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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: 28] [Impact Index Per Article: 14.0] [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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53
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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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54
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Zhang Y, Chan S, Park VY, Chang KT, Mehta S, Kim MJ, Combs FJ, Chang P, Chow D, Parajuli R, Mehta RS, Lin CY, Chien SH, Chen JH, Su MY. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol 2022; 29 Suppl 1:S135-S144. [PMID: 33317911 PMCID: PMC8192591 DOI: 10.1016/j.acra.2020.12.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/03/2023]
Abstract
RATIONALE AND OBJECTIVES Computer-aided methods have been widely applied to diagnose lesions on breast magnetic resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire set of images and detect suspicious lesions. MATERIALS AND METHODS Two DCE-MRI datasets were used, 241 patients acquired using non-fat-sat sequence for training, and 98 patients acquired using fat-sat sequence for testing. All patients have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance, and the trade-off between sensitivity and specificity, was analyzed using free response receiver operating characteristic. RESULTS When the precontrast and subtraction image of both breasts were used as input, the false positive from the heart and normal parenchymal enhancements could be minimized. The training set had 1469 positive slices (containing lesion) and 9135 negative slices. In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79. When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the training and 98 of 98 (100%) lesions in the testing datasets were identified. CONCLUSION Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize, and segment lesions. This may be integrated with other artificial intelligence algorithms to develop a fully automatic breast MRI diagnostic system.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Siwa Chan
- Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Vivian Youngjean Park
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Siddharth Mehta
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Min Jung Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Freddie J. Combs
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Peter Chang
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Daniel Chow
- Department of Radiological Sciences, University of California, Irvine, CA, United States
| | - Ritesh Parajuli
- Department of Medicine, University of California, Irvine, CA, United States
| | - Rita S. Mehta
- Department of Medicine, University of California, Irvine, CA, United States
| | - Chin-Yao Lin
- Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Sou-Hsin Chien
- Department of Medical Imaging, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan,School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA, United States,Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA, United States,Corresponding Author:Min-Ying Su, PhD, John Tu and Thomas Yuen Center for Functional Onco-Imaging, 164 Irvine Hall, University of California, Irvine, CA 92697-5020, USA, Tel: +1 (949) 824-4925; Fax: +1 (949) 824-3481;
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55
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Vamvakas A, Tsivaka D, Logothetis A, Vassiou K, Tsougos I. Breast Cancer Classification on Multiparametric MRI - Increased Performance of Boosting Ensemble Methods. Technol Cancer Res Treat 2022; 21:15330338221087828. [PMID: 35341421 PMCID: PMC8966070 DOI: 10.1177/15330338221087828] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating benign and malignant breast lesions. Methods: The dataset includes mpMR images of 140 female patients with mass-like breast lesions (70 benign and 70 malignant), consisting of Dynamic Contrast Enhanced (DCE) and T2-weighted sequences, and the Apparent Diffusion Coefficient (ADC) calculated from the Diffusion Weighted Imaging (DWI) sequence. Tumor masks were manually defined in all consecutive slices of the respective MRI volumes and 3D radiomic features were extracted with the Pyradiomics package. Feature dimensionality reduction was based on statistical tests and the Boruta wrapper. Hierarchical Clustering on Spearman's rank correlation coefficients between features and Random Forest classification for obtaining feature importance, were implemented for selecting the final feature subset. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) classifiers, were trained and tested with bootstrap validation in differentiating breast lesions. A Support Vector Machine (SVM) classifier was also exploited for comparison. The Receiver Operator Characteristic (ROC) curves and DeLong's test were utilized to evaluate the classification performances. Results: The final feature subset consisted of 5 features derived from the lesion shape and the first order histogram of DCE and ADC images volumes. XGboost and LGBM achieved statistically significantly higher average classification performances [AUC = 0.95 and 0.94 respectively], followed by Adaboost [AUC = 0.90], GB [AUC = 0.89] and SVM [AUC = 0.88]. Conclusion: Overall, the integration of Ensemble Learning methods within mpMRI radiomic analysis can improve the performance of computer-assisted diagnosis of breast cancer lesions.
