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Jing X, Wielema M, Monroy-Gonzalez AG, Stams TRG, Mahesh SVK, Oudkerk M, Sijens PE, Dorrius MD, van Ooijen PMA. Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability. J Magn Reson Imaging 2024; 60:80-91. [PMID: 37846440 DOI: 10.1002/jmri.29058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Accurate breast density evaluation allows for more precise risk estimation but suffers from high inter-observer variability. PURPOSE To evaluate the feasibility of reducing inter-observer variability of breast density assessment through artificial intelligence (AI) assisted interpretation. STUDY TYPE Retrospective. POPULATION Six hundred and twenty-one patients without breast prosthesis or reconstructions were randomly divided into training (N = 377), validation (N = 98), and independent test (N = 146) datasets. FIELD STRENGTH/SEQUENCE 1.5 T and 3.0 T; T1-weighted spectral attenuated inversion recovery. ASSESSMENT Five radiologists independently assessed each scan in the independent test set to establish the inter-observer variability baseline and to reach a reference standard. Deep learning and three radiomics models were developed for three classification tasks: (i) four Breast Imaging-Reporting and Data System (BI-RADS) breast composition categories (A-D), (ii) dense (categories C, D) vs. non-dense (categories A, B), and (iii) extremely dense (category D) vs. moderately dense (categories A-C). The models were tested against the reference standard on the independent test set. AI-assisted interpretation was performed by majority voting between the models and each radiologist's assessment. STATISTICAL TESTS Inter-observer variability was assessed using linear-weighted kappa (κ) statistics. Kappa statistics, accuracy, and area under the receiver operating characteristic curve (AUC) were used to assess models against reference standard. RESULTS In the independent test set, five readers showed an overall substantial agreement on tasks (i) and (ii), but moderate agreement for task (iii). The best-performing model showed substantial agreement with reference standard for tasks (i) and (ii), but moderate agreement for task (iii). With the assistance of the AI models, almost perfect inter-observer variability was obtained for tasks (i) (mean κ = 0.86), (ii) (mean κ = 0.94), and (iii) (mean κ = 0.94). DATA CONCLUSION Deep learning and radiomics models have the potential to help reduce inter-observer variability of breast density assessment. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Xueping Jing
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Mirjam Wielema
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Andrea G Monroy-Gonzalez
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Thom R G Stams
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Shekar V K Mahesh
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands
- Institute of Diagnostic Accuracy Research B.V., Groningen, The Netherlands
| | - Paul E Sijens
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Moran CJ. Ready the treasure at our feet, MRI feature analysis in young women with breast cancer. Eur Radiol 2024:10.1007/s00330-024-10840-2. [PMID: 38896233 DOI: 10.1007/s00330-024-10840-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 05/21/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024]
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97:763-769. [PMID: 38273675 PMCID: PMC11027289 DOI: 10.1093/bjr/tqae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/30/2023] [Accepted: 01/21/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
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Affiliation(s)
- Badera Al Mohammad
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Afnan Aldaradkeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Monther Gharaibeh
- Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia
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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [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] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Zhang L, Xiao Z, Jiang W, Luo C, Ye M, Yue G, Chen Z, Ouyang S, Liu Y. Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing. Health Inf Sci Syst 2023; 11:51. [PMID: 37954065 PMCID: PMC10632346 DOI: 10.1007/s13755-023-00255-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.
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Affiliation(s)
- Ling Zhang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Zhennan Xiao
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Wenchao Jiang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Chengbin Luo
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Ming Ye
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Guanghui Yue
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060 Guangdong China
| | - Zhiyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Shuman Ouyang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Yupin Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15:1998-2016. [DOI: 10.4251/wjgo.v15.i11.1998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.
AIM To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).
METHODS PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
RESULTS A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).
CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.
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Affiliation(s)
- Jun-Qi Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jun-Jie Mi
- Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
| | - Rong Wang
- Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Cheng K, Wang J, Liu J, Zhang X, Shen Y, Su H. Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care. AIMS Public Health 2023; 10:867-895. [PMID: 38187901 PMCID: PMC10764974 DOI: 10.3934/publichealth.2023057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 10/10/2023] [Indexed: 01/09/2024] Open
Abstract
Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.
