Journal of Pathology and Translational Medicine

The Korean Breast Pathology Ki-67 Study Group

1 Article

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; DOI: https://doi.org/10.4132/jptm.2015.12.24

11,255 View
123 Download
25 Web of Science
27 Crossref

Background
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.

Citations

Citations to this article as recorded by

Comparative analysis of Ki-67 labeling index morphometry using deep learning, conventional image analysis, and manual counting
Mohammad Rizwan Alam, Kyung Jin Seo, Kwangil Yim, Phoebe Liang, Joe Yeh, Chifu Chang, Yosep Chong
Translational Oncology.2025; 51: 102159. CrossRef
Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review
Khaldoon Alhusari, Salam Dhou
Journal of Imaging.2025; 11(2): 38. CrossRef
Letter re: A critical appraisal of the DATA trial analysis on the prognostic and predictive value of the luminal-like subtype
M. Rizk, K. Mokbel
ESMO Open.2025; 10(5): 105067. CrossRef
Ki-67 Testing in Breast Cancer: Assessing Variability With Scoring Methods and Specimen Types and the Potential Subsequent Impact on Therapy Eligibility
Therese Bocklage, Virgilius Cornea, Caylin Hickey, Justin Miller, Jessica Moss, Mara Chambers, S. Emily Bachert
Applied Immunohistochemistry & Molecular Morphology.2024; 32(3): 119. CrossRef
Interobserver agreement and diagnostic challenges of Congo red staining for amyloid detection on fat pad aspiration biopsies
Levent Trabzonlu, T. Leif Helland, Melanie C. Kwan, Nathalie Kumiega, M. Lisa Zhang, Ivan Chebib, Vanda F. Torous
Journal of the American Society of Cytopathology.2024; 13(5): 359. CrossRef
Assessment of the Predictive Role of Ki-67 in Breast Cancer Patients’ Responses to Neoadjuvant Chemotherapy
Ghizlane Rais, Rania Mokfi, Farah Boutaggount, Meryem Maskrout, Soundouss Bennour, Chaymae Senoussi, Fadoua Rais
European Journal of Breast Health.2024; : 199. CrossRef
Improving the accuracy of reporting Ki-67 IHC by using an AI tool
Sahil Ajit Saraf, Aahan Singh, Wai Po Kevin Teng, Sencer Karakaya, M. Logaswari, Kaveh Taghipour, Rajasa Jialdasani, Li Yan Khor, Kiat Hon Lim, Sathiyamoorthy Selvarajan, Vani Ravikumar, Md Ali Osama, Priti Chatterjee, Santosh KV
Heliyon.2024; 10(22): e40193. CrossRef
Predictive Value of Ki-67 Index in Evaluating Sporadic Vestibular Schwannoma Recurrence: Systematic Review and Meta-analysis
Kunal Vakharia, Hirotaka Hasegawa, Christopher Graffeo, Mohammad H. A. Noureldine, Salomon Cohen-Cohen, Avital Perry, Matthew L. Carlson, Colin L. W. Driscoll, Maria Peris-Celda, Jamie J. Van Gompel, Michael J. Link
Journal of Neurological Surgery Part B: Skull Base.2023; 84(02): 119. CrossRef
Venous invasion and lymphatic invasion are correlated with the postoperative prognosis of pancreatic neuroendocrine neoplasm
Sho Kiritani, Junichi Arita, Yuichiro Mihara, Rihito Nagata, Akihiko Ichida, Yoshikuni Kawaguchi, Takeaki Ishizawa, Nobuhisa Akamatsu, Junichi Kaneko, Kiyoshi Hasegawa
Surgery.2023; 173(2): 365. CrossRef
Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques
S. Niyas, Ramya Bygari, Rachita Naik, Bhavishya Viswanath, Dhananjay Ugwekar, Tojo Mathew, J Kavya, Jyoti R Kini, Jeny Rajan
IEEE Journal of Translational Engineering in Health and Medicine.2023; 11: 161. CrossRef
Grade Progression and Intrapatient Tumor Heterogeneity as Potential Contributors to Resistance in Gastroenteropancreatic Neuroendocrine Tumors
Diana Grace Varghese, Jaydira Del Rivero, Emily Bergsland
Cancers.2023; 15(14): 3712. CrossRef
Diagnostic Role and Prognostic Impact of PSAP Immunohistochemistry: A Tissue Microarray Study on 31,358 Cancer Tissues
Laura Sophie Tribian, Maximilian Lennartz, Doris Höflmayer, Noémi de Wispelaere, Sebastian Dwertmann Rico, Clara von Bargen, Simon Kind, Viktor Reiswich, Florian Viehweger, Florian Lutz, Veit Bertram, Christoph Fraune, Natalia Gorbokon, Sören Weidemann, C
Diagnostics.2023; 13(20): 3242. CrossRef
AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images
Yiqing Liu, Tiantian Zhen, Yuqiu Fu, Yizhi Wang, Yonghong He, Anjia Han, Huijuan Shi
Cancers.2023; 16(1): 167. 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
Annals of Oncology.2022; 33(3): 234. CrossRef
A novel deep classifier framework for automated molecular subtyping of breast carcinoma using immunohistochemistry image analysis
Tojo Mathew, S. Niyas, C.I. Johnpaul, Jyoti R. Kini, Jeny Rajan
Biomedical Signal Processing and Control.2022; 76: 103657. CrossRef
Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility study
Matthias Choschzick, Mariam Alyahiaoui, Alexander Ciritsis, Cristina Rossi, André Gut, Patryk Hejduk, Andreas Boss
Heliyon.2021; 7(7): e07577. CrossRef
Oncotype DX Predictive Nomogram for Recurrence Score Output: The Novel System ADAPTED01 Based on Quantitative Immunochemistry Analysis
Fabio Marazzi, Roberto Barone, Valeria Masiello, Valentina Magri, Antonino Mulè, Angela Santoro, Federica Cacciatori, Luca Boldrini, Gianluca Franceschini, Francesca Moschella, Giuseppe Naso, Silverio Tomao, Maria Antonietta Gambacorta, Giovanna Mantini,
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; 57(3): 289. CrossRef
Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning
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
Assessment of Ki-67 for Predicting Effective Prognosis in Breast Cancer Subtypes
Sangjung Park, Sunyoung Park, Jungho Kim, Sungwoo Ahn, Kwang Hwa Park, Hyeyoung Lee
Biomedical Science Letters.2018; 24(1): 9. CrossRef
Quantitative tumor heterogeneity assessment on a nuclear population basis
Anne‐Sofie Wessel Lindberg, Knut Conradsen, Rasmus Larsen, Michael Friis Lippert, Rasmus Røge, Mogens Vyberg
Cytometry Part A.2017; 91(6): 574. CrossRef
A comparison of Ki-67 counting methods in luminal Breast Cancer: The Average Method vs. the Hot Spot Method
Min Hye Jang, Hyun Jung Kim, Yul Ri Chung, Yangkyu Lee, So Yeon Park, William B. Coleman
PLOS ONE.2017; 12(2): e0172031. CrossRef
A Novel Breast Cancer Index for Prediction of Distant Recurrence in HR+ Early-Stage Breast Cancer with One to Three Positive Nodes
Yi Zhang, Brock E. Schroeder, Piiha-Lotta Jerevall, Amy Ly, Hannah Nolan, Catherine A. Schnabel, Dennis C. Sgroi
Clinical Cancer Research.2017; 23(23): 7217. CrossRef

Author index