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Deng Y, Lu Y, Li X, Zhu Y, Zhao Y, Ruan Z, Mei N, Yin B, Liu L. Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study. Eur Radiol 2024; 34:5464-5476. [PMID: 38276982 DOI: 10.1007/s00330-024-10607-9] [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: 06/05/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/27/2024]
Abstract
OBJECTIVES To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. METHODS This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). RESULTS The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. CONCLUSIONS Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. CLINICAL RELEVANCE STATEMENT By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. KEY POINTS • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.
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Affiliation(s)
- Yalan Deng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yuqi Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Zhuoying Ruan
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
| | - Li Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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J W Partridge G, Darker I, J James J, Satchithananda K, Sharma N, Valencia A, Teh W, Khan H, Muscat E, J Michell M, Chen Y. How long does it take to read a mammogram? Investigating the reading time of digital breast tomosynthesis and digital mammography. Eur J Radiol 2024; 177:111535. [PMID: 38852330 DOI: 10.1016/j.ejrad.2024.111535] [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: 02/14/2024] [Revised: 05/16/2024] [Accepted: 05/27/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE To analyse digital breast tomosynthesis (DBT) reading times in the screening setting, compared to 2D full-field digital mammography (FFDM), and investigate the impact of reader experience and professional group on interpretation times. METHOD Reading time data were recorded in the PROSPECTS Trial, a prospective randomised trial comparing DBT plus FFDM or synthetic 2D mammography (S2D) to FFDM alone, in the National Health Service (NHS) breast screening programme, from January 2019-February 2023. Time to read DBT+FFDM or DBT+S2D and FFDM alone was calculated per case and reading times were compared between modalities using dependent T-tests. Reading times were compared between readers from different professional groups (radiologists and radiographer readers) and experience levels using independent T-tests. The learning curve effect of using DBT in screening on reading time was investigated using a Kruskal-Wallis test. RESULTS Forty-eight readers interpreted 1,242 FFDM batches (34,210 FFDM cases) and 973 DBT batches (13,983 DBT cases). DBT reading time was doubled compared to FFDM (2.09 ± 0.64 min vs. 0.98 ± 0.30 min; p < 0.001), and DBT+S2D reading was longer than DBT + FFDM (2.24 ± 0.62 min vs. 2.04 ± 0.46 min; p = 0.006). No difference was identified in reading time between radiologists and radiographers (2.06 ± 0.71 min vs. 2.14 ± 0.46 min, respectively; p = 0.71). Readers with five or more years of experience reading DBT were quicker than those with less experience (1.86 ± 0.56 min vs. 2.37 ± 0.65 min; p = 0.008), and DBT reading time decreased after less than 9 months accrued screening experience (p = 0.01). CONCLUSIONS DBT reading times were double those of FFDM in the screening setting, but there was a short learning curve effect with readers showing significant improvements in reading times within the first nine months of DBT experience. CLINICALTRIALS gov Identifier: NCT03733106.
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Affiliation(s)
- George J W Partridge
- University of Nottingham, School of Medicine, Translational Medical Sciences, Clinical Sciences Building, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, United Kingdom
| | - Iain Darker
- University of Nottingham, School of Medicine, Translational Medical Sciences, Clinical Sciences Building, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, United Kingdom
| | - Jonathan J James
- Nottingham University Hospitals NHS Trust, Nottingham Breast Institute, City Hospital Campus, Hucknall Road, Nottingham NG5 1PB, United Kingdom
| | - Keshthra Satchithananda
- Department of Breast Radiology and National Breast Screening Training Centre, King's College Hospital, Denmark Hill, London SE5 9RS, United Kingdom
| | - Nisha Sharma
- Leeds Breast Screening Unit, Leeds Teaching Hospital, York Road, Leeds, LS14 6UH, United Kingdom
| | - Alexandra Valencia
- Avon Breast Screening, Bristol Breast Care Centre, Bristol, BS10 5NB, United Kingdom
| | - William Teh
- North London Breast Screening Service, Edgware Community Hospital, London, HA8 9BA, United Kingdom
| | - Humaira Khan
- City, Sandwell and Walsall Breast Screening Service, Birmingham City Hospital, B18 7QH, United Kingdom
| | - Elizabeth Muscat
- South West London Breast Screening Service, St George's Hospital, London, SW17 0QT, United Kingdom
| | - Michael J Michell
- Department of Breast Radiology and National Breast Screening Training Centre, King's College Hospital, Denmark Hill, London SE5 9RS, United Kingdom
| | - Yan Chen
- University of Nottingham, School of Medicine, Translational Medical Sciences, Clinical Sciences Building, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, United Kingdom.
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Żydowicz WM, Skokowski J, Marano L, Polom K. Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients. J Clin Med 2024; 13:4337. [PMID: 39124604 PMCID: PMC11313674 DOI: 10.3390/jcm13154337] [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: 04/09/2024] [Revised: 05/22/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.
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Affiliation(s)
- Weronika Magdalena Żydowicz
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
| | - Jaroslaw Skokowski
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Luigi Marano
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Karol Polom
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznan, Poland
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Klein DS, Karmakar S, Jonnalagadda A, Abbey CK, Eckstein MP. Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images. J Med Imaging (Bellingham) 2024; 11:045501. [PMID: 38988989 PMCID: PMC11232702 DOI: 10.1117/1.jmi.11.4.045501] [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: 04/16/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/12/2024] Open
Abstract
Purpose Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3D search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors. Approach Sixteen nonexpert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC). Results The CNN-CADe improved the 3D search for the small microcalcification signal ( Δ AUC = 0.098 , p = 0.0002 ) and the 2D search for the large mass signal ( Δ AUC = 0.076 , p = 0.002 ). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D ( Δ Δ AUC = 0.066 , p = 0.035 ). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe ( r = - 0.528 , p = 0.036 ). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit ( Δ Δ AUC = 0.033 , p = 0.133 ). Conclusion The CNN-CADe brings unique performance benefits to the 3D (versus 2D) search of small signals by reducing errors caused by the underexploration of the volumetric data.
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Affiliation(s)
- Devi S. Klein
- University of California, Department of Psychological and Brain Sciences, Santa Barbara, California, United States
| | - Srijita Karmakar
- University of California, Department of Psychological and Brain Sciences, Santa Barbara, California, United States
| | - Aditya Jonnalagadda
- University of California, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Craig K. Abbey
- University of California, Department of Psychological and Brain Sciences, Santa Barbara, California, United States
| | - Miguel P. Eckstein
- University of California, Department of Psychological and Brain Sciences, Santa Barbara, California, United States
- University of California, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
- University of California, Department of Computer Science, Santa Barbara, California, United States
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Shimada Y, Ojima T, Takaoka Y, Sugano A, Someya Y, Hirabayashi K, Homma T, Kitamura N, Akemoto Y, Tanabe K, Sato F, Yoshimura N, Tsuchiya T. Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning. Surg Today 2024; 54:540-550. [PMID: 37864054 DOI: 10.1007/s00595-023-02756-z] [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: 03/20/2023] [Accepted: 09/13/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically. METHODS Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients. RESULTS The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons. CONCLUSION The deep learning model systems can be utilized in clinical applications via data expansion.