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Affiliation(s)
- Kai Cheng
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Jiangtao Wang
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Jian Liu
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Xiangsheng Zhang
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Yuanyuan Shen
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Hang Su
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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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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Hesso I, Kayyali R, Dolton DR, Joo K, Zacharias L, Charalambous A, Lavdaniti M, Stalika E, Ajami T, Acampa W, Boban J, Nabhani-Gebara S. Cancer care at the time of the fourth industrial revolution: an insight to healthcare professionals' perspectives on cancer care and artificial intelligence. Radiat Oncol 2023; 18:167. [PMID: 37814325 PMCID: PMC10561443 DOI: 10.1186/s13014-023-02351-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delivered across settings. The purpose of this study was to explore the perspectives and experiences of healthcare professionals (HCPs) on cancer treatment and the need for AI. This study is a part of the INCISIVE European Union H2020 project's development of user requirements, which aims to fully explore the potential of AI-based cancer imaging technologies. METHODS A mixed-methods research design was employed. HCPs participating in cancer care in the UK, Greece, Italy, Spain, Cyprus, and Serbia were first surveyed anonymously online. Twenty-seven HCPs then participated in semi-structured interviews. Appropriate statistical method was adopted to report the survey results by using SPSS. The interviews were audio recorded, verbatim transcribed, and then thematically analysed supported by NVIVO. RESULTS The survey drew responses from 95 HCPs. The occurrence of diagnostic delay was reported by 56% (n = 28/50) for breast cancer, 64% (n = 27/42) for lung cancer, 76% (n = 34/45) for colorectal cancer and 42% (n = 16/38) for prostate cancer. A proportion of participants reported the occurrence of false positives in the accuracy of the current imaging techniques used: 64% (n = 32/50) reported this for breast cancer, 60% (n = 25/42) for lung cancer, 51% (n = 23/45) for colorectal cancer and 45% (n = 17/38) for prostate cancer. All participants agreed that the use of technology would enhance the care pathway for cancer patients. Despite the positive perspectives toward AI, certain limitations were also recorded. The majority (73%) of respondents (n = 69/95) reported they had never utilised technology in the care pathway which necessitates the need for education and training in the qualitative finding; compared to 27% (n = 26/95) who had and were still using it. Most, 89% of respondents (n = 85/95) said they would be opened to providing AI-based services in the future to improve medical imaging for cancer care. Interviews with HCPs revealed lack of widespread preparedness for AI in oncology, several barriers to introducing AI, and a need for education and training. Provision of AI training, increasing public awareness of AI, using evidence-based technology, and developing AI based interventions that will not replace HCPs were some of the recommendations. CONCLUSION HCPs reported favourable opinions of AI-based cancer imaging technologies and noted a number of care pathway concerns where AI can be useful. For the future design and execution of the INCISIVE project and other comparable AI-based projects, the characteristics and recommendations offered in the current research can serve as a reference.
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Affiliation(s)
- Iman Hesso
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Reem Kayyali
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Debbie-Rose Dolton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Kwanyoung Joo
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Lithin Zacharias
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK
| | - Andreas Charalambous
- Cyprus University of Technology, Limassol, Cyprus
- University of Turku, Turku, Finland
| | | | - Evangelia Stalika
- International Hellenic University, Thessaloniki, Greece
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Tarek Ajami
- Urology Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Wanda Acampa
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Jasmina Boban
- Department of Radiology, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000, Novi Sad, Serbia
- Diagnostic Imaging Center, Oncology Institute of Vojvodine, Put Dr Goldmana 4, 21204, Sremska Kamenica, Serbia
| | - Shereen Nabhani-Gebara
- School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Penrhyn Road, Kingston Upon Thames, KT1 2EE, UK.
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You C, Shen Y, Sun S, Zhou J, Li J, Su G, Michalopoulou E, Peng W, Gu Y, Guo W, Cao H. Artificial intelligence in breast imaging: Current situation and clinical challenges. EXPLORATION (BEIJING, CHINA) 2023; 3:20230007. [PMID: 37933287 PMCID: PMC10582610 DOI: 10.1002/exp.20230007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/30/2023] [Indexed: 11/08/2023]
Abstract
Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.
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Affiliation(s)
- Chao You
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yiyuan Shen
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Shiyun Sun
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jiayin Zhou
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jiawei Li
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Guanhua Su
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina
| | | | - Weijun Peng
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yajia Gu
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Weisheng Guo
- Department of Minimally Invasive Interventional RadiologyKey Laboratory of Molecular Target and Clinical PharmacologySchool of Pharmaceutical Sciences and The Second Affiliated HospitalGuangzhou Medical UniversityGuangzhouChina
| | - Heqi Cao
- Department of Health SciencesNational Natural Science Foundation of ChinaBeijingChina
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15
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Mahmoud A, El-Sharkawy YH. Delineation and detection of breast cancer using novel label-free fluorescence. BMC Med Imaging 2023; 23:132. [PMID: 37716994 PMCID: PMC10505331 DOI: 10.1186/s12880-023-01095-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND Accurate diagnosis of breast cancer (BC) plays a crucial role in clinical pathology analysis and ensuring precise surgical margins to prevent recurrence. METHODS Laser-induced fluorescence (LIF) technology offers high sensitivity to tissue biochemistry, making it a potential tool for noninvasive BC identification. In this study, we utilized hyperspectral (HS) imaging data of stimulated BC specimens to detect malignancies based on altered fluorescence characteristics compared to normal tissue. Initially, we employed a HS camera and broadband spectrum light to assess the absorbance of BC samples. Notably, significant absorbance differences were observed in the 440-460 nm wavelength range. Subsequently, we developed a specialized LIF system for BC detection, utilizing a low-power blue laser source at 450 nm wavelength for ten BC samples. RESULTS Our findings revealed that the fluorescence distribution of breast specimens, which carries molecular-scale structural information, serves as an effective marker for identifying breast tumors. Specifically, the emission at 561 nm exhibited the greatest variation in fluorescence signal intensity for both tumor and normal tissue, serving as an optical predictive biomarker. To enhance BC identification, we propose an advanced image classification technique that combines image segmentation using contour mapping and K-means clustering (K-mc, K = 8) for HS emission image data analysis. CONCLUSIONS This exploratory work presents a potential avenue for improving "in-vivo" disease characterization using optical technology, specifically our LIF technique combined with the advanced K-mc approach, facilitating early tumor diagnosis in BC.
