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Wielema M, Sijens PE, Pijnappel RM, De Bock GH, Zorgdrager M, Kok MGJ, Rainer E, Varga R, Clauser P, Oudkerk M, Dorrius MD, Baltzer PAT. Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening. Eur Radiol 2024; 34:4730-4737. [PMID: 38008743 PMCID: PMC11213722 DOI: 10.1007/s00330-023-10321-y] [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: 04/11/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 11/28/2023]
Abstract
OBJECTIVES To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. METHODS Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). RESULTS Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). CONCLUSIONS Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. CLINICAL RELEVANCE STATEMENT Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. KEY POINTS • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.
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Affiliation(s)
- Mirjam Wielema
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Paul E Sijens
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ruud M Pijnappel
- Department of Radiology, Utrecht University, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geertruida H De Bock
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marcel Zorgdrager
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marius G J Kok
- Department of Radiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Eva Rainer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Raoul Varga
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Monique D Dorrius
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
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Shamir SB, Sasson AL, Margolies LR, Mendelson DS. New Frontiers in Breast Cancer Imaging: The Rise of AI. Bioengineering (Basel) 2024; 11:451. [PMID: 38790318 PMCID: PMC11117903 DOI: 10.3390/bioengineering11050451] [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: 03/21/2024] [Revised: 04/18/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
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Affiliation(s)
- Stephanie B. Shamir
- Department of Diagnostic, Molecular and Interventional Radiology, The Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
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Zhang H, Meng Z, Ru J, Meng Y, Wang K. Application and prospects of AI-based radiomics in ultrasound diagnosis. Vis Comput Ind Biomed Art 2023; 6:20. [PMID: 37828411 PMCID: PMC10570254 DOI: 10.1186/s42492-023-00147-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.
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Affiliation(s)
- Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jinyu Ru
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Yaqing Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China.
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Patel K, Huang S, Rashid A, Varghese B, Gholamrezanezhad A. A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer. Life (Basel) 2023; 13:2011. [PMID: 37895393 PMCID: PMC10608739 DOI: 10.3390/life13102011] [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: 08/27/2023] [Revised: 09/30/2023] [Accepted: 09/30/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist's workflow.
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Affiliation(s)
- Kishan Patel
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA (A.G.)
| | - Sherry Huang
- Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Arnav Rashid
- Department of Biological Sciences, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Bino Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA (A.G.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA (A.G.)
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Zhang Y, Liu YL, Nie K, Zhou J, Chen Z, Chen JH, Wang X, Kim B, Parajuli R, Mehta RS, Wang M, Su MY. Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification. Acad Radiol 2023; 30 Suppl 2:S161-S171. [PMID: 36631349 PMCID: PMC10515321 DOI: 10.1016/j.acra.2022.12.038] [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: 10/03/2022] [Revised: 12/10/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023]
Abstract
RATIONALE AND OBJECTIVES Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND METHODS Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis. RESULTS In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant. CONCLUSION ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, California; Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, California
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, California; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Bomi Kim
- Department of Radiological Sciences, University of California, Irvine, California; Department of Breast Radiology, Ilsan Hospital, Goyang, South Korea
| | - Ritesh Parajuli
- Department of Medicine, University of California, Irvine, United States
| | - Rita S Mehta
- Department of Medicine, University of California, Irvine, United States
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California; Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Alikhassi A, Li X, Au F, Kulkarni S, Ghai S, Allison G, Freitas V. False-positive incidental lesions detected on contrast-enhanced breast MRI: clinical and imaging features. Breast Cancer Res Treat 2023; 198:321-334. [PMID: 36740611 DOI: 10.1007/s10549-023-06861-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 01/08/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE To identify demographic and imaging features of MRI-detected enhancing lesions without clinical, ultrasound, and mammographic correlation associated with false-positive outcomes, impacting patient care. MATERIALS AND METHODS A retrospective multi-institutional study of imaging studies and patient's chart review of consecutive women with MRI-detected enhancing lesions without clinical, mammogram, or ultrasound correlation between January and December 2018, who underwent MRI-guided biopsy. According to the BI-RADS lexicon, lesions' frequency and imaging features were recorded. The demographic and imaging characteristics variables were correlated with histopathology as the gold standard and an uneventful follow-up of at least one year. Univariate logistic regression analysis was used to explore the correlation between the baseline variables such as age, genetic mutation, family history of breast cancer, personal history of breast cancer, MRI indication, background parenchymal enhancement, and MRI characteristic of the lesion with the false-positive results in main data and subgroup analysis. RESULTS Two hundred nineteen women (median age 49 years; range 26-85 years) with 219 MRI-detected enhancing lesions that underwent MRI-guided vacuum-assisted biopsy during the study period fulfilled the study criteria and formed the study cohort. Out of 219, 180 lesions (82.2%) yielded benign pathology results, including 137 benign outcomes (76%) and 43 high-risk lesions (24%). Most demographic and imaging characteristics variables did not help to differentiate malignant from benign lesions. The variables that showed statistically significant association with true-positive results in univariate analyses were age (OR 1.05; 95% CI 1.02-1.08; p = 0.0015), irregular mass-lesion shape when compared with oval/round mass lesion (OR 11.2; 95% CI 1.6-78.4; p = 0.015), and clumped and clustered ring of enhancement when compared with homogeneous (OR 3.22, 95% CI 1.40-7.40; p = 0.0058). For participants with mass breast lesion, the hyperintense signal on the T2-weighted sequence (compared to the normal fibroglandular signal) was significantly related to the false-positive result (OR 0.13; 95% CI 0.02-0.76; p = 0.024). CONCLUSION Young patients, oval/round mass-lesion shape, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of enhancing lesions depicted by MRI. For participants with mass breast lesion, T2-bright mass lesion showed significant association with false-positive result. It may impact the patient's management with a suggestion of follow-up rather than interventional procedure when these demographic and imaging parameters are present, consequently decreasing the patient's anxiety and health care costs.
