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Yu ZH, Hong YT, Chou CP. Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1372-1380. [PMID: 38897841 DOI: 10.1016/j.ultrasmedbio.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 05/12/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. MATERIALS AND METHODS This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. RESULTS The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830-0.917 vs. 0.792, 95% CI: 0.748-0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763-0.932 vs. 0.770, 95% CI: 0.709-0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. CONCLUSION The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations.
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Affiliation(s)
- Zi-Han Yu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Radiology, Jiannren Hospital, Kaohsiung, Taiwan
| | - Yu-Ting Hong
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chen-Pin Chou
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Fooyin University, Kaohsiung, Taiwan; Department of Pharmacy, College of Pharmacy, Tajen University, Pingtung, Taiwan.
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Resch D, Lo Gullo R, Teuwen J, Semturs F, Hummel J, Resch A, Pinker K. AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance. Radiol Imaging Cancer 2024; 6:e230149. [PMID: 38995172 PMCID: PMC11287230 DOI: 10.1148/rycan.230149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 04/23/2024] [Accepted: 05/30/2024] [Indexed: 07/13/2024]
Abstract
Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.
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Affiliation(s)
| | | | - Jonas Teuwen
- From the Department of Biomedical Imaging and Image-guided Therapy,
Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
(D.R.); Department of Radiology, Breast Imaging Service, Memorial
Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical
Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
(F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud
University Medical School, Vienna, Austria (A.R.); and Department of Radiology,
Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort
Washington Ave, New York, NY 10032 (K.P.)
| | - Friedrich Semturs
- From the Department of Biomedical Imaging and Image-guided Therapy,
Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
(D.R.); Department of Radiology, Breast Imaging Service, Memorial
Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical
Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
(F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud
University Medical School, Vienna, Austria (A.R.); and Department of Radiology,
Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort
Washington Ave, New York, NY 10032 (K.P.)
| | - Johann Hummel
- From the Department of Biomedical Imaging and Image-guided Therapy,
Division of Molecular and Gender Imaging, Medical University of Vienna, Austria
(D.R.); Department of Radiology, Breast Imaging Service, Memorial
Sloan-Kettering Cancer Center, New York, NY (R.L.G., J.T.); Center for Medical
Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
(F.S., J.H.); St Francis Hospital Vienna, Vienna, Austria (A.R.); Sigmund Freud
University Medical School, Vienna, Austria (A.R.); and Department of Radiology,
Division of Breast Imaging, Columbia University Irving Medical Center, 161 Fort
Washington Ave, New York, NY 10032 (K.P.)
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Iqbal U, Hsu YHE, Celi LA, Li YCJ. Artificial intelligence in healthcare: Opportunities come with landmines. BMJ Health Care Inform 2024; 31:e101086. [PMID: 38839426 PMCID: PMC11163668 DOI: 10.1136/bmjhci-2024-101086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 06/07/2024] Open
Affiliation(s)
- Usman Iqbal
- School of Population Health, Faculty of Medicine and Health, University of New South Wales (UNSW), Sydney, NSW, Australia
- Global Health and Health Security Department, College of Public Health, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information and Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Yi-Hsin Elsa Hsu
- Biotechnology Executive Master's Degree in Business Administration (BioTech EMBA), Taipei Medical University, Taipei, Taiwan
- School of Healthcare Administration, College of Management, Taipei Medical University, Taipei, Taiwan
- International Ph.D. Program in BioTech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
- Department of Humanities in Medicine, College of Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yu-Chuan Jack Li
- International Center for Health Information and Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science & Technology, Taipei Medical University, Taipei, Taiwan
- Department of Dermatology, Taipei Municipal Wanfang Hospital, Taipei, Taiwan
- The International Medical Informatics Association (IMIA), Zürich, Switzerland
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Waugh J, Evans J, Miocevic M, Lockie D, Aminzadeh P, Lynch A, Bell RJ. Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program. Eur Radiol 2024; 34:3947-3957. [PMID: 37955669 PMCID: PMC11166844 DOI: 10.1007/s00330-023-10396-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/15/2023] [Accepted: 09/05/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results. METHODS A total of 7533 prevalent round mammograms from 2017 were available for analysis. The AI program classified mammograms into deciles on the basis of breast cancer (BC) risk. BC diagnoses, including invasive BC (IBC) and ductal carcinoma in situ (DCIS), included those from the prevalent round, interval cancers, and cancers identified in the subsequent screening round two years later. Performance was assessed by sensitivity, specificity, positive and negative predictive values, and the proportion of women recalled by the radiologists and identified as higher risk by AI. RESULTS Radiologists identified 54 women with IBC and 13 with DCIS with a recall rate of 9.7%. In contrast, 51 of 54 of the IBCs and 12/13 cases of DCIS were within the higher AI score group (score 10), a recall equivalent of 10.6% (a difference of 