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Affiliation(s)
- Alaaeldin Mahmoud
- Optoelectronics and automatic control systems department, Military Technical College, Kobry El-Kobba, Cairo, Egypt.
| | - Yasser H El-Sharkawy
- Optoelectronics and automatic control systems department, Military Technical College, Kobry El-Kobba, Cairo, Egypt
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16
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Zhang J, Cui Z, Shi Z, Jiang Y, Zhang Z, Dai X, Yang Z, Gu Y, Zhou L, Han C, Huang X, Ke C, Li S, Xu Z, Gao F, Zhou L, Wang R, Liu J, Zhang J, Ding Z, Sun K, Li Z, Liu Z, Shen D. A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework. PATTERNS (NEW YORK, N.Y.) 2023; 4:100826. [PMID: 37720328 PMCID: PMC10499873 DOI: 10.1016/j.patter.2023.100826] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/25/2023] [Accepted: 07/21/2023] [Indexed: 09/19/2023]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Yingjia Jiang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan 410011, China
| | - Zhiliang Zhang
- School of Medical Imaging, Hangzhou Medical College, Zhejiang 310059, China
| | - Xiaoting Dai
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhenlu Yang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guizhou 550002, China
| | - Yuning Gu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Lei Zhou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Xiaomei Huang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Chenglu Ke
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Suyun Li
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Fei Gao
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
| | - Luping Zhou
- School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Rongpin Wang
- Department of Radiology, Guizhou Provincial People’s Hospital, Guizhou 550002, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Hunan 410011, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
| | - Zhongxiang Ding
- Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou 310003, China
| | - Kun Sun
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong 510080, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China
- Shanghai Clinical Research and Trial Center, Shanghai 200052, China
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Fatima Qizilbash F, Sartaj A, Qamar Z, Kumar S, Imran M, Mohammed Y, Ali J, Baboota S, Ali A. Nanotechnology revolutionises breast cancer treatment: harnessing lipid-based nanocarriers to combat cancer cells. J Drug Target 2023; 31:794-816. [PMID: 37525966 DOI: 10.1080/1061186x.2023.2243403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023]
Abstract
One of the most common cancers that occur in females is breast cancer. Despite the significant leaps and bounds that have been made in treatment of breast cancer, the disease remains one of the leading causes of death among women and a major public health challenge. The therapeutic efficacy of chemotherapeutics is hindered by chemoresistance and toxicity. Nano-based lipid drug delivery systems offer controlled drug release, nanometric size and site-specific targeting. Breast cancer treatment includes surgery, chemotherapy and radiotherapy. Despite this, no single method of treatment for the condition is currently effective due to cancer stem cell metastasis and chemo-resistance. Therefore, the employment of nanocarrier systems is necessary in order to target breast cancer stem cells. This article addresses breast cancer treatment options, including modern treatment procedures such as chemotherapy, etc. and some innovative therapeutic options highlighting the role of lipidic nanocarriers loaded with chemotherapeutic drugs such as nanoemulsion, solid-lipid nanoparticles, nanostructured lipid carriers and liposomes, and their investigations have demonstrated that they can limit cancer cell growth, reduce the risk of recurrence, as well as minimise post-chemotherapy metastasis. This article also explores FDA-approved lipid-based nanocarriers, commercially available formulations, and ligand-based formulations that are being considered for further research.
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Affiliation(s)
| | - Ali Sartaj
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
- Lloyd School of Pharmacy, Greater Noida, India
| | - Zufika Qamar
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
| | - Shobhit Kumar
- Department of Pharmaceutical Technology, Meerut Institute of Engineering and Technology (MIET), Meerut, India
| | - Mohammad Imran
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Yousuf Mohammed
- Therapeutics Research Group, Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- School of Pharmacy, The University of Queensland, Brisbane, Australia
| | - Javed Ali
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
| | - Sanjula Baboota
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
| | - Asgar Ali
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, New Delhi, India
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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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Zhang M, Wang C, Cai L, Zhao J, Xu Y, Xing J, Sun J, Zhang Y. Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images. Comput Struct Biotechnol J 2023; 22:17-26. [PMID: 37655162 PMCID: PMC10465855 DOI: 10.1016/j.csbj.2023.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.
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Affiliation(s)
- Mengyan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Cong Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Li Cai
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, 150040, China
| | - Jiyun Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ye Xu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Jiacheng Xing
- Beidahuang Industry Group General Hospital, 150060 Harbin, China
| | - Jianghong Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
- College of pathology, Qiqihar Medical University, Qiqihar 161042, China
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20
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Adam R, Dell'Aquila K, Hodges L, Maldjian T, Duong TQ. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Res 2023; 25:87. [PMID: 37488621 PMCID: PMC10367400 DOI: 10.1186/s13058-023-01687-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023] Open
Abstract
Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.
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Affiliation(s)
- Richard Adam
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Kevin Dell'Aquila
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Laura Hodges
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Takouhie Maldjian
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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21
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Zafar A, Tanveer J, Ali MU, Lee SW. BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization. Bioengineering (Basel) 2023; 10:825. [PMID: 37508852 PMCID: PMC10376009 DOI: 10.3390/bioengineering10070825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/04/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Jawad Tanveer
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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22
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Fatima GN, Fatma H, Saraf SK. Vaccines in Breast Cancer: Challenges and Breakthroughs. Diagnostics (Basel) 2023; 13:2175. [PMID: 37443570 DOI: 10.3390/diagnostics13132175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.