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Affiliation(s)
- Alexandros Vamvakas
- Medical Physics Department, Medical School, 37786University of Thessaly, Larissa, Greece
| | - Dimitra Tsivaka
- Medical Physics Department, Medical School, 37786University of Thessaly, Larissa, Greece
| | - Andreas Logothetis
- Medical Physics Laboratory, Medical School, 393206National and Kapodistrian University of Athens, Athens, Greece
| | - Katerina Vassiou
- Department of Anatomy and Radiology, Medical School, 37786University of Thessaly, Larissa, Greece
| | - Ioannis Tsougos
- Medical Physics Department, Medical School, 37786University of Thessaly, Larissa, Greece
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56
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Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103113] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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57
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Kataoka M, Honda M, Ohashi A, Yamaguchi K, Mori N, Goto M, Fujioka T, Mori M, Kato Y, Satake H, Iima M, Kubota K. Ultrafast Dynamic Contrast-enhanced MRI of the Breast: How Is It Used? Magn Reson Med Sci 2022; 21:83-94. [PMID: 35228489 PMCID: PMC9199976 DOI: 10.2463/mrms.rev.2021-0157] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Ultrafast dynamic contrast-enhanced (UF-DCE) MRI is a new approach to capture kinetic information in the very early post-contrast period with high temporal resolution while keeping reasonable spatial resolution. The detailed timing and shape of the upslope in the time–intensity curve are analyzed. New kinetic parameters obtained from UF-DCE MRI are useful in differentiating malignant from benign lesions and in evaluating prognostic markers of the breast cancers. Clinically, UF-DCE MRI contributes in identifying hypervascular lesions when the background parenchymal enhancement (BPE) is marked on conventional dynamic MRI. This review starts with the technical aspect of accelerated acquisition. Practical aspects of UF-DCE MRI include identification of target hypervascular lesions from marked BPE and diagnosis of malignant and benign lesions based on new kinetic parameters derived from UF-DCE MRI: maximum slope (MS), time to enhance (TTE), bolus arrival time (BAT), time interval between arterial and venous visualization (AVI), and empirical mathematical model (EMM). The parameters derived from UF-DCE MRI are compared in terms of their diagnostic performance and association with prognostic markers. Pitfalls of UF-DCE MRI in the clinical situation are also covered. Since UF-DCE MRI is an evolving technique, future prospects of UF-DCE MRI are discussed in detail by citing recent evidence. The topic covers prediction of treatment response, multiparametric approach using DWI-derived parameters, evaluation of tumor-related vessels, and application of artificial intelligence for UF-DCE MRI. Along with comprehensive literature review, illustrative clinical cases are used to understand the value of UF-DCE MRI.
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Affiliation(s)
- Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Maya Honda
- Department of Diagnostic Radiology, Kansai Electric Power Hospital
| | - Akane Ohashi
- Department of Translational Medicine, Diagnostic Radiology, Lund University, Skåne University hospital
| | - Ken Yamaguchi
- Department of Radiology, Faculty of Medicine, Saga University
| | - Naoko Mori
- Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine
| | - Mariko Goto
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University
| | - Yutaka Kato
- Department of Radiological Technology, Nagoya University Hospital
| | - Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine
| | - Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Saitama Medical Center
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58
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Frankhouser DE, Dietze E, Mahabal A, Seewaldt VL. Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging. FRONTIERS IN RADIOLOGY 2021; 1:735567. [PMID: 37492179 PMCID: PMC10364989 DOI: 10.3389/fradi.2021.735567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/11/2021] [Indexed: 07/27/2023]
Abstract
Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.
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Affiliation(s)
- David E. Frankhouser
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Eric Dietze
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
| | - Ashish Mahabal
- Department of Astronomy, Division of Physics, Mathematics, and Astronomy, California Institute of Technology (Caltech), Pasadena, CA, United States
| | - Victoria L. Seewaldt
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, United States
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59
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Bahl M. Updates in Artificial Intelligence for Breast Imaging. Semin Roentgenol 2021; 57:160-167. [PMID: 35523530 PMCID: PMC9077006 DOI: 10.1053/j.ro.2021.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) for breast imaging has rapidly moved from the experimental to implementation phase. As of this writing, Food and Drug Administration (FDA)-approved mammographic applications are available for triage, lesion detection and classification, and breast density assessment. For sonography and MRI, FDA-approved applications are available for lesion classification. Numerous other interpretive and noninterpretive AI applications are in the development phase. This article reviews AI applications for mammography, sonography, and MRI that are currently available for clinical use. In addition, clinical implementation and the future of AI for breast imaging are discussed.
