Snead David Rj, Azam Ayesha S, Thirlwall Jenny, Kimani Peter, Hiller Louise, Bickers Adam, Boyd Clinton, Boyle David, Clark David, Ellis Ian, Gopalakrishnan Kishore, Ilyas Mohammad, Kelly Paul, Loughrey Maurice, Neil Desley, Rakha Emad, Roberts Ian Sd, Sah Shatrughan, Soares Maria, Tsang YeeWah, Salto-Tellez Manuel, Higgins Helen, Howe Donna, Takyi Abigail, Chen Yan, Ignatowicz Agnieszka, Madan Jason, Nwankwo Henry, Partridge George, Dunn Janet
Histopathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Warwick Medical School, University of Warwick, Coventry, UK.
Health Technol Assess. 2025 Jul;29(30):1-75. doi: 10.3310/SPLK4325.
Digital pathology refers to the conversion of histopathology slides to digital image files for examination on computer workstations as opposed to conventional microscopes. Prior to adoption, it is important to demonstrate pathologists provide equivalent reports when using digital pathology in comparison to bright-field and immunofluorescent light microscopy, the current standard of care.
A multicentre comparison of digital pathology with light microscopy for reporting of histopathology slides, measuring variation within and between pathologists on both modalities.
A blinded crossover 2000-case study estimating clinical management concordance (identical diagnoses plus differences not affecting patient management). Each sample was assessed twice by four pathologists (once using light microscopy, once using digital pathology, the order randomly assigned and a 6-week gap between viewings). Random-effects logistic regression models, including crossed random-effects terms for case and pathologist, estimated percentage clinical management concordance. Findings were interpreted with reference to 98.3% concordance (Azam AS, Miligy IM, Kimani PKU, Maqbool H, Hewitt K, Rajpoot NM, Snead DRJ. Diagnostic concordance and discordance in digital pathology: a systematic review and meta-analysis. 2021;:448-55. https://doi.org/10.1136/jclinpath-2020-206764).
Sixteen consultant pathologists, four for each specialty, from six National Health Service laboratories. Experience ranged from 3 to 35 years. Some were early adopters of digital pathology, but the majority were new to digital pathology.
Eight viewings per sample (four pathologists with light microscopy and with digital pathology), culminating in a consensus ground truth, enabling measurement of agreement within and between readers. Samples enrolled reflected routine practice, included cancer screening biopsies, and were enriched for areas of difficulty [e.g. dysplasia (7, 10, 11)]. State-of-the-art digital pathology equipment designed for diagnosis, and holding either Conformité Européene or Food and Drug Administration approval, was used.
Intra-pathologist variation between reports issued on digital pathology and light microscopy, inter-pathologist variation against ground-truth diagnosis using light microscopy and digital pathology.
Pathologist-recorded reporting times, along with their confidence in diagnosis, analysis of eye-tracking evaluating examination techniques, and a qualitative study examining attitudes of pathologists and laboratory staff to digital pathology adoption.
Two thousand and twenty-four cases (608 breast, 607 gastrointestinal, 609 skin, 200 renal) were recruited, with breast and gastrointestinal including screening samples [207 (34%) breast, 250 (41%) gastrointestinal]. Overall, in light microscopy versus digital pathology comparisons, clinical management concordance levels were 99.95% (95% confidence interval 99.91 to 99.97). Similar results were observed within specialties [breast: 99.40% (95% confidence interval 99.06 to 99.62); gastrointestinal 99.96% (95% confidence interval 99.89 to 99.99); skin 99.99% (95% confidence interval 99.92 to 100.0); renal 99.99% (95% confidence interval 99.57 to 100.0)], and within screening cases [98.96% (95% confidence interval 98.42 to 99.32), breast 96.27% (94.63 to 97.43), gastrointestinal 99.93% (95% confidence interval 99.68 to 99.98)]. Reporting time between digital pathology and light microscopy was similar, but pathologists became faster on digital pathology with familiarity. Pathologists recorded high levels of confidence in their diagnosis with light microscopy, significantly higher than digital pathology.
Cytology cases and specialty groups outside those tested were not examined. The study used two digital pathology scanning systems. Other systems available on the market were not tested.
Clinical management concordance levels between the two modalities exceed the reference 98.3% in breast, gastrointestinal, skin and renal specialties, and pooled breast and large bowel cancer screening cases. Subgroup analysis of clinically significant differences revealed a range of differences including areas where interobserver variability is known to be high, which were distributed between reads performed with both platforms and without apparent trends to either.
