基于算法的先天性巨结肠症诊断——不同实验室和玻片扫描仪数据的稳健性评估和比较图像分析。

Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners.

机构信息

Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.

Department of Pathology, Soroka University Medical Center, 76 Wingate Street, 8486614, Be'er Sheva, Israel.

出版信息

Diagn Pathol. 2024 Feb 6;19(1):26. doi: 10.1186/s13000-024-01452-x.

DOI:10.1186/s13000-024-01452-x

PMID:38321431

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10845737/

Abstract

BACKGROUND

Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance.

METHODS

The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast.

RESULTS

The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners.

CONCLUSIONS

The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

摘要

背景

数字病理学幻灯片的制备、染色和扫描方式的差异造成了显著的分析前变异性。算法辅助工具必须能够应对这种变异性，才能在临床实践中应用。在之前的研究中，开发了一种决策支持算法来辅助先天性巨结肠症的诊断。在当前的研究中，我们测试了该算法的稳健性，同时评估了可能影响其性能的分析前因素。

方法

使用来自四个不同医疗中心（A-D）的数字病理学幻灯片和三种不同的扫描仪模型（由飞利浦、Hamamatsu 和 3DHISTECH 制造）获得的决策支持算法。本研究共使用了 192 例病例和 1782 张幻灯片。构建 RGB 直方图来比较来自不同医疗中心和扫描仪模型的图像，并突出颜色和对比度的差异。

结果

该算法能够正确识别来自所有医疗中心（均由飞利浦玻片扫描仪扫描）的 99.2%的病例中的神经节细胞，以及来自 3DHISTECH 和 Hamamatsu 品牌玻片扫描仪的 95.5%和 100%的玻片。中心 D 的总错误率低于其他医疗中心（分别为 3.9%、7.1%、10.8%和 6%），绝大多数错误为假阳性（3.45%比 0.45%的假阴性）。其他医疗中心的假阴性率高于假阳性率（分别为 6.81%、0.29%、9.8%、1.2%和 5.37%、0.63%）。飞利浦、Hamamatsu 和 3DHISTECH 品牌扫描仪的总错误率分别为 3.9%、3.2%和 9.8%。RGB 直方图显示，四个医疗中心之间的像素值分布存在显著差异，3DHISTECH 品牌扫描仪与飞利浦和 Hamamatsu 品牌扫描仪相比也存在显著差异。