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Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022; 22:187. [PMID: 36316665 PMCID: PMC9620604 DOI: 10.1186/s12880-022-00918-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/22/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.
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Affiliation(s)
- Iben Fasterholdt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mohammad Naghavi-Behzad
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Benjamin S. B. Rasmussen
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Radiology, Odense University Hospital, Odense, Denmark ,grid.7143.10000 0004 0512 5013CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
| | - Tue Kjølhede
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mette Maria Skjøth
- grid.7143.10000 0004 0512 5013Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Malene Grubbe Hildebrandt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark ,grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Kristian Kidholm
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
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Rojas F, Hernandez S, Lazcano R, Laberiano-Fernandez C, Parra ER. Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research. Front Oncol 2022; 12:889886. [PMID: 35832550 PMCID: PMC9271766 DOI: 10.3389/fonc.2022.889886] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.
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Capobianco E, Meroni PL. Value of digital biomarkers in precision medicine: implications in cancer, autoimmune diseases, and COVID-19. Expert Review of Precision Medicine and Drug Development 2021. [DOI: 10.1080/23808993.2021.1924055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Enrico Capobianco
- Institute of Data Science and Computing, University of Miami, Miami, FL, USA
| | - Pier Luigi Meroni
- Immunorheumatology Research Laboratory, IRCCS Istituto Auxologico Italiano, Milan, Italy
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Gupta R, Le H, Van Arnam J, Belinsky D, Hasan M, Samaras D, Kurc T, Saltz JH. Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics. Curr Pathobiol Rep 2020; 8:133-48. [DOI: 10.1007/s40139-020-00217-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Abstract
Purpose of Review
Our goal is to show how readily available Pathomics tissue analytics can be used to study tumor immune interactions in cancer. We provide a brief overview of how Pathomics complements traditional histopathologic examination of cancer tissue samples. We highlight a novel Pathomics application, Tumor-TILs, that quantitatively measures and generates maps of tumor infiltrating lymphocytes in breast, pancreatic, and lung cancer by leveraging deep learning computer vision applications to perform automated analyses of whole slide images.
Recent Findings
Tumor-TIL maps have been generated to analyze WSIs from thousands of cases of breast, pancreatic, and lung cancer. We report the availability of these tools in an effort to promote collaborative research and motivate future development of ensemble Pathomics applications to discover novel biomarkers and perform a wide range of correlative clinicopathologic research in cancer immunopathology and beyond.
Summary
Tumor immune interactions in cancer are a fascinating aspect of cancer pathobiology with particular significance due to the emergence of immunotherapy. We present simple yet powerful specialized Pathomics methods that serve as powerful clinical research tools and potential standalone clinical screening tests to predict clinical outcomes and treatment responses for precision medicine applications in immunotherapy.
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Panayides AS, Amini A, Filipovic ND, Sharma A, Tsaftaris SA, Young A, Foran D, Do N, Golemati S, Kurc T, Huang K, Nikita KS, Veasey BP, Zervakis M, Saltz JH, Pattichis CS. AI in Medical Imaging Informatics: Current Challenges and Future Directions. IEEE J Biomed Health Inform 2020; 24:1837-1857. [PMID: 32609615 PMCID: PMC8580417 DOI: 10.1109/jbhi.2020.2991043] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
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Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep learning workflow in radiology: a primer. Insights Imaging 2020; 11:22. [PMID: 32040647 PMCID: PMC7010882 DOI: 10.1186/s13244-019-0832-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 12/17/2019] [Indexed: 02/08/2023] Open
Abstract
Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification, monitoring, and prediction. This article provides step-by-step practical guidance for conducting a project that involves deep learning in radiology, from defining specifications, to deployment and scaling. Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to patient, data, model, and hardware selection. Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis. This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival. Challenges are discussed, including ethical considerations, cohorting, data collection, anonymization, and availability of expert annotations. The practical guidance may be adapted to any project that requires automated medical image analysis.
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Affiliation(s)
- Emmanuel Montagnon
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Milena Cerny
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | | | - Vincent Hamilton
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Thomas Derennes
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - André Ilinca
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
| | - Franck Vandenbroucke-Menu
- Department of Surgery, Hepatopancreatobiliary and Liver Transplantation Service, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Quebec, Canada
| | - Simon Turcotte
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
- Department of Surgery, Hepatopancreatobiliary and Liver Transplantation Service, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Quebec, Canada
| | | | - An Tang
- Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Université Montréal and CRCHUM, 1058 rue Saint-Denis, Montréal, Québec, H2X 3 J4, Canada
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Bona JP, Prior FW, Zozus MN, Brochhausen M. Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective. Yearb Med Inform 2019; 28:140-151. [PMID: 31419826 PMCID: PMC6697506 DOI: 10.1055/s-0039-1677912] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Objectives
: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms “semantic integration" and “knowledge representation“. This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs).
Methods
: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles.
Results
: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT.
Conclusions
: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
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Affiliation(s)
| | - Fred W Prior
- University of Arkansas for Medical Sciences, Arkansas, USA
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8
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Vu QD, Graham S, Kurc T, To MNN, Shaban M, Qaiser T, Koohbanani NA, Khurram SA, Kalpathy-Cramer J, Zhao T, Gupta R, Kwak JT, Rajpoot N, Saltz J, Farahani K. Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Front Bioeng Biotechnol 2019; 7:53. [PMID: 31001524 PMCID: PMC6454006 DOI: 10.3389/fbioe.2019.00053] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 03/01/2019] [Indexed: 12/12/2022] Open
Abstract
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.