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Affiliation(s)
- Yoshifumi Shimada
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Toshihiro Ojima
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yutaka Takaoka
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Aki Sugano
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Clinical Research, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Yoshiaki Someya
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Kenichi Hirabayashi
- Department of Diagnostic Pathology, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Takahiro Homma
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoya Kitamura
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yushi Akemoto
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Keitaro Tanabe
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Fumitaka Sato
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoki Yoshimura
- Department of Cardiovascular Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Tomoshi Tsuchiya
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
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Bae MS. AI Improves Cancer Detection and Reading Time of Digital Breast Tomosynthesis. Radiol Artif Intell 2024; 6:e240219. [PMID: 38747570 PMCID: PMC11140501 DOI: 10.1148/ryai.240219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 05/18/2024]
Affiliation(s)
- Min Sun Bae
- From the Department of Radiology, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Gyeonggi-do, Republic of Korea
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Monteiro Cordeiro N, Facina G, Pinto Nazário AC, Monteiro Sanvido V, Araujo Neto JT, Rodrigues Dos Santos E, Domingues da Silva M, Elias S. Towards precision medicine in breast imaging: A novel open mammography database with tailor-made 3D image retrieval for AI and teaching. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108117. [PMID: 38498955 DOI: 10.1016/j.cmpb.2024.108117] [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: 07/26/2023] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/20/2024]
Abstract
This project addresses the global challenge of breast cancer, particularly in low-resource settings, by creating a pioneering mammography database. Breast cancer, identified by the World Health Organization as a leading cause of cancer death among women, often faces diagnostic and treatment resource constraints in low- and middle-income countries. To enhance early diagnosis and address educational setbacks, the project focuses on leveraging artificial intelligence (AI) technologies through a comprehensive database. Developed in collaboration with Ambra Health, a cloud-based medical image management software, the database comprises 941 mammography images from 100 anonymized cases, with 62 % including 3D images. Accessible through http://mamografia.unifesp.br, the platform facilitates a simple registration process and an advanced search system based on 169 clinical and imaging variables. The website, customizable to the user's native language, ensures data security through an automatic anonymization system. By providing high-resolution, 3D digital images and supplementary clinical information, the platform aims to promote education and research in breast cancer diagnosis, representing a significant advancement in resource-constrained healthcare environments.
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Affiliation(s)
| | - Gil Facina
- Federal University of São Paulo, R. Marselhesa, 249 - Vila Mariana, São Paulo, SP 04020-060, Brazil
| | | | - Vanessa Monteiro Sanvido
- Federal University of São Paulo, R. Marselhesa, 249 - Vila Mariana, São Paulo, SP 04020-060, Brazil
| | | | | | | | - Simone Elias
- Federal University of São Paulo, R. Marselhesa, 249 - Vila Mariana, São Paulo, SP 04020-060, Brazil.
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Littrup PJ, Mehrmohammadi M, Duric N. Breast Tomographic Ultrasound: The Spectrum from Current Dense Breast Cancer Screenings to Future Theranostic Treatments. Tomography 2024; 10:554-573. [PMID: 38668401 PMCID: PMC11053617 DOI: 10.3390/tomography10040044] [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: 02/24/2024] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024] Open
Abstract
This review provides unique insights to the scientific scope and clinical visions of the inventors and pioneers of the SoftVue breast tomographic ultrasound (BTUS). Their >20-year collaboration produced extensive basic research and technology developments, culminating in SoftVue, which recently received the Food and Drug Administration's approval as an adjunct to breast cancer screening in women with dense breasts. SoftVue's multi-center trial confirmed the diagnostic goals of the tissue characterization and localization of quantitative acoustic tissue differences in 2D and 3D coronal image sequences. SoftVue mass characterizations are also reviewed within the standard cancer risk categories of the Breast Imaging Reporting and Data System. As a quantitative diagnostic modality, SoftVue can also function as a cost-effective platform for artificial intelligence-assisted breast cancer identification. Finally, SoftVue's quantitative acoustic maps facilitate noninvasive temperature monitoring and a unique form of time-reversed, focused US in a single theranostic device that actually focuses acoustic energy better within the highly scattering breast tissues, allowing for localized hyperthermia, drug delivery, and/or ablation. Women also prefer the comfort of SoftVue over mammograms and will continue to seek out less-invasive breast care, from diagnosis to treatment.
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Affiliation(s)
- Peter J. Littrup
- Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA; (M.M.); (N.D.)
- Delphinus Medical Technologies, Inc., Novi, MI 48374, USA
| | - Mohammad Mehrmohammadi
- Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA; (M.M.); (N.D.)
| | - Nebojsa Duric
- Department of Imaging Sciences, University of Rochester, Rochester, NY 14642, USA; (M.M.); (N.D.)
- Delphinus Medical Technologies, Inc., Novi, MI 48374, USA
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Guo Y, Zhang H, Yuan L, Chen W, Zhao H, Yu QQ, Shi W. Machine learning and new insights for breast cancer diagnosis. J Int Med Res 2024; 52:3000605241237867. [PMID: 38663911 PMCID: PMC11047257 DOI: 10.1177/03000605241237867] [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: 08/21/2023] [Accepted: 02/21/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.
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Affiliation(s)
- Ya Guo
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Heng Zhang
- Department of Laboratory Medicine, Shandong Daizhuang Hospital, Jining, Shandong Province, China
| | - Leilei Yuan
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Weidong Chen
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Haibo Zhao
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Qing-Qing Yu
- Phase I Clinical Research Centre, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Wenjie Shi
- Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular- and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, Magdeburg, Germany
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Cerekci E, Alis D, Denizoglu N, Camurdan O, Ege Seker M, Ozer C, Hansu MY, Tanyel T, Oksuz I, Karaarslan E. Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis. Eur J Radiol 2024; 173:111356. [PMID: 38364587 DOI: 10.1016/j.ejrad.2024.111356] [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: 08/23/2023] [Revised: 12/10/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there's a lack of quantitative evidence regarding their performance. OBJECTIVES To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms. METHODS Three radiologists drew ground-truth boxes on a balanced mammogram dataset of women (n = 1496 cancer-positive and negative scans) from three centers. A modified, pre-trained DL model was employed for breast cancer detection, using MLO and CC images. Saliency XAI methods, including Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM, were evaluated. We utilized the Pointing Game to assess these methods, determining if the maximum value of a saliency map aligned with the bounding boxes, representing the ratio of correctly identified lesions among all cancer patients, with a value ranging from 0 to 1. RESULTS The development sample included 2,244 women (75%), with the remaining 748 women (25%) in the testing set for unbiased XAI evaluation. The model's recall, precision, accuracy, and F1-Score in identifying cancer in the testing set were 69%, 88%, 80%, and 0.77, respectively. The Pointing Game Scores for Grad-CAM, Grad-CAM++, and Eigen-CAM were 0.41, 0.30, and 0.35 in women with cancer and marginally increased to 0.41, 0.31, and 0.36 when considering only true-positive samples. CONCLUSIONS While saliency-based methods provide some degree of explainability, they frequently fall short in delineating how DL models arrive at decisions in a considerable number of instances.
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Affiliation(s)
- Esma Cerekci
- Sisli Hamidiye Etfal Training and Research Hospital, Department of Radiology, Istanbul, Turkey.
| | - Deniz Alis
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, Turkey.
| | - Nurper Denizoglu
- Acibadem Healthcare Group, Department of Radiology, Istanbul, Turkey.
| | - Ozden Camurdan
- Acibadem Healthcare Group, Department of Radiology, Istanbul, Turkey.
| | - Mustafa Ege Seker
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey.
| | - Caner Ozer
- Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey.
| | - Muhammed Yusuf Hansu
- Istanbul Technical University, Department of Electronics and Communication Engineering, Istanbul, Turkey.
| | - Toygar Tanyel
- Istanbul Technical University, Department of Biomedical Engineering, Istanbul, Turkey.
| | - Ilkay Oksuz
- Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey.