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Affiliation(s)
- Afsaneh Alikhassi
- Division of Breast Imaging, Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada
| | - Xuan Li
- Department of Biostatistics-Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, 10Th Floor, Room 10-509, Toronto, ON, M5G 2M9, Canada
| | - Frederick Au
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Supriya Kulkarni
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Sandeep Ghai
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Grant Allison
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Vivianne Freitas
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada.
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Pesapane F, De Marco P, Rapino A, Lombardo E, Nicosia L, Tantrige P, Rotili A, Bozzini AC, Penco S, Dominelli V, Trentin C, Ferrari F, Farina M, Meneghetti L, Latronico A, Abbate F, Origgi D, Carrafiello G, Cassano E. How Radiomics Can Improve Breast Cancer Diagnosis and Treatment. J Clin Med 2023; 12:jcm12041372. [PMID: 36835908 PMCID: PMC9963325 DOI: 10.3390/jcm12041372] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Correspondence: ; Tel.: +39-02-574891
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rapino
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Eleonora Lombardo
- UOC of Diagnostic Imaging, Policlinico Tor Vergata University, 00133 Rome, Italy
| | - Luca Nicosia
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Priyan Tantrige
- Department of Radiology, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Chiara Trentin
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Federica Ferrari
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Mariagiorgia Farina
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Health Sciences, University of Milan, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Songeon J, Courvoisier S, Xin L, Agius T, Dabrowski O, Longchamp A, Lazeyras F, Klauser A. In vivo magnetic resonance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:math> P-Spectral Analysis With Neural Networks: 31P-SPAWNN. Magn Reson Med 2023; 89:40-53. [PMID: 36161342 PMCID: PMC9828468 DOI: 10.1002/mrm.29446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$$ {}^{31} $$</mml:annotation></mml:semantics> </mml:math> P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with the performance of the two approaches, are compared in this work. THEORY AND METHODS Convolutional neural network architectures have been proposed for the analysis and quantification of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$$ {}^{31} $$</mml:annotation></mml:semantics> </mml:math> P-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein. In vivo unlocalized free induction decay and three-dimensional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$$ {}^{31} $$</mml:annotation></mml:semantics> </mml:math> P-magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P-SPAWNN or traditional least-square fitting techniques. RESULTS The presented experiment has demonstrated both the reliability and accuracy of 31P-SPAWNN for estimating metabolite concentrations and spectral parameters. Simulated test data showed improved quantification using 31P-SPAWNN compared with LCModel. In vivo data analysis revealed higher accuracy at low signal-to-noise ratio using 31P-SPAWNN, yet with equivalent precision. Processing time using 31P-SPAWNN can be further shortened up to two orders of magnitude. CONCLUSION The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting.
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Affiliation(s)
- Julien Songeon
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Sébastien Courvoisier
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland,CIBM Center for Biomedical ImagingGenevaSwitzerland
| | - Lijing Xin
- CIBM Center for Biomedical ImagingGenevaSwitzerland,Animal Imaging and TechnologyEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Thomas Agius
- Department of Vascular SurgeryCentre Hospitalier Universitaire Vaudois and University of LausanneLausanneSwitzerland
| | - Oscar Dabrowski
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Alban Longchamp
- Department of Vascular SurgeryCentre Hospitalier Universitaire Vaudois and University of LausanneLausanneSwitzerland
| | - François Lazeyras
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland,CIBM Center for Biomedical ImagingGenevaSwitzerland
| | - Antoine Klauser
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland,CIBM Center for Biomedical ImagingGenevaSwitzerland
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9
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The potential of predictive and prognostic breast MRI (P2-bMRI). Eur Radiol Exp 2022; 6:42. [PMID: 35989400 PMCID: PMC9393116 DOI: 10.1186/s41747-022-00291-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.
