1
|
Singh A, Paruthy SB, Belsariya V, Chandra J N, Singh SK, Manivasagam SS, Choudhary S, Kumar MA, Khera D, Kuraria V. Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence. Cureus 2023; 15:e50203. [PMID: 38192969 PMCID: PMC10772314 DOI: 10.7759/cureus.50203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 01/10/2024] Open
Abstract
Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
Collapse
Affiliation(s)
- Arun Singh
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Shivani B Paruthy
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vivek Belsariya
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Nemi Chandra J
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Sunil Kumar Singh
- Surgical Oncology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | | | - Sushila Choudhary
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - M Anil Kumar
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Dhananjay Khera
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vaibhav Kuraria
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| |
Collapse
|
2
|
Drabiak K, Kyzer S, Nemov V, El Naqa I. AI and machine learning ethics, law, diversity, and global impact. Br J Radiol 2023; 96:20220934. [PMID: 37191072 PMCID: PMC10546451 DOI: 10.1259/bjr.20220934] [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] [Received: 09/30/2022] [Revised: 03/20/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023] Open
Abstract
Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective healthcare with impressive technical capability to mimic human cognitive capabilities. However, widespread application of this promising technology has been limited in the medical domain and expectations have been tampered by ethical challenges and concerns regarding patient privacy, legal responsibility, trustworthiness, and fairness. To balance technical innovation with ethical applications of AI/ML, developers must demonstrate the AI functions as intended and adopt strategies to minimize the risks for failure or bias. This review describes the new ethical challenges created by AI/ML for clinical care and identifies specific considerations for its practice in medicine. We provide an overview of regulatory and legal issues applicable in Europe and the United States, a description of technical aspects to consider, and present recommendations for trustworthy AI/ML that promote transparency, minimize risks of bias or error, and protect the patient well-being.
Collapse
Affiliation(s)
- Katherine Drabiak
- Colleges of Public Health and Medicine, University of South Florida, Tampa, FL, USA
| | - Skylar Kyzer
- Colleges of Public Health and Medicine, University of South Florida, Tampa, FL, USA
| | - Valerie Nemov
- Colleges of Public Health and Medicine, University of South Florida, Tampa, FL, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| |
Collapse
|
3
|
Hadjiiski L, Cha K, Chan HP, Drukker K, Morra L, Näppi JJ, Sahiner B, Yoshida H, Chen Q, Deserno TM, Greenspan H, Huisman H, Huo Z, Mazurchuk R, Petrick N, Regge D, Samala R, Summers RM, Suzuki K, Tourassi G, Vergara D, Armato SG. AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging. Med Phys 2023; 50:e1-e24. [PMID: 36565447 DOI: 10.1002/mp.16188] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/13/2022] [Accepted: 11/22/2022] [Indexed: 12/25/2022] Open
Abstract
Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support.
Collapse
Affiliation(s)
- Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kenny Cha
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Lia Morra
- Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy
| | - Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Berkman Sahiner
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Thomas M Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
| | - Hayit Greenspan
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv, Israel & Department of Radiology, Ichan School of Medicine, Tel Aviv University, Mt Sinai, New York, New York, USA
| | - Henkjan Huisman
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Zhimin Huo
- Tencent America, Palo Alto, California, USA
| | - Richard Mazurchuk
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.,Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Ravi Samala
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ronald M Summers
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland, USA
| | - Kenji Suzuki
- Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
| | | | - Daniel Vergara
- Department of Radiology, Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Samuel G Armato
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| |
Collapse
|
4
|
Castro E, Costa Pereira J, Cardoso JS. Symmetry-based regularization in deep breast cancer screening. Med Image Anal 2023; 83:102690. [PMID: 36446314 DOI: 10.1016/j.media.2022.102690] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
Breast cancer is the most common and lethal form of cancer in women. Recent efforts have focused on developing accurate neural network-based computer-aided diagnosis systems for screening to help anticipate this disease. The ultimate goal is to reduce mortality and improve quality of life after treatment. Due to the difficulty in collecting and annotating data in this domain, data scarcity is - and will continue to be - a limiting factor. In this work, we present a unified view of different regularization methods that incorporate domain-known symmetries in the model. Three general strategies were followed: (i) data augmentation, (ii) invariance promotion in the loss function, and (iii) the use of equivariant architectures. Each of these strategies encodes different priors on the functions learned by the model and can be readily introduced in most settings. Empirically we show that the proposed symmetry-based regularization procedures improve generalization to unseen examples. This advantage is verified in different scenarios, datasets and model architectures. We hope that both the principle of symmetry-based regularization and the concrete methods presented can guide development towards more data-efficient methods for breast cancer screening as well as other medical imaging domains.
Collapse
Affiliation(s)
- Eduardo Castro
- INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
| | - Jose Costa Pereira
- INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; Huawei Technologies R&D, Noah's Ark Lab, Gridiron building, 1 Pancras Square, 5th floor, London N1C 4AG, United Kingdom
| | - Jaime S Cardoso
- INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| |
Collapse
|
5
|
Dileep G, Gianchandani Gyani SG. Artificial Intelligence in Breast Cancer Screening and Diagnosis. Cureus 2022; 14:e30318. [PMID: 36381716 PMCID: PMC9650950 DOI: 10.7759/cureus.30318] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/15/2022] [Indexed: 11/05/2022] Open
Abstract
Cancer is a disease that continues to plague our modern society. Among all types of cancer, breast cancer is now the most common type of cancer occurring in women worldwide. Various factors, including genetics, lifestyle, and the environment, have contributed to the rise in the prevalence of breast cancer among women of all socioeconomic strata. Therefore, proper screening for early diagnosis and treatment becomes a major factor when fighting the disease. Artificial intelligence (AI) continues to revolutionize various spheres of our lives with its numerous applications. Using AI in the existing screening process makes obtaining results even easier and more convenient. Faster, more accurate results are some of the benefits of AI methods in breast cancer screening. Nonetheless, there are many challenges in the process of the integration of AI that needs to be addressed systematically. The following is a review of the application of AI in breast cancer screening.
Collapse
|
6
|
Ueda D, Yamamoto A, Onoda N, Takashima T, Noda S, Kashiwagi S, Morisaki T, Fukumoto S, Shiba M, Morimura M, Shimono T, Kageyama K, Tatekawa H, Murai K, Honjo T, Shimazaki A, Kabata D, Miki Y. Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets. PLoS One 2022; 17:e0265751. [PMID: 35324962 PMCID: PMC8947392 DOI: 10.1371/journal.pone.0265751] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/07/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography. Methods Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model’s sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets. Results The hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45–0.47 mFPI and had partial AUCs of 0.93 in both test datasets. Conclusions The DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.
Collapse
Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
- * E-mail:
| | - Akira Yamamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Naoyoshi Onoda
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Tsutomu Takashima
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Satoru Noda
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Shinichiro Kashiwagi
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Tamami Morisaki
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, Osaka City University, Osaka, Japan
- Department of Premier Preventive Medicine, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Shinya Fukumoto
- Department of Premier Preventive Medicine, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Masatsugu Shiba
- Department of Gastroenterology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Mina Morimura
- Department of General Practice, Osaka City University Hospital, Osaka, Japan
| | - Taro Shimono
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Ken Kageyama
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiroyuki Tatekawa
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Kazuki Murai
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Takashi Honjo
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Akitoshi Shimazaki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Daijiro Kabata
- Department of Medical Statistics, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| |
Collapse
|
7
|
Zhao W, Kang Q, Qian F, Li K, Zhu J, Ma B. Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto's Thyroiditis on Ultrasound. J Clin Endocrinol Metab 2022; 107:953-963. [PMID: 34907442 PMCID: PMC8947219 DOI: 10.1210/clinem/dgab870] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto's thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence. METHODS We retrospectively collected ultrasound images from patients with and without HT from 2 hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled 9 convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification. The model's diagnostic performance was validated and compared to 2 hospital validation sets. We also compared the accuracy of CAD-HT against seniors/junior radiologists. Subgroup analysis of CAD-HT performance for different thyroid hormone levels (hyperthyroidism, hypothyroidism, and euthyroidism) was also evaluated. RESULTS 39 280 ultrasound images from 21 118 patients were included in this study. The accuracy, sensitivity, and specificity of the HT-CAD model were 0.892, 0.890, and 0.895, respectively. HT-CAD performance between 2 hospitals was not significantly different. The HT-CAD model achieved a higher performance (P < 0.001) when compared to senior radiologists, with a nearly 9% accuracy improvement. HT-CAD had almost similar accuracy (range 0.87-0.894) for the 3 subgroups based on thyroid hormone level. CONCLUSION The HT-CAD strategy based on CNN significantly improved the radiologists' diagnostic accuracy of HT. Our model demonstrates good performance and robustness in different hospitals and for different thyroid hormone levels.
