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Champendal M, Marmy L, Malamateniou C, Sá Dos Reis C. Artificial intelligence to support person-centred care in breast imaging - A scoping review. J Med Imaging Radiat Sci 2023; 54:511-544. [PMID: 37183076 DOI: 10.1016/j.jmir.2023.04.001] [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: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023]
Abstract
AIM To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies. METHODS The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage. RESULTS 3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used. CONCLUSIONS The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
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Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Laurent Marmy
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Christina Malamateniou
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, UK.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
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Park S, Ahn S, Kim JY, Kim J, Han HJ, Hwang D, Park J, Park HS, Park S, Kim GM, Sohn J, Jeong J, Song YU, Lee H, Kim SI. Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts. Int J Mol Sci 2022; 23:ijms23169140. [PMID: 36012405 PMCID: PMC9409068 DOI: 10.3390/ijms23169140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 11/29/2022] Open
Abstract
Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.
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Affiliation(s)
- Sunyoung Park
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
| | - Sungwoo Ahn
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
| | - Jee Ye Kim
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Jungho Kim
- Department of Clinical Laboratory Science, College of Health Sciences, Catholic University of Pusan, Busan 46252, Korea
| | - Hyun Ju Han
- Avison Biomedical Research Center, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Dasom Hwang
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
| | - Jungmin Park
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Hyung Seok Park
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Seho Park
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Gun Min Kim
- Department of Medical Oncology, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Joohyuk Sohn
- Department of Medical Oncology, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Joon Jeong
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea
| | - Yong Uk Song
- Division of Business Administration, College of Government and Business, Yonsei University, Wonju 26493, Korea
| | - Hyeyoung Lee
- Department of Biomedical Laboratory Science, College of Health Sciences, Yonsei University, Wonju 26493, Korea
- Correspondence: (H.L.); (S.I.K.)
| | - Seung Il Kim
- Department of Surgery, Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (H.L.); (S.I.K.)
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Mishra J, Kumar B, Targhotra M, Sahoo PK. Advanced and futuristic approaches for breast cancer diagnosis. FUTURE JOURNAL OF PHARMACEUTICAL SCIENCES 2020. [DOI: 10.1186/s43094-020-00113-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast cancer is the most frequent cancer and one of the most common causes of death in women, impacting almost 2 million women each year. Tenacity or perseverance of breast cancer in women is very high these days with an extensive increasing rate of 3 to 5% every year. Along with hurdles faced during treatment of breast tumor, one of the crucial causes of delay in treatment is invasive and poor diagnostic techniques for breast cancer hence the early diagnosis of breast tumors will help us to improve its management and treatment in the initial stage.
Main body
Present review aims to explore diagnostic techniques for breast cancer that are currently being used, recent advancements that aids in prior detection and evaluation and are extensively focused on techniques that are going to be future of breast cancer detection with better efficiency and lesser pain to patients so that it helps to a physician to prevent delay in treatment of cancer. Here, we have discussed mammography and its advanced forms that are the need of current era, techniques involving radiation such as radionuclide methods, the potential of nanotechnology by using nanoparticle in breast cancer, and how the new inventions such as breath biopsy, and X-ray diffraction of hair can simply use as a prominent method in breast cancer early and easy detection tool.
Conclusion
It is observed significantly that advancement in detection techniques is helping in early diagnosis of breast cancer; however, we have to also focus on techniques that will improve the future of cancer diagnosis in like optical imaging and HER2 testing.
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Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk. Diagn Interv Imaging 2020; 101:811-819. [PMID: 32819886 DOI: 10.1016/j.diii.2020.07.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 07/07/2020] [Accepted: 07/27/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To evaluate the agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist, as well as the impact on the assessment of breast cancer risk (BCR) at 5 years. MATERIALS AND METHODS We retrospectively included 311 consecutive women (mean age, 55.6±8.5 [SD]; range: 40-74 years) without a personal history of breast cancer who underwent routine mammography between January 1, 2019 and February 28, 2019. Mammographic breast density (MBD) was independently evaluated by a junior and a senior reader on digital mammography (DM) and synthetic mammography (SM) using BI-RADS (5th edition) and by an AI software. For each MBD, BCR at 5 years was estimated per woman by the AI software. Interobserver agreement for MBD between the two readers and the AI software were evaluated by quadratic κ coefficients. Reproducibility of BCR was assessed by intraclass correlation coefficient (ICC). RESULTS Agreement for MBD assessment on DM and SM was almost perfect between senior and junior radiologists (κ=0.88 [95% CI: 0.84-0.92] and κ=0.86 [95% CI: 0.82-0.90], respectively) and substantial between the senior radiologist and AI (κ=0.79; 95% CI: 0.73-0.84). There was substantial agreement between DM and SM for the senior radiologist (κ=0.79; 95% CI: 0.74-0.84). BCR evaluation at 5 years was highly reproducible between the two radiologists on DM and SM (ICC=0.98 [95% CI: 0.97-0.98] for both), between BCR evaluation based on DM and SM evaluated by the senior (ICC=0.96; 95% CI: 0.95-0.97) or junior radiologist (ICC=0.97; 95% CI: 0.96-0.98) and between the senior radiologist and AI (ICC=0.96; 95% CI: 0.95-0.97). CONCLUSION This preliminary study demonstrates a very good agreement for BCR evaluation based on the evaluation of MBD by a senior radiologist, junior radiologist and AI software.
