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Chouaïd C, Gendarme S, Auliac JB. Artificial intelligence to finally enable precision medicine for the management of resected non-small-cell lung cancer. Ann Oncol 2023; 34:565-566. [PMID: 37182802 DOI: 10.1016/j.annonc.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/16/2023] Open
Affiliation(s)
- C Chouaïd
- Service de Pneumologie, CHI Créteil, Créteil; Inserm U955, UPEC, IMRB, Créteil, France.
| | - S Gendarme
- Service de Pneumologie, CHI Créteil, Créteil; Inserm U955, UPEC, IMRB, Créteil, France
| | - J-B Auliac
- Service de Pneumologie, CHI Créteil, Créteil
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2
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Osher N, Kang J, Krishnan S, Rao A, Baladandayuthapani V. SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data. Front Genet 2023; 14:1175603. [PMID: 37274781 PMCID: PMC10232864 DOI: 10.3389/fgene.2023.1175603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/25/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes. Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.
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Affiliation(s)
- Nathaniel Osher
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Santhoshi Krishnan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Arvind Rao
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Veerabhadran Baladandayuthapani
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
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3
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Huang S, Yang J, Shen N, Xu Q, Zhao Q. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 2023; 89:30-37. [PMID: 36682439 DOI: 10.1016/j.semcancer.2023.01.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.
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Affiliation(s)
- Shigao Huang
- Department of Radiation Oncology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shanxi, China
| | - Jie Yang
- Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Na Shen
- Hong Kong Shue Yan University, Hong Kong, China
| | - Qingsong Xu
- Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macau SAR, China.
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4
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Sakamoto T, Furukawa T, Pham HHN, Kuroda K, Tabata K, Kashima Y, Okoshi EN, Morimoto S, Bychkov A, Fukuoka J. A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma. Histopathology 2022; 81:758-769. [PMID: 35989443 PMCID: PMC9826135 DOI: 10.1111/his.14779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/02/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023]
Abstract
AIMS The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumour cellularity in lung cancer samples and propose a protocol to apply it to routine practice. METHODS AND RESULTS We developed a quantitative model of lung adenocarcinoma that was validated and tested on 50 cases, and a collaborative workflow where pathologists could access the AI results and adjust their original tumour cellularity scores (adjusted-score) that we tested on 151 cases. The adjusted-score was validated by comparing them with a ground truth established by manual annotation of haematoxylin and eosin slides with reference to immunostains with thyroid transcription factor-1 and napsin A. For training, validation, testing the AI and testing the collaborative workflow, we used 40, 10, 50 and 151 whole slide images of lung adenocarcinoma, respectively. The sensitivity and specificity of tumour segmentation were 97 and 87%, respectively, and the accuracy of nuclei recognition was 99%. One pathologist's visually estimated scores were compared to the adjusted-score, and the pathologist's scores were altered in 87% of cases. Comparison with the ground truth revealed that the adjusted-score was more precise than the pathologists' scores (P < 0.05). CONCLUSION We proposed a collaborative workflow between AI and pathologists as a model to improve daily practice and enhance the prediction of tumour cellularity for genetic tests.
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Affiliation(s)
- Taro Sakamoto
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan
| | - Tomoi Furukawa
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan
| | - Hoa H N Pham
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan
| | - Kishio Kuroda
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan,Department of Pathology, Kameda Medical CenterKamogawaJapan
| | - Kazuhiro Tabata
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan
| | - Yukio Kashima
- Department of Pathology, Awaji Medical CenterSumotoJapan
| | - Ethan N Okoshi
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan
| | - Shimpei Morimoto
- Innovation Platform and Office for Precision Medicine (iPOP), Graduate School of Biomedical SciencesNagasaki UniversityNagasakiJapan
| | - Andrey Bychkov
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan,Department of Pathology, Kameda Medical CenterKamogawaJapan
| | - Junya Fukuoka
- Department of PathologyNagasaki University Graduate School of Biomedical SciencesNagasakiJapan,Department of Pathology, Kameda Medical CenterKamogawaJapan
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5
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Jiang X, Xiao G, Li Q. A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data. Stat Med 2022; 41:4647-4665. [DOI: 10.1002/sim.9530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 05/13/2022] [Accepted: 07/01/2022] [Indexed: 12/13/2022]
Affiliation(s)
- Xi Jiang
- Department of Statistical Science Southern Methodist University Dallas Texas USA
- Department of Population and Data Sciences The University of Texas Southwestern Medical Center Dallas Texas USA
| | - Guanghua Xiao
- Department of Population and Data Sciences The University of Texas Southwestern Medical Center Dallas Texas USA
| | - Qiwei Li
- Department of Mathematical Sciences The University of Texas at Dallas Richardson Texas USA
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6
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Cai L, Xiao G, Gerber D, D Minna J, Xie Y. Lung Cancer Computational Biology and Resources. Cold Spring Harb Perspect Med 2022; 12:a038273. [PMID: 34751162 PMCID: PMC8805643 DOI: 10.1101/cshperspect.a038273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.