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Affiliation(s)
- Gul Naz Fatima
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
| | - Hera Fatma
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
| | - Shailendra K Saraf
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
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23
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Zhang R, Wang P, Bian Y, Fan Y, Li J, Liu X, Shen J, Hu Y, Liao X, Wang H, Song C, Li W, Wang X, Sun M, Zhang J, Wang M, Wang S, Shen Y, Zhang X, Jia Q, Tan J, Li N, Wang S, Xu L, Wu W, Zhang W, Meng Z. Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging. BMC Med Imaging 2023; 23:84. [PMID: 37328753 PMCID: PMC10273563 DOI: 10.1186/s12880-023-01037-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 05/29/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. METHODS We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named "YOLO" to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). RESULTS Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value < 0.05). CONCLUSION The AI system of our study showed excellent predictive accuracy in the diagnosis of MPI protocols, and therefore might be potentially helpful to aid radiologists in clinical practice and develop more sophisticated models.
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Affiliation(s)
- Ruyi Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Peng Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Yanzhu Bian
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Yan Fan
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Jianming Li
- Department of Nuclear Medicine, Teda International Cardiovascular Hospital, Tianjin, China
| | - Xuehui Liu
- Department of Nuclear Medicine, Tianjin Third Central Hospital, Tianjin, China
| | - Jie Shen
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, China
| | - Yujing Hu
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Xianghe Liao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - He Wang
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Chengyu Song
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Wangxiao Li
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072
| | - Xiaojie Wang
- Department of Nuclear Medicine, Teda International Cardiovascular Hospital, Tianjin, China
| | - Momo Sun
- Department of Nuclear Medicine, Tianjin First Central Hospital, Tianjin, China
| | - Jianping Zhang
- Department of Nuclear Medicine, Tianjin Third Central Hospital, Tianjin, China
| | - Miao Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Shen Wang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Yiming Shen
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Xuemei Zhang
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Qiang Jia
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Jian Tan
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Ning Li
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Sen Wang
- Department of Nuclear Medicine, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Lingyun Xu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Weiming Wu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052
| | - Wei Zhang
- School of Microelectronics, Tianjin University, Weijin Road No. 92, Nankai District, Tianjin, China, 300072.
| | - Zhaowei Meng
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, Anshan Road No. 154, Heping District, Tianjin, China, 300052.
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Ying W. Phenomic Studies on Diseases: Potential and Challenges. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:285-299. [PMID: 36714223 PMCID: PMC9867904 DOI: 10.1007/s43657-022-00089-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 01/23/2023]
Abstract
The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.
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Affiliation(s)
- Weihai Ying
- Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030 China
- Collaborative Innovation Center for Genetics and Development, Shanghai, 200043 China
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25
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Odhiambo P, Okello H, Wakaanya A, Wekesa C, Okoth P. Mutational signatures for breast cancer diagnosis using artificial intelligence. J Egypt Natl Canc Inst 2023; 35:14. [PMID: 37184779 DOI: 10.1186/s43046-023-00173-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 04/19/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Breast cancer is the most common female cancer worldwide. Its diagnosis and prognosis remain scanty, imprecise, and poorly documented. Previous studies have indicated that some genetic mutational signatures are suspected to lead to progression of various breast cancer scenarios. There is paucity of data on the role of AI tools in delineating breast cancer mutational signatures. This study sought to investigate the relationship between breast cancer genetic mutational profiles using artificial intelligence models with a view to developing an accurate prognostic prediction based on breast cancer genetic signatures. Prior research on breast cancer has been based on symptoms, origin, and tumor size. It has not been investigated whether diagnosis of breast cancer can be made utilizing AI platforms like Cytoscape, Phenolyzer, and Geneshot with potential for better prognostic power. This is the first ever attempt for a combinatorial approach to breast cancer diagnosis using different AI platforms. METHOD Artificial intelligence (AI) are mathematical algorithms that simulate human cognitive abilities and solve difficult healthcare issues such as complicated biological abnormalities like those experienced in breast cancer scenarios. The current models aimed to predict outcomes and prognosis by correlating imaging phenotypes with genetic mutations, tumor profiles, and hormone receptor status and development of imaging biomarkers that combine tumor and patient-specific features. Geneshotsav 2021, Cytoscape 3.9.1, and Phenolyzer Nature Methods, 12:841-843 (2015) tools, were used to mine breast cancer-associated mutational signatures and provided useful alternative computational tools for discerning pathways and enriched networks of genes of similarity with the overall goal of providing a systematic view of the variety of mutational processes that lead to breast cancer development. The development of novel-tailored pharmaceuticals, as well as the distribution of prospective treatment alternatives, would be aided by the collection of massive datasets and the use of such tools as diagnostic markers. RESULTS Specific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.
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Affiliation(s)
- Patrick Odhiambo
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya.
| | - Harrison Okello
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Annette Wakaanya
- Department of Mathematics, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Clabe Wekesa
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
| | - Patrick Okoth
- Department of Biological Sciences, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P.O. Box 190, Kakamega, 50100, Kenya
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Wang W, Wang Y. Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer. Diagnostics (Basel) 2023; 13:diagnostics13091582. [PMID: 37174975 PMCID: PMC10177566 DOI: 10.3390/diagnostics13091582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/27/2023] [Accepted: 04/09/2023] [Indexed: 05/15/2023] Open
Abstract
Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.