| | - Ercan Karaarslan
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, Turkey
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Zhao Z, Nie C, Zhao L, Xiao D, Zheng J, Zhang H, Yan P, Jiang X, Zhao H. Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas. Eur Radiol 2024; 34:2468-2479. [PMID: 37812296 PMCID: PMC10957672 DOI: 10.1007/s00330-023-10252-8] [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: 10/27/2022] [Revised: 07/05/2023] [Accepted: 07/15/2023] [Indexed: 10/10/2023]
Abstract
OBJECTIVE The purpose of this study was to develop and validate a nomogram combined multiparametric MRI and clinical indicators for identifying the WHO grade of meningioma. MATERIALS AND METHODS Five hundred and sixty-eight patients were included in this study, who were diagnosed pathologically as having meningiomas. Firstly, radiomics features were extracted from CE-T1, T2, and 1-cm-thick tumor-to-brain interface (BTI) images. Then, difference analysis and the least absolute shrinkage and selection operator were orderly used to select the most representative features. Next, the support vector machine algorithm was conducted to predict the WHO grade of meningioma. Furthermore, a nomogram incorporated radiomics features and valuable clinical indicators was constructed by logistic regression. The performance of the nomogram was assessed by calibration and clinical effectiveness, as well as internal validation. RESULTS Peritumoral edema volume and gender are independent risk factors for predicting meningioma grade. The multiparametric MRI features incorporating CE-T1, T2, and BTI features showed the higher performance for prediction of meningioma grade with a pooled AUC = 0.885 (95% CI, 0.821-0.946) and 0.860 (95% CI, 0.788-0.923) in the training and test groups, respectively. Then, a nomogram with a pooled AUC = 0.912 (95% CI, 0.876-0.961), combined radiomics score, peritumoral edema volume, and gender improved diagnostic performance compared to radiomics model or clinical model and showed good calibration as the true results. Moreover, decision curve analysis demonstrated satisfactory clinical effectiveness of the proposed nomogram. CONCLUSIONS A novel nomogram is simple yet effective in differentiating WHO grades of meningioma and thus can be used in patients with meningiomas. CLINICAL RELEVANCE STATEMENT We proposed a nomogram that included clinical indicators and multi-parameter radiomics features, which can accurately, objectively, and non-invasively differentiate WHO grading of meningioma and thus can be used in clinical work. KEY POINTS • The study combined radiomics features and clinical indicators for objectively predicting the meningioma grade. • The model with CE-T1 + T2 + brain-to-tumor interface features demonstrated the best predictive performance by investigating seven different radiomics models. • The nomogram potentially has clinical applications in distinguishing high-grade and low-grade meningiomas.
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Affiliation(s)
- Zhen Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuansheng Nie
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Zhao
- International Education College of Henan University, Kaifeng, China
| | - Dongdong Xiao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianglin Zheng
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Zhang
- Department of Geriatric Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengfei Yan
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Jiang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Hongyang Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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13
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Grignaffini F, Barbuto F, Troiano M, Piazzo L, Simeoni P, Mangini F, De Stefanis C, Onetti Muda A, Frezza F, Alisi A. The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review. Diagnostics (Basel) 2024; 14:388. [PMID: 38396427 PMCID: PMC10887838 DOI: 10.3390/diagnostics14040388] [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: 12/27/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
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Affiliation(s)
- Flavia Grignaffini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Francesco Barbuto
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Maurizio Troiano
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Patrizio Simeoni
- National Transport Authority (NTA), D02 WT20 Dublin, Ireland;
- Faculty of Lifelong Learning, South East Technological University (SETU), R93 V960 Carlow, Ireland
| | - Fabio Mangini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Cristiano De Stefanis
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | | | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Anna Alisi
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
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Bassi E, Russo A, Oliboni E, Zamboni F, De Santis C, Mansueto G, Montemezzi S, Foti G. The role of an artificial intelligence software in clinical senology: a mammography multi-reader study. LA RADIOLOGIA MEDICA 2024; 129:202-210. [PMID: 38082194 DOI: 10.1007/s11547-023-01751-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/07/2023] [Indexed: 02/21/2024]
Abstract
PURPOSE To evaluate the diagnostic role of a dedicated AI software in detecting anomalous breast findings on mammography and tomosynthesis images in the clinical setting, stand-alone and as aid of four readers. METHODS A total of 210 patients with complete clinical and radiologic records were retrospectively analyzed. Pathology was used as the reference standard for patients undergoing surgery or biopsy, and a 1-year follow-up was used to confirm no change in the remaining patients. The image evaluation was performed by four readers with different levels of experience (a junior and three senior breast radiologists) using a 5-point Likert scale moving from 1 (definitively no cancer) to 5 (definitively cancer). The positivity of mammograms was assessed on the presence of any breast lesion (masses, architectural distortions, asymmetries, calcifications), including malignant and benign ones. A multi-reader multi-case analysis was performed. A p value < 0.05 was considered statistically significant. RESULTS The stand-alone AI system achieved an accuracy of 71% (69% sensitivity and 73% specificity), which is overall lower than the value achieved by readers without AI. However, with the aid of AI, a significant increase of accuracy (p value = 0.004) and specificity (p value = 0.04) was achieved for the less experienced radiologist and a senior one. CONCLUSION The use of AI software as a second reader for breast lesions assessment could play a crucial role in the clinical setting, by increasing sensitivity and specificity, especially for less experienced radiologists.
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Affiliation(s)
- Enrica Bassi
- Department of Radiology, Verona University Hospital, Verona, Italy
| | - Anna Russo
- Department of Radiology, IRCCS Sacro Cuore Hospital, Via Don A. Sempreboni 10, 37024, Negrar (VR), Italy
| | - Eugenio Oliboni
- Department of Radiology, IRCCS Sacro Cuore Hospital, Via Don A. Sempreboni 10, 37024, Negrar (VR), Italy
| | - Federico Zamboni
- Department of Radiology, IRCCS Sacro Cuore Hospital, Via Don A. Sempreboni 10, 37024, Negrar (VR), Italy
| | - Cecilia De Santis
- Department of Radiology, IRCCS Sacro Cuore Hospital, Via Don A. Sempreboni 10, 37024, Negrar (VR), Italy
| | | | - Stefania Montemezzi
- Department of Radiology, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Giovanni Foti
- Department of Radiology, IRCCS Sacro Cuore Hospital, Via Don A. Sempreboni 10, 37024, Negrar (VR), Italy.
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [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: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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Tang R, Li J, Zhao P, Zhang Z, Yin H, Ding H, Xu N, Yang Z, Wang Z. Utility of machine learning for identifying stapes fixation on ultra-high-resolution CT. Jpn J Radiol 2024; 42:69-77. [PMID: 37561264 DOI: 10.1007/s11604-023-01475-2] [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: 04/06/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE Imaging diagnosis of stapes fixation (SF) is challenging owing to a lack of definite evidence. We developed a comprehensive machine learning (ML) model to identify SF on ultra-high-resolution CT. MATERIALS AND METHODS We retrospectively enrolled 109 participants (143 ears) and divided them into the training set (115 ears) and test set (28 ears). Stapes mobility (SF or non-SF) was determined by surgical inspection. In the ML analysis, rectangular regions of interest were placed on consecutive axial slices in the training set. Radiomic features were extracted and fed into the training session. The test set was analyzed using 7 ML models (support vector machine, k nearest neighbor, decision tree, random forest, extra trees, eXtreme Gradient Boosting, and Light Gradient Boosting Machine) and by 2 dedicated neuroradiologists. Diagnostic performance (sensitivity, specificity and accuracy, with surgical findings as the reference) was compared between the radiologists and the optimal ML model by using the McNemar test. RESULTS The mean age of the participants was 42.3 ± 17.5 years. The Light Gradient Boosting Machine (LightGBM) model showed the highest sensitivity (0.83), specificity (0.81), accuracy (0.82) and area under the curve (0.88) for detecting SF among the 7 ML models. The neuroradiologists achieved good sensitivities (0.75 and 0.67), moderate-to-good specificities (0.63 and 0.56) and good accuracies (0.68 and 0.61). This model showed no statistical differences with the neuroradiologists (P values 0.289-1.000). CONCLUSIONS Compared to the neuroradiologists, the LightGBM model achieved competitive diagnostic performance in identifying SF, and has the potential to be a supportive tool in clinical practice.
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Affiliation(s)
- Ruowei Tang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Jia Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China.
| | - Zhengyu Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Hongxia Yin
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Heyu Ding
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Ning Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China.
