Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling

Zhang, Sanguo; Fan, Yu; Zhong, Tingyan; Ma, Shuangge

doi:10.1038/s41598-020-72201-5

Cited by 7 publications

(8 citation statements)

References 42 publications

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“…The feature‐processing pipeline has been described in Zhang et al . (2020). We further remove features with standard deviations less than 1 to focus on the “more interesting” features.…”

Section: Discussionmentioning

confidence: 99%

“…Here we analyze the imaging features extracted using cellprofiler from the Biometrics, 00 0000 histopathological slides of the TCGA LUAD (lung adenocarcinoma) patients, with data downloaded from the TCGA portal (TCGA, 2021). The feature processing pipeline has been described in Zhang et al (2020). We further remove features with standard deviations less than 1 to focus on the "more interesting" features.…”

Section: Heterogeneity Analysis Of Regulatory T Cells In Non-small-cell Lung Cancermentioning

confidence: 99%

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Gaussian graphical model‐based heterogeneity analysis via penalized fusion

Ren

Zhang

et al. 2021

Biometrics

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Heterogeneity is a hallmark of cancer, diabetes, cardiovascular diseases, and many other complex diseases. This study has been partly motivated by the unsupervised heterogeneity analysis for complex diseases based on molecular and imaging data, for which, network-based analysis, by accommodating the interconnections among variables, can be more informative than that limited to mean, variance, and other simple distributional properties. In the literature, there has been very limited research on network-based heterogeneity analysis, and a common limitation shared by the existing techniques is that the number of subgroups needs to be specified a priori or in an ad hoc manner. In this article, we develop a penalized fusion approach for heterogeneity analysis based on the Gaussian Graphical Model (GGM). It applies penalization to the mean and precision matrix parameters to generate regularized and interpretable estimates. More importantly, a fusion penalty is imposed to "automatedly" determine the number of subgroups and generate more concise, reliable, and interpretable estimation. Consistency properties are rigorously established, and an effective computational algorithm is developed. The heterogeneity analysis of nonsmall-cell lung cancer based on single-cell gene expression data of the Wnt pathway and that of lung adenocarcinoma based on histopathological imaging data not only demonstrate the practical applicability of the proposed approach but also lead to interesting new findings.

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Section: Discussionmentioning

confidence: 99%

Section: Heterogeneity Analysis Of Regulatory T Cells In Non-small-cell Lung Cancermentioning

confidence: 99%

Gaussian graphical model‐based heterogeneity analysis via penalized fusion

Ren

Zhang

et al. 2021

Biometrics

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“…In the past few years, pathological imaging features have been shown as informative for modeling prognosis and other cancer outcomes/phenotypes (Yuan et al., 2012). There have also been studies on associating pathological imaging features with omics measurements and integrating pathological imaging and omics data for modeling cancer outcomes (Zhang et al., 2020). However, to the best of our knowledge, these studies focus on main effects and are not in the context of interaction analysis.…”

Section: Introductionmentioning

confidence: 99%

Pathological Imaging-Assisted Cancer Gene–Environment Interaction Analysis

Fang

Zhang

et al. 2023

Biometrics

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Gene–environment (G–E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main‐effect‐only analysis, G–E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the “main effects, interactions” variable selection hierarchy. Effort has been made to bring in additional information to assist cancer G–E interaction analysis. In this study, we take a strategy different from the existing literature and borrow information from pathological imaging data. Such data are a “byproduct” of biopsy, enjoys broad availability and low cost, and has been shown as informative for modeling prognosis and other cancer outcomes/phenotypes in recent studies. Building on penalization, we develop an assisted estimation and variable selection approach for G–E interaction analysis. The approach is intuitive, can be effectively realized, and has competitive performance in simulation. We further analyze The Cancer Genome Atlas (TCGA) data on lung adenocarcinoma (LUAD). The outcome of interest is overall survival, and for G variables, we analyze gene expressions. Assisted by pathological imaging data, our G–E interaction analysis leads to different findings with competitive prediction performance and stability.

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“…On the negative side, this process is labor‐intensive, and the extracted information is limited by the available biological knowledge. Applications of the first type of imaging features have been considered by Li et al, 8 Zhang et al, 9 and others. The pipeline for extracting the second type of imaging features is sketched in the lower panel of Figure 1 and based on automated signal processing.…”

Section: Introductionmentioning

confidence: 99%

Bayesian hierarchical finite mixture of regression for histopathological imaging‐based cancer data analysis

Huang

et al. 2022

Statistics in Medicine

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Cancer is heterogeneous, and for seemingly similar cancer patients, the associations between an outcome/phenotype and covariates can be different. To describe such differences, finite mixture of regression (FMR) and other modeling techniques have been developed. “Classic” FMR analysis has usually been based on clinical, demographic, and molecular variables. More recently, histopathological imaging data—which is a byproduct of biopsy and enjoys broader data availability and higher cost‐effectiveness—has been increasingly used in cancer modeling, although it is noted that its application to cancer FMR analysis still remains limited. In this article, we further advance cancer FMR analysis based on histopathological imaging data. Significantly advancing from the existing analyses under heterogeneity and homogeneity, our goal is to simultaneously use two types of histopathological imaging features, which are extracted based on domain‐specific biomedical knowledge and using automated signal processing software, respectively. A significant modeling/methodological advancement is that, to reflect the “increased resolution” of the second type of imaging features over the first type, we impose a hierarchy in the mixture structures. An effective and flexible Bayesian approach is proposed. Simulation shows its competitiveness over several alternatives. The TCGA lung cancer data is analyzed, and interesting heterogeneous structures different from using the alternatives are found. Overall, this study provides a new venue for FMR analysis for cancer and other complex diseases.

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Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling

Cited by 7 publications

References 42 publications

Gaussian graphical model‐based heterogeneity analysis via penalized fusion

Gaussian graphical model‐based heterogeneity analysis via penalized fusion

Pathological Imaging-Assisted Cancer Gene–Environment Interaction Analysis

Bayesian hierarchical finite mixture of regression for histopathological imaging‐based cancer data analysis

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