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Woo-Hee Jung 4 Articles
Pathologic Evaluation of Breast Cancer after Neoadjuvant Therapy
Cheol Keun Park, Woo-Hee Jung, Ja Seung Koo
J Pathol Transl Med. 2016;50(3):173-180.   Published online April 11, 2016
  • 11,842 View
  • 369 Download
  • 27 Citations
AbstractAbstract PDF
Breast cancer, one of the most common cancers in women, has various treatment modalities. Neoadjuvant therapy (NAT) has been used in many clinical trials because it is easy to evaluate the treatment response to therapeutic agents in a short time period; consequently, NAT is currently a standard treatment modality for large-sized and locally advanced breast cancers, and its use in early-stage breast cancer is becoming more common. Thus, chances to encounter breast tissue from patients treated with NAT is increasing. However, systems for handling and evaluating such specimens have not been established. Several evaluation systems emphasize a multidisciplinary approach to increase the accuracy of breast cancer assessment. Thus, detailed and systematic evaluation of clinical, radiologic, and pathologic findings is important. In this review, we compare the major problems of each evaluation system and discuss important points for handling and evaluating NAT-treated breast specimens.


Citations to this article as recorded by  
  • Good practice: The experiences with the utilization of residual cancer burden—A single institution study
    Anita Sejben, Fanni Hegedűs, Szintia Almási, Márton Berta, Orsolya Oláh‐Németh, Tamás Zombori
    Thoracic Cancer.2023;[Epub]     CrossRef
  • Concurrent Chemo-radiation As a Means of Achieving Pathologic Complete Response in Triple Negative Breast Cancer
    Maryam Nemati Shafaee, Shalini Makawita, Bora Lim, Matthew J Ellis, Michelle S Ludwig
    Clinical Breast Cancer.2022; 22(4): e536.     CrossRef
  • Pathology after neoadjuvant treatment – How to assess residual disease
    Giuseppe Viale, Nicola Fusco
    The Breast.2022; 62: S25.     CrossRef
  • Efficacy Evaluation of Neoadjuvant Chemotherapy in Breast Cancer by MRI
    Yongguang Liu, Mingxiang Wu, Wenyong Tan, Jingshan Gong, Jie Ma, Mohammad Farukh Hashmi
    Contrast Media & Molecular Imaging.2022; 2022: 1.     CrossRef
  • Predictive Role of Soluble IL-6R, TNF-R1/2, and Cell Adhesion Molecules Serum Levels in the Preoperative and Adjuvant Therapy in Women with Nonmetastatic Breast Cancer: A Preliminary Study
    Weronika Bulska-Będkowska, Paulina Czajka-Francuz, Sylwia Cisoń-Jurek, Aleksander J. Owczarek, Tomasz Francuz, Jerzy Chudek
    Journal of Interferon & Cytokine Research.2022; 42(11): 557.     CrossRef
  • The prognostic role of lymph node ratio in breast cancer patients received neoadjuvant chemotherapy: A dose-response meta-analysis
    Jinzhao Liu, Yifei Li, Weifang Zhang, Chenhui Yang, Chao Yang, Liang Chen, Mingjian Ding, Liang Zhang, Xiaojun Liu, Guozhong Cui, Yunjiang Liu
    Frontiers in Surgery.2022;[Epub]     CrossRef
  • The Role of miR-375-3p, miR-210-3p and Let-7e-5p in the Pathological Response of Breast Cancer Patients to Neoadjuvant Therapy
    Lorena Alexandra Lisencu, Andrei Roman, Simona Visan, Eduard-Alexandru Bonci, Andrei Pașca, Emilia Grigorescu, Elena Mustea, Andrei Cismaru, Alexandru Irimie, Cosmin Lisencu, Loredana Balacescu, Ovidiu Balacescu, Oana Tudoran
    Medicina.2022; 58(10): 1494.     CrossRef
  • Post-Neoadjuvant Treatment Strategies for Patients with Early Breast Cancer
    Elisa Agostinetto, Flavia Jacobs, Véronique Debien, Alex De Caluwé, Catalin-Florin Pop, Xavier Catteau, Philippe Aftimos, Evandro de Azambuja, Laurence Buisseret