Collapse
Affiliation(s)
- Wanjun Zhao
- Department of Thyroid Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Qingbo Kang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Feiyan Qian
- Department of Rehabilitation, Shaoxing Central Hospital, Shaoxing, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jingqiang Zhu
- Department of Thyroid Surgery, West China Hospital, Sichuan University, Chengdu, China
- Correspondence: Jingqiang Zhu, MD, Department of Thyroid Surgery, West China Hospital, Sichuan University, Chengdu 610041, China. ; or Buyun Ma, MD, Department of Ultrasonography, West China Hospital of Sichuan University, Chengdu 610041, China.
| | - Buyun Ma
- Department of Ultrasonography, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
8
|
Artificial Intelligence (AI) for Screening Mammography, From the AI Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:369-380. [PMID: 35018795 DOI: 10.2214/ajr.21.27071] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment, and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
Collapse
|
9
|
Treviño M, Birdsong G, Carrigan A, Choyke P, Drew T, Eckstein M, Fernandez A, Gallas BD, Giger M, Hewitt SM, Horowitz TS, Jiang YV, Kudrick B, Martinez-Conde S, Mitroff S, Nebeling L, Saltz J, Samuelson F, Seltzer SE, Shabestari B, Shankar L, Siegel E, Tilkin M, Trueblood JS, Van Dyke AL, Venkatesan AM, Whitney D, Wolfe JM. Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration. JNCI Cancer Spectr 2021; 6:6491257. [DOI: 10.1093/jncics/pkab099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 10/20/2021] [Accepted: 11/11/2021] [Indexed: 11/14/2022] Open
Abstract
Abstract
Medical image interpretation is central to detecting, diagnosing, and staging cancer and many other disorders. At a time when medical imaging is being transformed by digital technologies and artificial intelligence, understanding the basic perceptual and cognitive processes underlying medical image interpretation is vital for increasing diagnosticians’ accuracy and performance, improving patient outcomes, and reducing diagnostician burn-out. Medical image perception remains substantially understudied. In September of 2019, the National Cancer Institute convened a multidisciplinary panel of radiologists and pathologists together with researchers working in medical image perception and adjacent fields of cognition and perception for the “Cognition and Medical Image Perception Think Tank.” The Think Tank’s key objectives were: to identify critical unsolved problems related to visual perception in pathology and radiology from the perspective of diagnosticians; to discuss how these clinically relevant questions could be addressed through cognitive and perception research; to identify barriers and solutions for transdisciplinary collaborations; to define ways to elevate the profile of cognition and perception research within the medical image community; to determine the greatest needs to advance medical image perception; and to outline future goals and strategies to evaluate progress. The Think Tank emphasized diagnosticians’ perspectives as the crucial starting point for medical image perception research, with diagnosticians describing their interpretation process and identifying perceptual and cognitive problems that arise. This paper reports the deliberations of the Think Tank participants to address these objectives and highlight opportunities to expand research on medical image perception.
Collapse
Affiliation(s)
- Melissa Treviño
- National Cancer Institute, United States of America
- National Center for Complementary and Integrative Health, United States of America
| | - George Birdsong
- Emory University School of Medicine, United States of America
| | | | - Peter Choyke
- National Cancer Institute, United States of America
| | | | - Miguel Eckstein
- University of California, Santa Barbara, United States of America
| | - Anna Fernandez
- National Cancer Institute, United States of America
- Booz Allen Hamilton, United States of America
| | | | | | | | | | | | - Bonnie Kudrick
- Transportation Security Administration, United States of America
| | | | | | | | - Joseph Saltz
- Stony Brook University, United States of America
| | | | - Steven E Seltzer
- Brigham and Women’s Hospital, United States of America
- Harvard Medical School, United States of America
| | - Behrouz Shabestari
- National Institute of Biomedical Imaging and Bioengineering, United States of America
| | | | - Eliot Siegel
- University of Maryland School of Medicine, United States of America
| | - Mike Tilkin
- American College of Radiology, United States of America
| | | | | | | | - David Whitney
- University of California, Berkeley, United States of America
| | - Jeremy M Wolfe
- Brigham and Women’s Hospital, United States of America
- Harvard Medical School, United States of America
| |
Collapse
|
10
|
Hooshmand S, Reed WM, Suleiman ME, Brennan PC. SCREENING MAMMOGRAPHY: DIAGNOSTIC EFFICACY-ISSUES AND CONSIDERATIONS FOR THE 2020S. RADIATION PROTECTION DOSIMETRY 2021; 197:54-62. [PMID: 34729603 DOI: 10.1093/rpd/ncab160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/04/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
Diagnostic efficacy in medical imaging is ultimately a reflection of radiologist performance. This can be influenced by numerous factors, some of which are patient related, such as the physical size and density of the breast, and machine related, where some lesions are difficult to visualise on traditional imaging techniques. Other factors are human reader errors that occur during the diagnostic process, which relate to reader experience and their perceptual and cognitive oversights. Given the large-scale nature of breast cancer screening, even small increases in diagnostic performance equate to large numbers of women saved. It is important to identify the causes of diagnostic errors and how detection efficacy can be improved. This narrative review will therefore explore the various factors that influence mammographic performance and the potential solutions used in an attempt to ameliorate the errors made.
Collapse
Affiliation(s)
- Sahand Hooshmand
- Faculty of Medicine and Health, The Discipline of Medical Imaging Sciences, The University of Sydney, Susan Wakil Health Building (D18), Sydney, NSW 2050, Australia
| | - Warren M Reed
- Faculty of Medicine and Health, The Discipline of Medical Imaging Sciences, The University of Sydney, Susan Wakil Health Building (D18), Sydney, NSW 2050, Australia
| | - Mo'ayyad E Suleiman
- Faculty of Medicine and Health, The Discipline of Medical Imaging Sciences, The University of Sydney, Susan Wakil Health Building (D18), Sydney, NSW 2050, Australia
| | - Patrick C Brennan
- Faculty of Medicine and Health, The Discipline of Medical Imaging Sciences, The University of Sydney, Susan Wakil Health Building (D18), Sydney, NSW 2050, Australia
| |
Collapse
|
11
|
Fischer G, De Silvestro A, Müller M, Frauenfelder T, Martini K. Computer-Aided Detection of Seven Chest Pathologies on Standard Posteroanterior Chest X-Rays Compared to Radiologists Reading Dual-Energy Subtracted Radiographs. Acad Radiol 2021; 29:e139-e148. [PMID: 34706849 DOI: 10.1016/j.acra.2021.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/06/2021] [Accepted: 09/21/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES Retrospective performance evaluation of a computer-aided detection (CAD) system on standard posteroanterior (PA) chest radiographs (PA-CXR) in detection of pulmonary nodules, infectious consolidation, pneumothorax, pleural effusion, aortic calcification, cardiomegaly and rib fractures compared to radiologists analyzing PA-CXR including dual-energy subtraction radiography (further termed as DESR). MATERIALS AND METHODS PA-CXR/DESR images of 197 patients were included. All patients underwent chest CT (gold standard) within a short interval (mean 28 hours). All images were evaluated by three blinded readers for the presence of pulmonary nodules, infectious consolidation, pneumothorax, pleural effusion, aortic calcification, cardiomegaly, and rib fractures. Meanwhile PA-CXR were analyzed by a CAD software. CAD results were compared to the majority result of the three readers. Sensitivity and specificity were calculated. McNemar's test was applied to test for significant differences. Interobserver agreement was defined using Cohen's kappa (κ). RESULTS Sensitivity of the CAD software was significantly higher (p < 0.05) for detection of infectious consolidation and pulmonary nodules (67.9% vs 26.8% and 54% vs 35.6%, respectively; p < 0.001) compared to radiologists analyzing DESR images. For the residual evaluated pathologies no statistical significant differences could be found. Overall, mean inter observer agreement between the three radiologists was moderate (k = 0.534). The best interobserver agreement could be reached for pneumothorax (k = 0.708) and pleural effusion (k = 0.699), while the worst was obtained for rib fractures (k = 0.412). CONCLUSION The CAD system has the potential to improve the detection of infectious consolidation and pulmonary nodules on CXR images.
