Hot spot detection for breast cancer in Ki-67 stained slides: image dependent filtering approach

Author(s):  
M. Khalid Khan Niazi ◽  
Erinn Downs-Kelly ◽  
Metin N. Gurcan
2021 ◽  
pp. 1-11
Author(s):  
Brian S. Finkelman ◽  
Amanda Meindl ◽  
Carissa LaBoy ◽  
Brannan Griffin ◽  
Suguna Narayan ◽  
...  

BACKGROUND: Ki-67 immunohistochemistry (IHC) staining is a widely used cancer proliferation assay; however, its limitations could be improved with automated scoring. The OncotypeDXTM Recurrence Score (ORS), which primarily evaluates cancer proliferation genes, is a prognostic indicator for breast cancer chemotherapy response; however, it is more expensive and slower than Ki-67. OBJECTIVE: To compare manual Ki-67 (mKi-67) with automated Ki-67 (aKi-67) algorithm results based on manually selected Ki-67 “hot spots” in breast cancer, and correlate both with ORS. METHODS: 105 invasive breast carcinoma cases from 100 patients at our institution (2011–2013) with available ORS were evaluated. Concordance was assessed via Cohen’s Kappa (κ). RESULTS: 57/105 cases showed agreement between mKi-67 and aKi-67 (κ 0.31, 95% CI 0.18–0.45), with 41 cases overestimated by aKi-67. Concordance was higher when estimated on the same image (κ 0.53, 95% CI 0.37–0.69). Concordance between mKi-67 score and ORS was fair (κ 0.27, 95% CI 0.11–0.42), and concordance between aKi-67 and ORS was poor (κ 0.10, 95% CI −0.03–0.23). CONCLUSIONS: These results highlight the limits of Ki-67 algorithms that use manual “hot spot” selection. Due to suboptimal concordance, Ki-67 is likely most useful as a complement to, rather than a surrogate for ORS, regardless of scoring method.


2014 ◽  
Author(s):  
Jesper Molin ◽  
Kavitha Shaga Devan ◽  
Karin Wårdell ◽  
Claes Lundström
Keyword(s):  
Hot Spot ◽  
Ki 67 ◽  

Author(s):  
F. Z. Mohammed ◽  
Lamis Gamal ◽  
Mohamed Farouk Mosa ◽  
Mohamed Ibraheim Aref

Background: Breast cancer (BC) is a well-documented major cause of female morbidity and mortality worldwide. Ongoing research era is focusing on the establishment of diagnostic and prognostic markers, helping for early pick up of the cases, proper prognosis evaluation and clarifying reliable treatment strategy. Aim of the Study: This study aimed to evaluate the role of  Ki-67 as prognostic marker for breast cancer  in Egyptian females population. Patients and Methods: 120 BC patients and 30 age and BMI matching health controls are the subjects of the study, Ki-67 index values were investigated by immunohistochemistry that was performed on 5-lm slides of formalin-fixed and paraffin-embedded archival tumor tissue (core needle biopsy samples). Antigen retrieval was performed in a micro-oven in citrate buffer pH 6 for 20 minutes. Ki-67–stained slides were captured digitally at a hot spot at 3200 magnification. The Ki-67 labeling index was measured using digital image analysis software. Image analysis was performed by an experienced pathologist. Ki-67 index values were correlated with the clinicopathologic aspects of the BC patients. Results: Our study showed that Ki-67 index values revealed gradual increase with disease severity and correlated with poor prognosis aspects. Conclusion: Ki-67 index values are shown to be associated with breast cancer prognosis,  supporting their role as prognostic biomarkers.


PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0172031 ◽  
Author(s):  
Min Hye Jang ◽  
Hyun Jung Kim ◽  
Yul Ri Chung ◽  
Yangkyu Lee ◽  
So Yeon Park

Oncology ◽  
2015 ◽  
Vol 90 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Nobuyuki Arima ◽  
Reiki Nishimura ◽  
Tomofumi Osako ◽  
Yasuyuki Nishiyama ◽  
Mamiko Fujisue ◽  
...  

2020 ◽  
Vol 10 (21) ◽  
pp. 7761
Author(s):  
Zaneta Swiderska-Chadaj ◽  
Jaime Gallego ◽  
Lucia Gonzalez-Lopez ◽  
Gloria Bueno

Ki67 hot-spot detection and its evaluation in invasive breast cancer regions play a significant role in routine medical practice. The quantification of cellular proliferation assessed by Ki67 immunohistochemistry is an established prognostic and predictive biomarker that determines the choice of therapeutic protocols. In this paper, we present three deep learning-based approaches to automatically detect and quantify Ki67 hot-spot areas by means of the Ki67 labeling index. To this end, a dataset composed of 100 whole slide images (WSIs) belonging to 50 breast cancer cases (Ki67 and H&E WSI pairs) was used. Three methods based on CNN classification were proposed and compared to create the tumor proliferation map. The best results were obtained by applying the CNN to the mutual information acquired from the color deconvolution of both the Ki67 marker and the H&E WSIs. The overall accuracy of this approach was 95%. The agreement between the automatic Ki67 scoring and the manual analysis is promising with a Spearman’s ρ correlation of 0.92. The results illustrate the suitability of this CNN-based approach for detecting hot-spots areas of invasive breast cancer in WSI.


2018 ◽  
Author(s):  
A Noske ◽  
J Ettl ◽  
SI Anders ◽  
A Hapfelmeier ◽  
K Steiger ◽  
...  

2008 ◽  
Vol 68 (05) ◽  
Author(s):  
MP Lux ◽  
PA Fasching ◽  
MG Schrauder ◽  
CR Löhberg ◽  
FG Wiesner ◽  
...  
Keyword(s):  

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