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60
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Liu Y, Wang X, Li J, Hao L, Zhao T, Zou H, Xu D. Deep Learning Technology in Pathological Image Analysis of Breast Tissue. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9610830. [PMID: 34868535 PMCID: PMC8635881 DOI: 10.1155/2021/9610830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/16/2021] [Accepted: 10/15/2021] [Indexed: 12/28/2022]
Abstract
To explore the application value of the multilevel pyramid convolutional neural network (MPCNN) model based on convolutional neural network (CNN) in breast histopathology image analysis, in this study, based on CNN algorithm and softmax classifier (SMC), a sparse autoencoder (SAE) is introduced to optimize it. The sliding window method is used to identify cells, and the CNN + SMC pathological image cell detection method is established. Furthermore, the local region active contour (LRAC) is introduced to optimize it and the LRAC fine segmentation model driven by local Gaussian distribution is established. On this basis, the sparse automatic encoder is further introduced to optimize it and the MPCNN model is established. The proposed algorithm is evaluated on the pathological image data set. The results showed that the Acc value, F value, and Re value of pathological cell detection of CNN + SMC algorithm were significantly higher than those of the other two algorithms (P < 0.05). The Dice, OL, Sen, and Spe values of pathological image regional segmentation of CNN algorithm were significantly higher than those of the other two algorithms, and the difference was statistically significant (P < 0.05). The accuracy, recall, and F-measure of the optimized CNN algorithm for detecting breast histopathological images were 85.25%, 89.27%, and 80.09%, respectively. In the two databases with segmentation standards, the segmentation accuracy of MPCNN is 55%, 73.1%, 78.8%, and 82.1%. In the deep convolution network model, the training time of the MPCNN algorithm is about 80 min. It shows that when the feature dimension is low, the feature map extracted by MPCNN is more effective than the traditional feature extraction method.
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Affiliation(s)
- Yanan Liu
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Xiaoyan Wang
- Breast Department, Qiqihar First Hospital, Qiqihar 161006, Heilongjiang, China
| | - Jingyu Li
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Liguo Hao
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Tianyu Zhao
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - He Zou
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
| | - Dongbin Xu
- Medical Technology Department, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China
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61
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Zhou J, Liu YL, Zhang Y, Chen JH, Combs FJ, Parajuli R, Mehta RS, Liu H, Chen Z, Zhao Y, Pan Z, Wang M, Yu R, Su MY. BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning. Front Oncol 2021; 11:728224. [PMID: 34790569 PMCID: PMC8591227 DOI: 10.3389/fonc.2021.728224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/11/2021] [Indexed: 11/24/2022] Open
Abstract
Background A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions. Materials and Methods A total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing. Results The diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A–5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets. Conclusion Diagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.
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Affiliation(s)
- Jiejie Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Freddie J Combs
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ritesh Parajuli
- Department of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Rita S Mehta
- Department of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Huiru Liu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Youfan Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Risheng Yu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
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Ganesan K, Wang Y, Gao F, Liu Q, Zhang C, Li P, Zhang J, Chen J. Targeting Engineered Nanoparticles for Breast Cancer Therapy. Pharmaceutics 2021; 13:pharmaceutics13111829. [PMID: 34834243 PMCID: PMC8623926 DOI: 10.3390/pharmaceutics13111829] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/11/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022] Open
Abstract
Breast cancer (BC) is the second most common cancer in women globally after lung cancer. Presently, the most important approach for BC treatment consists of surgery, followed by radiotherapy and chemotherapy. The latter therapeutic methods are often unsuccessful in the treatment of BC because of their various side effects and the damage incurred to healthy tissues and organs. Currently, numerous nanoparticles (NPs) have been identified and synthesized to selectively target BC cells without causing any impairments to the adjacent normal tissues or organs. Based on an exploratory study, this comprehensive review aims to provide information on engineered NPs and their payloads as promising tools in the treatment of BC. Therapeutic drugs or natural bioactive compounds generally incorporate engineered NPs of ideal sizes and shapes to enhance their solubility, circulatory half-life, and biodistribution, while reducing their side effects and immunogenicity. Furthermore, ligands such as peptides, antibodies, and nucleic acids on the surface of NPs precisely target BC cells. Studies on the synthesis of engineered NPs and their impact on BC were obtained from PubMed, Science Direct, and Google Scholar. This review provides insights on the importance of engineered NPs and their methodology for validation as a next-generation platform with preventive and therapeutic effects against BC.
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Affiliation(s)
- Kumar Ganesan
- Li Ka Shing Faculty of Medicine, School of Chinese Medicine, The University of Hong Kong, Hong Kong, China; (K.G.); (Y.W.); (Q.L.)
| | - Yan Wang
- Li Ka Shing Faculty of Medicine, School of Chinese Medicine, The University of Hong Kong, Hong Kong, China; (K.G.); (Y.W.); (Q.L.)
| | - Fei Gao
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; (F.G.); (C.Z.)
| | - Qingqing Liu
- Li Ka Shing Faculty of Medicine, School of Chinese Medicine, The University of Hong Kong, Hong Kong, China; (K.G.); (Y.W.); (Q.L.)
- Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518063, China
| | - Chen Zhang
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; (F.G.); (C.Z.)
| | - Peng Li
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, China;
| | - Jinming Zhang
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; (F.G.); (C.Z.)
- Correspondence: (J.Z.); (J.C.); Tel.: +852-3917-6479 (J.C.)
| | - Jianping Chen
- Li Ka Shing Faculty of Medicine, School of Chinese Medicine, The University of Hong Kong, Hong Kong, China; (K.G.); (Y.W.); (Q.L.)
- Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518063, China
- Correspondence: (J.Z.); (J.C.); Tel.: +852-3917-6479 (J.C.)
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Whitney HM, Li H, Ji Y, Liu P, Giger ML. Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel) 2021; 13:cancers13194809. [PMID: 34638294 PMCID: PMC8508003 DOI: 10.3390/cancers13194809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/16/2021] [Accepted: 09/18/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Batch harmonization of radiomic features extracted from magnetic resonance images of breast lesions from two databases was applied to an artificial intelligence/machine learning classification workflow. Training and independent test sets from the two databases, as well as the combination of them, were used in pre-harmonization and post-harmonization forms to investigate the generalizability of performance in the task of distinguishing between malignant and benign lesions. Most training and independent test scenarios were statistically equivalent, demonstrating that batch harmonization with feature selection harmonization can potentially develop generalizable classification models. Abstract Radiomic features extracted from medical images may demonstrate a batch effect when cases come from different sources. We investigated classification performance using training and independent test sets drawn from two sources using both pre-harmonization and post-harmonization features. In this retrospective study, a database of thirty-two radiomic features, extracted from DCE-MR images of breast lesions after fuzzy c-means segmentation, was collected. There were 944 unique lesions in Database A (208 benign lesions, 736 cancers) and 1986 unique lesions in Database B (481 benign lesions, 1505 cancers). The lesions from each database were divided by year of image acquisition into training and independent test sets, separately by database and in combination. ComBat batch harmonization was conducted on the combined training set to minimize the batch effect on eligible features by database. The empirical Bayes estimates from the feature harmonization were applied to the eligible features of the combined independent test set. The training sets (A, B, and combined) were then used in training linear discriminant analysis classifiers after stepwise feature selection. The classifiers were then run on the A, B, and combined independent test sets. Classification performance was compared using pre-harmonization features to post-harmonization features, including their corresponding feature selection, evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit. Four out of five training and independent test scenarios demonstrated statistically equivalent classification performance when compared pre- and post-harmonization. These results demonstrate that translation of machine learning techniques with batch data harmonization can potentially yield generalizable models that maintain classification performance.
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Affiliation(s)
- Heather M. Whitney
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
- Department of Physics, Wheaton College, Wheaton, IL 60187, USA
- Correspondence: (H.M.W.); (M.L.G.)
| | - Hui Li
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
| | - Yu Ji
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
- Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China;
| | - Peifang Liu
- Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China;
| | - Maryellen L. Giger
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
- Correspondence: (H.M.W.); (M.L.G.)
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Abstract
Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.
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Affiliation(s)
- Anabel M Scaranelo
- Medical Imaging Department, 12366University of Toronto, Ontario, Canada.,Breast Imaging Division, Joint Department of Medical Imaging, University of Health Network, Sinai Health and Women's College Hospital, Toronto, Ontario, Canada
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Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, Lungren MP, Hargreaves BA, Langlotz CP. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2021; 54:357-371. [PMID: 32830874 PMCID: PMC8639049 DOI: 10.1002/jmri.27331] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/27/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
| | - Christopher M Sandino
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Elizabeth K Cole
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - David B Larson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Matthew P Lungren
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
| | - Curtis P Langlotz
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
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66
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Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, Li J, Ye HR, Cui XW, Dietrich CF. Artificial Intelligence in Medical Imaging of the Breast. Front Oncol 2021; 11:600557. [PMID: 34367938 PMCID: PMC8339920 DOI: 10.3389/fonc.2021.600557] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 07/07/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.