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Bahl M, Do S. Beyond the AJR: An International Competition Advances Artificial Intelligence Research. AJR Am J Roentgenol 2024; 222:e2329644. [PMID: 37222276 DOI: 10.2214/ajr.23.29644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Affiliation(s)
- Manisha Bahl
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
| | - Synho Do
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
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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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Hossain S, Azam S, Montaha S, Karim A, Chowa SS, Mondol C, Zahid Hasan M, Jonkman M. Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model. Heliyon 2023; 9:e21369. [PMID: 37885728 PMCID: PMC10598544 DOI: 10.1016/j.heliyon.2023.e21369] [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: 06/14/2023] [Revised: 10/11/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study. Method Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images. Result The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset. Conclusion The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.
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Affiliation(s)
- Shahed Hossain
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| | - Sidratul Montaha
- Department of Computer Science, University of Calgary, Calgary, AB, T2N 1N4, Canada
| | - Asif Karim
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
| | - Sadia Sultana Chowa
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Chaity Mondol
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Md Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, 0909, NT, Australia
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Heine J, Fowler EEE, Weinfurtner RJ, Hume E, Tworoger SS. Breast density analysis of digital breast tomosynthesis. Sci Rep 2023; 13:18760. [PMID: 37907569 PMCID: PMC10618274 DOI: 10.1038/s41598-023-45402-x] [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: 08/24/2023] [Accepted: 10/19/2023] [Indexed: 11/02/2023] Open
Abstract
Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case-control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.
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Affiliation(s)
- John Heine
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
| | - Erin E E Fowler
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - R Jared Weinfurtner
- Diagnostic Imaging and Interventional Radiology, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - Emma Hume
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
| | - Shelley S Tworoger
- Cancer Epidemiology Department, Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA
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21
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Chikarmane SA, Offit LR, Giess CS. Synthetic Mammography: Benefits, Drawbacks, and Pitfalls. Radiographics 2023; 43:e230018. [PMID: 37768863 DOI: 10.1148/rg.230018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Digital breast tomosynthesis (DBT) allows three-dimensional assessment of breast tissue; however, DBT requires a two-dimensional (2D) image for comparison with prior mammograms and accurate interpretation of calcifications. Traditionally, full-field digital mammography (FFDM) has been performed after the DBT image acquisition. Synthetic mammography (SM), the 2D reconstruction of the tomosynthesis slice dataset, has been designed to replace FFDM. Advantages of SM include decreased image acquisition time and decreased radiation exposure, with maintained or improved screening performance metrics. Because SM algorithms give extra weight to lesion-like characteristics (eg, calcifications and architectural distortions), they may enable increased visibility of these characteristics relative to that at FFDM. Although SM algorithms were designed to improve lesion identification, they have led to varied outcomes in studies reported in the literature. Compared with FFDM, SM has been reported to be associated with a higher false-positive rate for calcifications, decreased conspicuity of asymmetries, lower breast density assessments, and imaging artifacts (eg, metallic artifact, bright-band artifact, blurring of the axilla, and truncation artifact). The authors review the literature on SM, including its implementation, benefits, and artifacts. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.
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Affiliation(s)
- Sona A Chikarmane
- From the Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (S.A.C., C.S.G.); and Harvard Medical School, Boston, MA (L.R.O.)
| | - Lily R Offit
- From the Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (S.A.C., C.S.G.); and Harvard Medical School, Boston, MA (L.R.O.)
| | - Catherine S Giess
- From the Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (S.A.C., C.S.G.); and Harvard Medical School, Boston, MA (L.R.O.)
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22
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Gegios AR, Peterson MS, Fowler AM. Breast Cancer Screening and Diagnosis: Recent Advances in Imaging and Current Limitations. PET Clin 2023; 18:459-471. [PMID: 37296043 DOI: 10.1016/j.cpet.2023.04.003] [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] [Indexed: 06/12/2023]
Abstract
Breast cancer detection has a significant impact on population health. Although there are many breast imaging modalities, mammography is the predominant tool for breast cancer screening. The introduction of digital breast tomosynthesis to mammography has contributed to increased cancer detection rates and decreased recall rates. In average-risk women, starting annual screening mammography at age 40 years has demonstrated the highest mortality reduction. In intermediate- and high-risk women as well as in those with dense breasts, additional modalities, including MRI, ultrasound, and molecular breast imaging, can also be considered for adjunct screening to improve the detection of mammographically occult malignancy.
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Affiliation(s)
- Alison R Gegios
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA
| | - Molly S Peterson
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA
| | - Amy M Fowler
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
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23
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Liu J, Lei J, Ou Y, Zhao Y, Tuo X, Zhang B, Shen M. Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis. Clin Exp Med 2023; 23:2341-2356. [PMID: 36242643 DOI: 10.1007/s10238-022-00895-0] [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: 05/06/2022] [Accepted: 09/12/2022] [Indexed: 12/24/2022]
Abstract
Breast cancer was the fourth leading cause of cancer-related death worldwide, and early mammography screening could decrease the breast cancer mortality. Artificial intelligence (AI)-assisted diagnose system based on machine learning (ML) methods can help improve the screening accuracy and efficacy. This study aimed to systematically review and make a meta-analysis on the diagnostic accuracy of mammography diagnosis of breast cancer through various ML methods. Springer Link, Science Direct (Elsevier), IEEE Xplore, PubMed and Web of Science were searched for relevant studies published from January 2000 to September 2021. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42021284227). A Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the included studies, and reporting was evaluated using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The pooled summary estimates for sensitivity, specificity, the area under the receiver operating characteristic curve (AUC) for three ML methods (convolutional neural network [CNN], artificial neural network [ANN], support vector machine [SVM]) were calculated. A total of 32 studies with 23,804 images were included in the meta-analysis. The overall pooled estimate for sensitivity, specificity and AUC was 0.914 [95% CI 0.868-0.945], 0.916 [95% CI 0.873-0.945] and 0.945 for mammography diagnosis of breast cancer through three ML methods. The pooled sensitivity, specificity and AUC of CNN were 0.961 [95% CI 0.886-0.988], 0.950 [95% CI 0.924-0.967] and 0.974. The pooled sensitivity, specificity and AUC of ANN were 0.837 [95% CI 0.772-0.886], 0.894 [95% CI 0.764-0.957] and 0.881. The pooled sensitivity, specificity and AUC of SVM were 0.889 [95% CI 0.807-0.939], 0.843 [95% CI 0.724-0.916] and 0.913. Machine learning methods (especially CNN) show excellent performance in mammography diagnosis of breast cancer screening based on retrospective studies. More rigorous prospective studies are needed to evaluate the longitudinal performance of AI.
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Affiliation(s)
- Junjie Liu
- School of Medicine, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China
| | - Jiangjie Lei
- School of Medicine, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China
| | - Yuhang Ou
- School of Medicine, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China
| | - Yilong Zhao
- School of Medicine, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China
| | - Xiaofeng Tuo
- School of Medicine, Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, People's Republic of China
| | - Baoming Zhang
- College of Stomatology, Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, People's Republic of China
- Key laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, People's Republic of China
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, People's Republic of China
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, People's Republic of China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, People's Republic of China.