    Cancers.2022; 14(21): 5467.     CrossRef
  • Tumor Microenvironment in Breast Cancer—Updates on Therapeutic Implications and Pathologic Assessment
    Joshua J. Li, Julia Y. Tsang, Gary M. Tse
    Cancers.2021; 13(16): 4233.     CrossRef
  • SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
    Nicholas Petrick, Shazia Akbar, Kenny H. Cha, Sharon Nofech-Mozes, Berkman Sahiner, Marios A. Gavrielides, Jayashree Kalpathy-Cramer, Karen Drukker, Anne L. Martel, for the BreastPathQ Challenge Group
    Journal of Medical Imaging.2021;[Epub]     CrossRef
  • Diagnostic performance of digital breast tomosynthesis for predicting response to neoadjuvant systemic therapy in breast cancer patients: A comparison with magnetic resonance imaging, ultrasound, and full-field digital mammography
    Ryusuke Murakami, Hitomi Tani, Shinichiro Kumita, Nachiko Uchiyama
    Acta Radiologica Open.2021; 10(12): 205846012110637.     CrossRef
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    Laia Bernet, María Angeles Montero Fernández
    Revista de Senología y Patología Mamaria.2021; 34: S25.     CrossRef
  • Neoadjuvant chemotherapy in non‐metastatic breast cancer: a study on practice trends in a regional cancer treatment service
    Edmond Ang, Navin Wewala, Rebecca Carroll, Garry Forgeson, Malcolm Anderson, Jennifer Fernando, Jody Jordan, Richard Isaacs
    Internal Medicine Journal.2020; 50(3): 315.     CrossRef
  • Examination of Tumor Regression Grading Systems in Breast Cancer Patients Who Received Neoadjuvant Therapy
    Anita Sejben, Renáta Kószó, Zsuzsanna Kahán, Gábor Cserni, Tamás Zombori
    Pathology & Oncology Research.2020; 26(4): 2747.     CrossRef
  • Integrating evolutionary dynamics into cancer therapy
    Robert A. Gatenby, Joel S. Brown
    Nature Reviews Clinical Oncology.2020; 17(11): 675.     CrossRef
  • Assessing the accuracy of conventional gadolinium‐enhanced breast MRI in measuring the nodal response to neoadjuvant chemotherapy (NAC) in breast cancer
    Lisa Christine Murphy, Edel Marie Quinn, Zeeshan Razzaq, Claire Brady, Vicki Livingstone, Lorna Duddy, Josephine Barry, Henry Paul Redmond, Mark Anthony Corrigan
    The Breast Journal.2020; 26(11): 2151.     CrossRef
  • Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning
    Joon Ho Choi, Hyun-Ah Kim, Wook Kim, Ilhan Lim, Inki Lee, Byung Hyun Byun, Woo Chul Noh, Min-Ki Seong, Seung-Sook Lee, Byung Il Kim, Chang Woon Choi, Sang Moo Lim, Sang-Keun Woo
    Scientific Reports.2020;[Epub]     CrossRef
  • Patterns of Regression in Breast Cancer after Primary Systemic Treatment
    Tamás Zombori, Gábor Cserni
    Pathology & Oncology Research.2019; 25(3): 1153.     CrossRef
  • The Role of Neutrophil-lymphocyte Ratio and Platelet-lymphocyte Ratio in Predicting Neoadjuvant Chemotherapy Response in Breast Cancer
    Hee Yeon Kim, Tae Hyun Kim, Hye Kyoung Yoon, Anbok Lee
    Journal of Breast Cancer.2019; 22(3): 425.     CrossRef
  • Higher underestimation of tumour size post-neoadjuvant chemotherapy with breast magnetic resonance imaging (MRI)—A concordance comparison cohort analysis
    Wen-Pei Wu, Hwa-Koon Wu, Chih-Jung Chen, Chih-Wie Lee, Shou-Tung Chen, Dar-Ren Chen, Chen-Te Chou, Chi Wei Mok, Hung-Wen Lai, Pascal A. T. Baltzer
    PLOS ONE.2019; 14(10): e0222917.     CrossRef
  • Multimodal image-guided surgery of HER2-positive breast cancer using [111In]In-DTPA-trastuzumab-IRDye800CW in an orthotopic breast tumor model
    Marion M. Deken, Desirée L. Bos, Willemieke S. F. J. Tummers, Taryn L. March, Cornelis J. H. van de Velde, Mark Rijpkema, Alexander L. Vahrmeijer
    EJNMMI Research.2019;[Epub]     CrossRef
  • Mammographic density is a potential predictive marker of pathological response after neoadjuvant chemotherapy in breast cancer
    Ida Skarping, Daniel Förnvik, Hanna Sartor, Uffe Heide-Jørgensen, Sophia Zackrisson, Signe Borgquist
    BMC Cancer.2019;[Epub]     CrossRef
  • ALDH1 and tumor infiltrating lymphocytes as predictors for neoadjuvant chemotherapy response in breast cancer
    Anbok Lee, Kyu Yeoun Won, Sung-Jig Lim, Sun Young Cho, Sang-Ah Han, SaeGwang Park, Jeong-Yoon Song
    Pathology - Research and Practice.2018; 214(5): 619.     CrossRef
  • Early Prediction of Response to Neoadjuvant Chemotherapy Using Dynamic Contrast-Enhanced MRI and Ultrasound in Breast Cancer
    Yunju Kim, Sung Hun Kim, Byung Joo Song, Bong Joo Kang, Kwang-il Yim, Ahwon Lee, Yoonho Nam
    Korean Journal of Radiology.2018; 19(4): 682.     CrossRef
  • Outcomes of neoadjuvant and adjuvant chemotherapy in stage 2 and 3 non-small cell lung cancer: an analysis of the National Cancer Database
    Matthew MacLean, Xin Luo, Shidan Wang, Kemp Kernstine, David E. Gerber, Yang Xie
    Oncotarget.2018; 9(36): 24470.     CrossRef
  • Automatic cellularity assessment from post-treated breast surgical specimens
    Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne L. Martel
    Cytometry Part A.2017; 91(11): 1078.     CrossRef
  • The importance of tissue confirmation of metastatic disease in patients with breast cancer: lesson from a brain metastasis case
    Jingxian Ding, Pinghua Hu, Jun Chen, Xiaobo Wu, Yali Cao
    Oncoscience.2016; 3(9-10): 268.     CrossRef
Interobserver Variability of Ki-67 Measurement in Breast Cancer
Yul Ri Chung, Min Hye Jang, So Yeon Park, Gyungyub Gong, Woo-Hee Jung, The Korean Breast Pathology Ki- Study Group
J Pathol Transl Med. 2016;50(2):129-137.   Published online February 15, 2016
  • 8,645 View
  • 101 Download
  • 17 Citations
AbstractAbstract PDF
As measurement of Ki-67 proliferation index is an important part of breast cancer diagnostics, we conducted a multicenter study to examine the degree of concordance in Ki-67 counting and to find factors that lead to its variability. Methods: Thirty observers from thirty different institutions reviewed Ki-67–stained slides of 20 different breast cancers on whole sections and tissue microarray (TMA) by online system. Ten of the 20 breast cancers had hot spots of Ki-67 expression. Each observer scored Ki-67 in two different ways: direct counting (average vs. hot spot method) and categorical estimation. Intraclass correlation coefficient (ICC) of Ki-67 index was calculated for comparative analysis. Results: For direct counting, ICC of TMA was slightly higher than that of whole sections using average method (0.895 vs 0.858). The ICC of tumors with hot spots was lower than that of tumors without (0.736 vs 0.874). In tumors with hot spots, observers took an additional counting from the hot spot; the ICC of whole sections using hot spot method was still lower than that of TMA (0.737 vs 0.895). In categorical estimation, Ki-67 index showed a wide distribution in some cases. Nevertheless, in tumors with hot spots, the range of distribution in Ki-67 categories was decreased with hot spot method and in TMA platform. Conclusions: Interobserver variability of Ki-67 index for direct counting and categorical estimation was relatively high. Tumors with hot spots showed greater interobserver variability as opposed to those without, and restricting the measurement area yielded lower interobserver variability.