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Affiliation(s)
- Yu-Meng Lei
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Miao Yin
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Mei-Hui Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Jing Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Shu-E Zeng
- Department of Medical Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Jun Li
- Department of Medical Ultrasound, The First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China
| | - Hua-Rong Ye
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Beau Site, Salem und Permanence, Bern, Switzerland
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Hunt C, Montgomery S, Berkenpas JW, Sigafoos N, Oakley JC, Espinosa J, Justice N, Kishaba K, Hippe K, Si D, Hou J, Ding H, Cao R. Recent Progress of Machine Learning in Gene Therapy. Curr Gene Ther 2021; 22:132-143. [PMID: 34161210 DOI: 10.2174/1566523221666210622164133] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/15/2021] [Accepted: 04/02/2021] [Indexed: 11/22/2022]
Abstract
With new developments in biomedical technology, it is now a viable therapeutic treatment to alter genes with techniques like CRISPR. At the same time, it is increasingly cheaper to do whole genome sequencing, resulting in rapid advancement in gene therapy and editing in precision medicine. Thus, understanding the current industry and academic applications of gene therapy provides an important backdrop to future scientific developments. Additionally, machine learning and artificial intelligence techniques allow for the reduction of time and money spent in the development of new gene therapy products and techniques. In this paper, we survey the current progress of gene therapy treatments for several diseases and explore machine learning applications in gene therapy. We also discuss the ethical implications of gene therapy and the use of machine learning in precision medicine. Machine learning and gene therapy are both topics gaining popularity in various publications, and we conclude that there is still room for continued research and application of machine learning techniques in the gene therapy field.
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Affiliation(s)
- Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States
| | - Sandra Montgomery
- Department of Physics, Pacific Lutheran University, Tacoma, WA, United States
| | | | - Noel Sigafoos
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States
| | - John Christian Oakley
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States
| | - Jacob Espinosa
- Department of Mathematics, Pacific Lutheran University, Tacoma, WA, United States
| | - Nicola Justice
- Department of Mathematics, Pacific Lutheran University, Tacoma, WA, United States
| | - Kiyomi Kishaba
- Department of Humanities, Pacific Lutheran University, Tacoma, WA, United States
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States
| | - Dong Si
- Division of Computing Software Systems, University of Washington-Bothell, Bothell, WA, United States
| | - Jie Hou
- Department of Computer Science, Saint Louis University, St. Louis, MO, United States
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA, United States
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Hu Q, Whitney HM, Li H, Ji Y, Liu P, Giger ML. Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI. Radiol Artif Intell 2021; 3:e200159. [PMID: 34235439 PMCID: PMC8231792 DOI: 10.1148/ryai.2021200159] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 02/04/2021] [Accepted: 02/09/2021] [Indexed: 04/16/2023]
Abstract
PURPOSE To develop a deep transfer learning method that incorporates four-dimensional (4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant breast lesions. MATERIALS AND METHODS The retrospective dataset is composed of 1990 distinct lesions (1494 malignant and 496 benign) from 1979 women (mean age, 47 years ± 10). Lesions were split into a training and validation set of 1455 lesions (acquired in 2015-2016) and an independent test set of 535 lesions (acquired in 2017). Features were extracted from a convolutional neural network (CNN), and lesions were classified as benign or malignant using support vector machines. Volumetric information was collapsed into two dimensions by taking the maximum intensity projection (MIP) at the image level or feature level within the CNN architecture. Performances were evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit and were compared using the DeLong test. RESULTS The image MIP and feature MIP methods yielded AUCs of 0.91 (95% CI: 0.87, 0.94) and 0.93 (95% CI: 0.91, 0.96), respectively, for the independent test set. The feature MIP method achieved higher performance than the image MIP method (∆AUC 95% CI: 0.003, 0.051; P = .03). CONCLUSION Incorporating 4D information in DCE MRI by MIP of features in deep transfer learning demonstrated superior classification performance compared with using MIP images as input in the task of distinguishing between benign and malignant breast lesions.Keywords: Breast, Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), MR-Dynamic Contrast Enhanced, Supervised learning, Support vector machines (SVM), Transfer learning, Volume Analysis © RSNA, 2021.
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69
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Eroğlu Y, Yildirim M, Çinar A. Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Comput Biol Med 2021; 133:104407. [PMID: 33901712 DOI: 10.1016/j.compbiomed.2021.104407] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/08/2021] [Accepted: 04/13/2021] [Indexed: 12/25/2022]
Abstract
Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.
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Affiliation(s)
- Yeşim Eroğlu
- Department of Radiology, Firat University School of Medicine, Elazig, Turkey.
| | | | - Ahmet Çinar
- Computer Engineering Department, Firat University, Elazig, Turkey.