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Affiliation(s)
- Wei Wang
- College of Computer Science and Technology, Guizhou University, Guiyang 550001, China
- Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China
- Guizhou Provincial People's Hospital, Guiyang 550001, China
| | - Yisong Wang
- College of Computer Science and Technology, Guizhou University, Guiyang 550001, China
- Institute for Artificial Intelligence, Guizhou University, Guiyang 550001, China
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27
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Puchnin V, Jandaliyeva A, Hurshkainen A, Solomakha G, Nikulin A, Petrova P, Lavrenteva A, Andreychenko A, Shchelokova A. Quadrature transceive wireless coil: Design concept and application for bilateral breast MRI at 1.5 T. Magn Reson Med 2023; 89:1251-1264. [PMID: 36336799 DOI: 10.1002/mrm.29507] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 09/20/2022] [Accepted: 10/09/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE Development of a novel quadrature inductively driven transceive wireless coil for breast MRI at 1.5 T. METHODS A quadrature wireless coil (HHMM-coil) design has been developed as a combination of two linearly polarized coils: a pair of 'metasolenoid' coils (MM-coil) and a pair of Helmholtz-type coils (HH-coil). The MM-coil consisted of an array of split-loop resonators. The HH-coil design included two electrically connected flat spirals. All the wireless coils were coupled to a whole-body birdcage coil. The HHMM-coil was studied and compared to the linear coils in terms of transmit and SAR efficiencies via numerical simulations. A prototype of HHMM-coil was built and tested on a 1.5 T scanner in a phantom and healthy volunteer. We also proposed an extended design of the HHMM-coil and compared its performance to a dedicated breast array. RESULTS Numerical simulations of the HHMM-coil with a female voxel model have shown more than a 2.5-fold increase in transmit efficiency and a 1.7-fold enhancement of SAR efficiency compared to the linearly polarized coils. Phantom and in vivo imaging showed good agreement with the numerical simulations. Moreover, the HHMM-coil provided good image quality, visualizing all areas of interest similar to a multichannel breast array with a 32% reduction in signal-to-noise ratio. CONCLUSION The proposed quadrature HHMM-coil allows the B 1 + $$ {\mathrm{B}}_1^{+} $$ -field to be significantly better focused in the region-of-interest compared to the linearly polarized coils. Thus, the HHMM-coil provides high-quality breast imaging on a 1.5 T scanner using a whole-body birdcage coil for transmit and receive.
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Affiliation(s)
- Viktor Puchnin
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | | | - Anna Hurshkainen
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Georgiy Solomakha
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Anton Nikulin
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Polina Petrova
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
| | - Anna Lavrenteva
- Medical Institute named after Berezin Sergey (MIBS), St. Petersburg, Russia
| | - Anna Andreychenko
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia.,Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Health Care Department, Moscow, Russia
| | - Alena Shchelokova
- School of Physics and Engineering, ITMO University, St. Petersburg, Russia
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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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29
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AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? Digit Health 2023; 9:20552076231152167. [PMID: 36762024 PMCID: PMC9903019 DOI: 10.1177/20552076231152167] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/03/2023] [Indexed: 01/28/2023] Open
Abstract
Background Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications. Objective To evaluate physicians and medical students' attitude and perception on AI applications in healthcare. Method A web-based survey was disseminated by email to physicians and medical students. Results A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians' employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians' legal liability in case of a medical error. Conclusion Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.
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Affiliation(s)
- Adhari AlZaabi
- Human and Clinical Anatomy Department, College of Medicine and Health Science, Muscat, Sultanate of Oman,Adhari AlZaabi, Human and Clinical Anatomy Department, College of Medicine and Health Science, Alkhodh, P.O 123, Muscat, Sultanate of Oman.
Abdulrahman AalAbdulsalam, College of Science, Sultan Qaboos University, Muscat, Sultanate of Oman.
| | - Saleh AlMaskari
- Human and Clinical Anatomy Department, College of Medicine and Health Science, Muscat, Sultanate of Oman
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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Ming W, Li F, Zhu Y, Bai Y, Gu W, Liu Y, Sun X, Liu X, Liu H. Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique. Comput Biol Med 2022; 150:106147. [PMID: 36201887 DOI: 10.1016/j.compbiomed.2022.106147] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/06/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.
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Affiliation(s)
- Wenlong Ming
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Fuyu Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Yanhui Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China; Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, 210023, PR China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China
| | - Xiaoan Liu
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China.
