Browning Lisa, Jesus Christine, Malacrino Stefano, Guan Yue, White Kieron, Puddle Alison, Alham Nasullah Khalid, Haghighat Maryam, Colling Richard, Birks Jacqueline, Rittscher Jens, Verrill Clare
Department of Cellular Pathology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK.
Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK.
Diagnostics (Basel). 2024 May 9;14(10):990. doi: 10.3390/diagnostics14100990.
Digital pathology continues to gain momentum, with the promise of artificial intelligence to aid diagnosis and for assessment of features which may impact prognosis and clinical management. Successful adoption of these technologies depends upon the quality of digitised whole-slide images (WSI); however, current quality control largely depends upon manual assessment, which is inefficient and subjective. We previously developed PathProfiler, an automated image quality assessment tool, and in this feasibility study we investigate its potential for incorporation into a diagnostic clinical pathology setting in real-time. A total of 1254 genitourinary WSI were analysed by PathProfiler. PathProfiler was developed and trained on prostate tissue and, of the prostate biopsy WSI, representing 46% of the WSI analysed, 4.5% were flagged as potentially being of suboptimal quality for diagnosis. All had concordant subjective issues, mainly focus-related, 54% severe enough to warrant remedial action which resulted in improved image quality. PathProfiler was less reliable in assessment of non-prostate surgical resection-type cases, on which it had not been trained. PathProfiler shows potential for incorporation into a digitised clinical pathology workflow, with opportunity for image quality improvement. Whilst its reliability in the current form appears greatest for assessment of prostate specimens, other specimen types, particularly biopsies, also showed benefit.
数字病理学的发展势头持续增强，有望借助人工智能辅助诊断，并评估可能影响预后和临床管理的特征。这些技术的成功应用取决于数字化全切片图像（WSI）的质量；然而，目前的质量控制很大程度上依赖于人工评估，既低效又主观。我们之前开发了PathProfiler，一种自动化图像质量评估工具，在这项可行性研究中，我们调查了将其实时纳入诊断临床病理学环境的潜力。PathProfiler共分析了1254份泌尿生殖系统WSI。PathProfiler是基于前列腺组织开发和训练的，在所分析的前列腺活检WSI中，占WSI的46%，有4.5%被标记为可能存在诊断质量欠佳的情况。所有这些都存在一致的主观问题，主要与聚焦有关，54%严重到需要采取补救措施，从而提高了图像质量。PathProfiler在评估未经训练的非前列腺手术切除类型病例时可靠性较低。PathProfiler显示出纳入数字化临床病理学工作流程的潜力，有提高图像质量的机会。虽然其当前形式在评估前列腺标本时可靠性似乎最高，但其他标本类型，特别是活检标本，也显示出益处。