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Wang T, Shuai JJ, Li X, Wen Z. Impact of full field digital mammography diagnosis for female patients with breast cancer. Medicine (Baltimore) 2019; 98:e15175. [PMID: 31008938 PMCID: PMC6494235 DOI: 10.1097/md.0000000000015175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Previous clinical studies have reported that full field digital mammography (FFDM) can be used for diagnosis on breast cancer (BC) with promising outcome results. However, no study systematically investigates its diagnostic impact on female patients with BC. Thus, this systematic review will assess the accurate of FFDM diagnosis on BC. METHODS In this study, we will perform a comprehensive search strategy in the databases as follows: Cochrane Library, EMBASE, MEDILINE, PSYCINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Allied and Complementary Medicine Database, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, VIP Information, and Wanfang Data from inception to February 28, 2019. All case-controlled studies exploring the impacts of FFDM diagnosis for patients BC will be fully considered for inclusion in this study. Two authors will independently scan the title and abstracts for relevance, and assess full texts for inclusion. They will also independently extract data and will assess methodological qualify for each included study by using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. RevMan V.5.3 software (London, UK) and Stata V.12.0 software (Texas, USA) will be used to pool the data and to conduct the meta-analysis. RESULTS The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of FFDM will be used to determine the diagnostic accuracy of FFDM for the diagnosis of patients with BC. CONCLUSION Its findings will provide latest evidence for the diagnostic accuracy of FFDM in female patients with BC. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42019125338.
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Affiliation(s)
- Tuan Wang
- Department of Radiology, Affiliated Tumor Hospital of Xinjiang Medical University
| | - Jian-jun Shuai
- Department of Imaging Center, Traditional Chinese Medicine Hospital of Xinjiang Uyghur Autonomous Region
| | - Xing Li
- Department of Nuclear Magnetic
| | - Zhi Wen
- Department of Computed Tomography, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
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A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification. AJR Am J Roentgenol 2019; 212:259-270. [DOI: 10.2214/ajr.18.20391] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Chang HH, Lin YJ, Zhuang AH. An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images. J Digit Imaging 2019; 32:148-161. [PMID: 30088157 PMCID: PMC6382639 DOI: 10.1007/s10278-018-0110-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Abstract
Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks. In our approach, parallel computing with the GPU architecture is first developed to accelerate the computation of the conventional bilateral filter. Subsequently, a back propagation network (BPN) scheme using significant image texture features as the input is established to estimate the GPU-based bilateral filter parameters and its denoising process. The k-fold cross validation method is exploited to evaluate the performance of the proposed automatic restoration framework. A wide variety of T1-weighted brain MR images were employed to train and evaluate this parameter-free decision system with GPU-based bilateral filtering, which resulted in a speed-up factor of 208 comparing to the CPU-based computation. The proposed filter parameter prediction system achieved a mean absolute percentage error (MAPE) of 6% and was classified as "high accuracy". Our automatic denoising framework dramatically removed noise in numerous brain MR images and outperformed several state-of-the-art methods based on the peak signal-to-noise ratio (PSNR). The usage of image texture features associated with the BPN to estimate the GPU-based bilateral filter parameters and to automate the denoising process is feasible and validated. It is suggested that this automatic restoration system is advantageous to various brain MR image-processing applications.
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Affiliation(s)
- Herng-Hua Chang
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan.
| | - Yu-Ju Lin
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan
| | - Audrey Haihong Zhuang
- Department of Radiation Oncology, Keck Medical School, University of Southern California, Los Angeles, CA, USA
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Abstract
OBJECTIVE The purpose of this article is to discuss potential applications of artificial intelligence (AI) in breast imaging and limitations that may slow or prevent its adoption. CONCLUSION The algorithms of AI for workflow improvement and outcome analyses are advancing. Using imaging data of high quality and quantity, AI can support breast imagers in diagnosis and patient management, but AI cannot yet be relied on or be responsible for physicians' decisions that may affect survival. Education in AI is urgently needed for physicians.
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Peng T, Wang Y, Xu TC, Shi L, Jiang J, Zhu S. Detection of Lung Contour with Closed Principal Curve and Machine Learning. J Digit Imaging 2018; 31:520-533. [PMID: 29450843 DOI: 10.1007/s10278-018-0058-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10-2. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.
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Affiliation(s)
- Tao Peng
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Yihuai Wang
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China.
| | - Thomas Canhao Xu
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Lianmin Shi
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Jianwu Jiang
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
| | - Shilang Zhu
- School of Computer Science & Technology, Soochow University, No.1 Shizi Road, Suzhou, Jiangsu, 215006, China
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