Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Li, Hui; Chen, Linyan; Zeng, Hao; Liao, Qimeng; Ji, Jianrui; Ma, Xuelei

doi:10.3389/fonc.2021.636451

Cited by 15 publications

(13 citation statements)

References 51 publications

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“…All of them are closely related to the immune mechanisms of cancer, and corresponded to the pathway of immune escape and tumor spread. To the best of our knowledge, the biological significance revealed by our study was the most related to the immune mechanisms of cancer compared to previous studies ( 18 , 29 ) which used morphological features to make the prognostic prediction of cancer patients.…”

Section: Resultsmentioning

confidence: 66%

“…to the pathway of immune escape and tumor spread. To the best of our knowledge, the biological significance revealed by our study was the most related to the immune mechanisms of cancer compared to previous studies (18,29) which used morphological features to make the prognostic prediction of cancer patients. In contrast, without tumor segmentation, the model using features from pieces randomly selected in the whole image does not have a very interpretable biological meaning.…”

Section: Enrichment Analysismentioning

confidence: 63%

“…With the development of more accurate and more interpretable AI algorithms, deep learning has shown its magnificent power in dealing with biomedical images, such as tumor region identification, metastasis detection, and patient prognosis ( 30 ). Also, pathological images may be more insightful, interpretable, and sensitive than radiometric images because alterations in tumor molecular pathways are frequently mirrored in cell shape ( 18 ). Here, for our tumor region segmentation part, we utilized the Eff-UNet as our tumor region segmentation model for the H&E stained pathological images.…”

Section: Discussionmentioning

confidence: 99%

“…To avoid the potential risks, selecting small pieces from the same type of region may serve as an effective way. In addition, although there existed some studies exploring the biological significance and the interpretability of the prognosis prediction model based on quantitative morphological features, the results were not closely related to the mechanisms of cancer immunology ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

et al. 2023

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Colorectal cancer (CRC) is now the third most common malignancy to cause mortality worldwide, and its prognosis is of great importance. Recent CRC prognostic prediction studies mainly focused on biomarkers, radiometric images, and end-to-end deep learning methods, while only a few works paid attention to exploring the relationship between the quantitative morphological features of patients' tissue slides and their prognosis. However, existing few works in this area suffered from the drawback of choosing the cells randomly from the whole slides, which contain the non-tumor region that lakes information about prognosis. In addition, the existing works, which tried to demonstrate their biological interpretability using patients' transcriptome data, failed to show the biological meaning closely related to cancer. In this study, we proposed and evaluated a prognostic model using morphological features of cells in the tumor region. The features were first extracted by the software CellProfiler from the tumor region selected by Eff-Unet deep learning model. Features from different regions were then averaged for each patient as their representative, and the Lasso-Cox model was used to select the prognosis-related features. The prognostic prediction model was at last constructed using the selected prognosis-related features and was evaluated through KM estimate and cross-validation. In terms of biological meaning, Gene Ontology (GO) enrichment analysis of the expressed genes that correlated with the prognostically significant features was performed to show the biological interpretability of our model.With the help of tumor segmentation, our model achieved better statistical significance and better biological interpretability compared to the results without tumor segmentation. Statistically, the Kaplan Meier (KM) estimate of our model showed that the model using features in the tumor region has a higher C-index, a lower p-value, and a better performance on cross-validation than the model without tumor segmentation. In addition, revealing the pathway of the immune escape and the spread of the tumor, the model with tumor segmentation demonstrated a biological meaning much more related to cancer immunobiology than the model without tumor segmentation. Our prognostic prediction model using quantitive morphological features from tumor regions was almost as good as the TNM tumor staging system as they had a close C-index, and our model can be combined with the TNM tumor stage system to make a better prognostic prediction. And to the best of our knowledge, the biological mechanisms in our study were the most relevant to the immune mechanism of cancer compared to the previous studies.

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Section: Resultsmentioning

confidence: 66%

Section: Enrichment Analysismentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

et al. 2023

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“…We also quantified the characteristics of their pathology images and found significant differences in major features between subgroups, including texture and intensity. The quantitative features of these major pathologies have often been used to develop machine learning models for disease diagnosis [ 28 ]. BIE enriched the “brisk diffuse” TILs pattern, showing that it had a relatively strong immune infiltrate within the tumor.…”

Section: Discussionmentioning

confidence: 99%

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer

Zhang

Ma²,

Zhao³

et al. 2022

IJMS

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Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and “Boruta” feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.

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Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

Li,

Yu,

Tian

et al. 2023

Medical Physics

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BackgroundCutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.PurposeThe purpose of this study was to explore the potential application value of pathomics signatures.MethodsPathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature‐gene links.ResultsThe clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e‐04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high‐risk group based on gene expression and pathomics signatures was significantly lower than that of the low‐risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p‐values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc.ConclusionThis study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature‐based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.

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Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

Cited by 15 publications

References 51 publications

Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

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