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70
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Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer. Biosci Rep 2021; 41:228103. [PMID: 33734319 PMCID: PMC8024874 DOI: 10.1042/bsr20203391] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 02/28/2021] [Accepted: 03/17/2021] [Indexed: 12/13/2022] Open
Abstract
Background: The aim of the present study was to confirm the role of Brachyury in breast cancer and to verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients. Methods: We conducted a retrospective review of the medical records to obtain patient information, and made the patient’s paraffin tissue into tissue chips for staining analysis. We selected 303 patients for research and implemented four machine learning algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. Results: The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (P=0.0335); patients with breast cancer with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (P=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of patients with breast cancer. The results showed that the decision tree model had the best performance (AUC = 0.781). Conclusions: Brachyury is highly expressed in breast cancer and indicates that patients had a poor prognosis. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of patients with breast cancer.
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71
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Dietzel M, Clauser P, Kapetas P, Schulz-Wendtland R, Baltzer PAT. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. ROFO-FORTSCHR RONTG 2021; 193:898-908. [PMID: 33535260 DOI: 10.1055/a-1346-0095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. METHODS AND RESULTS This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. CONCLUSION Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. KEY POINTS · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.. CITATION FORMAT · Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
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Affiliation(s)
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | | | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
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72
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Artificial intelligence in oncology. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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73
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Drukker K, Yan P, Sibley A, Wang G. Biomedical imaging and analysis through deep learning. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00004-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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74
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Giger ML. AI/Machine Learning in Medical Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00052-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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75
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Pathak P, Jalal AS, Rai R. Breast Cancer Image Classification: A Review. Curr Med Imaging 2020; 17:720-740. [PMID: 33371857 DOI: 10.2174/0929867328666201228125208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/23/2020] [Accepted: 10/14/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer, such as mammogram, ultrasound, computed tomography and Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. OBJECTIVE This paper aims to cover the approaches used in the CAD system for the detection of breast cancer. METHODS In this paper, the methods used in CAD systems are categories into two classes: the conventional approach and artificial intelligence (AI) approach. RESULTS The conventional approach covers the basic steps of image processing, such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. CONCLUSION This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.
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Affiliation(s)
- Pooja Pathak
- Department of Mathematics, GLA University, Mathura, India
| | - Anand Singh Jalal
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Ritu Rai
- Department of Computer Engineering & Applications, GLA University, Mathura, India
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Fujioka T, Mori M, Kubota K, Oyama J, Yamaga E, Yashima Y, Katsuta L, Nomura K, Nara M, Oda G, Nakagawa T, Kitazume Y, Tateishi U. The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review. Diagnostics (Basel) 2020; 10:diagnostics10121055. [PMID: 33291266 PMCID: PMC7762151 DOI: 10.3390/diagnostics10121055] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/04/2020] [Accepted: 12/05/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women's health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
- Correspondence: ; Tel.: +81-3-5803-5311; Fax: +81-3-5803-0147
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
- Department of Radiology, Dokkyo Medical University, Tochigi 321-0293, Japan
| | - Jun Oyama
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Yuka Yashima
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Kyoko Nomura
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Miyako Nara
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
- Department of Breast Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo 113-8677, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (G.O.); (T.N.)
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (G.O.); (T.N.)
| | - Yoshio Kitazume
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
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Jiang Y, Edwards AV, Newstead GM. Artificial Intelligence Applied to Breast MRI for Improved Diagnosis. Radiology 2020; 298:38-46. [PMID: 33078996 DOI: 10.1148/radiol.2020200292] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.
