Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis
Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient...
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| Published in | Cancers Vol. 15; no. 19; p. 4824 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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01.10.2023
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| Abstract | Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies. |
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| AbstractList | Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies. Simple SummaryLung cancer is the leading cause of cancer death in the United States and worldwide. Currently, deep learning–based methods show significant advances and potential in pathology and can guide lung cancer diagnosis and prognosis prediction. In this study, we present a fully automated cellular-level survival prediction pipeline that uses histopathologic images of lung adenocarcinoma to predict survival risk based on dual global feature fusion. The results show meaningful, convincing, and comprehensible survival prediction ability and manifest the potential of our proposed pipeline for application to other malignancies.AbstractHistopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies. Lung cancer is the leading cause of cancer death in the United States and worldwide. Currently, deep learning–based methods show significant advances and potential in pathology and can guide lung cancer diagnosis and prognosis prediction. In this study, we present a fully automated cellular-level survival prediction pipeline that uses histopathologic images of lung adenocarcinoma to predict survival risk based on dual global feature fusion. The results show meaningful, convincing, and comprehensible survival prediction ability and manifest the potential of our proposed pipeline for application to other malignancies. Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies. Lung cancer is the leading cause of cancer death in the United States and worldwide. Currently, deep learning–based methods show significant advances and potential in pathology and can guide lung cancer diagnosis and prognosis prediction. In this study, we present a fully automated cellular-level survival prediction pipeline that uses histopathologic images of lung adenocarcinoma to predict survival risk based on dual global feature fusion. The results show meaningful, convincing, and comprehensible survival prediction ability and manifest the potential of our proposed pipeline for application to other malignancies. Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies. |
| Audience | Academic |
| Author | Diao, Songhui Zhu, Bo Showkatian, Eman Salehjahromi, Morteza Bandyopadhyay, Rukhmini Sujit, Sheeba J Chen, Pingjun Saad, Maliazurina B Muneer, Amgad Solis Soto, Luisa M Hong, Lingzhi Rojas, Frank R Wu, Jia Qin, Wenjian Wistuba, Ignacio I Zhang, Jianjun Behrens, Carmen Heymach, John V Aminu, Muhammad Kalhor, Neda Gibbons, Don L |
| AuthorAffiliation | 6 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 4 Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 3 Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 7 Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 5 Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA 2 Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China 1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China |
| AuthorAffiliation_xml | – name: 2 Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China – name: 5 Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – name: 7 Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – name: 4 Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – name: 1 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – name: 6 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – name: 3 Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA |
| Author_xml | – sequence: 1 givenname: Songhui orcidid: 0000-0002-0971-8591 surname: Diao fullname: Diao, Songhui organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 2 givenname: Pingjun orcidid: 0000-0003-0528-1713 surname: Chen fullname: Chen, Pingjun organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 3 givenname: Eman surname: Showkatian fullname: Showkatian, Eman organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 4 givenname: Rukhmini orcidid: 0000-0002-0422-9537 surname: Bandyopadhyay fullname: Bandyopadhyay, Rukhmini organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 5 givenname: Frank R surname: Rojas fullname: Rojas, Frank R organization: Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 6 givenname: Bo surname: Zhu fullname: Zhu, Bo organization: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 7 givenname: Lingzhi orcidid: 0000-0001-6553-3279 surname: Hong fullname: Hong, Lingzhi organization: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 8 givenname: Muhammad surname: Aminu fullname: Aminu, Muhammad organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 9 givenname: Maliazurina B surname: Saad fullname: Saad, Maliazurina B organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 10 givenname: Morteza orcidid: 0000-0002-7036-1683 surname: Salehjahromi fullname: Salehjahromi, Morteza organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 11 givenname: Amgad orcidid: 0000-0002-7157-3020 surname: Muneer fullname: Muneer, Amgad organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 12 givenname: Sheeba J orcidid: 0000-0001-8302-7046 surname: Sujit fullname: Sujit, Sheeba J organization: Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 13 givenname: Carmen surname: Behrens fullname: Behrens, Carmen organization: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 14 givenname: Don L surname: Gibbons fullname: Gibbons, Don L organization: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 15 givenname: John V surname: Heymach fullname: Heymach, John V organization: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 16 givenname: Neda surname: Kalhor fullname: Kalhor, Neda organization: Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 17 givenname: Ignacio I surname: Wistuba fullname: Wistuba, Ignacio I organization: Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 18 givenname: Luisa M orcidid: 0000-0002-1253-630X surname: Solis Soto fullname: Solis Soto, Luisa M organization: Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 19 givenname: Jianjun orcidid: 0000-0001-7872-3477 surname: Zhang fullname: Zhang, Jianjun organization: Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA – sequence: 20 givenname: Wenjian surname: Qin fullname: Qin, Wenjian organization: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China – sequence: 21 givenname: Jia orcidid: 0000-0001-8392-8338 surname: Wu fullname: Wu, Jia organization: Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA |
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| Keywords | global fusion cellular architecture embedded features survival prediction whole-slide image lung adenocarcinoma |
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| Snippet | Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has... Lung cancer is the leading cause of cancer death in the United States and worldwide. Currently, deep learning–based methods show significant advances and... Simple SummaryLung cancer is the leading cause of cancer death in the United States and worldwide. Currently, deep learning–based methods show significant... |
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| SubjectTerms | Adenocarcinoma Artificial intelligence Automation Cancer cellular architecture Classification Datasets Deep learning Design and construction Diagnosis embedded features Gene mapping Genomes global fusion Health aspects lung adenocarcinoma Lung cancer Malignancy Mechanization Medical diagnosis Medical prognosis Methods Morphology Oncology, Experimental Pathology Patients Pipelines Predictions Prognosis survival prediction Tumor microenvironment whole-slide image |
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| Title | Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis |
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