A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue

Abstract

IntroductionImmunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach.MethodsTissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently.ResultsANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists’ VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particular for the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction.ConclusionsOur experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.

Fractal Parameters of Tumour Microscopic Images as Prognostic Indicators of Clinical Outcome in Early Breast Cancer

“…The reproducibility of the analysis was based on the settings for the range of box sizes (scale window) defined as 2,4,8,16,32,64,128,256, 512, 1024 pixels. With the pixel size of 145 nm the grid lengths ranged between 0.29 m and 148 m. Furthermore, scan background setting was locked to white in order to avoid the inversion of the binary image from white to black background during analysis.…”

“…Breast cancer metastasis risk biomarkers have been a field of intensive research in the past decade with a focus on molecular prognosticators including transcriptional profiling [1], microRNA analysis [2], detection of circulating tumour cells in blood [3], proliferation [4], and stem cell markers [5], while research for novel histomorphological prognostic clues as a source of prognostic information has been largely neglected. The need for new prognostic approaches derives from the fact that molecular risk biomarkers often outperform the established clinicopathological prognosticators, but regrettably still exhibit insufficient prognostic accuracy, with the remaining unreliable therapeutic guidance.…”

“…Apart from the above mentioned molecular traces of a tumour, its histology reveals valued prognostic clues due to the fact that the tissue structure reflects the individual patterns of malignant cell growth. A demand for improvement of breast cancer risk prognosis has triggered the idea to implement alternatives to molecular approaches, with a resulting expansion of the digital pathology as a structure examination tool for medical images [4].…”

Reduction of mitomycin C resistance in human bladder cancer T24 cells by knocking-down ras oncogene

“…The percentage per section was determined. We estimated Ki-67 labelling indices (LIs) reflecting the proliferation activity of tumor cells according to a recently described method [22] . The Ki-67 LI was calculated as 100 × Ki-67 positive nuclei/(Ki-67-positive nuclei + Ki-67-negative nuclei) per image.…”

Medical Imaging Mutifractal Analysis in Prediction of Efficiency of Cancer Therapy

“…In parallel to standard molecular approaches, digital pathology emerged as a structure analysis tool to aid in detection, diagnosis (Cross 1997;Vasiljevic, Reljin et al 2012), risk assessment (Vasilescu, Giza et al 2012), chemotherapy efficacy assessment (Li, Hu et al 2014) and therapy prediction for cancer (Laurinavicius, Plancoulaine et al 2014). It is based on computational analysis of medical images by use of texture or fractal algorithms.…”