0.9% (CI -0.03 to 1.89%, p = 0.06). When IBCs were identified in the 2017 round, interval cancers classified as false negatives or with minimal signs in 2017, and cancers from the 2019 round were combined, the radiologists identified 54/67 and 59/67 were in the highest risk AI category (sensitivity 80.6% and 88.06 % respectively, a difference that was not different statistically). CONCLUSIONS As the performance of AI was comparable to that of expert radiologists, future AI roles in screening could include replacing one reader and supporting arbitration, reducing workload and false positive results. CLINICAL RELEVANCE STATEMENT AI analysis of consecutive prevalent screening mammograms from the Australian BreastScreen program demonstrated the algorithm's ability to match the cancer detection of experienced radiologists, additionally identifying five interval cancers (false negatives), and the majority of the false positive recalls. KEY POINTS • The AI program was almost as sensitive as the radiologists in terms of identifying prevalent lesions (51/54 for invasive breast cancer, 63/67 when including ductal carcinoma in situ). • If selected interval cancers and cancers identified in the subsequent screening round were included, the AI program identified more cancers than the radiologists (59/67 compared with 54/67, sensitivity 88.06 % and 80.6% respectively p = 0.24). • The high negative predictive value of a score of 1-9 would indicate a role for AI as a triage tool to reduce the recall rate (specifically false positives).
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Affiliation(s)
- John Waugh
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia.
| | - Jill Evans
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Miranda Miocevic
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Darren Lockie
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Parisa Aminzadeh
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Anne Lynch
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Robin J Bell
- Women's Health Research Program, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
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Nicolis O, De Los Angeles D, Taramasco C. A contemporary review of breast cancer risk factors and the role of artificial intelligence. Front Oncol 2024; 14:1356014. [PMID: 38699635 PMCID: PMC11063273 DOI: 10.3389/fonc.2024.1356014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/25/2024] [Indexed: 05/05/2024] Open
Abstract
Background Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics. Methods A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses. Results Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies. Conclusion The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.
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Affiliation(s)
- Orietta Nicolis
- Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
- Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile
| | - Denisse De Los Angeles
- Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
- Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile
| | - Carla Taramasco
- Engineering Faculty, Universidad Andres Bello, Viña del Mar, Chile
- Centro para la Prevención y Control del Cáncer (CECAN), Santiago, Chile
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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Pinker K. Implementing AI in breast imaging: challenges to turn the gadget into gain. Eur Radiol 2024; 34:2093-2095. [PMID: 37667145 DOI: 10.1007/s00330-023-10205-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 09/06/2023]
Affiliation(s)
- Katja Pinker
- Department of Radiology - Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66Th Street, Room 707, New York, NY, 10065, USA.
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Pruski M. What does it mean for a clinical AI to be just: conflicts between local fairness and being fit-for-purpose? JOURNAL OF MEDICAL ETHICS 2024:jme-2023-109675. [PMID: 38423759 DOI: 10.1136/jme-2023-109675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
There have been repeated calls to ensure that clinical artificial intelligence (AI) is not discriminatory, that is, it provides its intended benefit to all members of society irrespective of the status of any protected characteristics of individuals in whose healthcare the AI might participate. There have also been repeated calls to ensure that any clinical AI is tailored to the local population in which it is being used to ensure that it is fit-for-purpose. Yet, there might be a clash between these two calls since tailoring an AI to a local population might reduce its effectiveness when the AI is used in the care of individuals who have characteristics which are not represented in the local population. Here, I explore the bioethical concept of local fairness as applied to clinical AI. I first introduce the discussion concerning fairness and inequalities in healthcare and how this problem has continued in attempts to develop AI-enhanced healthcare. I then discuss various technical aspects which might affect the implementation of local fairness. Next, I introduce some rule of law considerations into the discussion to contextualise the issue better by drawing key parallels. I then discuss some potential technical solutions which have been proposed to address the issue of local fairness. Finally, I outline which solutions I consider most likely to contribute to a fit-for-purpose and fair AI.
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Affiliation(s)
- Michal Pruski
- Department of Medical Physics and Clinical Engineering, Cardiff and Vale UHB, Cardiff, UK
- School of Health Sciences, The University of Manchester, Manchester, UK
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Mackenzie A, Lewis E, Loveland J. Successes and challenges in extracting information from DICOM image databases for audit and research. Br J Radiol 2023; 96:20230104. [PMID: 37698251 PMCID: PMC10607388 DOI: 10.1259/bjr.20230104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 09/13/2023] Open
Abstract
In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.