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24
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Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol 2023; 95:75-87. [PMID: 37499847 DOI: 10.1016/j.semcancer.2023.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/29/2023]
Abstract
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
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Affiliation(s)
- Yichen Meng
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Yue Yang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Miao Hu
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Zheng Zhang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
| | - Xuhui Zhou
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
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25
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Yoen H, Chang JM. Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography. J Breast Cancer 2023; 26:504-513. [PMID: 37704383 PMCID: PMC10625864 DOI: 10.4048/jbc.2023.26.e39] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/21/2023] [Accepted: 08/17/2023] [Indexed: 09/15/2023] Open
Abstract
Despite recent advances in artificial intelligence (AI) software with improved performance in mammography screening for breast cancer, insufficient data are available on its performance in detecting cancers that were initially missed on mammography. In this study, we aimed to determine whether AI software-aided mammography could provide additional value in identifying cancers detected through supplemental screening ultrasound. We searched our database from 2017 to 2018 and included 238 asymptomatic patients (median age, 50 years; interquartile range, 45-57 years) diagnosed with breast cancer using supplemental ultrasound. Two unblinded radiologists retrospectively reviewed the mammograms using commercially available AI software and identified the reasons for missed detection. Clinicopathological characteristics of AI-detected and AI-undetected cancers were compared using univariate and multivariate logistic regression analyses. A total of 253 cancers were detected in 238 patients using ultrasound. In an unblinded review, the AI software failed to detect 187 of the 253 (73.9%) mammography cases with negative findings in retrospective observations. The AI software detected 66 cancers (26.1%), of which 42 (63.6%) exhibited indiscernible findings obscured by overlapping dense breast tissues, even with the knowledge of magnetic resonance imaging and post-wire localization mammography. The remaining 24 cases (36.4%) were considered interpretive errors by the radiologists. Invasive tumor size was associated with AI detection after multivariable analysis (odds ratio, 2.2; 95% confidence intervals, 1.5-3.3; p < 0.001). In the control group of 160 women without cancer, the AI software identified 19 false positives (11.9%, 19/160). Although most ultrasound-detected cancers were not detected on mammography with the use of AI, the software proved valuable in identifying breast cancers with indiscernible abnormalities or those that clinicians may have overlooked.
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Affiliation(s)
- Heera Yoen
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.
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26
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Song C, Chen X, Tang C, Xue P, Jiang Y, Qiao Y. Artificial intelligence for HPV status prediction based on disease-specific images in head and neck cancer: A systematic review and meta-analysis. J Med Virol 2023; 95:e29080. [PMID: 37691329 DOI: 10.1002/jmv.29080] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/14/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023]
Abstract
Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer (HNC) is crucial to identify at-risk populations, stratify patients, personalized treatment options, and predict prognosis. Artificial intelligence (AI) is an emerging tool to dissect imaging features. This systematic review and meta-analysis aimed to evaluate the performance of AI to predict the HPV positivity through the HPV-associated diseased images in HNC patients. A systematic literature search was conducted in databases including Ovid-MEDLINE, Embase, and Web of Science Core Collection for studies continuously published from inception up to October 30, 2022. Search strategies included keywords such as "artificial intelligence," "head and neck cancer," "HPV," and "sensitivity & specificity." Duplicates, articles without HPV predictions, letters, scientific reports, conference abstracts, or reviews were excluded. Binary diagnostic data were then extracted to generate contingency tables and then used to calculate the pooled sensitivity (SE), specificity (SP), area under the curve (AUC), and their 95% confidence interval (CI). A random-effects model was used for meta-analysis, four subgroup analyses were further explored. Totally, 22 original studies were included in the systematic review, 15 of which were eligible to generate 33 contingency tables for meta-analysis. The pooled SE and SP for all studies were 79% (95% CI: 75-82%) and 74% (95% CI: 69-78%) respectively, with an AUC of 0.83 (95% CI: 0.79-0.86). When only selecting one contingency table with the highest accuracy from each study, our analysis revealed a pooled SE of 79% (95% CI: 75-83%), SP of 75% (95% CI: 69-79%), and an AUC of 0.84 (95% CI: 0.81-0.87). The respective heterogeneities were moderate (I2 for SE and SP were 51.70% and 51.01%) and only low (35.99% and 21.44%). This evidence-based study showed an acceptable and promising performance for AI algorithms to predict HPV status in HNC but was not comparable to the routine p16 immunohistochemistry. The exploitation and optimization of AI algorithms warrant further research. Compared with previous studies, future studies anticipate to make progress in the selection of databases, improvement of international reporting guidelines, and application of high-quality deep learning algorithms.
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Affiliation(s)
- Cheng Song
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xu Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Tang
- Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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27
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Zhang M, Wang C, Cai L, Zhao J, Xu Y, Xing J, Sun J, Zhang Y. Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images. Comput Struct Biotechnol J 2023; 22:17-26. [PMID: 37655162 PMCID: PMC10465855 DOI: 10.1016/j.csbj.2023.08.012] [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: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.
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Affiliation(s)
- Mengyan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Cong Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Li Cai
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, 150040, China
| | - Jiyun Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ye Xu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Jiacheng Xing
- Beidahuang Industry Group General Hospital, 150060 Harbin, China
| | - Jianghong Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
- College of pathology, Qiqihar Medical University, Qiqihar 161042, China
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28
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Zafar A, Tanveer J, Ali MU, Lee SW. BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization. Bioengineering (Basel) 2023; 10:825. [PMID: 37508852 PMCID: PMC10376009 DOI: 10.3390/bioengineering10070825] [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: 05/25/2023] [Revised: 07/04/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Jawad Tanveer
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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29
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Arasu VA, Habel LA, Achacoso NS, Buist DSM, Cord JB, Esserman LJ, Hylton NM, Glymour MM, Kornak J, Kushi LH, Lewis DA, Liu VX, Lydon CM, Miglioretti DL, Navarro DA, Pu A, Shen L, Sieh W, Yoon HC, Lee C. Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study. Radiology 2023; 307:e222733. [PMID: 37278627 PMCID: PMC10315521 DOI: 10.1148/radiol.222733] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/05/2023] [Accepted: 04/18/2023] [Indexed: 06/07/2023]
Abstract
Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Vignesh A. Arasu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Laurel A. Habel
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Ninah S. Achacoso
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Diana S. M. Buist
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Jason B. Cord
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Laura J. Esserman
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Nola M. Hylton
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - M. Maria Glymour
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - John Kornak
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Lawrence H. Kushi
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Donald A. Lewis
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Vincent X. Liu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Caitlin M. Lydon
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Diana L. Miglioretti
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Daniel A. Navarro
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Albert Pu
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Li Shen
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Weiva Sieh
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Hyo-Chun Yoon
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
| | - Catherine Lee
- From the Division of Research, Kaiser Permanente Northern California,
2000 Broadway, Oakland, CA 94612 (V.A.A., L.A.H., N.S.A., L.H.K., V.X.L.,
C.M.L., C.L.); Department of Radiology, Kaiser Permanente Northern California,
Vallejo Medical Center, Vallejo, Calif (V.A.A.); Kaiser Permanente Washington
Health Research Institute, Seattle, Wash (D.S.M.B.); Department of Radiology,
Southern California Permanente Medical Group, Orange County, Irvine, Calif
(J.B.C.); Department of Surgery (L.J.E.), Department of Radiology and Biomedical
Imaging (N.M.H.), and Department of Epidemiology and Biostatistics (M.M.G.,
J.K.), University of California–San Francisco, San Francisco, Calif;
Department of Medical Imaging Technology and Informatics, Southern California
Permanente Medical Group, Pasadena, Calif (D.A.L.); Department of Biostatistics,
University of California–Davis, Davis, Calif (D.L.M.); The Technology
Group, The Permanente Medical Group, Oakland, Calif (D.A.N.); KP Information
Technology, Kaiser Foundation Health Plan Inc and Kaiser Foundation Hospitals,
Oakland, Calif (A.P.); Department of Artificial Intelligence and Human Health
and Nash Family Department of Neuroscience (L.S.) and Department of Population
Health Science and Policy, Department of Genetics and Genomic Sciences (W.S.),
Icahn School of Medicine at Mount Sinai, New York, NY; and Department of
Radiology, Hawaii Permanente Medical Group, Moanalua Medical Center, Honolulu,
Hawaii (H.C.Y.)
