Bodén Anna C S, Molin Jesper, Garvin Stina, West Rebecca A, Lundström Claes, Treanor Darren
Department of Clinical Pathology, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Centre for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
Histopathology. 2021 Aug;79(2):210-218. doi: 10.1111/his.14356. Epub 2021 May 30.
One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and efficiency. Whereas stand-alone DIA has great potential benefit for research, little is known about the effect of DIA assistance in clinical use. The aim of this study was to investigate the clinical use characteristics of a DIA application for Ki67 proliferation assessment. Specifically, the human-in-the-loop interplay between DIA and pathologists was studied.
We retrospectively investigated breast cancer Ki67 areas assessed with human-in-the-loop DIA and compared them with visual and automatic approaches. The results, expressed as standard deviation of the error in the Ki67 index, showed that visual estimation ('eyeballing') (14.9 percentage points) performed significantly worse (P < 0.05) than DIA alone (7.2 percentage points) and DIA with human-in-the-loop corrections (6.9 percentage points). At the overall level, no improvement resulting from the addition of human-in-the-loop corrections to the automatic DIA results could be seen. For individual cases, however, human-in-the-loop corrections could address major DIA errors in terms of poor thresholding of faint staining and incorrect tumour-stroma separation.
The findings indicate that the primary value of human-in-the-loop corrections is to address major weaknesses of a DIA application, rather than fine-tuning the DIA quantifications.
临床实践中采用数字病理学的主要驱动因素之一是引入数字图像分析（DIA）以协助诊断任务的可能性。这有望提高准确性、可重复性和效率。虽然独立的DIA在研究中具有巨大的潜在益处，但关于DIA辅助在临床应用中的效果却知之甚少。本研究的目的是调查用于Ki67增殖评估的DIA应用程序的临床使用特征。具体而言，研究了DIA与病理学家之间的人在回路相互作用。
我们回顾性研究了通过人在回路DIA评估的乳腺癌Ki67区域，并将其与视觉和自动方法进行比较。结果以Ki67指数误差的标准差表示，显示视觉估计（“目测”）（14.9个百分点）的表现明显差于单独的DIA（7.2个百分点）和带有人在回路校正的DIA（6.9个百分点）（P < 0.05）。在总体水平上，未观察到在自动DIA结果中添加人在回路校正带来的改善。然而，对于个别病例，人在回路校正可以解决DIA在微弱染色阈值设定不佳和肿瘤-基质分离不正确方面的主要误差。
研究结果表明，人在回路校正的主要价值在于解决DIA应用程序的主要弱点，而不是微调DIA定量。