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Zhu J, Geng J, Shan W, Zhang B, Shen H, Dong X, Liu M, Li X, Cheng L. Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI. Front Oncol 2022; 12:946580. [PMID: 36033449 PMCID: PMC9402900 DOI: 10.3389/fonc.2022.946580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
Importance The utilization of artificial intelligence for the differentiation of benign and malignant breast lesions in multiparametric MRI (mpMRI) assists radiologists to improve diagnostic performance. Objectives To develop an automated deep learning model for breast lesion segmentation and characterization and to evaluate the characterization performance of AI models and radiologists. Materials and methods For lesion segmentation, 2,823 patients were used for the training, validation, and testing of the VNet-based segmentation models, and the average Dice similarity coefficient (DSC) between the manual segmentation by radiologists and the mask generated by VNet was calculated. For lesion characterization, 3,303 female patients with 3,607 pathologically confirmed lesions (2,213 malignant and 1,394 benign lesions) were used for the three ResNet-based characterization models (two single-input and one multi-input models). Histopathology was used as the diagnostic criterion standard to assess the characterization performance of the AI models and the BI-RADS categorized by the radiologists, in terms of sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). An additional 123 patients with 136 lesions (81 malignant and 55 benign lesions) from another institution were available for external testing. Results Of the 5,811 patients included in the study, the mean age was 46.14 (range 11–89) years. In the segmentation task, a DSC of 0.860 was obtained between the VNet-generated mask and manual segmentation by radiologists. In the characterization task, the AUCs of the multi-input and the other two single-input models were 0.927, 0.821, and 0.795, respectively. Compared to the single-input DWI or DCE model, the multi-input DCE and DWI model obtained a significant increase in sensitivity, specificity, and accuracy (0.831 vs. 0.772/0.776, 0.874 vs. 0.630/0.709, 0.846 vs. 0.721/0.752). Furthermore, the specificity of the multi-input model was higher than that of the radiologists, whether using BI-RADS category 3 or 4 as a cutoff point (0.874 vs. 0.404/0.841), and the accuracy was intermediate between the two assessment methods (0.846 vs. 0.773/0.882). For the external testing, the performance of the three models remained robust with AUCs of 0.812, 0.831, and 0.885, respectively. Conclusions Combining DCE with DWI was superior to applying a single sequence for breast lesion characterization. The deep learning computer-aided diagnosis (CADx) model we developed significantly improved specificity and achieved comparable accuracy to the radiologists with promise for clinical application to provide preliminary diagnoses.
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Affiliation(s)
- Jingjin Zhu
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Jiahui Geng
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Boya Zhang
- School of Medicine, Nankai University, Tianjin, China
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Huaqing Shen
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Xiaohan Dong
- Department of Radiology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Mei Liu
- Department of Pathology, Chinese People’s Liberation Army General Hospital, Beijing, China
| | - Xiru Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing, China
- *Correspondence: Liuquan Cheng, ; Xiru Li,
| | - Liuquan Cheng
- Department of Radiology, Chinese People’s Liberation Army General Hospital, Beijing, China
- *Correspondence: Liuquan Cheng, ; Xiru Li,
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11
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Galati F, Rizzo V, Trimboli RM, Kripa E, Maroncelli R, Pediconi F. MRI as a biomarker for breast cancer diagnosis and prognosis. BJR Open 2022; 4:20220002. [PMID: 36105423 PMCID: PMC9459861 DOI: 10.1259/bjro.20220002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/05/2022] Open
Abstract
Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.
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Affiliation(s)
- Francesca Galati
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Veronica Rizzo
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | | | - Endi Kripa
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Roberto Maroncelli
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
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12
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Breast Tumor Identification in Ultrafast MRI Using Temporal and Spatial Information. Cancers (Basel) 2022; 14:cancers14082042. [PMID: 35454949 PMCID: PMC9027362 DOI: 10.3390/cancers14082042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/05/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Simple Summary The diagnosis of breast cancer with MRI is based on both morphological evaluation and kinetic curve assessment. Current computer-aided diagnosis methods for malignancy determination mainly focus on morphology features but ignored the temporal information in dynamic contrast-enhanced MRI sequences. Malignant and benign lesions usually have different enhancement patterns during the wash-in phase. Ultrafast breast MRI with high temporal resolution can capture the inflow of contrast in breast lesions. This advantage of ultrafast MRI enables the combination of both temporal and spatial information for automatic breast lesion analysis model development. We found that temporal information helps to significantly improve the performance of breast lesion classification. This suggests that ultrafast MRI provides useful information for malignancy identification and temporal information, which is indispensable for similar model development. Abstract Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization.
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13
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Liu MZ, Swintelski C, Sun S, Siddique M, Desperito E, Jambawalikar S, Ha R. Weakly Supervised Deep Learning Approach to Breast MRI Assessment. Acad Radiol 2022; 29 Suppl 1:S166-S172. [PMID: 34108114 DOI: 10.1016/j.acra.2021.03.032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification. MATERIALS AND METHODS In this IRB approved study, the dataset consisted of 278,685 image slices from 438 patients. The weakly supervised network was based on the Resnet-101 architecture. Training was implemented using the Adam optimizer and a final SoftMax score threshold of 0.5 was used for two class classification (malignant or benign). 278,685 image slices were combined into 92,895 3-channel images. 79,871 (85%) images were used for training and validation while 13,024 (15%) images were separated for testing. Of the testing dataset, 11,498 (88%) were benign and 1531 (12%) were malignant. Model performance was assessed. RESULTS The weakly supervised network achieved an AUC of 0.92 (SD ± 0.03) in distinguishing malignant from benign images. The model had an accuracy of 94.2% (SD ± 3.4) with a sensitivity and specificity of 74.4% (SD ± 8.5) and 95.3% (SD ± 3.3) respectively. CONCLUSION It is feasible to use a weakly supervised deep learning approach to assess breast MRI images without the need for pixel-by-pixel segmentation yielding a high degree of specificity in lesion classification.