Collapse
|
12
|
Zheng J, Sun H, Wu S, Jiang K, Peng Y, Yang X, Zhang F, Li M. 3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection. IEEE J Biomed Health Inform 2021; 25:764-773. [PMID: 32750942 DOI: 10.1109/jbhi.2020.3003316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
False positives (FPs) reduction is indispensable for clustered microcalcifications (MCs) detection in digital breast tomosynthesis (DBT), since there might be excessive false candidates in the detection stage. Considering that DBT volume has an anisotropic resolution, we proposed a novel 3D context-aware convolutional neural network (CNN) to reduce FPs, which consists of a 2D intra-slices feature extraction branch and a 3D inter-slice features fusion branch. In particular, 3D anisotropic convolutions were designed to learn representations from DBT volumes and inter-slice information fusion is only performed on the feature map level, which could avoid the influence of anisotropic resolution of DBT volume. The proposed method was evaluated on a large-scale Chinese women population of 877 cases with 1754 DBT volumes and compared with 8 related methods. Experimental results show that the proposed network achieved the best performance with an accuracy of 92.68% for FPs reduction with an AUC of 97.65%, and the FPs are 0.0512 per DBT volume at a sensitivity of 90%. This also proved that making full use of 3D contextual information of DBT volume can improve the performance of the classification algorithm.
Collapse
|
13
|
Chan HP, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys 2021; 47:e218-e227. [PMID: 32418340 DOI: 10.1002/mp.13764] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 05/13/2019] [Accepted: 05/13/2019] [Indexed: 12/15/2022] Open
Abstract
Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then be used to assist clinicians in their decision-making process. The recent success of the deep learning technology in machine learning spurs new research and development efforts to improve CAD performance and to develop CAD for many other complex clinical tasks. In this paper, we discuss the potential and challenges in developing CAD tools using deep learning technology or artificial intelligence (AI) in general, the pitfalls and lessons learned from CAD in screening mammography and considerations needed for future implementation of CAD or AI in clinical use. It is hoped that the past experiences and the deep learning technology will lead to successful advancement and lasting growth in this new era of CAD, thereby enabling CAD to deliver intelligent aids to improve health care.
Collapse
Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109-5842, USA
| | - Lubomir M Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109-5842, USA
| | - Ravi K Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109-5842, USA
| |
Collapse
|
14
|
Pacilè S, Lopez J, Chone P, Bertinotti T, Grouin JM, Fillard P. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiol Artif Intell 2020; 2:e190208. [PMID: 33937844 DOI: 10.1148/ryai.2020190208] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 06/27/2020] [Accepted: 07/07/2020] [Indexed: 02/06/2023]
Abstract
Purpose To evaluate the benefits of an artificial intelligence (AI)-based tool for two-dimensional mammography in the breast cancer detection process. Materials and Methods In this multireader, multicase retrospective study, 14 radiologists assessed a dataset of 240 digital mammography images, acquired between 2013 and 2016, using a counterbalance design in which half of the dataset was read without AI and the other half with the help of AI during a first session and vice versa during a second session, which was separated from the first by a washout period. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were assessed as endpoints. Results The average AUC across readers was 0.769 (95% CI: 0.724, 0.814) without AI and 0.797 (95% CI: 0.754, 0.840) with AI. The average difference in AUC was 0.028 (95% CI: 0.002, 0.055, P = .035). Average sensitivity was increased by 0.033 when using AI support (P = .021). Reading time changed dependently to the AI-tool score. For low likelihood of malignancy (< 2.5%), the time was about the same in the first reading session and slightly decreased in the second reading session. For higher likelihood of malignancy, the reading time was on average increased with the use of AI. Conclusion This clinical investigation demonstrated that the concurrent use of this AI tool improved the diagnostic performance of radiologists in the detection of breast cancer without prolonging their workflow.Supplemental material is available for this article.© RSNA, 2020.
Collapse
Affiliation(s)
- Serena Pacilè
- Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.)
| | - January Lopez
- Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.)
| | - Pauline Chone
- Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.)
| | - Thomas Bertinotti
- Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.)
| | - Jean Marie Grouin
- Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.)
| | - Pierre Fillard
- Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.)
| |
Collapse
|
15
|
Dif N, Elberrichi Z. A New Deep Learning Model Selection Method for Colorectal Cancer Classification. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2020. [DOI: 10.4018/ijsir.2020070105] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Deep learning is one of the most commonly used techniques in computer-aided diagnosis systems. Their exploitation for histopathological image analysis is important because of the complex morphology of whole slide images. However, the main limitation of these methods is the restricted number of available medical images, which can lead to an overfitting problem. Many studies have suggested the use of static ensemble learning methods to address this issue. This article aims to propose a new dynamic ensemble deep learning method. First, it generates a set of models based on the transfer learning strategy from deep neural networks. Then, the relevant subset of models is selected by the particle swarm optimization algorithm and combined by voting or averaging methods. The proposed approach was tested on a histopathological dataset for colorectal cancer classification, based on seven types of CNNs. The method has achieved accurate results (94.52%) by the Resnet121 model and the voting strategy, which provides important insights into the efficiency of dynamic ensembling in deep learning.
Collapse
Affiliation(s)
- Nassima Dif
- EEDIS Laboraory, Djillali Liabes University, Sidi Bel Abbes, Algeria
| | | |
Collapse
|
16
|
Noortman WA, Vriens D, Grootjans W, Tao Q, de Geus-Oei LF, Van Velden FH. Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2020; 64:278-290. [PMID: 32397702 DOI: 10.23736/s1824-4785.20.03263-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of handcrafted features combined with sophisticated statistical methods or machine learning algorithms for modelling, or deep learning algorithms that both learn features from raw data and perform modelling. These features have the potential to serve as non-invasive biomarkers for tumor characterization, prognostic stratification and response prediction, thereby contributing to precision medicine. However, especially in nuclear medicine, variable results are obtained when using radiomics for these purposes. Individual studies show promising results, but due to small numbers of patients per study and little standardization, it is difficult to compare and validate results on other datasets. This review describes the radiomic pipeline, its applications and the increasing role of artificial intelligence within the field. Furthermore, the challenges that need to be overcome to achieve clinical translation are discussed, so that, eventually, radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine, by providing the right treatment to the right patient, with the right dose, at the right time.
Collapse
Affiliation(s)
- Wyanne A Noortman
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands - .,Biomedical Photonic Imaging Group, University of Twente, Enschede, the Netherlands -
| | - Dennis Vriens
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| | - Willem Grootjans
- Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| | - Qian Tao
- Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands.,Biomedical Photonic Imaging Group, University of Twente, Enschede, the Netherlands
| | - Floris H Van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands
| |
Collapse
|
17
|
Chan HP, Samala RK, Hadjiiski LM. CAD and AI for breast cancer-recent development and challenges. Br J Radiol 2020; 93:20190580. [PMID: 31742424 PMCID: PMC7362917 DOI: 10.1259/bjr.20190580] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 11/13/2019] [Accepted: 11/17/2019] [Indexed: 12/15/2022] Open
Abstract
Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.
Collapse
Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Ravi K. Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | | |
Collapse
|
18
|
Qenam BA, Li T, Tapia K, Brennan PC. The roles of clinical audit and test sets in promoting the quality of breast screening: a scoping review. Clin Radiol 2020; 75:794.e1-794.e6. [PMID: 32139003 DOI: 10.1016/j.crad.2020.01.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/29/2020] [Indexed: 12/24/2022]
Abstract
Breast screening programmes enhance the probability of early breast cancer detection in many countries worldwide; however, the success of these efforts is highly dependent on the ability of breast screen readers to detect abnormalities in the screened population, which has low prevalence. Therefore, this task can be challenging. Clinical audit is a key quality assurance measure that aims to keep the screen reading performance within acceptable standards. Auditing, nonetheless, is a lengthy process, and its accuracy is dependent on available clinical data, which often can be limited. Mammographic standardised test sets are a different screen reading evaluation approach that provides participants with instant feedback based on a simulated environment. Although a test set provides unique evaluative qualities, its ability to represent clinical performance is debated. This article describes the distinctive roles of clinical audit and test sets in measuring and improving the quality of breast screening and highlights the relationship between test sets and clinical performance.
