Automated characterization and counting of Ki-67 protein for breast cancer prognosis: A quantitative immunohistochemistry approach

This study analyzed the correlation between the dynamic contrast-enhanced MRI (DCE-MRI) features with prognostic factors of breast cancer. Eighty-five breast cancer patients verified by pathology and immunohistochemistry underwent DCE-MRI examination. Spearman correlation analysis was used to analyze the DCE-MRI features [the strengthening types, shape, distribution, edge, internal reinforcement and the time-signal intensity curve (TIC) types] and the 4 immunohistochemical markers (ER, PR, Her-2, and Ki-67) by GraphPad InStat version 6.0 software. The enhanced morphology types, shapes, edge had significant correlation with the expression of ER (P = .001, P = .000, P = .001, respectively), PR (P = .045, P = .015, P = .000, respectively) and Ki-67 (P = .039, P = .000, P = .024, respectively), and no significant correlation with Her-2 expression (P = .906, P = .074, P = .679, respectively) was observed. There was significant correlation between internal enhancement patterns and Ki-67 expression (P = .004), and no significant correlation between internal enhancement patterns and the expression of ER, PR, and Her-2 (P = .208, P = .682, P = .437, respectively) was observed. TIC had significant correlation with ER, Ki-67 expressions (P = .022, P = .001, respectively), and no correlation with expressions of PR and Her-2 (P = .128, P = .391, respectively) was observed. The DCE-MRI features of breast cancer were well correlated with the expression of immunohistochemistry, and might also be helpful to evaluate the biological progress and prognosis.

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“…These in turn demonstrate higher expression of Ki-67, with a higher malignant degree and with worse prognosis of breast cancer. [ 11 ]…”

Section: Introductionmentioning

confidence: 99%

Correlation between the dynamic contrast-enhanced MRI features and prognostic factors in breast cancer

Tao

Bai

et al. 2018

Medicine

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“…Internationally, automatic analysis with the aid of artificial intelligence has covered a variety of diseases, ranging from "benign" conditions such as diabetic retinopathy and Alzheimer's disease [7], to malignant tumors such as breast cancer [26][27][28], lung cancer [29], liver cancer [30], skin cancer [31], osteosarcoma [32], and lymphoma [33,34], with an accuracy rate of 89.4-97.8%, and an AUC score of 0.85-0.94 [7,27,31]. In addition, various AI systems related to breast cancer have penetrated through different levels of IDC, such as histology-assisted and cytology-assisted diagnosis, mitotic cell count, lymph node metastasis assessment [9,10,18,22], breast cancer drug development and others [8], with an accuracy rate of 82.7-92.4% and an AUC score of 0.97 [27,28]. This also indicated that, with the help of AI, pathological diagnosis and index counting was safe, effective, and feasible [35].…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, a number of studies have reported that Ki-67 staining can be used as a reference index for the prognosis and personalized treatment of breast cancer patients, it is also closely related to the clinicopathological features and molecular typing of breast cancer patients [5][6][7]. Moreover, Ki-67 scoring can be used to distinguish luminal breast cancer subtypes (A/B) and, as a result, it certainly helps to define the best treatment strategy for each particular condition [8,9]. In triple negative breast cancer (TNBC), patients high Ki-67 scores seem to benefit more from the treatment [10].…”

Section: Introductionmentioning

confidence: 99%

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

et al. 2020

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Background: The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. Methods: We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. Results: The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.

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“…Color deconvolution (CD) proposed in Ruifrok and Johnston (2001) has dominated digital pathology image analysis as a pre-processing step for automated stain separation. It has been applied to Ki67 analysis for stain deconvolution and PI estimation (Kårsnäs et al, 2011; Shi et al, 2016; Mungle et al, 2017). CD is dependent on the Beer-Lambert (BL) law of absorption (Ruifrok and Johnston, 2001; Macenko et al, 2009) which characterizes each pure stain by an optical density (OD) vector of light in the red, green, and blue (RGB) intensity channels (Kårsnäs et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

Geread

Morreale

Dony

et al. 2019

Front. Bioeng. Biotechnol.

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Automated image analysis tools for Ki67 breast cancer digital pathology images would have significant value if integrated into diagnostic pathology workflows. Such tools would reduce the workload of pathologists, while improving efficiency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An “overstaining” threshold is implemented to adjust for background overstaining, and an automated nuclei radius estimator is designed to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67− nuclei. The method accurately quantified the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classified 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classifiers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantification accuracy.

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Automated characterization and counting of Ki-67 protein for breast cancer prognosis: A quantitative immunohistochemistry approach

Cited by 23 publications

References 22 publications

Correlation between the dynamic contrast-enhanced MRI features and prognostic factors in breast cancer

Correlation between the dynamic contrast-enhanced MRI features and prognostic factors in breast cancer

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

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