Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.
Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.
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特征驱动的局部细胞图（FLOCK）：用于肺癌预后和口咽癌的HPV状态的新的基于计算病理学的描述。
DOI：
10.1016/j.media.2020.101903
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发表时间：
2021-03
影响因子：
10.9
通讯作者：
Madabhushi A
中科院分区：
文献类型：
--
作者：
Lu C;Koyuncu C;Corredor G;Prasanna P;Leo P;Wang X;Janowczyk A;Bera K;Lewis J Jr;Velcheti V;Madabhushi A
不同癌症亚型的组织病理学图像中细胞核的局部空间排列已被证明具有预后价值。为了捕获局部核结构信息，提出了基于局部细胞簇图的测量方法。然而，传统的细胞图构建方法仅利用核空间邻近性，在构建图时不区分不同的细胞类型。本文提出了特征驱动局部细胞簇图（FLocK），这是一种通过同时考虑单个核的空间接近性和属性（如形状、大小、纹理）来构建局部细胞图的新方法。此外，我们还设计了一套新的定量图衍生指标，用于从羊群中提取，从而捕获不同近端核簇之间的相互作用。我们评估了从H&E染色组织图像中提取的FLocK特征在两种临床应用中的有效性：对早期非小细胞肺癌（ES-NSCLC）患者的短期和长期生存进行分类，以及预测口咽鳞状细胞癌（OP-SCCs）的人乳头瘤病毒（HPV）状态。在ES-NSCLC患者的长期与短期生存分类（训练队列，n=434）中，通过最小冗余和最大相关性（MRMR），在100次10倍交叉验证下，确定了与FLocK大小变化和相交FLocK距离相关的前10个判别性FLocK特征，并结合线性判别分类器，得出预测训练队列生存的平均AUC=0.68。这比其他最先进的组织形态测量和深度学习分类器(细胞簇图（AUC=0.62），全局细胞图（AUC=0.56），核形状（AUC=0.54），核取向（AUC=0.61）， AlexNet （AUC=0.55), ResNet (AUC=0.56)）要好。在独立测试队列（n=150）中，基于絮凝体的分类器的AUC为0.70。在独立检测队列中，被确定为“高风险”的患者的总生存率明显较差，风险比（95%可信区间）= 2.24 (1.24-4.05),p = 0.01144。在对OP-SCC的HPV状态进行分类时，确定了与相交的部分相关的前三个FLocK特征来构建分类器，在训练队列（n=50）中的AUC为0.80，在独立测试队列（n=35）中的准确率为0.78。FLocK测量与细胞簇图、核取向和核形状的结合将训练AUC分别提高到0.87、0.91和0.85。与基于絮凝体的分类器相比，深度学习方法在该应用程序中的性能略好，在独立测试队列中，AlexNet的AUC=0.78， ResNet的AUC=0.81，基于絮凝体的分类器的AUC=0.76。然而，结合两个手工制作的特征：FLocK和核取向产生了更好的性能（AUC=0.84）。FLocK提供了一种独特的定量方法来分析实体瘤的组织学图像，并从不同的角度询问肿瘤形态，而不是现有的组织形态计量学。源代码可以在https://github.com/hacylu/FLocK上访问。
Local spatial arrangement of nuclei in histopathology images of different cancer subtypes has been shown to have prognostic value. In order to capture localized nuclear architectural information, local cell cluster graph-based measurements have been proposed. However, conventional ways of cell graph construction only utilize nuclear spatial proximity, and do not differentiate between different cell types while constructing the graph. In this paper, we present feature-driven local cell cluster graph (FLocK), a new approach to constructing local cell graphs by simultaneously considering spatial proximity and attributes of the individual nuclei (e.g. shape, size, texture). In addition, we have designed a new set of quantitative graph-derived metrics to be extracted from FLocKs, in turn capturing the interplay between different proximally located clusters of nuclei. We have evaluated the efficacy of FLocK features extracted from H&E stained tissue images in two clinical applications: to classify short-term vs. long-term survival among patients of early stage non-small cell lung cancer (ES-NSCLC), and also to predict Human Papillomavirus (HPV) status of Oropharyngeal Squamous Cell Carcinoma (OP-SCCs). In the classification of long-term vs. short-term survival among patients of ES-NSCLC (training cohort, n=434), the top 10 discriminative FLocK features related to the variation of FLocK sizes and intersected FLocK distance were identified, via the Minimum Redundancy and Maximum Relevance (MRMR), under 100 runs of 10-fold cross-validation, in conjunction with a linear discriminant classifier yielded a mean of AUC=0.68 for predicting survival in training cohort. This is better than when compared to other state-of-art histomorphometric and deep learning classifiers (cell cluster graphs (AUC=0.62), global cell graph (AUC=0.56), nuclear shape (AUC=0.54), nuclear orientation (AUC=0.61), AlexNet (AUC=0.55), ResNet (AUC=0.56)). The FLocK-based classifier yielded an AUC of 0.70 in an independent testing cohort (n=150). The patients identified as “high-risk” had significantly poorer overall survival in the independent testing cohort, with Hazard Ratio (95% Confident Interval) = 2.24 (1.24-4.05), p = 0.01144). In the classification of HPV status of OP-SCC, the top three FLocK features pertaining to the portion of intersected FLocKs were identified to construct a classifier, which yielded an AUC of 0.80 in the training cohort (n=50), and an accuracy of 0.78 in an independent testing cohort (n=35). The combination of FLocK measurements with cell cluster graphs, nuclear orientation, and nuclear shape improved the training AUC to 0.87, 0.91 and 0.85, respectively. Deep learning approaches yield marginally better performance compared to the FLocK-based classifier in this application, with AUC=0.78 for AlexNet, AUC=0.81 for ResNet, and AUC=0.76 for FLocK-based classifier in the independent testing cohort. However, the combination of two hand-crafted features: FLocK and nuclear orientation yielded a better performance (with an AUC=0.84). FLocK provides a unique and quantitative way to analyze histology image of solid tumor and interrogates tumor morphology from a different aspect compared to the existing histomorphometrics. The source code can be accessed at https://github.com/hacylu/FLocK.
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空间结构和肿瘤浸润淋巴细胞的排列，以预测早期非小细胞肺癌中复发的可能性。
DOI：
10.1158/1078-0432.ccr-18-2013
发表时间：
2019-03-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Corredor G;Wang X;Zhou Y;Lu C;Fu P;Syrigos K;Rimm DL;Yang M;Romero E;Schalper KA;Velcheti V;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
5.4
作者：
Lee G;Veltri RW;Zhu G;Ali S;Epstein JI;Madabhushi A
通讯作者：
Madabhushi A
用于组织病理学图像中细胞核检测的多通道自适应投票
影响因子：
4.6
作者：
Lu C;Xu H;Xu J;Gilmore H;Mandal M;Madabhushi A
通讯作者：
Madabhushi A
DOI：
10.1038/s41374-018-0095-7
发表时间：
2018-11
期刊：
Laboratory investigation; a journal of technical methods and pathology
影响因子：
--
作者：
Lu C;Romo-Bucheli D;Wang X;Janowczyk A;Ganesan S;Gilmore H;Rimm D;Madabhushi A
通讯作者：
Madabhushi A
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant