A ‘Real-Life’ Experience on Automated Digital Image Analysis of FGFR2 Immunohistochemistry in Breast Cancer

Diagnostics

Rafal Sadej¹  HannaM. Romanska²  Radzislaw Kordek²  Dominika Piasecka²  Marcin Braun²  Mateusz Bobrowski³ 

[1] Department of Molecular Enzymology and Oncology, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, 80-211 Gdansk, Poland;Department of Pathology, Chair of Oncology, Medical University of Lodz, 92-213 Lodz, Poland;Sysmex Polska Sp. z o.o., 02-486 Warszawa, Poland;

关键词: machine learning algorithm;    AI;    CaseViewer;    QuantCenter;    breast cancer;    FGFR2;   

DOI  :  10.3390/diagnostics10121060

来源: DOAJ

【 摘 要 】

We present here an assessment of a ‘real-life’ value of automated machine learning algorithm (AI) for examination of immunohistochemistry for fibroblast growth factor receptor-2 (FGFR2) in breast cancer (BC). Expression of FGFR2 in BC (n = 315) measured using a certified 3DHistech CaseViewer/QuantCenter software 2.3.0. was compared to the manual pathologic assessment in digital slides (PA). Results revealed: (i) substantial interrater agreement between AI and PA for dichotomized evaluation (Cohen’s kappa = 0.61); (ii) strong correlation between AI and PA H-scores (Spearman r = 0.85, p < 0.001); (iii) a small constant error and a significant proportional error (Passing–Bablok regression y = 0.51 × X + 29.9, p < 0.001); (iv) discrepancies in H-score in cases of extreme (strongest/weakest) or heterogeneous FGFR2 expression and poor tissue quality. The time of AI was significantly longer (568 h) than that of the pathologist (32 h). This study shows that the described commercial machine learning algorithm can reliably execute a routine pathologic assessment, however, in some instances, human expertise is essential.

【 授权许可】

Unknown