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Affiliation(s)
| | | | - John Loveland
- NCCPM, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
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11
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [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: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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Dembrower K, Crippa A, Colón E, Eklund M, Strand F. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit Health 2023; 5:e703-e711. [PMID: 37690911 DOI: 10.1016/s2589-7500(23)00153-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/21/2023] [Accepted: 07/28/2023] [Indexed: 09/12/2023]
Abstract
BACKGROUND Artificial intelligence (AI) as an independent reader of screening mammograms has shown promise, but there are few prospective studies. Our aim was to conduct a prospective clinical trial to examine how AI affects cancer detection and false positive findings in a real-world setting. METHODS ScreenTrustCAD was a prospective, population-based, paired-reader, non-inferiority study done at the Capio Sankt Göran Hospital in Stockholm, Sweden. Consecutive women without breast implants aged 40-74 years participating in population-based screening in the geographical uptake area of the study hospital were included. The primary outcome was screen-detected breast cancer within 3 months of mammography, and the primary analysis was to assess non-inferiority (non-inferiority margin of 0·15 relative reduction in breast cancer diagnoses) of double reading by one radiologist plus AI compared with standard-of-care double reading by two radiologists. We also assessed single reading by AI alone and triple reading by two radiologists plus AI compared with standard-of-care double reading by two radiologists. This study is registered with ClinicalTrials.gov, NCT04778670. FINDINGS From April 1, 2021, to June 9, 2022, 58 344 women aged 40-74 years underwent regular mammography screening, of whom 55 581 were included in the study. 269 (0·5%) women were diagnosed with screen-detected breast cancer based on an initial positive read: double reading by one radiologist plus AI was non-inferior for cancer detection compared with double reading by two radiologists (261 [0·5%] vs 250 [0·4%] detected cases; relative proportion 1·04 [95% CI 1·00-1·09]). Single reading by AI (246 [0·4%] vs 250 [0·4%] detected cases; relative proportion 0·98 [0·93-1·04]) and triple reading by two radiologists plus AI (269 [0·5%] vs 250 [0·4%] detected cases; relative proportion 1·08 [1·04-1·11]) were also non-inferior to double reading by two radiologists. INTERPRETATION Replacing one radiologist with AI for independent reading of screening mammograms resulted in a 4% higher non-inferior cancer detection rate compared with radiologist double reading. Our study suggests that AI in the study setting has potential for controlled implementation, which would include risk management and real-world follow-up of performance. FUNDING Swedish Research Council, Swedish Cancer Society, Region Stockholm, and Lunit.
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Affiliation(s)
- Karin Dembrower
- Breast Imaging Unit, Department of Radiology, Capio Sankt Göran Hospital, Sankt Göransplan, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
| | - Alessio Crippa
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Eugenia Colón
- Department of Pathology, Unilabs, Capio Sankt Göran Hospital, Sankt Göransplan, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Strand
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Breast Radiology Unit, Medical Diagnostics Karolinska, Karolinska University Hospital, Stockholm, Sweden
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Bhowmik A, Monga N, Belen K, Varela K, Sevilimedu V, Thakur SB, Martinez DF, Sutton EJ, Pinker K, Eskreis-Winkler S. Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model. Invest Radiol 2023; 58:710-719. [PMID: 37058323 DOI: 10.1097/rli.0000000000000976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
OBJECTIVES The aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers. MATERIALS AND METHODS In this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists. RESULTS In the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool. CONCLUSIONS Our automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.
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Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel) 2023; 13:2760. [PMID: 37685300 PMCID: PMC10487271 DOI: 10.3390/diagnostics13172760] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 09/10/2023] Open
Abstract
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
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Affiliation(s)
- Reabal Najjar
- Canberra Health Services, Australian Capital Territory 2605, Australia
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Asif A, Rajpoot K, Graham S, Snead D, Minhas F, Rajpoot N. Unleashing the potential of AI for pathology: challenges and recommendations. J Pathol 2023; 260:564-577. [PMID: 37550878 PMCID: PMC10952719 DOI: 10.1002/path.6168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 08/09/2023]
Abstract
Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Amina Asif
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
| | - Kashif Rajpoot
- School of Computer ScienceUniversity of BirminghamBirminghamUK
| | - Simon Graham
- Histofy Ltd, Birmingham Business ParkBirminghamUK
| | - David Snead
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Department of PathologyUniversity Hospitals Coventry & Warwickshire NHS TrustCoventryUK
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Cancer Research CentreUniversity of WarwickCoventryUK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, Department of Computer ScienceUniversity of WarwickCoventryUK
- Histofy Ltd, Birmingham Business ParkBirminghamUK
- Cancer Research CentreUniversity of WarwickCoventryUK
- The Alan Turing InstituteLondonUK
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16
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Giorgi Rossi P. Excluding specificity from research and development priorities is delaying the AI adoption in breast cancer screening. Eur Radiol 2023; 33:4597-4599. [PMID: 37219621 DOI: 10.1007/s00330-023-09733-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/24/2023]
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17
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Du RC, Ouyang YB, Hu Y. Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022. World J Gastroenterol 2023; 29:3561-3573. [PMID: 37389238 PMCID: PMC10303508 DOI: 10.3748/wjg.v29.i22.3561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/03/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
BACKGROUND Recently, artificial intelligence (AI) has been widely used in gastrointestinal endoscopy examinations.
AIM To comprehensively evaluate the application of AI-assisted endoscopy in detecting different digestive diseases using bibliometric analysis.
METHODS Relevant publications from the Web of Science published from 1990 to 2022 were extracted using a combination of the search terms “AI” and “endoscopy”. The following information was recorded from the included publications: Title, author, institution, country, endoscopy type, disease type, performance of AI, publication, citation, journal and H-index.
RESULTS A total of 446 studies were included. The number of articles reached its peak in 2021, and the annual citation numbers increased after 2006. China, the United States and Japan were dominant countries in this field, accounting for 28.7%, 16.8%, and 15.7% of publications, respectively. The Tada Tomohiro Institute of Gastroenterology and Proctology was the most influential institution. “Cancer” and “polyps” were the hotspots in this field. Colorectal polyps were the most concerning and researched disease, followed by gastric cancer and gastrointestinal bleeding. Conventional endoscopy was the most common type of examination. The accuracy of AI in detecting Barrett’s esophagus, colorectal polyps and gastric cancer from 2018 to 2022 is 87.6%, 93.7% and 88.3%, respectively. The detection rates of adenoma and gastrointestinal bleeding from 2018 to 2022 are 31.3% and 96.2%, respectively.
CONCLUSION AI could improve the detection rate of digestive tract diseases and a convolutional neural network-based diagnosis program for endoscopic images shows promising results.
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Affiliation(s)
- Ren-Chun Du
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Yao-Bin Ouyang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, United States
| | - Yi Hu
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong 999077, China
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18
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Ozcan BB, Patel BK, Banerjee I, Dogan BE. Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice. JOURNAL OF BREAST IMAGING 2023; 5:248-257. [PMID: 38416888 DOI: 10.1093/jbi/wbad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 03/01/2024]
Abstract
Artificial intelligence (AI) in breast imaging is a rapidly developing field with promising results. Despite the large number of recent publications in this field, unanswered questions have led to limited implementation of AI into daily clinical practice for breast radiologists. This paper provides an overview of the key limitations of AI in breast imaging including, but not limited to, limited numbers of FDA-approved algorithms and annotated data sets with histologic ground truth; concerns surrounding data privacy, security, algorithm transparency, and bias; and ethical issues. Ultimately, the successful implementation of AI into clinical care will require thoughtful action to address these challenges, transparency, and sharing of AI implementation workflows, limitations, and performance metrics within the breast imaging community and other end-users.
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Affiliation(s)
- B Bersu Ozcan
- The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA
| | | | - Imon Banerjee
- Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA
| | - Basak E Dogan
- The University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX, USA
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Ng AY, Glocker B, Oberije C, Fox G, Sharma N, James JJ, Ambrózay É, Nash J, Karpati E, Kerruish S, Kecskemethy PD. Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload. JOURNAL OF BREAST IMAGING 2023; 5:267-276. [PMID: 38416889 DOI: 10.1093/jbi/wbad010] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Indexed: 03/01/2024]
Abstract
OBJECTIVE To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. METHODS Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. RESULTS Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). CONCLUSION The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.
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Affiliation(s)
- Annie Y Ng
- Kheiron Medical Technologies, London, UK
| | - Ben Glocker
- Kheiron Medical Technologies, London, UK
- Imperial College London, Department of Computing, London, UK
| | | | | | - Nisha Sharma
- Leeds Teaching Hospital NHS Trust, Department of Radiology, Leeds, UK
| | - Jonathan J James
- Nottingham University Hospitals NHS Trust, Nottingham Breast Institute, Nottingham, UK
| | - Éva Ambrózay
- MaMMa Egészségügyi Zrt., Breast Diagnostic Department, Kecskemét, Hungary
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20
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Trepanier C, Huang A, Liu M, Ha R. Emerging uses of artificial intelligence in breast and axillary ultrasound. Clin Imaging 2023; 100:64-68. [PMID: 37243994 DOI: 10.1016/j.clinimag.2023.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/02/2023] [Indexed: 05/29/2023]
Abstract
Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also plays an important role in staging breast cancer by assessing axillary lymph nodes. However, its utility is limited by operator dependence, high recall rate, low positive predictive value and low specificity. These limitations present an opportunity for artificial intelligence (AI) to improve diagnostic performance and pioneer novel uses of ultrasound. Research in developing AI for radiology has flourished over the past few years. A subset of AI, deep learning, uses interconnected computational nodes to form a neural network, which extracts complex visual features from image data to train itself into a predictive model. This review summarizes several key studies evaluating AI programs' performance in predicting breast cancer and demonstrates that AI can assist radiologists and address limitations of ultrasound by acting as a decision support tool. This review also touches on how AI programs allow for novel predictive uses of ultrasound, particularly predicting molecular subtypes of breast cancer and response to neoadjuvant chemotherapy, which have the potential to change how breast cancer is managed by providing non-invasive prognostic and treatment data from ultrasound images. Lastly, this review explores how AI programs demonstrate improved diagnostic accuracy in predicting axillary lymph node metastasis. The limitations and future challenges in developing and implementing AI for breast and axillary ultrasound will also be discussed.
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Affiliation(s)
- Christopher Trepanier
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| | - Alice Huang
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| | - Michael Liu
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| | - Richard Ha
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
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21
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: (S.S.-A.); (F.B.M.)
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
- Correspondence: (S.S.-A.); (F.B.M.)
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Massafra R, Fanizzi A, Amoroso N, Bove S, Comes MC, Pomarico D, Didonna V, Diotaiuti S, Galati L, Giotta F, La Forgia D, Latorre A, Lombardi A, Nardone A, Pastena MI, Ressa CM, Rinaldi L, Tamborra P, Zito A, Paradiso AV, Bellotti R, Lorusso V. Analyzing breast cancer invasive disease event classification through explainable artificial intelligence. Front Med (Lausanne) 2023; 10:1116354. [PMID: 36817766 PMCID: PMC9932275 DOI: 10.3389/fmed.2023.1116354] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/13/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable. Methods Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis. Results Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames. Discussion Thus, our framework aims at shortening the distance between AI and clinical practice.
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Affiliation(s)
| | | | - Nicola Amoroso
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Domenico Pomarico
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | | | | | - Luisa Galati
- International Agency for Research on Cancer, Lyon, France
| | | | | | | | - Angela Lombardi
- Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, Bari, Italy
| | | | | | | | - Lucia Rinaldi
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
| | | | - Roberto Bellotti
- INFN, Sezione di Bari, Bari, Italy
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Vito Lorusso
- IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
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Vedantham S, Shazeeb MS, Chiang A, Vijayaraghavan GR. Artificial Intelligence in Breast X-Ray Imaging. Semin Ultrasound CT MR 2023; 44:2-7. [PMID: 36792270 PMCID: PMC9932302 DOI: 10.1053/j.sult.2022.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists' reading time. AI can also serve as a potential second 'reader' during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.
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Affiliation(s)
| | | | - Alan Chiang
- Department of Medical Imaging, University of Arizona, Tucson, AZ
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Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model. Tomography 2023; 9:105-129. [PMID: 36648997 PMCID: PMC9844448 DOI: 10.3390/tomography9010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/08/2023] [Accepted: 01/09/2023] [Indexed: 01/14/2023] Open
Abstract
Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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Magni V, Cozzi A, Schiaffino S, Colarieti A, Sardanelli F. Artificial intelligence for digital breast tomosynthesis: Impact on diagnostic performance, reading times, and workload in the era of personalized screening. Eur J Radiol 2023; 158:110631. [PMID: 36481480 DOI: 10.1016/j.ejrad.2022.110631] [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: 11/07/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022]
Abstract
The ultimate goals of the application of artificial intelligence (AI) to digital breast tomosynthesis (DBT) are the reduction of reading times, the increase of diagnostic performance, and the reduction of interval cancer rates. In this review, after outlining the journey from computer-aided detection/diagnosis systems to AI applied to digital mammography (DM), we summarize the results of studies where AI was applied to DBT, noting that long-term advantages of DBT screening and its crucial ability to decrease the interval cancer rate are still under scrutiny. AI has shown the capability to overcome some shortcomings of DBT in the screening setting by improving diagnostic performance and by reducing recall rates (from -2 % to -27 %) and reading times (up to -53 %, with an average 20 % reduction), but the ability of AI to reduce interval cancer rates has not yet been clearly investigated. Prospective validation is needed to assess the cost-effectiveness and real-world impact of AI models assisting DBT interpretation, especially in large-scale studies with low breast cancer prevalence. Finally, we focus on the incoming era of personalized and risk-stratified screening that will first see the application of contrast-enhanced breast imaging to screen women with extremely dense breasts. As the diagnostic advantage of DBT over DM was concentrated in this category, we try to understand if the application of AI to DM in the remaining cohorts of women with heterogeneously dense or non-dense breast could close the gap in diagnostic performance between DM and DBT, thus neutralizing the usefulness of AI application to DBT.
