Histopathological Imaging–Environment Interactions in Cancer Modeling

Xu, Yaqing; Zhong, Tingyan; Wu, Mengyun; Ma, Shuangge

doi:10.3390/cancers11040579

Cited by 10 publications

(10 citation statements)

References 37 publications

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“…The histopathological images re ect the morphological features of tumor cells and histological structure of their microenvironment, which can be in uenced by individual immune function and environment in addition to molecular changes. [14,15] For instance, a previous study [16] has found that there is a signi cant correlation between TP53 mutation in lung adenocarcinoma and pathological characteristics of tumor cells. Another study has given evidence of the correlation between ampli cations of PDGFRA, EGFR, MDM2 and speci c image features in glioblastoma.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Zeng

Chen

et al. 2020

Preprint

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Background: Colon adenocarcinoma (COAD) is one of the highest morbidity cancers all over the world. Its 5-year survival is no more than 60% even in European countries with the highest survival rates. The histopathological information is crucial for the prognosis and therapy of COAD. Application of the digital whole slide imaging system enables us to read histopathological sections digitally. Apart from that, cancer genomics is also an important prognostic factor.Methods: To identify prognosis biomarkers of COAD, we downloaded whole-slide histopathological images from TCIA database. After processing these images, histopathological features were extracted by CellProfiler. Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination were followed applied, screening out 5 prognosis-related features. Weighted gene co-expression network analysis (WGCNA) was operated to find co-expression gene module correlated with prognosis-related features. The samples were divided into a training set and a testing set on a scale of 70% and 30%. Random forest was applied to construct histopathologic-genomic prognosis factor (HGPF) using prognosis-related features and genomic data. After that, we combined HGPF and clinical characteristics with nomogram and verify its predictive efficacy.Results: The time-dependent ROC was drawn to evaluate the efficacy of prognostic model. In the training set, 1-year, 3-year and 5-year AUCs are respectively 0.948, 0.916, 0.933. In the testing set, 1-year, 3-year and 5-year AUCs are respectively 0.913, 0.894, 0.924. In addition, patients were separated into high-risk survival group and low-risk survival group by HGPF. Survival analysis indicates that the low-risk patients’ survival was significantly better than high-risk patients’ in both training set and testing set. It is suggested that histopathological image features have certain ability to predict COAD survival, which can be further improved by means of multi-omics combination.Conclusions: In conclusion, this study constructs an integrative prognosis model based on histopathological and genomic features, which may have some guidance effect on prognosis and clinical decision of COAD patients. Furthermore, the underlying biological mechanisms of this multi-omics model require further study.

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Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Zeng

Chen

et al. 2020

Preprint

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“…The information revealed by the cancer omics profile and histopathological images is not only relatively independent but also has commonality to a certain extent. The morphological features of tumor cells and histological structure of the tumor microenvironment can be influenced by molecular changes, individual immune function, and environment (15)(16)(17). For instance, a previous study (18) has found that there is a significant correlation between the TP53 mutation and pathological characteristics of tumor cells in lung adenocarcinoma.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Chen

Zeng

et al. 2021

Front. Oncol.

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BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

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“…However, such studies are still relatively limited. With the consideration that tumor properties can be affected by molecular changes, there have been studies modeling the relationships between imaging features and molecular changes 11,12 . Considering the cost-effectiveness and routineness of biopsy and histopathological images, imaging-based modeling, if effective, can have great potential.…”

Section: Introductionmentioning

confidence: 99%

“…However, such studies are still relatively scarce. With the consideration that tumor properties as reflected in histopathological images can be affected by molecular changes, there have been studies modeling the relationships between imaging features and molecular changes 12,13 . Such studies are biologically well-grounded.…”

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confidence: 99%

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

Zhang

Fan

Zhong

et al. 2020

Sci Rep

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For lung and many other cancers, prognosis is essentially important, and extensive modeling has been carried out. Cancer is a genetic disease. In the past 2 decades, diverse molecular data (such as gene expressions and DNA mutations) have been analyzed in prognosis modeling. More recently, histopathological imaging data, which is a “byproduct” of biopsy, has been suggested as informative for prognosis. In this article, with the TCGA LUAD and LUSC data, we examine and directly compare modeling lung cancer overall survival using gene expressions versus histopathological imaging features. High-dimensional penalization methods are adopted for estimation and variable selection. Our findings include that gene expressions have slightly better prognostic performance, and that most of the gene expressions are weakly correlated imaging features. This study may provide additional insight into utilizing the two types of important data in cancer prognosis modeling and into lung cancer overall survival.

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Histopathological Imaging–Environment Interactions in Cancer Modeling

Cited by 10 publications

References 37 publications

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

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

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