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Affiliation(s)
- Ling Cai
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - David Gerber
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - John D Minna
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
- Harrold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Population and Data Sciences, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
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7
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Song Y, Zhou X, Kang J, Aung MT, Zhang M, Zhao W, Needham BL, Kardia SLR, Liu Y, Meeker JD, Smith JA, Mukherjee B. Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators. Stat Med 2021; 40:6038-6056. [PMID: 34404112 PMCID: PMC9257993 DOI: 10.1002/sim.9168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 07/30/2021] [Accepted: 08/05/2021] [Indexed: 01/18/2023]
Abstract
We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure variable to an outcome of interest. Correlations among mediators are commonly observed in modern data analysis; examples include the activated voxels within connected regions in brain image data, regulatory signals driven by gene networks in genome data, and correlated exposure data from the same source. When correlations are present among active mediators, mediation analysis that fails to account for such correlation can be suboptimal and may lead to a loss of power in identifying active mediators. Building upon a recent high-dimensional mediation analysis framework, we propose two Bayesian hierarchical models, one with a Gaussian mixture prior that enables correlated mediator selection and the other with a Potts mixture prior that accounts for the correlation among active mediators in mediation analysis. We develop efficient sampling algorithms for both methods. Various simulations demonstrate that our methods enable effective identification of correlated active mediators, which could be missed by using existing methods that assume prior independence among active mediators. The proposed methods are applied to the LIFECODES birth cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) and identified new active mediators with important biological implications.
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Affiliation(s)
- Yanyi Song
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Max T. Aung
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Min Zhang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan USA
| | - Belinda L. Needham
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan USA
| | | | - Yongmei Liu
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina USA
| | - John D. Meeker
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan USA
| | - Jennifer A. Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
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Abstract
Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.
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9
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Zhang M, Sheffield T, Zhan X, Li Q, Yang DM, Wang Y, Wang S, Xie Y, Wang T, Xiao G. Spatial molecular profiling: platforms, applications and analysis tools. Brief Bioinform 2021; 22:bbaa145. [PMID: 32770205 PMCID: PMC8138878 DOI: 10.1093/bib/bbaa145] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/26/2020] [Accepted: 06/09/2020] [Indexed: 12/24/2022] Open
Abstract
Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.
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Affiliation(s)
- Minzhe Zhang
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Thomas Sheffield
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Xiaowei Zhan
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Qiwei Li
- Department of Mathematics Sciences at University of Texas at Dallas
| | - Donghan M Yang
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Yunguan Wang
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Shidan Wang
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Yang Xie
- Quantitative Biomedical Research Center at the University of Texas Southwestern Medical Center
| | - Tao Wang
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
| | - Guanghua Xiao
- Department of Population and Data Sciences at University of Texas Southwestern Medical Center
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10
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Bayesian Joint Modeling of Single-Cell Expression Data and Bulk Spatial Transcriptomic Data. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09308-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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11
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Lan Z, Reich BJ, Bandyopadhyay D. A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging. CAN J STAT 2021. [DOI: 10.1002/cjs.11601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Zhou Lan
- Center for Outcomes Research and Evaluation Yale School of Medicine U.S.A
| | - Brian J. Reich
- Department of Statistics North Carolina State University U.S.A
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12
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Cong L, Feng W, Yao Z, Zhou X, Xiao W. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer. J Cancer 2020; 11:3615-3622. [PMID: 32284758 PMCID: PMC7150458 DOI: 10.7150/jca.43268] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/05/2020] [Indexed: 01/01/2023] Open
Abstract
Lung cancer is one of the main causes of cancer-related death in the world. The identification and characteristics of malignant cells are essential for the diagnosis and treatment of primary or metastatic cancers. Deep learning is a new field of artificial intelligence, which can be used for computer aided diagnosis and scientific research of lung cancer pathology by analyzing and learning through establishment and simulation of human brain. In this review, we will introduce the application, progress and problems of deep learning in pathology of lung cancer and make prospects for its future development.
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Affiliation(s)
- Lei Cong
- Department of Oncology, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Wanbing Feng
- Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhigang Yao
- Department of Pathology, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Xiaoming Zhou
- Department of Scientific Research, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Wei Xiao
- Department of Scientific Research, Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
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13
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Wang S, Wang T, Yang L, Yang DM, Fujimoto J, Yi F, Luo X, Yang Y, Yao B, Lin S, Moran C, Kalhor N, Weissferdt A, Minna J, Xie Y, Wistuba II, Mao Y, Xiao G. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 2019; 50:103-110. [PMID: 31767541 PMCID: PMC6921240 DOI: 10.1016/j.ebiom.2019.10.033] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 10/16/2019] [Accepted: 10/16/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone. METHODS In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/. FINDINGS The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage. INTERPRETATION The analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX
| | - Lin Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Faliu Yi
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Xin Luo
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Yikun Yang
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Bo Yao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - ShinYi Lin
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Cesar Moran
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Neda Kalhor
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Annikka Weissferdt
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, Department of Internal Medicine and Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX.
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