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    IEEE Journal of Translational Engineering in Health and Medicine.2023; 11: 161.     CrossRef
  • Expression of estrogen and progesterone receptors, HER2 protein and Ki-67 proliferation index in breast carcinoma in both tumor tissue and tissue microarray
    UP Hacısalihoğlu, MA Dogan
    Biotechnic & Histochemistry.2022; 97(4): 298.     CrossRef
  • Diffusive Ki67 and vimentin are associated with worse recurrence-free survival of upper tract urothelial carcinoma: A retrospective cohort study from bench to bedside
    Che Hsueh Yang, Wei Chun Weng, Yen Chuan Ou, Yi Sheng Lin, Li Hua Huang, Chin Heng Lu, Tang Yi Tsao, Chao Yu Hsu, Min Che Tung
    Urologic Oncology: Seminars and Original Investigations.2022; 40(3): 109.e21.     CrossRef
  • Should Ki-67 be adopted to select breast cancer patients for treatment with adjuvant abemaciclib?
    P. Tarantino, H.J. Burstein, N.U. Lin, I.E. Krop, E.P. Winer, S.J. Schnitt, E.P. Hamilton, S.A. Hurvitz, H.S. Rugo, G. Curigliano, S.M. Tolaney
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    Tojo Mathew, S. Niyas, C.I. Johnpaul, Jyoti R. Kini, Jeny Rajan
    Biomedical Signal Processing and Control.2022; 76: 103657.     CrossRef
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    Heliyon.2021; 7(7): e07577.     CrossRef
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    Clinical Breast Cancer.2020; 20(5): e600.     CrossRef
  • Study of Ki-67 index in the molecular subtypes of breast cancer: Inter-observer variability and automated scoring
    Divya Meermira, Meenakshi Swain, Swarnalata Gowrishankar
    Indian Journal of Cancer.2020;[Epub]     CrossRef
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    Darshana Govind, Kuang-Yu Jen, Karen Matsukuma, Guofeng Gao, Kristin A. Olson, Dorina Gui, Gregory. E. Wilding, Samuel P. Border, Pinaki Sarder
    Scientific Reports.2020;[Epub]     CrossRef
  • Practical approaches to automated digital image analysis of Ki-67 labeling index in 997 breast carcinomas and causes of discordance with visual assessment
    Ah-Young Kwon, Ha Young Park, Jiyeon Hyeon, Seok Jin Nam, Seok Won Kim, Jeong Eon Lee, Jong-Han Yu, Se Kyung Lee, Soo Youn Cho, Eun Yoon Cho, Irina V. Lebedeva
    PLOS ONE.2019; 14(2): e0212309.     CrossRef
  • Evaluation of Ki-67 Index in Core Needle Biopsies and Matched Breast Cancer Surgical Specimens
    Soomin Ahn, Junghye Lee, Min-Sun Cho, Sanghui Park, Sun Hee Sung
    Archives of Pathology & Laboratory Medicine.2018; 142(3): 364.     CrossRef
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  • A comparison of Ki-67 counting methods in luminal Breast Cancer: The Average Method vs. the Hot Spot Method
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Primary Mucinous Cystadenocarcinoma of the Breast: Cytologic Finding and Expression of MUC5 Are Different from Mucinous Carcinoma
Sung Eun Kim, Ji Hye Park, SoonWon Hong, Ja Seung Koo, Joon Jeong, Woo-Hee Jung
Korean J Pathol. 2012;46(6):611-616.   Published online December 26, 2012
  • 6,640 View
  • 44 Download
  • 9 Citations
AbstractAbstract PDF