The use of digital pathology for cytology samples remains an area for further research.
This study is registered as ISRCTN14513591.
This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/84/07) and is published in full in ; Vol. 29, No. 30. See the NIHR Funding and Awards website for further award information.
数字病理学是指将组织病理学切片转换为数字图像文件,以便在计算机工作站上进行检查,而非使用传统显微镜。在采用数字病理学之前,重要的是要证明与当前的标准护理方法——明场和免疫荧光光学显微镜相比,病理学家在使用数字病理学进行诊断时能够提供等效的报告。
进行一项多中心研究,比较数字病理学与光学显微镜在组织病理学切片报告中的应用,评估两种方式下病理学家内部及之间的差异。
一项双盲交叉的2000例病例研究,评估临床管理一致性(相同诊断加上不影响患者管理的差异)。每个样本由四位病理学家评估两次(一次使用光学显微镜,一次使用数字病理学,评估顺序随机分配,两次评估间隔6周)。随机效应逻辑回归模型,包括病例和病理学家的交叉随机效应项,用于估计临床管理一致性百分比。研究结果参考98.3%的一致性进行解读(阿扎姆·阿斯、米利吉·伊姆、基马尼·普库、马克布尔·赫、休伊特·凯、拉杰普特·纳姆、斯内德·德里杰。数字病理学中的诊断一致性和不一致性:系统评价和荟萃分析。2021年;448 - 55页。https://doi.org/10.1136/jclinpath - 2020 - 206764)。
来自六个国民保健服务实验室的16位顾问病理学家,每个专业4位。经验范围从3年到35年。一些是数字病理学的早期采用者,但大多数是数字病理学新手。
每个样本进行8次评估(四位病理学家分别使用光学显微镜和数字病理学进行评估),最终达成共识的真实诊断结果,以便测量读者内部及之间的一致性。纳入的样本反映了常规实践,包括癌症筛查活检样本,并增加了疑难区域的样本[如发育异常(7、10、11)]。使用了经欧洲合格评定或美国食品药品监督管理局批准的、用于诊断的先进数字病理学设备。
病理学家在数字病理学和光学显微镜下出具的报告之间的内部差异,以及病理学家与基于光学显微镜和数字病理学的真实诊断之间的差异。
病理学家记录的报告时间,以及他们对诊断的信心,通过眼动追踪评估检查技术的分析,以及一项定性研究以考察病理学家和实验室工作人员对采用数字病理学的态度。
共招募了2024例病例(608例乳腺、607例胃肠道、609例皮肤、200例肾脏),乳腺和胃肠道样本包括筛查样本[207例(34%)乳腺、250例(41%)胃肠道]。总体而言,在光学显微镜与数字病理学的比较中,临床管理一致性水平为99.95%(95%置信区间99.91至99.97)。各专业内也观察到类似结果[乳腺:99.40%(95%置信区间99.06至99.62);胃肠道99.96%(95%置信区间99.89至99.99);皮肤99.99%(95%置信区间99.92至至100.0);肾脏99.99%(95%置信区间99.57至100.0)],筛查病例中也是如此[98.96%(95%置信区间98.42至99.32),乳腺96.27%(94.63至97.43),胃肠道99.93%(95%置信区间99.68至99.98)]。数字病理学和光学显微镜之间的报告时间相似,但随着熟悉程度的提高,病理学家使用数字病理学报告的速度更快。病理学家对光学显微镜诊断的信心水平较高,显著高于数字病理学。
未检查所测试专业以外的细胞学病例和专业组。本研究使用了两种数字病理学扫描系统。未测试市场上的其他系统。
在乳腺、胃肠道、皮肤和肾脏专业以及汇总的乳腺癌和大肠癌筛查病例中,两种方式的临床管理一致性水平超过了参考的98.3%。对具有临床意义的差异进行的亚组分析揭示了一系列差异,包括已知观察者间变异性较高的区域,这些差异分布在两个平台的读数之间,且没有明显的偏向趋势。
数字病理学在细胞学样本中的应用仍是一个有待进一步研究的领域。
本研究注册为ISRCTN14513591。
本研究由国家卫生与保健研究机构(NIHR)卫生技术评估项目资助(NIHR资助编号:17/84/07),并全文发表于;第29卷,第30期。有关更多资助信息,请访问NIHR资助与奖项网站。