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Affiliation(s)
- Michael Z Liu
- Department of Medical Physics, Columbia University Medical Center, New York, NY 10032-3784
| | - Cara Swintelski
- Department of Radiology, Columbia University Medical Center, New York, NY 10032
| | - Shawn Sun
- College of Physicians and Surgeons, Columbia University, New York, NY, 10027
| | - Maham Siddique
- Department of Radiology, Columbia University Medical Center, New York, NY 10032
| | - Elise Desperito
- Department of Radiology, Columbia University Medical Center, New York, NY 10032
| | - Sachin Jambawalikar
- Department of Medical Physics, Columbia University Medical Center, New York, NY 10032-3784
| | - Richard Ha
- Associate Professor of Radiology, Director of Research and Education, Breast Imaging Section, Columbia University Medical Center, New York Presbyterian Hospital, New York, NY 10032.
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14
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Zhang Y, Chan S, Park VY, Chang KT, Mehta S, Kim MJ, Combs FJ, Chang P, Chow D, Parajuli R, Mehta RS, Lin CY, Chien SH, Chen JH, Su MY. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol 2022; 29 Suppl 1:S135-S144. [PMID: 33317911 PMCID: PMC8192591 DOI: 10.1016/j.acra.2020.12.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/03/2023]
Abstract
RATIONALE AND OBJECTIVES Computer-aided methods have been widely applied to diagnose lesions on breast magnetic resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire set of images and detect suspicious lesions. MATERIALS AND METHODS Two DCE-MRI datasets were used, 241 patients acquired using non-fat-sat sequence for training, and 98 patients acquired using fat-sat sequence for testing. All patients have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance, and the trade-off between sensitivity and specificity, was analyzed using free response receiver operating characteristic. RESULTS When the precontrast and subtraction image of both breasts were used as input, the false positive from the heart and normal parenchymal enhancements could be minimized. The training set had 1469 positive slices (containing lesion) and 9135 negative slices. In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79. When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the training and 98 of 98 (100%) lesions in the testing datasets were identified. CONCLUSION Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize, and segment lesions. This may be integrated with other artificial intelligence algorithms to develop a fully automatic breast MRI diagnostic system.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Siwa Chan
- Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Vivian Youngjean Park
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Siddharth Mehta
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Min Jung Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Freddie J. Combs
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Peter Chang
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Daniel Chow
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Ritesh Parajuli
- Department of Medicine, University of California, Irvine, CA, United States
| | - Rita S. Mehta
- Department of Medicine, University of California, Irvine, CA, United States
| | - Chin-Yao Lin
- Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Sou-Hsin Chien
- Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA, United States,Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, United States,Corresponding Author:Min-Ying Su, PhD, John Tu and Thomas Yuen Center for Functional Onco-Imaging, 164 Irvine Hall, University of California, Irvine, CA 92697-5020, USA, Tel: +1 (949) 824-4925; Fax: +1 (949) 824-3481;
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15
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Frankhouser DE, Dietze E, Mahabal A, Seewaldt VL. Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging. FRONTIERS IN RADIOLOGY 2021; 1:735567. [PMID: 37492179 PMCID: PMC10364989 DOI: 10.3389/fradi.2021.735567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/11/2021] [Indexed: 07/27/2023]
Abstract
Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.
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Affiliation(s)
- David E. Frankhouser
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Eric Dietze
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Ashish Mahabal
- Department of Astronomy, Division of Physics, Mathematics, and Astronomy, California Institute of Technology (Caltech), Pasadena, CA, United States
| | - Victoria L. Seewaldt
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
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16
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Galati F, Trimboli RM, Pediconi F. Special Issue "Advances in Breast MRI". Diagnostics (Basel) 2021; 11:diagnostics11122297. [PMID: 34943534 PMCID: PMC8700161 DOI: 10.3390/diagnostics11122297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022] Open
Affiliation(s)
- Francesca Galati
- Department of Radiological, Oncological and Pathological Sciences, Sapienza—University of Rome, 00161 Rome, Italy;
| | | | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza—University of Rome, 00161 Rome, Italy;
- Correspondence: ; Tel.: +39-06-4455602; Fax: +39-06-490243
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17
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Bahl M. Updates in Artificial Intelligence for Breast Imaging. Semin Roentgenol 2021; 57:160-167. [PMID: 35523530 PMCID: PMC9077006 DOI: 10.1053/j.ro.2021.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) for breast imaging has rapidly moved from the experimental to implementation phase. As of this writing, Food and Drug Administration (FDA)-approved mammographic applications are available for triage, lesion detection and classification, and breast density assessment. For sonography and MRI, FDA-approved applications are available for lesion classification. Numerous other interpretive and noninterpretive AI applications are in the development phase. This article reviews AI applications for mammography, sonography, and MRI that are currently available for clinical use. In addition, clinical implementation and the future of AI for breast imaging are discussed.
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Abstract
Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.