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
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Baughan N, Douglas L, Giger ML. Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening. JOURNAL OF BREAST IMAGING 2022; 4:451-459. [PMID: 38416954 DOI: 10.1093/jbi/wbac052] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Indexed: 03/01/2024]
Abstract
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
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Affiliation(s)
- Natalie Baughan
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Maryellen L Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
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Yuan G, He Y, Cao QH, Tang MM, Xie ZL, Qiu Y, Zeng ZR, Peng S, Chen MH. Visceral adipose volume is correlated with surgical tissue fibrosis in Crohn's disease of the small bowel. Gastroenterol Rep (Oxf) 2022; 10:goac044. [PMID: 36042948 PMCID: PMC9420045 DOI: 10.1093/gastro/goac044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 03/30/2022] [Accepted: 05/04/2022] [Indexed: 11/16/2022] Open
Abstract
Background This study explored the diagnostic performance of visceral adiposity to predict the degree of intestinal inflammation and fibrosis. Methods The patients with Crohn’s disease (CD) who underwent surgical small bowel resection at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) between January 2007 and December 2017 were enrolled. We evaluated the intestinal imaging features of computed tomography enterography (CTE), including mesenteric inflammatory fat stranding, the target sign, mesenteric hypervascularity, bowel wall thickening, lymphadenopathy, stricture diameter, and maximal upstream diameter. We used A.K. software (Artificial Intelligence Kit, version 1.1) to calculate the visceral fat (VF) and subcutaneous fat (SF) volumes at the third lumbar vertebra level. Pathological tissue information was recorded. Diagnostic models were established based on the multivariate regression analysis results, and their effectiveness was evaluated by area under the curve (AUC) and decision curve analyses. Results Overall, 48 patients with CD were included in this study. The abdominal VF/SF volume ratio (odds ratio, 1.20; 95% confidence interval, 1.05–1.38; P = 0.009) and the stenosis diameter/upstream intestinal dilatation diameter (ND) ratio (odds ratio, 0.90; 95% confidence interval, 0.82–0.99; P = 0.034) were independent risk factors for the severe fibrosis of the small intestine. The AUC values of the VF/SF ratio, the ND ratio, and their combination were 0.760, 0.673, and 0.804, respectively. The combination of the VS/SF volume ratio and ND ratio achieved the highest net benefit on the decision curve. Conclusion The VF volume on CTE can reflect intestinal fibrosis. The combination of the VF/SF volume ratio and ND ratio of CD patients assessed using CTE can help predict severe fibrosis stenosis of the small intestine.
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Affiliation(s)
| | | | | | - Mi-Mi Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Zong-Lin Xie
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yun Qiu
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Zhi-Rong Zeng
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Sui Peng
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Clinical Trial Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Min-Hu Chen
- Corresponding author. Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen University, 58 Zhongshan Road 2, Yuexiu Distinct, Guangzhou, Guangdong 510080, P. R. China. Tel: +86-13802957089;
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Mikhailova V, Anbarjafari G. Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning. Med Biol Eng Comput 2022; 60:2589-2600. [DOI: 10.1007/s11517-022-02623-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/15/2022] [Indexed: 10/17/2022]
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Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2541358. [PMID: 36092784 PMCID: PMC9453096 DOI: 10.1155/2022/2541358] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 08/20/2022] [Indexed: 01/23/2023]
Abstract
Background Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today's clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network's labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation. Results We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors. Conclusion Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.
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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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Yin HL, Jiang Y, Xu Z, Jia HH, Lin GW. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions. J Cancer Res Clin Oncol 2022; 149:2575-2584. [PMID: 35771263 DOI: 10.1007/s00432-022-04142-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/13/2022] [Indexed: 02/05/2023]
Abstract
PURPOSE To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category 4 (BI-RADS 4) lesions and to evaluate whether the combined diagnosis of these models could improve the diagnostic performance of radiologists. METHODS A total of 319 female patients with 319 pathologically confirmed BI-RADS 4 lesions were randomly divided into training, validation, and testing sets in this retrospective study. The three models were established based on contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and T2-weighted imaging using the training and validation sets. The artificial intelligence (AI) combination score was calculated according to the results of three models. The diagnostic performances of four radiologists with and without AI assistance were compared with the AI combination score on the testing set. The area under the curve (AUC), sensitivity, specificity, accuracy, and weighted kappa value were calculated to assess the performance. RESULTS The AI combination score yielded an excellent performance (AUC = 0.944) on the testing set. With AI assistance, the AUC for the diagnosis of junior radiologist 1 (JR1) increased from 0.833 to 0.885, and that for JR2 increased from 0.823 to 0.876. The AUCs of senior radiologist 1 (SR1) and SR2 slightly increased from 0.901 and 0.950 to 0.925 and 0.975 after AI assistance, respectively. CONCLUSION Combined diagnosis of multiparametric MRI-based deep learning models to differentiate TNBC from fibroadenoma magnetic resonance BI-RADS 4 lesions can achieve comparable performance to that of SRs and improve the diagnostic performance of JRs.
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Affiliation(s)
- Hao-Lin Yin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China
| | - Yu Jiang
- Department of Radiology, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China
| | - Zihan Xu
- Lung Cancer Center, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan, China
| | - Hui-Hui Jia
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China
| | - Guang-Wu Lin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Jing'an District, 221# Yan'anxi Road, Shanghai, 200040, China.
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Wu H, Ou S, Zhang H, Huang R, Yu S, Zhao M, Tai S. Advances in biomarkers and techniques for pancreatic cancer diagnosis. Cancer Cell Int 2022; 22:220. [PMID: 35761336 PMCID: PMC9237966 DOI: 10.1186/s12935-022-02640-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/18/2022] [Indexed: 12/31/2022] Open
Abstract
Pancreatic cancer is the most lethal type of malignancy and is characterized by high invasiveness without severe symptoms. It is difficult to detect PC at an early stage because of the low diagnostic accuracy of existing routine methods, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). Therefore, it is of value to develop new diagnostic techniques for early detection with high accuracy. In this review, we aim to highlight research progress on novel biomarkers, artificial intelligence, and nanomaterial applications on the diagnostic accuracy of pancreatic cancer.
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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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Bhowmik A, Eskreis-Winkler S. Deep learning in breast imaging. BJR Open 2022; 4:20210060. [PMID: 36105427 PMCID: PMC9459862 DOI: 10.1259/bjro.20210060] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/04/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022] Open
Abstract
Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
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Affiliation(s)
- Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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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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Monsour R, Dutta M, Mohamed AZ, Borkowski A, Viswanadhan NA. Neuroimaging in the Era of Artificial Intelligence: Current Applications. Fed Pract 2022; 39:S14-S20. [PMID: 35765692 PMCID: PMC9227741 DOI: 10.12788/fp.0231] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
BACKGROUND Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care. OBSERVATIONS AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology. CONCLUSIONS The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.