Collapse
Affiliation(s)
- B A Qenam
- BREAST, Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East St, Lidcombe, NSW, 2141, Australia; Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh, 11432, Saudi Arabia.
| | - T Li
- BREAST, Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East St, Lidcombe, NSW, 2141, Australia; Medical Image Optimisation and Perception Research Group (MIOPeG), Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East St, Lidcombe, NSW 2141, Australia
| | - K Tapia
- BREAST, Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East St, Lidcombe, NSW, 2141, Australia
| | - P C Brennan
- BREAST, Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East St, Lidcombe, NSW, 2141, Australia; Medical Image Optimisation and Perception Research Group (MIOPeG), Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East St, Lidcombe, NSW 2141, Australia
| |
Collapse
|
19
|
Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1213:3-21. [PMID: 32030660 PMCID: PMC7442218 DOI: 10.1007/978-3-030-33128-3_1] [Citation(s) in RCA: 227] [Impact Index Per Article: 56.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Deep learning is the state-of-the-art machine learning approach. The success of deep learning in many pattern recognition applications has brought excitement and high expectations that deep learning, or artificial intelligence (AI), can bring revolutionary changes in health care. Early studies of deep learning applied to lesion detection or classification have reported superior performance compared to those by conventional techniques or even better than radiologists in some tasks. The potential of applying deep-learning-based medical image analysis to computer-aided diagnosis (CAD), thus providing decision support to clinicians and improving the accuracy and efficiency of various diagnostic and treatment processes, has spurred new research and development efforts in CAD. Despite the optimism in this new era of machine learning, the development and implementation of CAD or AI tools in clinical practice face many challenges. In this chapter, we will discuss some of these issues and efforts needed to develop robust deep-learning-based CAD tools and integrate these tools into the clinical workflow, thereby advancing towards the goal of providing reliable intelligent aids for patient care.
Collapse
Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
| | - Ravi K Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
20
|
Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology 2019; 293:38-46. [PMID: 31385754 DOI: 10.1148/radiol.2019182908] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency. Purpose To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency. Materials and Methods In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists' assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage-simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05). Results The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001). Conclusion This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kontos and Conant in this issue.
Collapse
Affiliation(s)
- Adam Yala
- From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, WAC 240, Boston, Mass 02114-2698 (R.M., C.L.)
| | - Tal Schuster
- From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, WAC 240, Boston, Mass 02114-2698 (R.M., C.L.)
| | - Randy Miles
- From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, WAC 240, Boston, Mass 02114-2698 (R.M., C.L.)
| | - Regina Barzilay
- From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, WAC 240, Boston, Mass 02114-2698 (R.M., C.L.)
| | - Constance Lehman
- From the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (A.Y., T.S., R.B.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, WAC 240, Boston, Mass 02114-2698 (R.M., C.L.)
| |
Collapse
|
21
|
Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Boatsman JE, Hoffmeister JW. Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis. Radiol Artif Intell 2019; 1:e180096. [PMID: 32076660 DOI: 10.1148/ryai.2019180096] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/27/2019] [Accepted: 06/20/2019] [Indexed: 11/11/2022]
Abstract
Purpose To evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy. Materials and Methods A deep learning AI system was developed to identify suspicious soft-tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC), reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies. Results Radiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI]: 0.028, 0.087; P < .01), from 0.795 without AI to 0.852 with AI. Reading time decreased 52.7% (95% CI: 41.8%, 61.5%; P < .01), from 64.1 seconds without to 30.4 seconds with AI. Sensitivity increased from 77.0% without AI to 85.0% with AI (8.0%; 95% CI: 2.6%, 13.4%; P < .01), specificity increased from 62.7% without to 69.6% with AI (6.9%; 95% CI: 3.0%, 10.8%; noninferiority P < .01), and recall rate for noncancers decreased from 38.0% without to 30.9% with AI (7.2%; 95% CI: 3.1%, 11.2%; noninferiority P < .01). Conclusion The concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time.© RSNA, 2019See also the commentary by Hsu and Hoyt in this issue.
Collapse
Affiliation(s)
- Emily F Conant
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| | - Alicia Y Toledano
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| | - Senthil Periaswamy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| | - Sergei V Fotin
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| | - Jonathan Go
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| | - Justin E Boatsman
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| | - Jeffrey W Hoffmeister
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (E.F.C.); Biostatistics Consulting, Kensington, Md (A.Y.T.); iCAD, Nashua, NH (S.P., S.V.F., J.G., J.W.H.); and Intrinsic Imaging, Bolton, Mass (J.E.B.)
| |
Collapse
|
22
|
Brennan PC, Ganesan A, Eckstein MP, Ekpo EU, Tapia K, Mello-Thoms C, Lewis S, Juni MZ. Benefits of Independent Double Reading in Digital Mammography: A Theoretical Evaluation of All Possible Pairing Methodologies. Acad Radiol 2019; 26:717-723. [PMID: 30064917 DOI: 10.1016/j.acra.2018.06.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 06/19/2018] [Accepted: 06/19/2018] [Indexed: 10/28/2022]
Abstract
RATIONALE AND OBJECTIVES To establish the efficacy of pairing readers randomly and evaluate the merits of developing optimal pairing methodologies. MATERIALS AND METHODS Sensitivity, specificity, and proportion correct were computed for three different case sets that were independently read by 16 radiologists. Performance of radiologists as single readers was compared to expected double reading performance. We theoretically evaluated all possible pairing methodologies. Bootstrap resampling methods were used for statistical analyses. RESULTS Significant improvements in expected performance for double versus single reading (ie, delta performance) were shown for all performance measures and case-sets (p ≤ .003), with overall delta performance across all theoretically possible pairing schemes (n = 10,395) ranging between .05 and .08. Delta performance for the 20 best pairing schemes was significant (p < .001) and ranged between .07 and .10. Delta performance for 20 random pairing schemes was also significant (p ≤ .003) and ranged between .05 and .08. Delta performance for the 20 worst pairing schemes ranged between .03 and .06, reaching significance in delta proportion correct (p ≤ .021) for all three case-sets and in delta specificity for two case-sets (p ≤ .033) but not for a third case-set (p = .131), and not reaching significance in delta sensitivity for any of the three case-sets (.098 ≥ p ≥ .067). CONCLUSION Significant benefits accrue from double reading, and while random reader pairing achieves most double reading benefits, a strategic pairing approach may maximize the benefits of double reading.
Collapse
|
23
|
Lima ZS, Ebadi MR, Amjad G, Younesi L. Application of Imaging Technologies in Breast Cancer Detection: A Review Article. Open Access Maced J Med Sci 2019; 7:838-848. [PMID: 30962849 PMCID: PMC6447343 DOI: 10.3889/oamjms.2019.171] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/16/2019] [Accepted: 02/17/2019] [Indexed: 12/12/2022] Open
Abstract
One of the techniques utilised in the management of cancer in all stages is multiple biomedical imaging. Imaging as an important part of cancer clinical protocols can provide a variety of information about morphology, structure, metabolism and functions. Application of imaging technics together with other investigative apparatus including in fluids analysis and vitro tissue would help clinical decision-making. Mixed imaging techniques can provide supplementary information used to improve staging and therapy planning. Imaging aimed to find minimally invasive therapy to make better results and reduce side effects. Probably, the most important factor in reducing mortality of certain cancers is an early diagnosis of cancer via screening based on imaging. The most common cancer in women is breast cancer. It is considered as the second major cause of cancer deaths in females, and therefore it remained as an important medical and socio-economic issue. Medical imaging has always formed part of breast cancer care and has used in all phases of cancer management from detection and staging to therapy monitoring and post-therapeutic follow-up. An essential action to be performed in the preoperative staging of breast cancer based on breast imaging. The general term of breast imaging refers to breast sonography, mammography, and magnetic resonance tomography (MRT) of the breast (magnetic resonance mammography, MRM). Further development in technology will lead to increase imaging speed to meet physiological processes requirements. One of the issues in the diagnosis of breast cancer is sensitivity limitation. To overcome this limitation, complementary imaging examinations are utilised that traditionally includes screening ultrasound, and combined mammography and ultrasound. Development in targeted imaging and therapeutic agents calls for close cooperation among academic environment and industries such as biotechnological, IT and pharmaceutical industries.