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Affiliation(s)
- Anabel M Scaranelo
- Medical Imaging Department, 12366University of Toronto, Ontario, Canada.,Breast Imaging Division, Joint Department of Medical Imaging, University of Health Network, Sinai Health and Women's College Hospital, Toronto, Ontario, Canada
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19
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Siebelt M, Das D, Van Den Moosdijk A, Warren T, Van Der Putten P, Van Der Weegen W. Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints. Acta Orthop 2021; 92:254-257. [PMID: 33573429 PMCID: PMC8231380 DOI: 10.1080/17453674.2021.1884408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to different types of pathology. With this study, we investigated the proof-of-principle of ML-based diagnosis in patients with hip complaints using a digital questionnaire and the Kellgren and Lawrence (KL) osteoarthritis score.Patients and methods - 548 patients (> 55 years old) scheduled for consultation of hip complaints were asked to participate in this study and fill in an online questionnaire. Our questionnaire consists of 27 questions related to general history-taking and validated patient-related outcome measures (Oxford Hip Score and a Numeric Rating Scale for pain). 336 fully completed questionnaires were related to their classified diagnosis (either hip osteoarthritis, bursitis or tendinitis, or other pathology). Different AI techniques were used to relate questionnaire outcome and hip diagnoses. Resulting area under the curve (AUC) and classification accuracy (CA) are reported to identify the best scoring AI model. The accuracy of different ML models was compared using questionnaire outcome with and without radiologic KL scores for degree of osteoarthritis.Results - The most accurate ML model for diagnosis of patients with hip complaints was the Random Forest model (AUC 82%, 95% CI 0.78-0.86; CA 69%, CI 0.64-0.74) and most accurate analysis with addition of KL scores was with a Support Vector Machine model (AUC 89%, CI 0.86-0.92; CA 83%, CI 0.79-0.87).Interpretation - Analysis of self-reported online questionnaires related to hip complaints can differentiate between basic hip pathologies. The addition of radiological scores for osteoarthritis further improves these outcomes.
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Affiliation(s)
- Michiel Siebelt
- Department of Orthopedic Surgery, St Anna Hospital, Geldrop;
| | - Dirk Das
- Department of Orthopedic Surgery, St Anna Hospital, Geldrop;
| | | | - Tristan Warren
- Department of Orthopedic Surgery, St Anna Hospital, Geldrop;
| | - Peter Van Der Putten
- Leiden Institute of Advanced Computer Science, Leiden University Leiden, The Netherlands
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Dietzel M, Clauser P, Kapetas P, Schulz-Wendtland R, Baltzer PAT. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. ROFO-FORTSCHR RONTG 2021; 193:898-908. [PMID: 33535260 DOI: 10.1055/a-1346-0095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. METHODS AND RESULTS This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. CONCLUSION Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. KEY POINTS · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.. CITATION FORMAT · Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
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Affiliation(s)
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | | | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
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MRI-Derived Tumour-to-Breast Volume Is Associated with the Extent of Breast Surgery. Diagnostics (Basel) 2021; 11:diagnostics11020204. [PMID: 33573253 PMCID: PMC7912531 DOI: 10.3390/diagnostics11020204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/30/2020] [Accepted: 01/21/2021] [Indexed: 12/31/2022] Open
Abstract
The tumour-to-breast volume ratio (TBVR) is a metric that may help surgical decision making. In this retrospective Ethics-Committee–approved study, we assessed the correlation between magnetic resonance imaging (MRI)-derived TBVR and the performed surgery. The TBVR was obtained using a fully manual method for the segmentation of the tumour volume (TV) and a growing region semiautomatic method for the segmentation of the whole breast volume (WBV). Two specifically-trained residents (R1 and R2) independently segmented T1-weighted datasets of 51 cancer cases in 51 patients (median age 57 years). The intraobserver and interobserver TBVR reproducibility were calculated. Mann-Whitney U, Spearman correlations, and Bland-Altman statistics were used. Breast-conserving surgery (BCS) was performed in 31/51 cases (61%); mastectomy was performed in 20/51 cases (39%). The median TBVR was 2.08‰ (interquartile range 0.70–9.13‰) for Reader 1, and 2.28‰ (interquartile range 0.71–9.61‰) for Reader 2, with an 84% inter-reader reproducibility. The median segmentation times were 54 s for the WBV and 141 s for the TV. Significantly-lower TBVR values were observed in the breast-conserving surgery group (median 1.14‰, interquartile range 0.49–2.55‰) than in the mastectomy group (median 10.52‰, interquartile range 2.42–14.73‰) for both readers (p < 0.001). Large scale prospective studies are needed in order to validate MRI-derived TBVR as a predictor of the type of breast surgery.
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Dong F, She R, Cui C, Shi S, Hu X, Zeng J, Wu H, Xu J, Zhang Y. One step further into the blackbox: a pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound. Eur Radiol 2021; 31:4991-5000. [PMID: 33404698 DOI: 10.1007/s00330-020-07561-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 10/28/2020] [Accepted: 11/24/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To investigate how a DL model makes decisions in lesion classification with a newly defined region of evidence (ROE) by incorporating "explainable AI" (xAI) techniques. METHODS A data set of 785 2D breast ultrasound images acquired from 367 females. The DenseNet-121 was used to classify whether the lesion is benign or malignant. For performance assessment, classification results are evaluated by calculating accuracy, sensitivity, specificity, and receiver operating characteristic for experiments of both coarse and fine regions of interest (ROIs). The area under the curve (AUC) was evaluated, and the true-positive, false-positive, true-negative, and false-negative results with breakdown in high, medium, and low resemblance on test sets were also reported. RESULTS The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. The DL model captures ROE with high resemblance of physicians' consideration as they assess the image. CONCLUSIONS We have demonstrated the effectiveness of using DenseNet to classify breast lesions with limited quantity of 2D grayscale ultrasound image data. We have also proposed a new ROE-based metric system that can help physicians and patients better understand how AI makes decisions in reading images, which can potentially be integrated as a part of evidence in early screening or triaging of patients undergoing breast ultrasound examinations. KEY POINTS • The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. • The first model with coarse ROIs is slightly better than the second model with fine ROIs according to these evaluation metrics. • The results from coarse ROI and fine ROI are consistent and the peripheral tissue is also an impact factor in breast lesion classification.