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Affiliation(s)
- Robert Monsour
- University of South Florida Morsani College of Medicine, Tampa, Florida
| | - Mudit Dutta
- University of South Florida Morsani College of Medicine, Tampa, Florida
| | | | - Andrew Borkowski
- University of South Florida Morsani College of Medicine, Tampa, Florida
- James A. Haley Veterans’ Hospital, Tampa, Florida
| | - Narayan A. Viswanadhan
- University of South Florida Morsani College of Medicine, Tampa, Florida
- James A. Haley Veterans’ Hospital, Tampa, Florida
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Peng Y, Cheng Z, Gong C, Zheng C, Zhang X, Wu Z, Yang Y, Yang X, Zheng J, Shen J. Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Front Oncol 2022; 12:846775. [PMID: 35359387 PMCID: PMC8960929 DOI: 10.3389/fonc.2022.846775] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/26/2022] [Indexed: 01/02/2023] Open
Abstract
PurposeTo compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer.Materials and MethodsThis retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method.ResultsThe overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; P < 0.001) and comparable to that of the image-molecular RA model (0.755, 95% CI: 0.708, 0.802; P = 0.133). The AUROC of the image-kinetic-molecular DL model was 0.83 (95% CI: 0.816, 0.847), which was higher than that of the image-kinetic and image-molecular DL models (0.707, 95% CI: 0.654, 0.761; 0.79, 95% CI: 0.768, 0.812; P < 0.001) and higher than that of the image-kinetic-molecular RA model (0.778, 95% CI: 0.735, 0.828; P < 0.001).ConclusionsThe pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.
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Affiliation(s)
- Yunsong Peng
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Ziliang Cheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chang Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chushan Zheng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiang Zhang
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yaping Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaodong Yang
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jian Zheng
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- *Correspondence: Jian Zheng, ; Jun Shen,
| | - Jun Shen
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Jian Zheng, ; Jun Shen,
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Zhao C, Xiao M, Ma L, Ye X, Deng J, Cui L, Guo F, Wu M, Luo B, Chen Q, Chen W, Guo J, Li Q, Zhang Q, Li J, Jiang Y, Zhu Q. Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study. Front Oncol 2022; 12:804632. [PMID: 35223484 PMCID: PMC8867611 DOI: 10.3389/fonc.2022.804632] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/07/2022] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions. METHODS Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites. RESULTS A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157). CONCLUSIONS Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.
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Affiliation(s)
- Chenyang Zhao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mengsu Xiao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Ma
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinhua Ye
- Department of Ultrasound, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Deng
- Department of Ultrasound, First Affiliated Hospital, Nanjing Medical University, Nanjing, China
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Fajin Guo
- Department of Ultrasound, Beijing Hospital, Beijing, China
| | - Min Wu
- Department of Ultrasound, Nanjing Drum Tower Hospital, Nanjing, China
| | - Baoming Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Qin Chen
- Department of Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Wu Chen
- Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jun Guo
- Department of Ultrasound, Aero Space Central Hospital, Beijing, China
| | - Qian Li
- Department of Ultrasound, Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Qing Zhang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianchu Li
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxin Jiang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qingli Zhu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9227440. [PMID: 35265119 PMCID: PMC8901319 DOI: 10.1155/2022/9227440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/23/2022] [Accepted: 02/01/2022] [Indexed: 11/18/2022]
Abstract
This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patients with breast tumor were included in this study, all of whom underwent routine ultrasound examinations. Subsequently, TUI was used to assist in guiding the positioning, and the light scattering tomography system was used to further measure the lesions. A deep learning model was established to process the imaging results, and the pathological test results were undertaken as the gold standard for the efficiency of different imaging methods to diagnose the breast tumors. The results showed that, among 120 patients with breast tumor, 56 were benign lesions and 64 were malignant lesions. The average total amount of hemoglobin (HBT) of malignant lesions was significantly higher than that of benign lesions (P < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of TUI in the diagnosis of breast cancer were 90.4%, 75.6%, 81.4%, 84.7%, and 80.6%, respectively. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of ultrasound in the diagnosis of breast cancer were 81.7%, 64.9%, 70.5%, 75.9%, and 80.6%, respectively. In addition, for suspected breast malignant lesions, the combined application of ultrasound and tomography can increase the diagnostic specificity to 82.1% and the accuracy to 83.8%. Based on the above results, it was concluded that TUI combined with ultrasound had a significant effect on benign and malignant diagnosis of breast cancer and can significantly improve the specificity and accuracy of diagnosis. It also reflected that deep learning technology had a good auxiliary role in the examination of diseases and was worth the promotion of clinical application.
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Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2022; 38:527-538. [PMID: 35136337 DOI: 10.12788/fp.0174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. Observations With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. Conclusions AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.