Collapse
Affiliation(s)
- Zeinab Safarpour Lima
- Shahid Akbarabadi Clinical Research Development Unit (ShACRDU), Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mohammad Reza Ebadi
- Shohadaye Haft-e-tir Hospital, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Ghazaleh Amjad
- Shahid Akbarabadi Clinical Research Development Unit (ShACRDU), Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Ladan Younesi
- Shahid Akbarabadi Clinical Research Development Unit (ShACRDU), Iran University of Medical Sciences (IUMS), Tehran, Iran
| |
Collapse
|
24
|
Ventura-Alfaro CE. [Measurements errors in screening mammogram interpretation by radiologists]. ACTA ACUST UNITED AC 2019; 20:518-522. [PMID: 30843990 DOI: 10.15446/rsap.v20n4.52035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 02/12/2018] [Indexed: 11/09/2022]
Abstract
The timely detection of breast cancer is achieved through mammography; however, the quality of the procedure should be addressed for proper performance and interpretation. Despite recent improvements in quality assurance in mammography, interpretation still depends on each reader; therefore, errors can be made when interpreting screening mammograms, leading to unnecessary biopsies and/or overdiagnosis, with sustained physical, economic and psychological consequences. Since interpretation is related to the perceptive and cognitive ability of the radiologist, it is necessary to have extensive knowledge about the possible errors that may occur during interpretation, as well as of the way how they can be reduced, prevented and/or corrected to provide the patient with the highest possible level of safety.
Collapse
Affiliation(s)
- Carmelita E Ventura-Alfaro
- CV: MD. M. Sc. Ciencias con Area, de Concentración en Economía de la Salud. Ph. D. Ciencias con Area de Concentración en Epidemiología. Instituto Mexicano del Seguro Social, Delegación Jalisco. Jalisco, México.
| |
Collapse
|
25
|
Weinfurtner RJ, Mooney B, Forbus J. Specialized Second Opinion Review of Breast MRI Impacts Management and Increases Cancer Detection. J Am Coll Radiol 2019; 16:922-927. [PMID: 30833163 DOI: 10.1016/j.jacr.2019.01.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/04/2019] [Accepted: 01/05/2019] [Indexed: 12/27/2022]
Abstract
OBJECTIVE The aim of our study is to determine MRI review discrepancy frequency and the subsequent impact on patient management for patients pursuing breast imaging second opinions. METHODS A retrospective chart review was conducted on 1,000 consecutive patients with second opinion radiology interpretations performed by subspecialty-trained breast radiologists at a dedicated cancer center July 1 through December 31, 2016. Of these, 205 included review of outside breast MRI. Outside imaging reports were compared with second opinion reports to categorize breast MRI review discrepancies. These included relevant BI-RADS category changes or identification of additional extent of disease >4 cm. The discrepancy frequency, relevant alterations in patient management, and incremental cancer detection were measured. Statistical analyses were performed using Fisher's exact test. RESULTS Discrepant second opinion breast MRI review was seen in 36 of 205 patients (18%). Additional cancer was detected through image-guided biopsy in 3 of these 36 patients and through excision in 2 (5 of 205, 2%). Additionally, five biopsies yielded high-risk pathologic results without upstage on excision. Findings suspicious for additional extent of disease >4 cm were noted in five patients (2%) treated with mastectomies. Finally, five patients had BI-RADS category downgrades. Ultimately, completion of second opinion MRI review recommendations resulted in altered management in 10% of patients (20 of 205). The absence of prior imaging studies for comparison was associated with increased discrepancy frequency (P = .005). CONCLUSION Second opinion breast MRI review by subspecialized breast imaging radiologists increases cancer detection and results in clinically relevant changes in patient management.
Collapse
|
26
|
Min H, Chandra SS, Crozier S, Bradley AP. Multi-scale sifting for mammographic mass detection and segmentation. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/aafc07] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
27
|
Henriksen EL, Carlsen JF, Vejborg IMM, Nielsen MB, Lauridsen CA. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol 2019; 60:13-18. [PMID: 29665706 DOI: 10.1177/0284185118770917] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Early detection of breast cancer (BC) is crucial in lowering the mortality. PURPOSE To present an overview of studies concerning computer-aided detection (CAD) in screening mammography for early detection of BC and compare diagnostic accuracy and recall rates (RR) of single reading (SR) with SR + CAD and double reading (DR) with SR + CAD. MATERIAL AND METHODS PRISMA guidelines were used as a review protocol. Articles on clinical trials concerning CAD for detection of BC in a screening population were included. The literature search resulted in 1522 records. A total of 1491 records were excluded by abstract and 18 were excluded by full text reading. A total of 13 articles were included. RESULTS All but two studies from the SR vs. SR + CAD group showed an increased sensitivity and/or cancer detection rate (CDR) when adding CAD. The DR vs. SR + CAD group showed no significant differences in sensitivity and CDR. Adding CAD to SR increased the RR and decreased the specificity in all but one study. For the DR vs. SR + CAD group only one study reported a significant difference in RR. CONCLUSION All but two studies showed an increase in RR, sensitivity and CDR when adding CAD to SR. Compared to DR no statistically significant differences in sensitivity or CDR were reported. Additional studies based on organized population-based screening programs, with longer follow-up time, high-volume readers, and digital mammography are needed to evaluate the efficacy of CAD.
Collapse
Affiliation(s)
- Emilie L Henriksen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of technology, Faculty of Health and Technology, Metropolitan University College, Copenhagen, Denmark
| | - Jonathan F Carlsen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Ilse MM Vejborg
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Michael B Nielsen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Carsten A Lauridsen
- Department of Diagnostic Radiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of technology, Faculty of Health and Technology, Metropolitan University College, Copenhagen, Denmark
| |
Collapse
|
28
|
Wender RC, Brawley OW, Fedewa SA, Gansler T, Smith RA. A blueprint for cancer screening and early detection: Advancing screening's contribution to cancer control. CA Cancer J Clin 2019; 69:50-79. [PMID: 30452086 DOI: 10.3322/caac.21550] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
From the mid-20th century, accumulating evidence has supported the introduction of screening for cancers of the cervix, breast, colon and rectum, prostate (via shared decisions), and lung. The opportunity to detect and treat precursor lesions and invasive disease at a more favorable stage has contributed substantially to reduced incidence, morbidity, and mortality. However, as new discoveries portend advancements in technology and risk-based screening, we fail to fulfill the greatest potential of the existing technology, in terms of both full access among the target population and the delivery of state-of-the art care at each crucial step in the cascade of events that characterize successful cancer screening. There also is insufficient commitment to invest in the development of new technologies, incentivize the development of new ideas, and rapidly evaluate promising new technology. In this report, the authors summarize the status of cancer screening and propose a blueprint for the nation to further advance the contribution of screening to cancer control.
Collapse
Affiliation(s)
- Richard C Wender
- Chief Cancer Control Officer, American Cancer Society, Atlanta, GA
| | - Otis W Brawley
- Chief Medical Officer, American Cancer Society, Atlanta, GA
| | - Stacey A Fedewa
- Senior Principal Scientist, Department of Surveillance Research, American Cancer Society, Atlanta, GA
| | - Ted Gansler
- Strategic Director of Pathology Research, American Cancer Society, Atlanta, GA
| | - Robert A Smith
- Vice-President, Cancer Screening, Cancer Control Department, and Director, Center for Quality Cancer Screening and Research, American Cancer Society Atlanta, GA
| |
Collapse
|
29
|
Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology 2018; 290:305-314. [PMID: 30457482 DOI: 10.1148/radiol.2018181371] [Citation(s) in RCA: 234] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.