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Scaranelo AM. Standalone AI in Breast Cancer Screening: Where We Are and What Is to Be Achieved. Radiology 2023; 307:e230935. [PMID: 37219442 DOI: 10.1148/radiol.230935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Affiliation(s)
- Anabel M Scaranelo
- From the Department of Medical Imaging, Faculty of Medicine, University of Toronto, 263 McCaul St, 4th Floor, Toronto, ON, Canada M5T 1W7
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Isosalo A, Inkinen SI, Turunen T, Ipatti PS, Reponen J, Nieminen MT. Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data. Comput Biol Med 2023; 161:107023. [PMID: 37230016 DOI: 10.1016/j.compbiomed.2023.107023] [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: 12/31/2022] [Revised: 04/30/2023] [Accepted: 05/09/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Development of deep convolutional neural networks for breast cancer classification has taken significant steps towards clinical adoption. It is though unclear how the models perform for unseen data, and what is required to adapt them to different demographic populations. In this retrospective study, we adopt an openly available pre-trained mammography breast cancer multi-view classification model and evaluate it by utilizing an independent Finnish dataset. METHODS Transfer learning was used, and the pre-trained model was finetuned with 8,829 examinations from the Finnish dataset (4,321 normal, 362 malignant and 4,146 benign examinations). Holdout dataset with 2,208 examinations from the Finnish dataset (1,082 normal, 70 malignant and 1,056 benign examinations) was used in the evaluation. The performance was also evaluated on a manually annotated malignant suspect subset. Receiver Operating Characteristic (ROC) and Precision-Recall curves were used to performance measures. RESULTS The Area Under ROC [95%CI] values for malignancy classification obtained with the finetuned model for the entire holdout set were 0.82 [0.76, 0.87], 0.84 [0.77, 0.89], 0.85 [0.79, 0.90], and 0.83 [0.76, 0.89] for R-MLO, L-MLO, R-CC and L-CC views respectively. Performance on the malignant suspect subset was slightly better. On the auxiliary benign classification task performance remained low. CONCLUSIONS The results indicate that the model performs well also in an out-of-distribution setting. Finetuning allowed the model to adapt to some of the underlying local demographics. Future research should concentrate to identify breast cancer subgroups adversely affecting performance, as it is a requirement for increasing the model's readiness level for a clinical setting.
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Affiliation(s)
- A Isosalo
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - S I Inkinen
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland; HUS Diagnostic Center, Clinical Physiology and Nuclear Medicine, Helsinki University and Helsinki University Hospital, Helsinki, Finland
| | - T Turunen
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - P S Ipatti
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - J Reponen
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland; Medical Research Centre Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - M T Nieminen
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Medical Research Centre Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
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Quintana GI, Li Z, Vancamberg L, Mougeot M, Desolneux A, Muller S. Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification. Bioengineering (Basel) 2023; 10:bioengineering10050534. [PMID: 37237603 DOI: 10.3390/bioengineering10050534] [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: 03/19/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by the choice of patch size, as there is no unique patch size that is adapted to all lesion sizes. In addition, the impact of input image resolution on performance is not yet fully understood. In this work, we study the impact of patch size and image resolution on the classifier performance for 2D mammograms. To leverage the advantages of different patch sizes and resolutions, a multi patch-size classifier and a multi-resolution classifier are proposed. These new architectures perform multi-scale classification by combining different patch sizes and input image resolutions. The AUC is increased by 3% on the public CBIS-DDSM dataset and by 5% on an internal dataset. Compared with a baseline single patch size and single resolution classifier, our multi-scale classifier reaches an AUC of 0.809 and 0.722 in each dataset.
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Affiliation(s)
- Gonzalo Iñaki Quintana
- GE HealthCare, 283 Rue de la Minière, 78530 Buc, France
- ENS Paris-Saclay, Centre Borelli, 91190 Gif-sur-Yvette, France
| | - Zhijin Li
- GE HealthCare, 283 Rue de la Minière, 78530 Buc, France
| | | | | | - Agnès Desolneux
- ENS Paris-Saclay, Centre Borelli, 91190 Gif-sur-Yvette, France
| | - Serge Muller
- GE HealthCare, 283 Rue de la Minière, 78530 Buc, France
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33
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Alhussan AA, Eid MM, Towfek SK, Khafaga DS. Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm. Biomimetics (Basel) 2023; 8:163. [PMID: 37092415 PMCID: PMC10123690 DOI: 10.3390/biomimetics8020163] [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: 02/27/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023] Open
Abstract
According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women's death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.
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Affiliation(s)
- Amel Ali Alhussan
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Marwa M. Eid
- Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
| | - S. K. Towfek
- Delta Higher Institute for Engineering and Technology, Mansoura 35111, Egypt
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Zheng H, Lai V, Lu J, Hu D, Kang JK, Burman KD, Wartofsky L, Rosen JE. Efficacy of machine learning to identify clinical factors influencing levothyroxine dosage after total thyroidectomy. Am J Surg 2023; 225:694-698. [PMID: 36464545 DOI: 10.1016/j.amjsurg.2022.11.025] [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: 09/10/2022] [Revised: 10/26/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND We employed Machine Learning (ML) to evaluate potential additional clinical factors influencing replacement dosage requirements of levothyroxine. METHOD This was a retrospective study of patients who underwent total or completion thyroidectomy with benign pathology. Patients who achieved an euthyroid state were included in three different ML models. RESULTS Of the 487 patients included, mean age was 54.1 ± 14.1 years, 86.0% were females, 39.0% were White, 53.0% Black, 2.7% Hispanic, 1.4% Asian, and 3.9% Other. The Extreme Gradient Boosting (XGBoost) model achieved the highest accuracy at 61.0% in predicting adequate dosage compared to 47.0% based on 1.6 mcg/kg/day (p < 0.05). The Poisson regression indicated non-Caucasian race (p < 0.05), routine alcohol use (estimate = 0.03, p = 0.02), and osteoarthritis (estimate = -0.10, p < 0.001) in addition to known factors such as age (estimate = -0.003, p < 0.001), sex (female, estimate = -0.06, p < 0.001), and weight (estimate = 0.01, p < 0.001) were associated with the dosing of levothyroxine. CONCLUSIONS Along with weight, sex, age, and BMI, ML algorithms indicated that race, ethnicity, lifestyle and comorbidity factors also may impact levothyroxine dosing in post-thyroidectomy patients with benign conditions.
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Affiliation(s)
- Hui Zheng
- MedStar Washington Hospital Center, Division of Endocrine Surgery, USA; MedStar Georgetown University Hospital, Department of Surgery, USA.
| | - Victoria Lai
- MedStar Washington Hospital Center, Division of Endocrine Surgery, USA
| | - Jana Lu
- Georgetown University School of Medicine, USA
| | - Di Hu
- MedStar Health Research Institute, Center of Biostatistics, Informatics and Data Science, USA
| | - Jin K Kang
- MedStar Washington Hospital Center, Division of Endocrine Surgery, USA; MedStar Georgetown University Hospital, Department of Surgery, USA
| | - Kenneth D Burman
- MedStar Washington Hospital Center, Section of Endocrinology, USA
| | | | - Jennifer E Rosen
- MedStar Washington Hospital Center, Division of Endocrine Surgery, USA
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Heine J, Fowler EE, Weinfurtner RJ, Hume E, Tworoger SS. Breast Density Analysis Using Digital Breast Tomosynthesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.527911. [PMID: 36824710 PMCID: PMC9948963 DOI: 10.1101/2023.02.10.527911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We evaluated an automated percentage of breast density (BD) technique (PDa) with digital breast tomosynthesis (DBT) data. The approach is based on the wavelet expansion followed by analyzing signal dependent noise. Several measures were investigated as risk factors: normalized volumetric; total dense volume; average of the DBT slices (slice-mean); a two-dimensional (2D) metric applied to the synthetic images; and the mean and standard deviations of the pixel values. Volumetric measures were derived theoretically, and PDa was modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was investigated using the volume results. A matched case-control study (n = 426 pairs) was analyzed. Conditional logistic regression modeling, controlling body mass index and ethnicity, was used to estimate odds ratios (ORs) for each measure with 95% confidence intervals provided parenthetically. There were several significant findings: volumetric measure [OR = 1.43 (1.18, 1.72)], which produced an identical OR as the slice-mean measure as predicted; [OR =1.44 (1.18, 1.75)] when applied to the synthetic images; and mean of the pixel values (volume or 2D synthetic) [ORs ~ 1.31 (1.09, 1.57)]. PDa was modeled as 2nd degree polynomial (concave-down): its maximum value occurred at 0.41×(compressed breast thickness), which was similar across case-control groups, and was significant from this position [OR = 1.47 (1.21, 1.78)]. A standardized 2D synthetic image was produced, where each pixel value represents the percentage of BD above its location. The significant findings indicate the validity of the technique. Derivations supported by empirical analyses produced a new synthetic 2D standardized image technique. Ancillary to the objectives, the results provide evidence for understanding the percentage of BD measure applied to 2D mammograms. Notwithstanding the findings, the study design provides a template for investigating other measures such as texture.
