Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer

Corredor, Germán; Wang, Xiangxue; Zhou, Yu; Lu, Cheng; Fu, Pingfu; Syrigos, Konstantinos; Rimm, David L.; Yang, Michael; Romero, Eduardo; Schalper, Kurt A.; Velcheti, Vamsidhar; Madabhushi, Anant

doi:10.1158/1078-0432.ccr-18-2013

Cited by 189 publications

(192 citation statements)

References 40 publications

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“…As well as extracting each individual nucleus, determining the type of each nucleus can increase the diagnostic potential of current DP pipelines. For example, accurately classifying each nucleus to be from tumour or lymphocyte enables downstream analysis of tumour infiltrating lymphocytes (TILs), which have been shown to be predictive of cancer recurrence [7]. Yet, similar to nuclear segmentation, classifying the type of each nucleus is difficult, due to the high variance of nuclear appearance within each WSI.…”

Section: Introductionmentioning

confidence: 99%

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Graham

Raza

et al. 2019

Medical Image Analysis

760

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Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the nuclei of tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then, for each segmented instance the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels.

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Section: Introductionmentioning

confidence: 99%

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Graham

Raza

et al. 2019

Medical Image Analysis

760

601

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“…Microenvironmental factors like access to the vascular system and infiltration with immune cells differ greatly-for regions within the original tumor as well as between the main tumor and metastases, and across different time points [282]. This imposes different selective pressures on different tumor cells, driving the formation of tumor subclones and thus determining disease progression (including metastatic potential), patient outcome, and susceptibility to treatment ( [283,284] and Fig. 4).…”

Section: Challenge Ix: Inferring Population Genetic Parameters Of Tummentioning

confidence: 99%

Eleven grand challenges in single-cell data science

et al. 2020

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The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

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“…Probably the most popular 1) from all available FS methods that have widespread application is minimum redundancy maximum relevance (mRMR) FS [15]. It is widely used in many domains such as bioinformatics and multimedia processing [16,17]. The mRMR FS is a supervised filter method with a forward selection that uses mutual information as a dependency measure.…”

Section: Introductionmentioning

confidence: 99%

On some aspects of minimum redundancy maximum relevance feature selection

Bugata

Drotár

2019

Sci. China Inf. Sci.

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The feature selection is an important challenge in many areas of machine learning because it plays a crucial role in the interpretations of machine-driven decisions. There are various approaches to the feature selection problem and methods based on the information theory comprise an important group. Here, the minimum redundancy maximum relevance (mRMR) feature selection is undoubtedly the most popular one with widespread application. In this paper, we prove in contrast to an existing finding that the mRMR is not equivalent to Max-Dependency criterion for first-order incremental feature selection. We present another form of equivalence leading to a generalization of mRMR feature selection. Additionally, we compare several feature selection methods based on mRMR, Max-Dependency, and feature ranking, employing different measures of dependency. The results on high-dimensional real-world datasets show that the distance correlation is the suitable measure for dependency-based feature selection methods. The results also indicate that the Max-Dependency incremental algorithm combined with distance correlation appears to be a promising feature selection approach.

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Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non–Small Cell Lung Cancer

Abstract: A set of features related to density and spatial architecture of TILs was found to be associated with a likelihood of recurrence of early-stage NSCLC. This information could potentially be used for helping in treatment planning and management of early-stage NSCLC.

Cited by 189 publications

References 40 publications

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Eleven grand challenges in single-cell data science

On some aspects of minimum redundancy maximum relevance feature selection

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