Collapse
Affiliation(s)
- Alejandro Rodríguez-Ruiz
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Elizabeth Krupinski
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Jan-Jurre Mordang
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Kathy Schilling
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Sylvia H Heywang-Köbrunner
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Ioannis Sechopoulos
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Ritse M Mann
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (A.R.R., I.S., R.M.M.); Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga (E.K.); ScreenPoint Medical BV, Nijmegen, the Netherlands (J.J.M.); Lynn Women's Health & Wellness Institute, Boca Raton Regional Hospital, Boca Raton, Fla (K.S.); Referenzzentrum Mammographie Munich, Brustdiagnostik München and FFB, Munich, Germany (S.H.H.); and Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| |
Collapse
|
30
|
Katzen J, Dodelzon K. A review of computer aided detection in mammography. Clin Imaging 2018; 52:305-309. [PMID: 30216858 DOI: 10.1016/j.clinimag.2018.08.014] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 08/13/2018] [Accepted: 08/16/2018] [Indexed: 01/23/2023]
Abstract
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. It aims to decrease oversights made by interpreting radiologists. Here we review the efficacy of CAD and potential future directions.
Collapse
Affiliation(s)
- Janine Katzen
- Department of Radiology, Weill Cornell Medicine, 425 E 61st Street, New York, NY 10065, United States of America.
| | - Katerina Dodelzon
- Department of Radiology, Weill Cornell Medicine, 425 E 61st Street, New York, NY 10065, United States of America
| |
Collapse
|
31
|
Nawrocki T, Maldjian PD, Slasky SE, Contractor SG. Artificial Intelligence and Radiology: Have Rumors of the Radiologist's Demise Been Greatly Exaggerated? Acad Radiol 2018. [DOI: 10.1016/j.acra.2017.12.027] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
32
|
Kurvers RHJM, de Zoete A, Bachman SL, Algra PR, Ostelo R. Combining independent decisions increases diagnostic accuracy of reading lumbosacral radiographs and magnetic resonance imaging. PLoS One 2018; 13:e0194128. [PMID: 29614070 PMCID: PMC5882099 DOI: 10.1371/journal.pone.0194128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 02/26/2018] [Indexed: 11/18/2022] Open
Abstract
Diagnosing the causes of low back pain is a challenging task, prone to errors. A novel approach to increase diagnostic accuracy in medical decision making is collective intelligence, which refers to the ability of groups to outperform individual decision makers in solving problems. We investigated whether combining the independent ratings of chiropractors, chiropractic radiologists and medical radiologists can improve diagnostic accuracy when interpreting diagnostic images of the lumbosacral spine. Evaluations were obtained from two previously published studies: study 1 consisted of 13 raters independently rating 300 lumbosacral radiographs; study 2 consisted of 14 raters independently rating 100 lumbosacral magnetic resonance images. In both studies, raters evaluated the presence of "abnormalities", which are indicators of a serious health risk and warrant immediate further examination. We combined independent decisions of raters using a majority rule which takes as final diagnosis the decision of the majority of the group. We compared the performance of the majority rule to the performance of single raters. Our results show that with increasing group size (i.e., increasing the number of independent decisions) both sensitivity and specificity increased in both data-sets, with groups consistently outperforming single raters. These results were found for radiographs and MR image reading alike. Our findings suggest that combining independent ratings can improve the accuracy of lumbosacral diagnostic image reading.
Collapse
Affiliation(s)
- Ralf H. J. M. Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee, Berlin, Germany
- * E-mail:
| | - Annemarie de Zoete
- Department of Health Sciences, Amsterdam Public Health research institute, Vrije Universiteit, Amsterdam, the Netherlands
| | - Shelby L. Bachman
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee, Berlin, Germany
| | - Paul R. Algra
- Department of Radiology, Medical Centre Alkmaar, Alkmaar, the Netherlands
| | - Raymond Ostelo
- Department of Health Sciences, Amsterdam Public Health research institute, Vrije Universiteit, Amsterdam, the Netherlands
| |
Collapse
|
33
|
Concurrent Computer-Aided Detection Improves Reading Time of Digital Breast Tomosynthesis and Maintains Interpretation Performance in a Multireader Multicase Study. AJR Am J Roentgenol 2018; 210:685-694. [DOI: 10.2214/ajr.17.18185] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
34
|
Ekpo EU, Alakhras M, Brennan P. Errors in Mammography Cannot be Solved Through Technology Alone. Asian Pac J Cancer Prev 2018; 19:291-301. [PMID: 29479948 PMCID: PMC5980911 DOI: 10.22034/apjcp.2018.19.2.291] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2017] [Indexed: 12/18/2022] Open
Abstract
Mammography has been the frontline screening tool for breast cancer for decades. However, high error rates in the form of false negatives (FNs) and false positives (FPs) have persisted despite technological improvements. Radiologists still miss between 10% and 30% of cancers while 80% of woman recalled for additional views have normal outcomes, with 40% of biopsied lesions being benign. Research show that the majority of cancers missed is actually visible and looked at, but either go unnoticed or are deemed to be benign. Causal agents for these errors include human related characteristics resulting in contributory search, perception and decision-making behaviours. Technical, patient and lesion factors are also important relating to positioning, compression, patient size, breast density and presence of breast implants as well as the nature and subtype of the cancer itself, where features such as architectural distortion and triple-negative cancers remain challenging to detect on screening. A better understanding of these causal agents as well as the adoption of technological and educational interventions, which audits reader performance and provide immediate perceptual feedback, should help. This paper reviews the current status of our knowledge around error rates in mammography and explores the factors impacting it. It also presents potential solutions for maximizing diagnostic efficacy thus benefiting the millions of women who undergo this procedure each year.
Collapse
Affiliation(s)
- Ernest Usang Ekpo
- Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Sydney, Australia.
| | | | | |
Collapse
|
35
|
Simplified computer-aided detection scheme of microcalcification clusters in digital breast tomosynthesis images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1070-1073. [PMID: 28268510 DOI: 10.1109/embc.2016.7590888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A computer-aided detection (CADe) algorithm for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) images is suggested. The MC-like objects were enhanced by a Hessian-based 3D calcification response function, and a signal-to-noise ratio (SNR) enhanced image was also generated to screen the MC clustering seed objects. A connected component segmentation method was used to detect the cluster seed objects, which were considered as potential clustering centers of MCs. Bounding cubes for the accepted clustering seed candidate were generated and the overlapping cubes were combined and examined. After the MC clustering and false-positive (FP) reduction step, the average number of FPs was estimated to be 0.87 per DBT volume with a sensitivity of 90.5%.
Collapse
|
36
|
Aurora RN, Putcha N, Swartz R, Punjabi NM. Agreement Between Results of Home Sleep Testing for Obstructive Sleep Apnea with and Without a Sleep Specialist. Am J Med 2016; 129:725-30. [PMID: 26968467 PMCID: PMC4930550 DOI: 10.1016/j.amjmed.2016.02.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 11/24/2015] [Accepted: 02/04/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND Obstructive sleep apnea is a prevalent yet underdiagnosed condition associated with cardiovascular morbidity and mortality. Home sleep testing offers an efficient means for diagnosing obstructive sleep apnea but has been deployed primarily in clinical samples with a high pretest probability. The present study sought to assess whether obstructive sleep apnea can be diagnosed with home sleep testing in a nonreferred sample without involvement of a sleep medicine specialist. METHODS A study of community-based adults with untreated obstructive sleep apnea was undertaken. Misclassification of disease severity according to home sleep testing with and without involvement of a sleep medicine specialist was assessed, and agreement was characterized using scatter plots, Pearson's correlation coefficient, Bland-Altman analysis, and the κ statistic. Analyses were also conducted to assess whether any observed differences varied as a function of pretest probability of obstructive sleep apnea or subjective sleepiness. RESULTS The sample consisted of 191 subjects, with more than half (56.5%) having obstructive sleep apnea. Without involvement of a sleep medicine specialist, obstructive sleep apnea was not identified in only 5.8% of the sample. Analyses comparing the categorical assessment of disease severity with and without a sleep medicine specialist showed that in total, 32 subjects (16.8%) were misclassified. Agreement in the disease severity with and without a sleep medicine specialist was not influenced by the pretest probability or daytime sleep tendency. CONCLUSION Obstructive sleep apnea can be reliably identified with home sleep testing in a nonreferred sample, irrespective of the pretest probability of the disease.