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Konz N, Buda M, Gu H, Saha A, Yang J, Chłędowski J, Park J, Witowski J, Geras KJ, Shoshan Y, Gilboa-Solomon F, Khapun D, Ratner V, Barkan E, Ozery-Flato M, Martí R, Omigbodun A, Marasinou C, Nakhaei N, Hsu W, Sahu P, Hossain MB, Lee J, Santos C, Przelaskowski A, Kalpathy-Cramer J, Bearce B, Cha K, Farahani K, Petrick N, Hadjiiski L, Drukker K, Armato SG, Mazurowski MA. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open 2023; 6:e230524. [PMID: 36821110 PMCID: PMC9951043 DOI: 10.1001/jamanetworkopen.2023.0524] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
IMPORTANCE An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. OBJECTIVES To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021. MAIN OUTCOMES AND MEASURES The overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes. RESULTS A total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926. CONCLUSIONS AND RELEVANCE In this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.
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Affiliation(s)
- Nicholas Konz
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Mateusz Buda
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Hanxue Gu
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Ashirbani Saha
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
- Department of Oncology, McMaster University, Hamilton, Ontario, Canada
| | | | - Jakub Chłędowski
- Jagiellonian University, Kraków, Poland
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Jungkyu Park
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Jan Witowski
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Krzysztof J. Geras
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Yoel Shoshan
- Medical Image Analytics, IBM Research, Haifa, Israel
| | | | - Daniel Khapun
- Medical Image Analytics, IBM Research, Haifa, Israel
| | - Vadim Ratner
- Medical Image Analytics, IBM Research, Haifa, Israel
| | - Ella Barkan
- Medical Image Analytics, IBM Research, Haifa, Israel
| | | | - Robert Martí
- Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Akinyinka Omigbodun
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
| | - Chrysostomos Marasinou
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
| | - Noor Nakhaei
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
| | - William Hsu
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
- Department of Bioengineering, University of California Los Angeles Samueli School of Engineering
| | - Pranjal Sahu
- Department of Computer Science, Stony Brook University, Stony Brook, New York
| | - Md Belayat Hossain
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Juhun Lee
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Carlos Santos
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - Artur Przelaskowski
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown
| | - Benjamin Bearce
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown
| | - Kenny Cha
- US Food and Drug Administration, Silver Spring, Maryland
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland
| | | | | | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Samuel G. Armato
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Maciej A. Mazurowski
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
- Department of Computer Science, Duke University, Durham, North Carolina
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
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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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Zheng D, He X, Jing J. Overview of Artificial Intelligence in Breast Cancer Medical Imaging. J Clin Med 2023; 12:jcm12020419. [PMID: 36675348 PMCID: PMC9864608 DOI: 10.3390/jcm12020419] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/26/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.
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Goldberg JE, Reig B, Lewin AA, Gao Y, Heacock L, Heller SL, Moy L. New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis. Radiographics 2023; 43:e220060. [DOI: 10.1148/rg.220060] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Julia E. Goldberg
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Beatriu Reig
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Alana A. Lewin
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Yiming Gao
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Laura Heacock
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Samantha L. Heller
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Linda Moy
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
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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: 4] [Impact Index Per Article: 4.0] [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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Liu Y, Tong Y, Wan Y, Xia Z, Yao G, Shang X, Huang Y, Chen L, Chen DQ, Liu B. Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network. Front Oncol 2023; 13:1119743. [PMID: 37035200 PMCID: PMC10075355 DOI: 10.3389/fonc.2023.1119743] [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: 12/19/2022] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
Background Architectural distortion (AD) is a common imaging manifestation of breast cancer, but is also seen in benign lesions. This study aimed to construct deep learning models using mask regional convolutional neural network (Mask-RCNN) for AD identification in full-field digital mammography (FFDM) and evaluate the performance of models for malignant AD diagnosis. Methods This retrospective diagnostic study was conducted at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2011 and December 2020. Patients with AD in the breast in FFDM were included. Machine learning models for AD identification were developed using the Mask RCNN method. Receiver operating characteristics (ROC) curves, their areas under the curve (AUCs), and recall/sensitivity were used to evaluate the models. Models with the highest AUCs were selected for malignant AD diagnosis. Results A total of 349 AD patients (190 with malignant AD) were enrolled. EfficientNetV2, EfficientNetV1, ResNext, and ResNet were developed for AD identification, with AUCs of 0.89, 0.87, 0.81 and 0.79. The AUC of EfficientNetV2 was significantly higher than EfficientNetV1 (0.89 vs. 0.78, P=0.001) for malignant AD diagnosis, and the recall/sensitivity of the EfficientNetV2 model was 0.93. Conclusion The Mask-RCNN-based EfficientNetV2 model has a good diagnostic value for malignant AD.
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Affiliation(s)
- Yuanyuan Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yunfei Tong
- Department of Engineering, Shanghai Yanghe Huajian Artificial Intelligence Technology Co., Ltd, Shanghai, China
| | - Yun Wan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ziqiang Xia
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Guoyan Yao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaojing Shang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Huang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lijun Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Daniel Q. Chen
- Artificial Intelligence (AI), Research Lab, Boston Meditech Group, Burlington, MA, United States
- *Correspondence: Bo Liu, ; Daniel Q. Chen,
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Bo Liu, ; Daniel Q. Chen,
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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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Choi WJ, An JK, Woo JJ, Kwak HY. Comparison of Diagnostic Performance in Mammography Assessment: Radiologist with Reference to Clinical Information Versus Standalone Artificial Intelligence Detection. Diagnostics (Basel) 2022; 13:diagnostics13010117. [PMID: 36611409 PMCID: PMC9818877 DOI: 10.3390/diagnostics13010117] [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: 11/18/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
We compared diagnostic performances between radiologists with reference to clinical information and standalone artificial intelligence (AI) detection of breast cancer on digital mammography. This study included 392 women (average age: 57.3 ± 12.1 years, range: 30−94 years) diagnosed with malignancy between January 2010 and June 2021 who underwent digital mammography prior to biopsy. Two radiologists assessed mammographic findings based on clinical symptoms and prior mammography. All mammographies were analyzed via AI. Breast cancer detection performance was compared between radiologists and AI based on how the lesion location was concordant between each analysis method (radiologists or AI) and pathological results. Kappa coefficient was used to measure the concordance between radiologists or AI analysis and pathology results. Binominal logistic regression analysis was performed to identify factors influencing the concordance between radiologists’ analysis and pathology results. Overall, the concordance was higher in radiologists’ diagnosis than on AI analysis (kappa coefficient: 0.819 vs. 0.698). Impact of prior mammography (odds ratio (OR): 8.55, p < 0.001), clinical symptom (OR: 5.49, p < 0.001), and fatty breast density (OR: 5.18, p = 0.008) were important factors contributing to the concordance of lesion location between radiologists’ diagnosis and pathology results.