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Affiliation(s)
- Veronica Magni
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy.
| | - Andrea Cozzi
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Anna Colarieti
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy; Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy.
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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Baughan N, Douglas L, Giger ML. Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening. JOURNAL OF BREAST IMAGING 2022; 4:451-459. [PMID: 38416954 DOI: 10.1093/jbi/wbac052] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Indexed: 03/01/2024]
Abstract
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
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Affiliation(s)
- Natalie Baughan
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Maryellen L Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
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29
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Syed AH, Khan T. Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis. Front Oncol 2022; 12:854927. [PMID: 36267967 PMCID: PMC9578338 DOI: 10.3389/fonc.2022.854927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 08/30/2022] [Indexed: 01/27/2023] Open
Abstract
Objective In recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis. Methodology Therefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work. Results The present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified. Conclusion The present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.
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Affiliation(s)
- Asif Hassan Syed
- Department of Computer Science, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia,*Correspondence: Asif Hassan Syed,
| | - Tabrej Khan
- Department of Information Systems, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
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30
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Bell RJ. Changes to health screening - we need to remain vigilant. Climacteric 2022; 25:319-320. [PMID: 35801652 DOI: 10.1080/13697137.2022.2073812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Taylor-Phillips S, Seedat F, Kijauskaite G, Marshall J, Halligan S, Hyde C, Given-Wilson R, Wilkinson L, Denniston AK, Glocker B, Garrett P, Mackie A, Steele RJ. UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screening. Lancet Digit Health 2022; 4:e558-e565. [PMID: 35750402 DOI: 10.1016/s2589-7500(22)00088-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 03/04/2022] [Accepted: 04/06/2022] [Indexed: 10/18/2022]
Abstract
Artificial intelligence (AI) could have the potential to accurately classify mammograms according to the presence or absence of radiological signs of breast cancer, replacing or supplementing human readers (radiologists). The UK National Screening Committee's assessments of the use of AI systems to examine screening mammograms continues to focus on maximising benefits and minimising harms to women screened, when deciding whether to recommend the implementation of AI into the Breast Screening Programme in the UK. Maintaining or improving programme specificity is important to minimise anxiety from false positive results. When considering cancer detection, AI test sensitivity alone is not sufficiently informative, and additional information on the spectrum of disease detected and interval cancers is crucial to better understand the benefits and harms of screening. Although large retrospective studies might provide useful evidence by directly comparing test accuracy and spectrum of disease detected between different AI systems and by population subgroup, most retrospective studies are biased due to differential verification (ie, the use of different reference standards to verify the target condition among study participants). Enriched, multiple-reader, multiple-case, test set laboratory studies are also biased due to the laboratory effect (ie, radiologists' performance in retrospective, laboratory, observer studies is substantially different to their performance in a clinical environment). Therefore, assessment of the effect of incorporating any AI system into the breast screening pathway in prospective studies is required as it will provide key evidence for the effect of the interaction of medical staff with AI, and the impact on women's outcomes.
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Affiliation(s)
| | - Farah Seedat
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Goda Kijauskaite
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - John Marshall
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Steve Halligan
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Chris Hyde
- Exeter Test Group, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | | | - Alastair K Denniston
- Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Peter Garrett
- Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester, UK
| | - Anne Mackie
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Robert J Steele
- Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
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Homayoun H, Chan WY, Kuzan TY, Leong WL, Altintoprak KM, Mohammadi A, Vijayananthan A, Rahmat K, Leong SS, Mirza-Aghazadeh-Attari M, Ejtehadifar S, Faeghi F, Acharya UR, Ardakani AA. Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wardlaw JM, Mair G, von Kummer R, Williams MC, Li W, Storkey AJ, Trucco E, Liebeskind DS, Farrall A, Bath PM, White P. Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke 2022; 53:2393-2403. [PMID: 35440170 DOI: 10.1161/strokeaha.121.036204] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.
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Affiliation(s)
- Joanna M Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Grant Mair
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Rüdiger von Kummer
- Institute of Diagnostic and Interventional Neuroradiology, Universitätsklinikum Carl Gustav Carus, Dresden, Germany (R.v.K.)
| | - Michelle C Williams
- Centre for Cardiovascular Science, University of Edinburgh, Little France, United Kingdom (M.C.W.)
| | - Wenwen Li
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | | | - Emanuel Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee (E.T.)
| | | | - Andrew Farrall
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Little France, United Kingdom (J.M.W., G.M., W.L., A.F.)
| | - Philip M Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Queen's Medical Centre campus, United Kingdom (P.M.B.)