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Affiliation(s)
- Yulei Jiang
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL 60637
| | - Alexandra V Edwards
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL 60637
| | - Gillian M Newstead
- From the Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL 60637
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Fujioka T, Yashima Y, Oyama J, Mori M, Kubota K, Katsuta L, Kimura K, Yamaga E, Oda G, Nakagawa T, Kitazume Y, Tateishi U. Deep-learning approach with convolutional neural network for classification of maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging. Magn Reson Imaging 2020; 75:1-8. [PMID: 33045323 DOI: 10.1016/j.mri.2020.10.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 08/27/2020] [Accepted: 10/06/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE We aimed to evaluate deep learning approach with convolutional neural networks (CNNs) to discriminate between benign and malignant lesions on maximum intensity projections of dynamic contrast-enhanced breast magnetic resonance imaging (MRI). METHODS We retrospectively gathered maximum intensity projections of dynamic contrast-enhanced breast MRI of 106 benign (including 22 normal) and 180 malignant cases for training and validation data. CNN models were constructed to calculate the probability of malignancy using CNN architectures (DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, NasNetMobile, and Xception) with 500 epochs and analyzed that of 25 benign (including 12 normal) and 47 malignant cases for test data. Two human readers also interpreted these test data and scored the probability of malignancy for each case using Breast Imaging Reporting and Data System. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS The CNN models showed a mean AUC of 0.830 (range, 0.750-0.895). The best model was InceptionResNetV2. This model, Reader 1, and Reader 2 had sensitivities of 74.5%, 72.3%, and 78.7%; specificities of 96.0%, 88.0%, and 80.0%; and AUCs of 0.895, 0.823, and 0.849, respectively. No significant difference arose between the CNN models and human readers (p > 0.125). CONCLUSION Our CNN models showed comparable diagnostic performance in differentiating between benign and malignant lesions to human readers on maximum intensity projection of dynamic contrast-enhanced breast MRI.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuka Yashima
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jun Oyama
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan.
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan; Department of Radiology, Dokkyo Medical University, Tochigi, Japan
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koichiro Kimura
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoshio Kitazume
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
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Verde F, Romeo V, Stanzione A, Maurea S. Current trends of artificial intelligence in cancer imaging. Artif Intell Med Imaging 2020; 1:87-93. [DOI: 10.35711/aimi.v1.i3.87] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023] Open
Abstract
In this editorial, we discussed the current research status of artificial intelligence (AI) in Oncology, reviewing the basics of machine learning (ML) and deep learning (DL) techniques and their emerging applications on clinical and imaging cancer workflow. The growing amounts of available “big data” coupled to the increasing computational power have enabled the development of computer-based systems capable to perform advanced tasks in many areas of clinical care, especially in medical imaging. ML is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior instructions. DL is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks; convolutional neural networks are the most common DL models used in medical image analysis. AI methods have been proposed in many areas of oncology granting promising results in radiology-based clinical applications. In detail, we explored the emerging applications of AI in oncological risk assessment, lesion detection, characterization, staging, and therapy response. Critical issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical practice. Nevertheless, AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.
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Affiliation(s)
- Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Napoli 80131, Italy
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80
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Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 2020; 3:118. [PMID: 32984550 PMCID: PMC7486909 DOI: 10.1038/s41746-020-00324-0] [Citation(s) in RCA: 396] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 08/13/2020] [Indexed: 02/07/2023] Open
Abstract
At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.
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Affiliation(s)
- Stan Benjamens
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Bertalan Meskó
- The Medical Futurist Institute, Budapest, Hungary
- Department of Behavioural Sciences, Semmelweis University, Budapest, Hungary
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81
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Impact of Gene Biomarker Discovery Tools Based on Protein–Protein Interaction and Machine Learning on Performance of Artificial Intelligence Models in Predicting Clinical Stages of Breast Cancer. Interdiscip Sci 2020; 12:476-486. [DOI: 10.1007/s12539-020-00390-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 08/21/2020] [Accepted: 08/31/2020] [Indexed: 12/27/2022]
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82
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Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images. Diagnostics (Basel) 2020; 10:diagnostics10050330. [PMID: 32443922 PMCID: PMC7277981 DOI: 10.3390/diagnostics10050330] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/24/2022] Open
Abstract
We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.
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83
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Hu H. Recent Advances of Bioresponsive Nano-Sized Contrast Agents for Ultra-High-Field Magnetic Resonance Imaging. Front Chem 2020; 8:203. [PMID: 32266217 PMCID: PMC7100386 DOI: 10.3389/fchem.2020.00203] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 03/04/2020] [Indexed: 12/11/2022] Open
Abstract
The ultra-high-field magnetic resonance imaging (MRI) nowadays has been receiving enormous attention in both biomaterial research and clinical diagnosis. MRI contrast agents are generally comprising of T1-weighted and T2-weighted contrast agent types, where T1-weighted contrast agents show positive contrast enhancement with brighter images by decreasing the proton's longitudinal relaxation times and T2-weighted contrast agents show negative contrast enhancement with darker images by decreasing the proton's transverse relaxation times. To meet the incredible demand of MRI, ultra-high-field T2 MRI is gradually attracting the attention of research and medical needs owing to its high resolution and high accuracy for detection. It is anticipated that high field MRI contrast agents can achieve high performance in MRI imaging, where parameters of chemical composition, molecular structure and size of varied contrast agents show contrasted influence in each specific diagnostic test. This review firstly presents the recent advances of nanoparticle contrast agents for MRI. Moreover, multimodal molecular imaging with MRI for better monitoring is discussed during biological process. To fasten the process of developing better contrast agents, deep learning of artificial intelligent (AI) can be well-integrated into optimizing the crucial parameters of nanoparticle contrast agents and achieving high resolution MRI prior to the clinical applications. Finally, prospects and challenges are summarized.