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Affiliation(s)
- Won Jae Choi
- Department of Radiology, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Jin Kyung An
- Department of Radiology, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
- Correspondence: ; Tel.: +82-2-970-8290; Fax: +82-2-970-8346
| | - Jeong Joo Woo
- Department of Radiology, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
| | - Hee Yong Kwak
- Department of Surgery, Nowon Eulji University Hospital, Eulji University School of Medicine, Seoul 01830, Republic of Korea
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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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Guerrisi A, Falcone I, Valenti F, Rao M, Gallo E, Ungania S, Maccallini MT, Fanciulli M, Frascione P, Morrone A, Caterino M. Artificial Intelligence and Advanced Melanoma: Treatment Management Implications. Cells 2022; 11:cells11243965. [PMID: 36552729 PMCID: PMC9777238 DOI: 10.3390/cells11243965] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.
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Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Italia Falcone
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
- Correspondence:
| | - Fabio Valenti
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Marco Rao
- Enea-FSN-TECFIS-APAM, C.R. Frascati, via Enrico Fermi, 45, 00146 Rome, Italy
| | - Enzo Gallo
- Pathology Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy
| | - Maria Teresa Maccallini
- Departement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy
| | - Maurizio Fanciulli
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Pasquale Frascione
- Oncologic and Preventative Dermatology, IFO-San Gallicano Dermatological Institute-IRCCS, 00144 Rome, Italy
| | - Aldo Morrone
- Scientific Direction, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Mauro Caterino
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
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Sundell VM, Mäkelä T, Vitikainen AM, Kaasalainen T. Convolutional neural network -based phantom image scoring for mammography quality control. BMC Med Imaging 2022; 22:216. [PMID: 36476319 PMCID: PMC9727908 DOI: 10.1186/s12880-022-00944-w] [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: 06/21/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Visual evaluation of phantom images is an important, but time-consuming part of mammography quality control (QC). Consistent scoring of phantom images over the device's lifetime is highly desirable. Recently, convolutional neural networks (CNNs) have been applied to a wide range of image classification problems, performing with a high accuracy. The purpose of this study was to automate mammography QC phantom scoring task by training CNN models to mimic a human reviewer. METHODS Eight CNN variations consisting of three to ten convolutional layers were trained for detecting targets (fibres, microcalcifications and masses) in American College of Radiology (ACR) accreditation phantom images and the results were compared with human scoring. Regular and artificially degraded/improved QC phantom images from eight mammography devices were visually evaluated by one reviewer. These images were used in training the CNN models. A separate test set consisted of daily QC images from the eight devices and separately acquired images with varying dose levels. These were scored by four reviewers and considered the ground truth for CNN performance testing. RESULTS Although hyper-parameter search space was limited, an optimal network depth after which additional layers resulted in decreased accuracy was identified. The highest scoring accuracy (95%) was achieved with the CNN consisting of six convolutional layers. The highest deviation between the CNN and the reviewers was found at lowest dose levels. No significant difference emerged between the visual reviews and CNN results except in case of smallest masses. CONCLUSION A CNN-based automatic mammography QC phantom scoring system can score phantom images in a good agreement with human reviewers, and can therefore be of benefit in mammography QC.
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Affiliation(s)
- Veli-Matti Sundell
- grid.7737.40000 0004 0410 2071Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland ,grid.7737.40000 0004 0410 2071HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Teemu Mäkelä
- grid.7737.40000 0004 0410 2071Department of Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland ,grid.7737.40000 0004 0410 2071HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Anne-Mari Vitikainen
- grid.7737.40000 0004 0410 2071HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Touko Kaasalainen
- grid.7737.40000 0004 0410 2071HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290 Helsinki, Finland
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Discrimination between phyllodes tumor and fibro-adenoma: Does artificial intelligence-aided mammograms have an impact? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00734-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The indulgence of artificial intelligence (AI) has been considered recently in the work up for the detection and diagnosis of breast cancer through algorithms that could supply diagnosis as the radiologist do. The algorithm learns from a supervised and continuous input of large and new data sets unlike the standard programming, which requires clear step-by-step instructions. The aim of this study is to assess the ability of AI scanned mammograms to aid the ultrasound in the discrimination between phyllodes tumors and fibro-adenomas.
Results
This is a retrospective analysis included 374 proven phyllodes tumors (PT) and fibro-adenomas (FA). Digital mammogram and breast ultrasound was performed for all the cases and each breast was given a “Breast Imaging Reporting and Data System” (BI-RADS) score. Included mammograms were scanned by AI with resultant a qualitative heatmap and a quantitative abnormality scoring of suspicion percentage.
The study included 164 PT (43.9%) and 210 FA (56.1%). BI-RADS category 2 was assigned in 40.1%, category 3 in 38.2%, category 4 in 18.5% and category 5 in 3.2% with median value of the AI abnormality scoring of 23%, 44%, 65% and 90% respectively. Sensitivity and specificity of the conventional imaging were 59.2% and 75.8% respectively. The AI abnormality scoring of 49.5% upgraded the sensitivity to 89.6% and specificity to 94.8% in the ability to discriminate PT from FA masses.
Conclusion
Artificial intelligence-aided mammograms could be used as method of distinction between PT from FA detected on sono-mammogram. The color hue and the quantification of the abnormality scoring percentage could be used as a one setting method for specification and so guide clinicians in their decision of conservative management or the choice of the surgical procedure.
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Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations. JAMA Intern Med 2022; 182:1306-1312. [PMID: 36342705 PMCID: PMC10623674 DOI: 10.1001/jamainternmed.2022.4969] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Importance Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain. Objectives To describe the current FDA regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve the current system. Evidence Review Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021. Findings Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices. Conclusions and Relevance The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.
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Affiliation(s)
| | - Joseph S Ross
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
| | - Sanjay Aneja
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, Connecticut
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut
| | - Cary P Gross
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale School of Medicine, New Haven, Connecticut
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Ilana B Richman
- Section of General Medicine, Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale School of Medicine, New Haven, Connecticut
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Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, Huang Y, Sun HZ, Shi Y, Gao S, Lou Y, Chang Q, Zhao YH, Gao QL, Wu QJ. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine 2022; 53:101662. [PMID: 36147628 PMCID: PMC9486055 DOI: 10.1016/j.eclinm.2022.101662] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. METHODS The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. FINDINGS Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk). INTERPRETATION AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. FUNDING This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).
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Key Words
- AI, Artificial intelligence
- AUC, Area Under the Curve
- Artificial intelligence
- CT, Computed Tomography
- DL, Deep learning
- ML, Machine learning
- MRI, Magnetic Resonance Imaging
- Medical imaging
- Meta-analysis
- OC, Ovarian cancer
- Ovarian cancer
- SE, Sensitivity
- SP, Specificity
- US, Ultrasound
- XAI, Explainable artificial intelligence
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Affiliation(s)
- He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Yu Chen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Lou
- Department of Intelligent Medicine, China Medical University, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qing-Lei Gao
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynecology and Obstetrics, Tongji Hospital, Wuhan, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Corresponding author at: Department of Clinical Epidemiology, Department of Obstetrics and Gynecology, Clinical Research Center, Shengjing Hospital of China Medical University, Address: No. 36, San Hao Street, Shenyang, Liaoning 110004, PR China.
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Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022; 22:187. [PMID: 36316665 PMCID: PMC9620604 DOI: 10.1186/s12880-022-00918-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/22/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.
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Affiliation(s)
- Iben Fasterholdt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mohammad Naghavi-Behzad
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Benjamin S. B. Rasmussen
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Radiology, Odense University Hospital, Odense, Denmark ,grid.7143.10000 0004 0512 5013CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
| | - Tue Kjølhede
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mette Maria Skjøth
- grid.7143.10000 0004 0512 5013Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Malene Grubbe Hildebrandt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark ,grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Kristian Kidholm
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
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