Collapse
Affiliation(s)
- R Nisha Aurora
- Department of Medicine, Johns Hopkins University, Baltimore, Md
| | - Nirupama Putcha
- Department of Medicine, Johns Hopkins University, Baltimore, Md
| | - Rachel Swartz
- Department of Medicine, Johns Hopkins University, Baltimore, Md
| | - Naresh M Punjabi
- Department of Medicine, Johns Hopkins University, Baltimore, Md; Department of Epidemiology, Johns Hopkins University, Baltimore, Md.
| |
Collapse
|
37
|
Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8651573. [PMID: 27274993 PMCID: PMC4870350 DOI: 10.1155/2016/8651573] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 02/12/2016] [Accepted: 02/17/2016] [Indexed: 11/17/2022]
Abstract
We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.
Collapse
|
38
|
Pow RE, Mello-Thoms C, Brennan P. Evaluation of the effect of double reporting on test accuracy in screening and diagnostic imaging studies: A review of the evidence. J Med Imaging Radiat Oncol 2016; 60:306-14. [DOI: 10.1111/1754-9485.12450] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 02/26/2016] [Indexed: 12/01/2022]
Affiliation(s)
- Richard E Pow
- Medical Radiation Sciences; Faculty of Health Sciences; The University of Sydney; Sydney New South Wales Australia
| | - Claudia Mello-Thoms
- Medical Radiation Sciences; Faculty of Health Sciences; The University of Sydney; Sydney New South Wales Australia
| | - Patrick Brennan
- Medical Radiation Sciences; Faculty of Health Sciences; The University of Sydney; Sydney New South Wales Australia
| |
Collapse
|
39
|
Venkatesh SS, Levenback BJ, Sultan LR, Bouzghar G, Sehgal CM. Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis. ULTRASOUND IN MEDICINE & BIOLOGY 2015; 41:3148-3162. [PMID: 26354997 DOI: 10.1016/j.ultrasmedbio.2015.07.020] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Revised: 06/16/2015] [Accepted: 07/16/2015] [Indexed: 06/05/2023]
Abstract
The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods. AdaBoost also improved the diagnostic performance of the individual human observers and increased the agreement between their analyses. Pairing AdaBoost with selective pruning is a principled methodology for achieving high diagnostic performance without the added cost of an additional reader for differentiating solid breast masses by ultrasound.
Collapse
Affiliation(s)
- Santosh S Venkatesh
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Benjamin J Levenback
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laith R Sultan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ghizlane Bouzghar
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chandra M Sehgal
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| |
Collapse
|
40
|
Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. JAMA Intern Med 2015; 175:1828-37. [PMID: 26414882 PMCID: PMC4836172 DOI: 10.1001/jamainternmed.2015.5231] [Citation(s) in RCA: 348] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
IMPORTANCE After the US Food and Drug Administration (FDA) approved computer-aided detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid Services (CMS) provided increased payment in 2002, CAD technology disseminated rapidly. Despite sparse evidence that CAD improves accuracy of mammographic interpretations and costs over $400 million a year, CAD is currently used for most screening mammograms in the United States. OBJECTIVE To measure performance of digital screening mammography with and without CAD in US community practice. DESIGN, SETTING, AND PARTICIPANTS We compared the accuracy of digital screening mammography interpreted with (n = 495 818) vs without (n = 129 807) CAD from 2003 through 2009 in 323 973 women. Mammograms were interpreted by 271 radiologists from 66 facilities in the Breast Cancer Surveillance Consortium. Linkage with tumor registries identified 3159 breast cancers in 323 973 women within 1 year of the screening. MAIN OUTCOMES AND MEASURES Mammography performance (sensitivity, specificity, and screen-detected and interval cancers per 1000 women) was modeled using logistic regression with radiologist-specific random effects to account for correlation among examinations interpreted by the same radiologist, adjusting for patient age, race/ethnicity, time since prior mammogram, examination year, and registry. Conditional logistic regression was used to compare performance among 107 radiologists who interpreted mammograms both with and without CAD. RESULTS Screening performance was not improved with CAD on any metric assessed. Mammography sensitivity was 85.3% (95% CI, 83.6%-86.9%) with and 87.3% (95% CI, 84.5%-89.7%) without CAD. Specificity was 91.6% (95% CI, 91.0%-92.2%) with and 91.4% (95% CI, 90.6%-92.0%) without CAD. There was no difference in cancer detection rate (4.1 in 1000 women screened with and without CAD). Computer-aided detection did not improve intraradiologist performance. Sensitivity was significantly decreased for mammograms interpreted with vs without CAD in the subset of radiologists who interpreted both with and without CAD (odds ratio, 0.53; 95% CI, 0.29-0.97). CONCLUSIONS AND RELEVANCE Computer-aided detection does not improve diagnostic accuracy of mammography. These results suggest that insurers pay more for CAD with no established benefit to women.
Collapse
Affiliation(s)
| | | | | | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco
| | - Anna N A Tosteson
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Dartmouth College, Lebanon, New Hampshire
| | - Diana L Miglioretti
- Group Health Research Institute, Seattle, Washington5Department of Public Health Sciences, School of Medicine, University of California, Davis
| | | |
Collapse
|
41
|
Wolf M, Krause J, Carney PA, Bogart A, Kurvers RHJM. Collective intelligence meets medical decision-making: the collective outperforms the best radiologist. PLoS One 2015; 10:e0134269. [PMID: 26267331 PMCID: PMC4534443 DOI: 10.1371/journal.pone.0134269] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 07/07/2015] [Indexed: 11/24/2022] Open
Abstract
While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (“majority”, “quorum”, and “weighted quorum”) when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.
Collapse
Affiliation(s)
- Max Wolf
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587, Berlin, Germany
| | - Jens Krause
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587, Berlin, Germany; Faculty of Life Sciences, Humboldt-University of Berlin, Berlin, Germany
| | - Patricia A Carney
- Departments of Family Medicine and Pubic Health & Preventive Medicine, Knight Cancer Institute, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Portland, Oregon, United States of America
| | - Andy Bogart
- RAND Corporation, 1776 Main Street, Santa Monica, CA, 90407-2138, United States of America
| | - Ralf H J M Kurvers
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587, Berlin, Germany
| |
Collapse
|
42
|
Zyout I, Czajkowska J, Grzegorzek M. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography. Comput Med Imaging Graph 2015; 46 Pt 2:95-107. [PMID: 25795630 DOI: 10.1016/j.compmedimag.2015.02.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 01/24/2015] [Accepted: 02/16/2015] [Indexed: 10/23/2022]
Abstract
The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique.
Collapse
Affiliation(s)
- Imad Zyout
- Communications, Electronics and Computer Engineering Department, Tafila Technical University, Tafila 66110, Jordan.
| | - Joanna Czajkowska
- Department of Computer Science and Medical Equipment, Faculty of Biomedical Engineering, Silesian University of Technology, ul. Roosevelta 40, 41-800 Zabrze, Poland
| | - Marcin Grzegorzek
- Institute for Vision and Graphics, University of Siegen, Hoerlindstr. 3, 57076 Siegen, Germany
| |
Collapse
|
43
|
Bargalló X, Santamaría G, del Amo M, Arguis P, Ríos J, Grau J, Burrel M, Cores E, Velasco M. Single reading with computer-aided detection performed by selected radiologists in a breast cancer screening program. Eur J Radiol 2014; 83:2019-23. [DOI: 10.1016/j.ejrad.2014.08.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 08/13/2014] [Indexed: 10/24/2022]
|
44
|
Petrick N, Sahiner B, Armato SG, Bert A, Correale L, Delsanto S, Freedman MT, Fryd D, Gur D, Hadjiiski L, Huo Z, Jiang Y, Morra L, Paquerault S, Raykar V, Samuelson F, Summers RM, Tourassi G, Yoshida H, Zheng B, Zhou C, Chan HP. Evaluation of computer-aided detection and diagnosis systems. Med Phys 2014; 40:087001. [PMID: 23927365 DOI: 10.1118/1.4816310] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.
Collapse
Affiliation(s)
- Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland 20993, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
45
|
Levman J. Longitudinal disease detection rates for the evaluation of disease detection technologies with application in high-risk breast cancer screening. J Clin Diagn Res 2014; 7:2932-5. [PMID: 24551678 DOI: 10.7860/jcdr/2013/5693.3794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Accepted: 09/22/2013] [Indexed: 11/24/2022]
Abstract
CONTEXT This study presents a longitudinal simulation of disease screening at a variety of different test sensitivities. AIMS It is demonstrated that the difference between the performance of high quality tests and poor quality tests are relatively small in terms of the commonly used longitudinally measured disease detection rate. STATISTICAL ANALYSIS This simulation study is focused on the screening of patients at high-risk for breast cancer and thus used plausible rates of new cases of disease and initial disease prevalence for this population. RESULTS AND CONCLUSIONS The effects of varying the rate at which the disease enters the population and the initial disease prevalence is also discussed and was determined to not affect this study's conclusions regarding the inappropriateness of the use of the longitudinally measured disease detection rate for the evaluation of screening technologies.