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne and Newcastle upon Tyne Hospitals NHS Trust, United Kingdom (P.W.)
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Larsen M, Aglen CF, Hoff SR, Lund-Hanssen H, Hofvind S. Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations. Eur Radiol 2022; 32:8238-8246. [PMID: 35704111 DOI: 10.1007/s00330-022-08909-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 12/09/2022]
Abstract
OBJECTIVES Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists in screen-reading. METHODS A total of 122,969 digital screening examinations performed between 2009 and 2018 in BreastScreen Norway were retrospectively processed by an AI system, which scored the examinations from 1 to 10; 1 indicated low suspicion of malignancy and 10 high suspicion. Results were merged with information about screening outcome and used to explore consensus, recall, and cancer detection for 11 different scenarios of combining AI and radiologists. RESULTS Recall was 3.2%, screen-detected cancer 0.61% and interval cancer 0.17% after independent double reading and served as reference values. In a scenario where examinations with AI scores 1-5 were considered negative and 6-10 resulted in standard independent double reading, the estimated recall was 2.6% and screen-detected cancer 0.60%. When scores 1-9 were considered negative and score 10 double read, recall was 1.2% and screen-detected cancer 0.53%. In these two scenarios, potential rates of screen-detected cancer could be up to 0.63% and 0.56%, if the interval cancers selected for consensus were detected at screening. In the former scenario, screen-reading volume would be reduced by 50%, while the latter would reduce the volume by 90%. CONCLUSION Several theoretical scenarios with AI and radiologists have the potential to reduce the volume in screen-reading without affecting cancer detection substantially. Possible influence on recall and interval cancers must be evaluated in prospective studies. KEY POINTS • Different scenarios using artificial intelligence in combination with radiologists could reduce the screen-reading volume by 50% and result in a rate of screen-detected cancer ranging from 0.59% to 0.60%, compared to 0.61% after standard independent double reading • The use of artificial intelligence in combination with radiologists has the potential to identify negative screening examinations with high precision in mammographic screening and to reduce the rate of interval cancer.
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Affiliation(s)
- Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Camilla F Aglen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Solveig R Hoff
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Håkon Lund-Hanssen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway. .,Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway.
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Bhowmik A, Eskreis-Winkler S. Deep learning in breast imaging. BJR Open 2022; 4:20210060. [PMID: 36105427 PMCID: PMC9459862 DOI: 10.1259/bjro.20210060] [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: 10/05/2021] [Revised: 04/04/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022] Open
Abstract
Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
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Affiliation(s)
- Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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36
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Ha R, Jairam MP. A review of artificial intelligence in mammography. Clin Imaging 2022; 88:36-44. [DOI: 10.1016/j.clinimag.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/28/2022] [Accepted: 05/12/2022] [Indexed: 11/16/2022]
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Advancements in Oncology with Artificial Intelligence—A Review Article. Cancers (Basel) 2022; 14:cancers14051349. [PMID: 35267657 PMCID: PMC8909088 DOI: 10.3390/cancers14051349] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research. Abstract Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.
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X-ray dosimetry in breast cancer screening: 2D and 3D mammography. Eur J Radiol 2022; 151:110278. [DOI: 10.1016/j.ejrad.2022.110278] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 11/21/2022]
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Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res 2022; 24:14. [PMID: 35184757 PMCID: PMC8859891 DOI: 10.1186/s13058-022-01509-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
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Affiliation(s)
- Aimilia Gastounioti
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Shyam Desai
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Vinayak S Ahluwalia
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
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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: 22] [Impact Index Per Article: 11.0] [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.
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Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer. Tomography 2022; 8:131-141. [PMID: 35076612 PMCID: PMC8788419 DOI: 10.3390/tomography8010011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 11/17/2022] Open
Abstract
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.
