An adaptive positivity thresholding method for automated Ki67 hotspot detection (AKHoD) in breast cancer biopsies. (November 2017)

• Record Type: Journal Article
• Authors: Pilutti, David; Della Mea, Vincenzo; Pegolo, Enrico; La Marra, Francesco; Antoniazzi, Fulvio; Di Loreto, Carla
• Is Part Of: Computerized medical imaging and graphics. Volume 61(2017)
• Page Start: 28
• Page End: 34
• Publication Date: 2017-11
• Subjects: Digital pathology -- Ki67 -- Automated hotspot detection -- Breast cancer -- Image analysis; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2017.04.005
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: The proliferative activity of breast cancer tissue can be estimated using the Ki67 biomarker. The percentage of positivity of such biomarker is correlated with proliferation and consequently with the prognosis of a breast tumor. Ki67 marked tissue samples are analyzed by an experienced pathologist who identifies the most active areas of tumor cell proliferation called hotspots, and estimates the positivity of each case. A method for the Automated Ki67 Hotspot Detection (AKHoD) is presented in this work. The main objective of the AKHoD method is to automatically and efficiently provide the pathologist with suggestions about Ki67 hotspot areas as a decision support. The input of AKHoD is a digital slide that is divided in tiles. For each tile, AKHoD provides a rough estimate of positivity and cellularity, summarized in very low resolution positivity and cellularity images. In a second step, an adaptive thresholding is applied to such positivity image to identify the most positive connected and convex areas, within cellularity limits set by current guidelines (that is, 500–2000). The method has been preliminarily validated on 50 digital slides for which three expert pathologists provided gold standard hotspots. 82% of the gold standard hotspots have been successfully recognized by the system, spending an average of 54 s per slide. While further validation is needed taking into account also patients follow-up, this first experimentation suggests that the proposed method could be adequate for supporting the pathologist in hotspot detection.