Collapse
Affiliation(s)
- Jacob Levman
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK. Imaging Research, Sunnybrook Research Institute, University of Toronto , Toronto, Ontario, Canada
| |
Collapse
|
46
|
Bargalló X, Velasco M, Santamaría G, Del Amo M, Arguis P, Sánchez Gómez S. Role of computer-aided detection in very small screening detected invasive breast cancers. J Digit Imaging 2014; 26:572-7. [PMID: 23131867 DOI: 10.1007/s10278-012-9550-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
This study aims to assess computer-aided detection (CAD) performance with full-field digital mammography (FFDM) in very small (equal to or less than 1 cm) invasive breast cancers. Sixty-eight invasive breast cancers less than or equal to 1 cm were retrospectively studied. All cases were detected with FFDM in women aged 49-69 years from our breast cancer screening program. Radiological characteristics of lesions following BI-RADS descriptors were recorded and compared with CAD sensitivity. Age, size, BI-RADS classification, breast density type, histological type of the neoplasm, and role of the CAD were also assessed. Per-study specificity and mass false-positive rate were determined by using 100 normal consecutive studies. Thirty-seven (54.4 %) masses, 17 (25 %) calcifications, 6 (8.8 %) masses with calcifications, 7 (10.3 %) architectural distortions, and 1 asymmetry (1.5 %) were found. CAD showed an overall sensitivity of 86.7 % (masses, 86.5 %; calcifications, 100 %; masses with calcifications, 100 %; and architectural distortion, 57.14 %), CAD failed to detect 9 out of 68 cases: 5 of 37 masses, 3 of 7 architectural distortions, and 1 of 1 asymmetry. Fifteen out of 37 masses were hyperdense, and all of them were detected by CAD. No association was seen among mass morphology or margins and detectability. Per-study specificity and CAD false-positive rate was 26 % and 1.76 false marks per study. In conclusion, CAD shows a high sensitivity and a low specificity. Lesion size, histology, and breast density do not influence sensitivity. Mammographic features, mass density, and thickness of the spicules in architectural distortions do influence.
Collapse
Affiliation(s)
- Xavier Bargalló
- Department of Radiology (CDIC), Hospital Clínic de Barcelona, C/Villarroel,170, 08036, Barcelona, Spain.
| | | | | | | | | | | |
Collapse
|
47
|
Is full-field digital mammography more accurate than screen-film mammography in overall population screening? A systematic review and meta-analysis. Breast 2013; 22:217-24. [DOI: 10.1016/j.breast.2013.02.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Revised: 01/15/2013] [Accepted: 02/11/2013] [Indexed: 11/18/2022] Open
|
48
|
Antonios D, Dimitrios VA, Theoharis T. A new user-friendly visual environment for breast MRI data analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 110:411-423. [PMID: 23414601 DOI: 10.1016/j.cmpb.2012.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2012] [Revised: 12/29/2012] [Accepted: 12/30/2012] [Indexed: 06/01/2023]
Abstract
In this paper a novel, user friendly visual environment for Breast MRI Data Analysis is presented (BreDAn). Given planar MRI images before and after IV contrast medium injection, BreDAn generates kinematic graphs, color maps of signal increase and decrease and finally detects high risk breast areas. The advantage of BreDAn, which has been validated and tested successfully, is the automation of the radiodiagnostic process in an accurate and reliable manner. It can potentially facilitate radiologists' workload.
Collapse
Affiliation(s)
- Danelakis Antonios
- National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, University Campus, Ilissia, 15784 Athens, Greece.
| | | | | |
Collapse
|
49
|
Fenton JJ, Xing G, Elmore JG, Bang H, Chen SL, Lindfors KK, Baldwin LM. Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees. Ann Intern Med 2013; 158:580-7. [PMID: 23588746 PMCID: PMC3772716 DOI: 10.7326/0003-4819-158-8-201304160-00002] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Computer-aided detection (CAD) has rapidly diffused into screening mammography practice despite limited and conflicting data on its clinical effect. OBJECTIVE To determine associations between CAD use during screening mammography and the incidence of ductal carcinoma in situ (DCIS) and invasive breast cancer, invasive cancer stage, and diagnostic testing. DESIGN Retrospective cohort study. SETTING Medicare program. PARTICIPANTS Women aged 67 to 89 years having screening mammography between 2001 and 2006 in U.S. SEER (Surveillance, Epidemiology and End Results) regions (409 459 mammograms from 163 099 women). MEASUREMENTS Incident DCIS and invasive breast cancer within 1 year after mammography, invasive cancer stage, and diagnostic testing within 90 days after screening among women without breast cancer. RESULTS From 2001 to 2006, CAD prevalence increased from 3.6% to 60.5%. Use of CAD was associated with greater DCIS incidence (adjusted odds ratio [OR], 1.17 [95% CI, 1.11 to 1.23]) but no difference in invasive breast cancer incidence (adjusted OR, 1.00 [CI, 0.97 to 1.03]). Among women with invasive cancer, CAD was associated with greater likelihood of stage I to II versus III to IV cancer (adjusted OR, 1.27 [CI, 1.14 to 1.41]). In women without breast cancer, CAD was associated with increased odds of diagnostic mammography (adjusted OR, 1.28 [CI, 1.27 to 1.29]), breast ultrasonography (adjusted OR, 1.07 [CI, 1.06 to 1.09]), and breast biopsy (adjusted OR, 1.10 [CI, 1.08 to 1.12]). LIMITATION Short follow-up for cancer stage, potential unmeasured confounding, and uncertain generalizability to younger women. CONCLUSION Use of CAD during screening mammography among Medicare enrollees is associated with increased DCIS incidence, the diagnosis of invasive breast cancer at earlier stages, and increased diagnostic testing among women without breast cancer. PRIMARY FUNDING SOURCE Center for Healthcare Policy and Research, University of California, Davis.
Collapse
Affiliation(s)
- Joshua J Fenton
- University of California, Davis, Department of Family and Community Medicine, 4860 Y Street, Suite 2300, Sacramento, CA 95817, USA.
| | | | | | | | | | | | | |
Collapse
|
50
|
Krontiras H, Bramlett R, Umphrey H. How do I screen patients for breast cancer? Curr Treat Options Oncol 2013; 14:88-96. [PMID: 23315271 DOI: 10.1007/s11864-012-0218-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Breast cancer is a complex, heterogeneous disease. Approximately 230,000 women are diagnosed with breast cancer in the United States each year and approximately 40,000 women die each year with breast cancer. Although prevention of the disease would be preferred, no real prospects are available in the near future that would be applicable to the majority of women who are at risk for breast cancer. Early detection remains an effective way to decrease mortality from breast cancer, treating it at an early stage when it is likely curable. Unfortunately, screening does have its limitations. Not all breast cancers can be identified with routine screening. Some breast cancers despite early detection still result in poor outcomes. Furthermore, false-positive results are not infrequently seen in women undergoing screening mammography. Most patients experience significant anxiety when called back for additional studies or a biopsy. Not to mention the additional cost and potential side effects and complications of invasive procedures. In addition, there are breast cancers that may be indolent and otherwise not a threat to patients. In fact, some studies show that up to one quarter of cancers detected by screening may represent overdiagnosis. Currently, however, there are no proven methods to discern with complete certainty the cancers that would progress to lethal disease from those that would not. Women should be counseled regarding the risks and benefits of screening. Women at average risk should initiate screening mammography annually at the age of 40 years. Women at significant increased risk for breast cancer should be screened earlier. MRI has been shown to increase detection of breast cancer in women at increased risk and should be used as an adjunct to mammography in this high-risk patient population.
Collapse
Affiliation(s)
- Helen Krontiras
- Department of Surgery, Section of Surgical Oncology, University of Alabama at Birmingham, 1922 7th Avenue South KB 321, Birmingham, AL 35294, USA.
| | | | | |
Collapse
|