
Oncology Letters.2013; 6(2): 393. CrossRef - Epigenetic change in kidney tumor: downregulation of histone acetyltransferase MYST1 in human renal cell carcinoma
Yong Wang, Rui Zhang, Donglu Wu, Zhihua Lu, Wentao Sun, Yong Cai, Chunxi Wang, Jingji Jin
Journal of Experimental & Clinical Cancer Research.2013;[Epub] CrossRef
- Stabilization of MOF (KAT8) by USP10 promotes esophageal squamous cell carcinoma proliferation and metastasis through epigenetic activation of ANXA2/Wnt signaling
Case Report
- Immunohistochemical Identification of Pneumocystis jirovecii in Liquid-based Cytology of Bronchoalveolar Lavage: Nine Cases Report.
- Jeong Hyeon Lee, Ji Young Lee, Mi Ran Shin, Hyeong Kee Ahn, Chul Whan Kim, Insun Kim
- Korean J Pathol. 2011;45(1):115-118.
- DOI: https://doi.org/10.4132/KoreanJPathol.2011.45.1.115
- 3,619 View
- 34 Download
- 3 Crossref
-
Abstract
PDF
- Pneumocystis pneumonia (PCP) is caused by the yeast-like fungus Pneumocystis jirovecii, which is specific to humans. PCP could be a source of opportunistic infection in adults that are immunosuppressed and children with prematurity or malnutrition. The diagnosis should be confirmed by identification of the causative organism, by analysis of the sputum, a bronchoalveolar lavage or a tissue biopsy. In both histologic and cytologic specimens, the cysts are contained within frothy exudates, which form aggregated clumps. The cysts often collapse forming crescent-shaped bodies that resemble ping-pong balls. We recently diagnosed nine cases of PCP using an immunohistochemical stain for Pneumocystis. The patients consisted of five human immunodeficiency virus positive individuals, two renal transplant recipients, and two patients with a malignant disease. All nine patients were infected with P. jirovecii, which was positive for monoclonal antibody 3F6. In conclusion, the immunohistochemical stain used in this report is a new technique for the detection of P. jirovecii infection.
-
Citations
Citations to this article as recorded by- Metabolic Changes in Serum Metabolome of Beagle Dogs Fed Black Ginseng
Dahye Yoon, Ye Jin Kim, Wan Kyu Lee, Bo Ram Choi, Seon Min Oh, Young Seob Lee, Jae Kwang Kim, Dae Young Lee
Metabolites.2020; 10(12): 517. CrossRef - Effects of Red or Black Ginseng Extract in a Rat Model of Inflammatory Temporomandibular Joint Pain
Hyeon-Jeong Lee, Yun-Kyung Kim, Ja-Hyeong Choi, Jung-Hwa Lee, Hye-Jin Kim, Mi-Gyung Seong, Min-Kyung Lee
Journal of Dental Hygiene Science.2017; 17(1): 65. CrossRef - Value of Bronchoalveolar Lavage Fluid Cytology in the Diagnosis ofPneumocystis jiroveciiPneumonia: A Review of 30 Cases
Ji-Youn Sung, Joungho Han, Young Lyun Oh, Gee Young Suh, Kyeongman Jeon, Taeeun Kim
Tuberculosis and Respiratory Diseases.2011; 71(5): 322. CrossRef
- Metabolic Changes in Serum Metabolome of Beagle Dogs Fed Black Ginseng
First
Prev
