Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability.
Computational Analysis of Pathological Image Enables Interpretable Prediction for Microsatellite Instability.
复制标题
病理图像的计算分析能够对微卫星不稳定性进行可解释的预测
DOI：
10.3389/fonc.2022.825353
复制
发表时间：
2022
影响因子：
4.7
通讯作者：
中科院分区：
文献类型：
--
作者：
微卫星不稳定性（MSI）与几种肿瘤类型相关，在指导患者治疗决策方面变得越来越重要;然而，合理区分MSI与其对应物在临床实践中具有挑战性。在本研究中，建立了可解释的病理图像分析策略，以帮助医学专家识别MSI。这些策略仅需要无处不在的苏木素和伊红染色的全切片图像，并且在从癌症基因组图谱中收集的三个队列中表现良好。结合机器学习和图像处理技术，建立了基于病理图像的MSI诊断智能模型，为MSI的诊断提供了图像级和病理特征级的决策依据。这些策略实现了两个层次的可解释性。首先，通过基于深度学习生成重要区域的定位热图来实现图像级可解释性。其次，通过特征重要性和病态特征交互分析，获得特征级的可解释性。有趣的是，从图像级和特征级的可解释性，颜色和纹理特征，以及它们的相互作用，被证明是主要贡献的MSI预测。开发的透明机器学习管道能够有效地检测MSI，并为病理学家提供全面的临床见解。智能管道中易于理解的热图和特征反映了MSI肿瘤中细胞外和细胞内酸碱平衡的变化。
Microsatellite instability (MSI) is associated with several tumor types and has become increasingly vital in guiding patient treatment decisions; however, reasonably distinguishing MSI from its counterpart is challenging in clinical practice. In this study, interpretable pathological image analysis strategies are established to help medical experts to identify MSI. The strategies only require ubiquitous hematoxylin and eosin–stained whole-slide images and perform well in the three cohorts collected from The Cancer Genome Atlas. Equipped with machine learning and image processing technique, intelligent models are established to diagnose MSI based on pathological images, providing the rationale of the decision in both image level and pathological feature level. The strategies achieve two levels of interpretability. First, the image-level interpretability is achieved by generating localization heat maps of important regions based on deep learning. Second, the feature-level interpretability is attained through feature importance and pathological feature interaction analysis. Interestingly, from both the image-level and feature-level interpretability, color and texture characteristics, as well as their interaction, are shown to be mostly contributed to the MSI prediction. The developed transparent machine learning pipeline is able to detect MSI efficiently and provide comprehensive clinical insights to pathologists. The comprehensible heat maps and features in the intelligent pipeline reflect extra- and intra-cellular acid–base balance shift in MSI tumor.
登录
查看更多内容
DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
4.6
作者：
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者：
Hamilton PW
影响因子：
11.1
作者：
Jacobson, Nicholas C.;Bentley, Kate H.;Coppersmith, Daniel D. L.
通讯作者：
Coppersmith, Daniel D. L.
影响因子：
2.7
作者：
Cheng DT;Prasad M;Chekaluk Y;Benayed R;Sadowska J;Zehir A;Syed A;Wang YE;Somar J;Li Y;Yelskaya Z;Wong D;Robson ME;Offit K;Berger MF;Nafa K;Ladanyi M;Zhang L
通讯作者：
Zhang L
影响因子：
82.9
作者：
Hanse, Ronald J.;Pritchard, Colin C.;Salipante, Stephen J.
通讯作者：
Salipante, Stephen J.