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Hickman SE, Woitek R, Le EPV, Im YR, Mouritsen Luxhøj C, Aviles-Rivero AI, Baxter GC, MacKay JW, Gilbert FJ. Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis. Radiology 2022; 302:88-104. [PMID: 34665034 PMCID: PMC8717814 DOI: 10.1148/radiol.2021210391] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/14/2021] [Accepted: 08/05/2021] [Indexed: 01/03/2023]
Abstract
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for screening mammography workflow. Materials and Methods Ovid Embase, Ovid Medline, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science literature databases were searched for relevant studies published from January 2012 to September 2020. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42019156016). Stand-alone technology was defined as a ML algorithm that can be used independently of a human reader. Studies were quality assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and the Prediction Model Risk of Bias Assessment Tool, and reporting was evaluated using the Checklist for Artificial Intelligence in Medical Imaging. A primary meta-analysis included the top-performing algorithm and corresponding reader performance from which pooled summary estimates for the area under the receiver operating characteristic curve (AUC) were calculated using a bivariate model. Results Fourteen articles were included, which detailed 15 studies for stand-alone detection (n = 8) and triage (n = 7). Triage studies reported that 17%-91% of normal mammograms identified could be read by adapted screening, while "missing" an estimated 0%-7% of cancers. In total, an estimated 185 252 cases from three countries with more than 39 readers were included in the primary meta-analysis. The pooled sensitivity, specificity, and AUC was 75.4% (95% CI: 65.6, 83.2; P = .11), 90.6% (95% CI: 82.9, 95.0; P = .40), and 0.89 (95% CI: 0.84, 0.98), respectively, for algorithms, and 73.0% (95% CI: 60.7, 82.6), 88.6% (95% CI: 72.4, 95.8), and 0.85 (95% CI: 0.78, 0.97), respectively, for readers. Conclusion Machine learning (ML) algorithms that demonstrate a stand-alone application in mammographic screening workflows achieve or even exceed human reader detection performance and improve efficiency. However, this evidence is from a small number of retrospective studies. Therefore, further rigorous independent external prospective testing of ML algorithms to assess performance at preassigned thresholds is required to support these claims. ©RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Whitman and Moseley in this issue.
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Affiliation(s)
- Sarah E. Hickman
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Ramona Woitek
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Elizabeth Phuong Vi Le
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Yu Ri Im
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Carina Mouritsen Luxhøj
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Angelica I. Aviles-Rivero
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Gabrielle C. Baxter
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - James W. MacKay
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
| | - Fiona J. Gilbert
- From the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M.,
F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of
Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus,
Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's
Hospital, Cambridge University Hospitals National Health Service Foundation
Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and
Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.);
Department of Pure Mathematics and Mathematical Statistics, University of
Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University
of East Anglia, Norwich, England (J.W.M.)
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Chung C, Jaffray DA. Cancer Needs a Robust "Metadata Supply Chain" to Realize the Promise of Artificial Intelligence. Cancer Res 2021; 81:5810-5812. [PMID: 34853038 PMCID: PMC9306309 DOI: 10.1158/0008-5472.can-21-1929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/04/2021] [Accepted: 10/07/2021] [Indexed: 01/07/2023]
Abstract
Profound advances in computational methods, including artificial intelligence (AI), present the opportunity to use the exponentially growing volume and complexity of available cancer measurements toward data-driven personalized care. While exciting, this opportunity has highlighted the disconnect between the promise of compute and the supply of high-quality data. The current paradigm of ad-hoc aggregation and curation of data needs to be replaced with a "metadata supply chain" that provides robust data in context with known provenance, that is, lineage and comprehensive data governance that will allow the promise of AI technology to be realized to its full potential in clinical practice.
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Affiliation(s)
- Caroline Chung
- The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - David A Jaffray
- The University of Texas MD Anderson Cancer Center, Houston, Texas
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Target motion management in breast cancer radiation therapy. Radiol Oncol 2021; 55:393-408. [PMID: 34626533 PMCID: PMC8647788 DOI: 10.2478/raon-2021-0040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/04/2021] [Indexed: 12/25/2022] Open
Abstract
Background Over the last two decades, breast cancer remains the main cause of cancer deaths in women. To treat this type of cancer, radiation therapy (RT) has proved to be efficient. RT for breast cancer is, however, challenged by intrafractional motion caused by respiration. The problem is more severe for the left-sided breast cancer due to the proximity to the heart as an organ-at-risk. While particle therapy results in superior dose characteristics than conventional RT, due to the physics of particle interactions in the body, particle therapy is more sensitive to target motion. Conclusions This review highlights current and emerging strategies for the management of intrafractional target motion in breast cancer treatment with an emphasis on particle therapy, as a modern RT technique. There are major challenges associated with transferring real-time motion monitoring technologies from photon to particles beams. Surface imaging would be the dominant imaging modality for real-time intrafractional motion monitoring for breast cancer. The magnetic resonance imaging (MRI) guidance and ultra high dose rate (FLASH)-RT seem to be state-of-the-art approaches to deal with 4D RT for breast cancer.
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Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
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Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Hovey RC, Koledova Z. Evolution and Self-renewal of the Journal of Mammary Gland Biology and Neoplasia. J Mammary Gland Biol Neoplasia 2021; 26:217-220. [PMID: 34648096 PMCID: PMC8515151 DOI: 10.1007/s10911-021-09500-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 11/03/2022] Open
Affiliation(s)
- Russell C Hovey
- Department of Animal Science, University of California, Davis, CA, USA.
| | - Zuzana Koledova
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
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