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Affiliation(s)
- Fajin Dong
- The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, People's Republic of China.,Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, People's Republic of China
| | - Ruilian She
- Department of Obstetrics and Gynecology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, People's Republic of China
| | - Chen Cui
- The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, People's Republic of China
| | - Siyuan Shi
- The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, People's Republic of China
| | - Xuqiao Hu
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, People's Republic of China
| | - Jieying Zeng
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, People's Republic of China
| | - Huaiyu Wu
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, People's Republic of China
| | - Jinfeng Xu
- Department of Ultrasound, First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, 518020, People's Republic of China.
| | - Yun Zhang
- The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Health, and The State and Shandong Province Joint Key Laboratory of Translational Cardiovascular Medicine, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan, 250012, People's Republic of China.
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Pathak P, Jalal AS, Rai R. Breast Cancer Image Classification: A Review. Curr Med Imaging 2020; 17:720-740. [PMID: 33371857 DOI: 10.2174/0929867328666201228125208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/23/2020] [Accepted: 10/14/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer, such as mammogram, ultrasound, computed tomography and Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. OBJECTIVE This paper aims to cover the approaches used in the CAD system for the detection of breast cancer. METHODS In this paper, the methods used in CAD systems are categories into two classes: the conventional approach and artificial intelligence (AI) approach. RESULTS The conventional approach covers the basic steps of image processing, such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. CONCLUSION This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.
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Affiliation(s)
- Pooja Pathak
- Department of Mathematics, GLA University, Mathura, India
| | - Anand Singh Jalal
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Ritu Rai
- Department of Computer Engineering & Applications, GLA University, Mathura, India
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The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.
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Meyer-Bäse A, Morra L, Meyer-Bäse U, Pinker K. Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:6805710. [PMID: 32934610 PMCID: PMC7474774 DOI: 10.1155/2020/6805710] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/17/2020] [Accepted: 05/28/2020] [Indexed: 12/12/2022]
Abstract
Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.
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Affiliation(s)
- Anke Meyer-Bäse
- Department of Scientific Computing, Florida State University, Tallahassee, Florida 32310-4120, USA
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, Italy
| | - Uwe Meyer-Bäse
- Department of Electrical and Computer Engineering, Florida A&M University and Florida State University, Tallahassee, Florida 32310-4120, USA
| | - Katja Pinker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA
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Retson TA, Eghtedari M. Computer-Aided Detection/Diagnosis in Breast Imaging: A Focus on the Evolving FDA Regulations for Using Software as a Medical Device. CURRENT RADIOLOGY REPORTS 2020. [DOI: 10.1007/s40134-020-00350-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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MRI-guided vacuum-assisted breast biopsy: experience of a single tertiary referral cancer centre and prospects for the future. Med Oncol 2020; 37:36. [PMID: 32221708 DOI: 10.1007/s12032-020-01358-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 03/02/2020] [Indexed: 01/11/2023]
Abstract
MRI-guided vacuum-assisted breast biopsy (VABB) is used for suspicious breast cancer (BC) lesions which are detectable only with MRI: because the high sensitivity but limited specificity of breast MRI it is a fundamental tool in breast imaging divisions. We analyse our experience of MRI-guided VABB and critically discuss the potentialities of diffusion-weighted imaging (DWI) and artificial intelligence (AI) in this matter. We retrospectively analysed a population of consecutive women underwent VABB at our tertiary referral BC centre from 01/2011 to 01/2019. Reference standard was histological diagnosis or at least 1-year negative follow-up. McNemar, Mann-Whitney and χ2 tests at 95% level of significance were used as statistical exams. 217 women (mean age = 52, 18-72 years) underwent MRI-guided VABB; 11 were excluded and 208 MRI-guided VABB lesions were performed: 34/208 invasive carcinomas, 32/208 DCIS, 8/208 LCIS, 3/208 high-risk lesions and 131/208 benign lesions were reported. Accuracy of MRI-guided VABB was 97%. The predictive features for malignancy were mass with irregular shape (OR 8.4; 95% CI 0.59-31.6), size of the lesion (OR 4.4; 95% CI 1.69-9.7) and mass with irregular/spiculated margins (OR 5.4; 95% CI 6.8-31.1). Six-month follow-up showed 4 false-negative cases (1.9%). Invasive BC showed a statistically significant higher hyperintense signal at DWI compared to benign lesions (p = 0.03). No major complications occurred. MR-guided VABB showed high accuracy. Benign-concordant lesions should be followed up with breast MRI in 6-12 months due to the risk of false-negative results. DWI and AI applications showed potential benefit as support tools for radiologists.
