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Diao S, Chen P, Showkatian E, Bandyopadhyay R, Rojas FR, Zhu B, Hong L, Aminu M, Saad MB, Salehjahromi M, Muneer A, Sujit SJ, Behrens C, Gibbons DL, Heymach JV, Kalhor N, Wistuba II, Solis Soto LM, Zhang J, Qin W, Wu J. Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis. Cancers (Basel) 2023; 15:4824. [PMID: 37835518 PMCID: PMC10571722 DOI: 10.3390/cancers15194824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
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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Affiliation(s)
- Songhui Diao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Pingjun Chen
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eman Showkatian
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rukhmini Bandyopadhyay
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Frank R. Rojas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bo Zhu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lingzhi Hong
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Muhammad Aminu
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amgad Muneer
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sheeba J. Sujit
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L. Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Neda Kalhor
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ignacio I. Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Luisa M. Solis Soto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenjian Qin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jia Wu
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Zhang KP, Liao YF, Qiu B, Zheng YK, Yu LK, He GH, Chen QN, Sun DH. 3D Printed Embedded Metamaterials. Small 2021; 17:e2103262. [PMID: 34672425 DOI: 10.1002/smll.202103262] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 09/08/2021] [Indexed: 06/13/2023]
Abstract
The manufacturing of 3D and conformal metamaterials remains a major challenge. The projection micro-stereolithography 3D printing technology combined with the liquid metal filling method is employed here to fabricate the metamaterials, which are characterized with embedded features that can effectively protect the metal resonance layer from external influence, and integrated molding of macro-micro structures and function-structure. To demonstrate the robustness and flexibility of the proposed method, three types of metamaterials are fabricated: 3D orthogonal split-ring resonator metamaterial, bionic compound eye conformal metamaterial, and a five-layer broadband conformal metamaterial in the form of hemispherical moth-eye, which are costly, tedious, and time consuming in conventional fabrication methods. And the layout of the filling channel is optimized and the polydimethylsiloxane coating post-treatment process is applied to smooth the surface roughness caused by the staircase effect of 3D printing to improve the transmission performance of metamaterial devices. The transmission properties are measured using terahertz time-domain spectroscopy system and the experimental results show that the method proposed in this paper makes metamaterial manufacture no longer limited to complex structures, which effectively expands the application range of metamaterials.
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Affiliation(s)
- Kun Peng Zhang
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Yan Fei Liao
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Bin Qiu
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Yue Kun Zheng
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Ling Ke Yu
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Gong Han He
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Qin Nan Chen
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
| | - Dao Heng Sun
- Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
- Shenzhen Research Institute of Xiamen University, Shenzhen, 518000, China
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