Ki67 as a Biomarker of Prognosis and Prediction: Is it Ready for Use in Routine Pathology Practice?

Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma

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.

A Scoring Method for Immunohistochemical Staining on Ki67

An accurate interpretation of immunohistochemistry (IHC) staining results is crucial for precise disease diagnosis. In this study, we present a novel scoring method for interpreting and reporting of IHC staining assay results for the nuclear-type molecule. On the basis of the histologic characteristics, the samples were subdivided into 3 basic structural units and tissue subtypes including covered, mosaic, and mesenchymal subtypes. A cut-off of moderate-positive (2+) cells and 10% as the differential expression were applied to stratify the results into 11 grade scoring system (0 to X level). The observer can directly identify and count the number and percentage of positive cells from IHC staining data. Furthermore, Ki67 staining results in 88 carcinoma specimens were re-evaluated to determine the ease, reliability, reproducibility, and variance among different observers. The results indicated the consistency ratio of 68.0% for the mosaic subtype and 80% for the mesenchymal subtype, and 68.2% for the covered subtype by 5 experienced pathologists independently. Using 10% as the cut-off threshold, the consistency ratio of 92.5%, 96.8%, and 92.9% was noted for mosaic, mesenchymal, and covered subtypes, respectively. Besides, the correlation of counts revealed excellent agreement among the 5 independent pathologists. Overall, the proposed IHC scoring method is a novel, simple, reliable, and reproducible grading system for accurate interpretation of IHC staining data. Furthermore, the presented practical grading approach has the potential to improve the clinical evaluation of the IHC staining data for personalized therapy.

IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

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.