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Hu H. Recent Advances of Bioresponsive Nano-Sized Contrast Agents for Ultra-High-Field Magnetic Resonance Imaging. Front Chem 2020; 8:203. [PMID: 32266217 PMCID: PMC7100386 DOI: 10.3389/fchem.2020.00203] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 03/04/2020] [Indexed: 12/11/2022] Open
Abstract
The ultra-high-field magnetic resonance imaging (MRI) nowadays has been receiving enormous attention in both biomaterial research and clinical diagnosis. MRI contrast agents are generally comprising of T1-weighted and T2-weighted contrast agent types, where T1-weighted contrast agents show positive contrast enhancement with brighter images by decreasing the proton's longitudinal relaxation times and T2-weighted contrast agents show negative contrast enhancement with darker images by decreasing the proton's transverse relaxation times. To meet the incredible demand of MRI, ultra-high-field T2 MRI is gradually attracting the attention of research and medical needs owing to its high resolution and high accuracy for detection. It is anticipated that high field MRI contrast agents can achieve high performance in MRI imaging, where parameters of chemical composition, molecular structure and size of varied contrast agents show contrasted influence in each specific diagnostic test. This review firstly presents the recent advances of nanoparticle contrast agents for MRI. Moreover, multimodal molecular imaging with MRI for better monitoring is discussed during biological process. To fasten the process of developing better contrast agents, deep learning of artificial intelligent (AI) can be well-integrated into optimizing the crucial parameters of nanoparticle contrast agents and achieving high resolution MRI prior to the clinical applications. Finally, prospects and challenges are summarized.
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Affiliation(s)
- Hailong Hu
- School of Aeronautics and Astronautics, Central South University, Changsha, China
- Research Center in Intelligent Thermal Structures for Aerospace, Central South University, Changsha, China
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Pesapane F, Suter MB, Rotili A, Penco S, Nigro O, Cremonesi M, Bellomi M, Jereczek-Fossa BA, Pinotti G, Cassano E. Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation? Med Oncol 2020; 37:29. [PMID: 32180032 DOI: 10.1007/s12032-020-01353-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 02/26/2020] [Indexed: 02/06/2023]
Abstract
The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy's biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.
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Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy.
| | | | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Olga Nigro
- Medical Oncology, ASST Sette Laghi, Viale Borri 57, 21100, Varese, VA, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Massimo Bellomi
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiology, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Graziella Pinotti
- Medical Oncology, ASST Sette Laghi, Viale Borri 57, 21100, Varese, VA, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
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Bovbjerg ML, Irvin V. Current Resources for Evidence-Based Practice, March 2020. J Obstet Gynecol Neonatal Nurs 2020; 49:212-222. [PMID: 32061594 DOI: 10.1016/j.jogn.2020.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of the concept of overdiagnosis and implications for research and commentaries on reviews focused on exogenous progestogen to maintain pregnancies and cancer risk among children conceived using fertility treatments.
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Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019; 26:1695-1706. [PMID: 31405724 PMCID: PMC6878163 DOI: 10.1016/j.acra.2019.07.006] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 01/10/2023]
Abstract
RATIONALE AND OBJECTIVES The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. MATERIALS AND METHODS The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic. RESULTS The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications. CONCLUSION These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.
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Affiliation(s)
- Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
| | - Laura Heacock
- Department of Radiology, NYU Langone, New York, New York
| | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Robert D Boutin
- Department of Radiology, University of California Davis School of Medicine, Sacramento, California
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania, Philadelphia Pennsylvania
| | - Jason Itri
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157
| | - Christopher G Filippi
- Department of Radiology, Donald and Barbara School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, NY, New York
| | - Rao P Gullapalli
- Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - James Lee
- Department of Radiology, University of Kentucky, Lexington, Kentucky
| | | | - Tara Retson
- Department of Radiology, University of California San Diego, San Diego, California
| | - Kendra Godwin
- Medical Library, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joey Nicholson
- NYU Health Sciences Library, NYU School of Medicine, NYU Langone Health, New York, New York
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
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Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019; 10:105. [PMID: 31673823 PMCID: PMC6823335 DOI: 10.1186/s13244-019-0798-3] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 09/26/2019] [Indexed: 12/19/2022] Open
Abstract
We report the results of a survey conducted among ESR members in November and December 2018, asking for expectations about artificial intelligence (AI) in 5-10 years. Of 24,000 ESR members contacted, 675 (2.8%) completed the survey, 454 males (67%), 555 (82%) working at academic/public hospitals. AI impact was mostly expected (≥ 30% of responders) on breast, oncologic, thoracic, and neuro imaging, mainly involving mammography, computed tomography, and magnetic resonance. Responders foresee AI impact on: job opportunities (375/675, 56%), 218/375 (58%) expecting increase, 157/375 (42%) reduction; reporting workload (504/675, 75%), 256/504 (51%) expecting reduction, 248/504 (49%) increase; radiologist's profile, becoming more clinical (364/675, 54%) and more subspecialised (283/675, 42%). For 374/675 responders (55%) AI-only reports would be not accepted by patients, for 79/675 (12%) accepted, for 222/675 (33%) it is too early to answer. For 275/675 responders (41%) AI will make the radiologist-patient relation more interactive, for 140/675 (21%) more impersonal, for 259/675 (38%) unchanged. If AI allows time saving, radiologists should interact more with clinicians (437/675, 65%) and/or patients (322/675, 48%). For all responders, involvement in AI-projects is welcome, with different roles: supervision (434/675, 64%), task definition (359/675, 53%), image labelling (197/675, 29%). Of 675 responders, 321 (48%) do not currently use AI, 138 (20%) use AI, 205 (30%) are planning to do it. According to 277/675 responders (41%), radiologists will take responsibility for AI outcome, while 277/675 (41%) suggest shared responsibility with other professionals. To summarise, responders showed a general favourable attitude towards AI.