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Affiliation(s)
- Quoc Dang Vu
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Simon Graham
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Minh Nguyen Nhat To
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Muhammad Shaban
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Talha Qaiser
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | | | - Syed Ali Khurram
- School of Clinical Dentistry, The University of Sheffield, Sheffield, United Kingdom
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Harvard Medical School and Mass General Hospital, Boston, MA, United States
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
- Department of Pathology, Stony Brook University, Stony Brook, NY, United States
| | - Jin Tae Kwak
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
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9
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García-Figueiras R, Baleato-González S, Padhani AR, Luna-Alcalá A, Vallejo-Casas JA, Sala E, Vilanova JC, Koh DM, Herranz-Carnero M, Vargas HA. How clinical imaging can assess cancer biology. Insights Imaging 2019; 10:28. [PMID: 30830470 PMCID: PMC6399375 DOI: 10.1186/s13244-019-0703-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 11/08/2018] [Indexed: 02/07/2023] Open
Abstract
Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment.
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Affiliation(s)
- Roberto García-Figueiras
- Department of Radiology, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain.
| | - Sandra Baleato-González
- Department of Radiology, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England, HA6 2RN, UK
| | - Antonio Luna-Alcalá
- Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, OH, USA
- MRI Unit, Clínica Las Nieves, Health Time, Jaén, Spain
| | - Juan Antonio Vallejo-Casas
- Unidad de Gestión Clínica de Medicina Nuclear. IMIBIC. Hospital Reina Sofía. Universidad de Córdoba, Córdoba, Spain
| | - Evis Sala
- Department of Radiology and Cancer Research UK Cambridge Center, Cambridge, CB2 0QQ, UK
| | - Joan C Vilanova
- Department of Radiology, Clínica Girona and IDI, Lorenzana 36, 17002, Girona, Spain
| | - Dow-Mu Koh
- Department of Radiology, Royal Marsden Hospital & Institute of Cancer Research, Fulham Road, London, SW3 6JJ, UK
| | - Michel Herranz-Carnero
- Nuclear Medicine Department, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Galicia, Spain
- Molecular Imaging Program, IDIS, USC, Santiago de Compostela, Galicia, Spain
| | - Herbert Alberto Vargas
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, Radiology, 1275 York Av. Radiology Academic Offices C-278, New York, NY, 10065, USA
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10
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Fedorov A, Schwier M, Clunie D, Herz C, Pieper S, Kikinis R, Tempany C, Fennessy F. An annotated test-retest collection of prostate multiparametric MRI. Sci Data 2018; 5:180281. [PMID: 30512014 PMCID: PMC6278692 DOI: 10.1038/sdata.2018.281] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 10/26/2018] [Indexed: 12/13/2022] Open
Abstract
Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results.
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Affiliation(s)
- Andriy Fedorov
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Schwier
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Christian Herz
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ron Kikinis
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Fraunhofer MEVIS, Bremen, Germany
- Mathematics/Computer Science Faculty, University of Bremen, Bremen, Germany
| | - Clare Tempany
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Fiona Fennessy
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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11
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Huang X, Shu C, Chen L, Yao B. Impact of sex, body mass index and initial pathologic diagnosis age on the incidence and prognosis of different types of cancer. Oncol Rep 2018; 40:1359-1369. [PMID: 29956810 PMCID: PMC6072401 DOI: 10.3892/or.2018.6529] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Accepted: 06/13/2018] [Indexed: 02/07/2023] Open
Abstract
Cancer represents a significant challenge for humankind, as early diagnosis and treatment are difficult to achieve. To systemically investigate the effect of sex, body mass index (BMI) and age on cancer incidence and prognosis, the data from 14,504 cases of cancer were downloaded from The Cancer Genome Atlas (TCGA). BMI was used to categorize each person as underweight, normal weight, overweight or obese. Two‑ and five‑year survival rates were applied to estimate the prognosis for each cancer type. All data were statistically analyzed. We identified that males were more susceptible to lung, liver and skin cancer when compared with females, whereas females were more susceptible to thyroid, breast and adrenal cortex cancer. High BMI (>25) was positively associated with the occurrence of cancer, although patients with high BMI at the time of initial diagnosis had higher two/five‑year survival rates. The survival rates for cancer were positively correlated with the age at initial pathologic diagnosis. Some types of cancer were associated with particularly young ages of onset, including adrenocortical carcinoma, cervical and endocervical cancers, brain lower grade glioma, pheochromocytoma and paraganglioma, testicular germ cell tumors and thyroid carcinoma. Hence, the early diagnosis and prognosis for these cancers need to be improved. In conclusion, sex, BMI and age are associated with the incidence and survival rates for cancers. These results could be used to supplement precision and personalized medicine.
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Affiliation(s)
- Xuan Huang
- Reproductive Medical Center, Jinling Hospital Affiliated to The Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China
| | - Chuanjun Shu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, P.R. China
| | - Li Chen
- Reproductive Medical Center, Jinling Hospital Affiliated to The Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China
| | - Bing Yao
- Reproductive Medical Center, Jinling Hospital Affiliated to The Medical School of Nanjing University, Nanjing, Jiangsu 210002, P.R. China
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Hsu W, Park S, Kahn CE. Sensor, Signal, and Imaging Informatics. Yearb Med Inform 2017; 26:120-124. [PMID: 29063550 DOI: 10.15265/iy-2017-019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook. Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.
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