Mucinous cystadenocarcinoma (MCA) in the breast is a rare neoplasm. There have been 13 cases of primary breast MCA reported. The MCA presents as a large, partially cystic mass in postmenopausal woman with a good prognosis. The microscopic findings resemble those of ovarian, pancreatic, or appendiceal MCA. The aspiration findings showed mucin-containing cell clusters in the background of mucin and necrotic material. The cell clusters had intracytoplasmic mucin displacing atypical nuclei to the periphery. Histologically, the tumor revealed an abundant mucin pool with small floating clusters of mucin-containing tumor cells. There were also small cysts lined by a single layer of tall columnar mucinous cells, resembling those of the uterine endocervix. The cancer cells were positive for mucin (MUC) 5 and negative for MUC2 and MUC6. This mucin profile is different from ordinary mucinous carcinoma and may be a unique characteristic of breast MCA.


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    Anupma Nayak, Ira J. Bleiweiss, Kimberly Dumoff, Tawfiqul A. Bhuiya
    International Journal of Surgical Pathology.2018; 26(8): 749.     CrossRef
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    N Tsoukalas, M Kiakou, M Tolia, ID Kostakis, M Galanopoulos, G Nakos, D Tryfonopoulos, G Kyrgias, G Koumakis
    The Annals of The Royal College of Surgeons of England.2018; 100(5): e132.     CrossRef
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    Minjung Seong, Eun Young Ko, Boo-Kyung Han, Soo Youn Cho, Eun Yoon Cho, Se Kyung Lee, Jeong Eon Lee
    Journal of Breast Cancer.2016; 19(3): 330.     CrossRef
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The Clinicopathologic Features of Molecular Apocrine Breast Cancer
Yoon Jin Cha, Woo-Hee Jung, Ja Seung Koo
Korean J Pathol. 2012;46(2):169-176.   Published online April 25, 2012
  • 5,774 View
  • 45 Download
  • 12 Citations
AbstractAbstract PDF

To elucidate the clinicopathologic features and their implications on the immunohistochemistry in cases of molecular apocrine breast cancer (MABC).


Immunohistochemical (IHC) staining for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER-2), cytokeratin (CK) 5/6, epidermal growth factor receptor (EGFR), androgen receptor (AR), gamma-glutamyltrasferase 1 (GGT1) and Ki-67 was performed on tissue microarray breast cancer samples from 204 patients. Phenotypes of breast cancer were divided based on the IHC status of ER, AR and GGT1 into the following: luminal type, ER positive and AR and/or GGT1 positive; basal type, ER, AR, and GGT1 negative; non-basal type, ER positive and AR and GGT1 negative; and MABC type, ER negative and AR and/or GGT1 positive.


In our series of patients (n=204), there were 26 cases of MABC. Besides, there were 18, 60, and 100 cases of luminal type, basal type and non-basal type, respectively. The MABC demonstrated apocrine histology and a higher prevalence of HER-2 positivity than other phenotypes. With the basal type, the MABC manifested a more frequent expression of CK5/6 and EGFR and a higher Ki-67 index than other phenotypes (p<0.001). There were no significant differences in patient prognosis between the phenotypes of breast cancer.


MABC are distinguishable from other phenotypes based on the apocrine histology and a higher expression rate of HER-2.


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JPTM : Journal of Pathology and Translational Medicine