Cancer Research and Treatment

Jeong Hwan Park

2 Articles

Genitourinary cancer

Clear Cell Adenocarcinoma of Urethra: Clinical and Pathologic Implications and Characterization of Molecular Aberrations: Boram Song, Seok Hyun Lee, Jeong Hwan Park, Kyung Chul Moon; Cancer Res Treat. 2024;56(1):280-293. Published online September 11, 2023; DOI: https://doi.org/10.4143/crt.2023.577

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Purpose
This study aimed to evaluate the molecular features of clear cell adenocarcinoma (CCA) of the urinary tract and investigate its pathogenic pathways and possible actionable targets.
Materials and Methods
We retrospectively collected the data of patients with CCA between January 1999 and December 2016; the data were independently reviewed by two pathologists. We selected five cases of urinary CCA, based on the clinicopathological features. We analyzed these five cases by whole exome sequencing (WES) and subsequent bioinformatics analyses to determine the mutational spectrum and possible pathogenic pathways.
Results
All patients were female with a median age of 62 years. All tumors were located in the urethra and showed aggressive behavior with disease progression. WES revealed several genetic alterations, including driver gene mutations (AMER1, ARID1A, CHD4, KMT2D, KRAS, PBRM1, and PIK3R1) and mutations in other important genes with tumor-suppressive and oncogenic roles (CSMD3, KEAP1, SMARCA4, and CACNA1D). We suggest putative pathogenic pathways (chromatin remodeling pathway, mitogen-activated protein kinase signaling pathway, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin pathway, and Wnt/β-catenin pathway) as candidates for targeted therapies.
Conclusion
Our findings shed light on the molecular background of this extremely rare tumor with poor prognosis and can help improve treatment options.

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Urethral clear cell adenocarcinoma in an adult female: A rare case report
Yacob Sheiferawe Seman, Michael Teklehaimanot Abera, Fadil Nuredin Abrar, Tesfaye Kebede Legesse, Mesfin Asefa Tola, Tsiyon Nigusie Alemu
Urology Case Reports.2025; 58: 102882. CrossRef
Association between CACNA1D polymorphisms and hypospadias in a southern Chinese population
Ye He, Binyao Li, Xinying Zhao, Lingling Pan, Yanqing Liu, Chaoting Lan, Fuming Deng, Wen Fu, Yan Zhang, Xiaoyu Zuo
Journal of Pediatric Urology.2024; 20(3): 438.e1. CrossRef
The L‐type calcium channel CaV1.3: A potential target for cancer therapy
Xuerun Liu, Boqiang Shen, Jingyi Zhou, Juan Hao, Jianliu Wang
Journal of Cellular and Molecular Medicine.2024;[Epub] CrossRef

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Breast cancer

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer: Young-Gon Kim, In Hye Song, Hyunna Lee, Sungchul Kim, Dong Hyun Yang, Namkug Kim, Dongho Shin, Yeonsoo Yoo, Kyowoon Lee, Dahye Kim, Hwejin Jung, Hyunbin Cho, Hyungyu Lee, Taeu Kim, Jong Hyun Choi, Changwon Seo, Seong il Han, Young Je Lee, Young Seo Lee, Hyung-Ryun Yoo, Yongju Lee, Jeong Hwan Park, Sohee Oh, Gyungyub Gong; Cancer Res Treat. 2020;52(4):1103-1111. Published online June 30, 2020; DOI: https://doi.org/10.4143/crt.2020.337

Abstract PDF PubReader ePub

Purpose
Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients.
Materials and Methods
A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve).
Results
The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy.
Conclusion
In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting

Citations

Citations to this article as recorded by

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer
Young-Gon Kim, In Hye Song, Seung Yeon Cho, Sungchul Kim, Milim Kim, Soomin Ahn, Hyunna Lee, Dong Hyun Yang, Namkug Kim, Sungwan Kim, Taewoo Kim, Daeyoung Kim, Jonghyeon Choi, Ki-Sun Lee, Minuk Ma, Minki Jo, So Yeon Park, Gyungyub Gong
Cancer Research and Treatment.2023; 55(2): 513. CrossRef
Artificial intelligence and frozen section histopathology: A systematic review
Benjamin G. Gorman, Mark A. Lifson, Nahid Y. Vidal
Journal of Cutaneous Pathology.2023; 50(9): 852. CrossRef
Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections
Young-Gon Kim, Sungchul Kim, Cristina Eunbee Cho, In Hye Song, Hee Jin Lee, Soomin Ahn, So Yeon Park, Gyungyub Gong, Namkug Kim
Scientific Reports.2020;[Epub] CrossRef

8,070 View
201 Download
14 Web of Science
3 Crossref

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