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Schiaffino S, Calabrese M, Melani EF, Trimboli RM, Cozzi A, Carbonaro LA, Di Leo G, Sardanelli F. Upgrade Rate of Percutaneously Diagnosed Pure Atypical Ductal Hyperplasia: Systematic Review and Meta-Analysis of 6458 Lesions. Radiology 2019; 294:76-86. [PMID: 31660803 DOI: 10.1148/radiol.2019190748] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Management of percutaneously diagnosed pure atypical ductal hyperplasia (ADH) is an unresolved clinical issue. Purpose To calculate the pooled upgrade rate of percutaneously diagnosed pure ADH. Materials and Methods A search of MEDLINE and EMBASE databases was performed in October 2018. Preferred Reporting Items for Systematic Reviews and Meta-Analyses, or PRISMA, guidelines were followed. A fixed- or random-effects model was used, along with subgroup and meta-regression analyses. The Newcastle-Ottawa scale was used for study quality, and the Egger test was used for publication bias. Results Of 521 articles, 93 were analyzed, providing data for 6458 ADHs (5911 were managed with surgical excision and 547 with follow-up). Twenty-four studies used core-needle biopsy; 44, vacuum-assisted biopsy; 21, both core-needle and vacuum-assisted biopsy; and four, unspecified techniques. Biopsy was performed with stereotactic guidance in 29 studies; with US guidance in nine, with MRI guidance in nine, and with mixed guidance in eight. Overall heterogeneity was high (I2 = 80%). Subgroup analysis according to management yielded a pooled upgrade rate of 29% (95% confidence interval [CI]: 26%, 32%) for surgically excised lesions and 5% (95% CI: 4%, 8%) for lesions managed with follow-up (P < .001). Heterogeneity was entirely associated with surgically excised lesions (I2 = 78%) rather than those managed with follow-up (I2 = 0%). Most variability was explained by guidance and needle caliper (P = .15). At subgroup analysis of surgically excised lesions, the pooled upgrade rate was 42% (95% CI: 31%, 53%) for US guidance, 23% (95% CI: 19%, 27%) for stereotactic biopsy, and 32% (95% CI: 22%, 43%) for MRI guidance, with heterogeneity (52%, 63%, and 56%, respectively) still showing the effect of needle caliper. When the authors considered patients with apparent complete lesion removal after biopsy (subgroups in 14 studies), the pooled upgrade rate was 14% (95% CI: 8%, 23%). Study quality was low to medium; the risk of publication bias was low (P = .10). Conclusion Because of a pooled upgrade rate higher than 2% (independent of biopsy technique, needle size, imaging guidance, and apparent complete lesion removal), atypical ductal hyperplasia diagnosed with percutaneous needle biopsy should be managed with surgical excision. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Brem in this issue.
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Affiliation(s)
- Simone Schiaffino
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Massimo Calabrese
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Enrico Francesco Melani
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Rubina Manuela Trimboli
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Andrea Cozzi
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Luca Alessandro Carbonaro
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Giovanni Di Leo
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
| | - Francesco Sardanelli
- From the Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy (S.S., L.A.C., G.D.L., F.S.); Unit of Radiology, IRCCS Policlinico San Martino, Genoa, Italy (M.C.); Unit of Radiology, Ente Ospedaliero Ospedali Galliera, Genoa, Italy (E.F.M.); and Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy (R.M.T., A.C., F.S.)
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Artificial intelligence and breast screening: French Radiology Community position paper. Diagn Interv Imaging 2019; 100:553-566. [DOI: 10.1016/j.diii.2019.08.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 08/20/2019] [Accepted: 08/26/2019] [Indexed: 01/02/2023]
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What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging 2019; 10:44. [PMID: 30949865 PMCID: PMC6449411 DOI: 10.1186/s13244-019-0738-2] [Citation(s) in RCA: 159] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 03/20/2019] [Indexed: 02/08/2023] Open
Abstract
This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution.Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.
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Lassau N, Estienne T, de Vomecourt P, Azoulay M, Cagnol J, Garcia G, Majer M, Jehanno E, Renard-Penna R, Balleyguier C, Bidault F, Caramella C, Jacques T, Dubrulle F, Behr J, Poussange N, Bocquet J, Montagne S, Cornelis F, Faruch M, Bresson B, Brunelle S, Jalaguier-Coudray A, Amoretti N, Blum A, Paisant A, Herreros V, Rouviere O, Si-Mohamed S, Di Marco L, Hauger O, Garetier M, Pigneur F, Bergère A, Cyteval C, Fournier L, Malhaire C, Drape JL, Poncelet E, Bordonne C, Cauliez H, Budzik JF, Boisserie M, Willaume T, Molière S, Peyron Faure N, Caius Giurca S, Juhan V, Caramella T, Perrey A, Desmots F, Faivre-Pierre M, Abitbol M, Lotte R, Istrati D, Guenoun D, Luciani A, Zins M, Meder JF, Cotten A. Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI. Diagn Interv Imaging 2019; 100:199-209. [DOI: 10.1016/j.diii.2019.02.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 02/04/2019] [Indexed: 12/18/2022]
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