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Affiliation(s)
- L Brannon Thomas
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Stephen M Mastorides
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | | | - Colleen E Jakey
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Andrew A Borkowski
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
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Gu WQ, Cai SM, Liu WD, Zhang Q, Shi Y, Du LJ. Combined molybdenum target X-ray and magnetic resonance imaging examinations improve breast cancer diagnostic efficacy. World J Clin Cases 2022; 10:485-491. [PMID: 35097073 PMCID: PMC8771396 DOI: 10.12998/wjcc.v10.i2.485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/19/2021] [Accepted: 12/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Early-stage breast cancer patients often lack specific clinical manifestations, making diagnosis difficult. Molybdenum target X-ray and magnetic resonance imaging (MRI) examinations both have their own advantages. Thus, a combined examination methodology may improve early breast cancer diagnoses.
AIM To explore the combined diagnostic efficacy of molybdenum target X-ray and MRI examinations in breast cancer.
METHODS Patients diagnosed with breast cancer at our hospital from March 2019 to April 2021 were recruited, as were the same number of patients during the same period with benign breast tumors. Both groups underwent molybdenum target X-ray and MRI examinations, and diagnoses were given based on each exam. The single (i.e., X-ray or MRI) and combined (i.e., using both methods) diagnoses were counted, and the MRI-related examination parameters (e.g., T-wave peak, peak and early enhancement rates, and apparent diffusion coefficient) were compared between the groups.
RESULTS In total, 63 breast cancer patients and 63 benign breast tumor patients were recruited. MRI detected 53 breast cancer cases and 61 benign breast tumor cases. Molybdenum target X-ray detected 50 breast cancer cases and 60 benign breast tumor cases. The combined methodology detected 61 breast cancer cases and 61 benign breast tumor cases. The sensitivity (96.83%) and accuracy (96.83%) of the combined methodology were higher than single-method MRI (84.13% and 90.48%, respectively) and molybdenum target X-ray (79.37% and 87.30%, respectively) (P < 0.05). The combined methodology specificity (96.83%) did not differ from single-method MRI (96.83%) or molybdenum target X-ray (95.24%) (P > 0.05). The T-wave peak (169.43 ± 32.05) and apparent diffusion coefficient (1.01 ± 0.23) were lower in the breast cancer group than in the benign tumor group (228.86 ± 46.51 and 1.41 ± 0.35, respectively). However, the peak enhancement rate (1.08 ± 0.24) and early enhancement rate (1.07 ± 0.26) were significantly higher in the breast cancer group than in the benign tumor group (0.83 ± 0.19 and 0.75 ± 0.19, respectively) (P < 0.05).
CONCLUSION Combined molybdenum target X-ray and MRI examinations for diagnosing breast cancer improved the diagnostic sensitivity and accuracy, minimizing the missed- and misdiagnoses risks and promoting timely treatment intervention.
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Affiliation(s)
- Wen-Quan Gu
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Sun-Mei Cai
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Wei-Dong Liu
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Qi Zhang
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Ying Shi
- Department of Radiology, Punan Hospital, Shanghai 200126, China
| | - Li-Juan Du
- Department of Radiology, Punan Hospital, Shanghai 200126, China
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Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: Trends & directions. Comput Biol Med 2022; 142:105221. [PMID: 35016100 DOI: 10.1016/j.compbiomed.2022.105221] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 12/18/2022]
Abstract
Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.
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Affiliation(s)
- Shahid Munir Shah
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| | - Rizwan Ahmed Khan
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan.
| | - Sheeraz Arif
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| | - Unaiza Sajid
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
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Performance of artificial intelligence for biventricular cardiovascular magnetic resonance volumetric analysis in the clinical setting. Int J Cardiovasc Imaging 2022; 38:2413-2424. [PMID: 36434343 PMCID: PMC9700578 DOI: 10.1007/s10554-022-02649-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 05/09/2022] [Indexed: 12/14/2022]
Abstract
Cardiovascular magnetic resonance (CMR) derived ventricular volumes and function guide clinical decision-making for various cardiac pathologies. We aimed to evaluate the efficiency and clinical applicability of a commercially available artificial intelligence (AI) method for performing biventricular volumetric analysis. Three-hundred CMR studies (100 with normal CMR findings, 50 dilated cardiomyopathy, 50 hypertrophic cardiomyopathy, 50 ischaemic heart disease and 50 congenital or valvular heart disease) were randomly selected from database. Manual biventricular volumetric analysis (CMRtools) results were derived from clinical reports and automated volumetric analyses were performed using short axis volumetry AI function of CircleCVI42 v5.12 software. For 20 studies, a combined method of manually adjusted AI contours was tested and all three methods were timed. Clinicians` confidence in AI method was assessed using an online survey. Although agreement was better for left ventricle than right ventricle, AI analysis results were comparable to manual method. Manual adjustment of AI contours further improved agreement: within subject coefficient of variation decreased from 5.0% to 4.5% for left ventricular ejection fraction (EF) and from 9.9% to 7.1% for right ventricular EF. Twenty manual analyses were performed in 250 min 12 s whereas same task took 5 min 48 s using AI method. Clinicians were open to adopt AI but concerns about accuracy and validity were raised. The AI method provides clinically valid outcomes and saves significant time. To address concerns raised by survey participants and overcome shortcomings of the automated myocardial segmentation, visual assessment of contours and performing manual corrections where necessary appears to be a practical approach.
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Siddiqui MF, Mouna A, Nicolas G, Rahat SAA, Mitalipova A, Emmanuel N, Tashmatova N. Computational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target Prediction. COMPUTATIONAL INTELLIGENCE IN ONCOLOGY 2022:233-250. [DOI: 10.1007/978-981-16-9221-5_14] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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