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Affiliation(s)
- Hailong Hu
- School of Aeronautics and Astronautics, Central South University, Changsha, China
- Research Center in Intelligent Thermal Structures for Aerospace, Central South University, Changsha, China
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El Naqa I, Haider MA, Giger ML, Ten Haken RK. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93:20190855. [PMID: 31965813 PMCID: PMC7055429 DOI: 10.1259/bjr.20190855] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/12/2020] [Accepted: 01/13/2020] [Indexed: 12/15/2022] Open
Abstract
Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.
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Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Masoom A Haider
- Department of Medical Imaging and Lunenfeld-Tanenbaum Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Zhou J, Zhang Y, Chang KT, Lee KE, Wang O, Li J, Lin Y, Pan Z, Chang P, Chow D, Wang M, Su MY. Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue. J Magn Reson Imaging 2019; 51:798-809. [PMID: 31675151 DOI: 10.1002/jmri.26981] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 10/10/2019] [Accepted: 10/11/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE Retrospective. POPULATION In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Jiejie Zhou
- Department of Radiology, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, California, USA
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, California, USA
| | - Kyoung Eun Lee
- Department of Radiology, Inje University Seoul Paik Hospital, Inje University, Seoul, Korea
| | - Ouchen Wang
- Department of Thyroid and Breast Surgery, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiance Li
- Department of Radiology, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yezhi Lin
- Information Technology Center, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhifang Pan
- Information Technology Center, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China
| | - Peter Chang
- Department of Radiological Sciences, University of California, Irvine, California, USA
| | - Daniel Chow
- Department of Radiological Sciences, University of California, Irvine, California, USA
| | - Meihao Wang
- Department of Radiology, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA
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Song DY, Kim SY, Bong G, Kim JM, Yoo HJ. The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review. Soa Chongsonyon Chongsin Uihak 2019; 30:145-152. [PMID: 32595335 PMCID: PMC7298904 DOI: 10.5765/jkacap.190027] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/02/2019] [Accepted: 09/16/2019] [Indexed: 02/06/2023] Open
Abstract
Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.
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Affiliation(s)
- Da-Yea Song
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - So Yoon Kim
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.,Curriculum and Instruction, Lynch School of Education, Boston College, Chestnut Hill, MA, USA
| | - Guiyoung Bong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jong Myeong Kim
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hee Jeong Yoo
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
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87
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Ji Y, Li H, Edwards AV, Papaioannou J, Ma W, Liu P, Giger ML. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging 2019; 19:64. [PMID: 31533838 PMCID: PMC6751793 DOI: 10.1186/s40644-019-0252-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 09/11/2019] [Indexed: 11/30/2022] Open
Abstract
Background As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resonance imaging (MRI) dataset within a single institution for subsequent use as a computer aid for radiologists. Methods Computer analysis was conducted on consecutive dynamic contrast-enhanced MRI (DCE-MRI) studies from 1483 breast cancer and 496 benign patients who underwent MRI examinations between February 2015 and October 2017; with the age ranges of the cancer and benign patients being 19 to 77 and 16 to 76 years old, respectively. Cases were separated into a training dataset (years 2015 & 2016; 1444 cases) and an independent testing dataset (year 2017; 535 cases) based solely on MRI examination date. After radiologist indication of the lesion, the computer automatically segmented and extracted radiomic features, which were subsequently merged with a support-vector machine (SVM) to yield a lesion signature. Area under the receiving operating characteristic (ROC) curve (AUC) with 95% confidence intervals (CI) served as the primary figure of merit in the statistical evaluation for this clinical classification task. Results In the task of distinguishing malignant and benign breast lesions DCE-MRI, the trained predictive model yielded an AUC value of 0.89 (95% CI: 0.858, 0.922) on the independent image set. AUC values of 0.88 (95% CI: 0.845, 0.926) and 0.90 (95% CI: 0.837, 0.940) were obtained for mass lesions only and non-mass lesions only, respectively. Compared with actual clinical management decisions, the predictive model achieved 99.5% sensitivity with 9.6% fewer recommended biopsies. Conclusion On an independent, consecutive clinical dataset within a single institution, a trained machine learning system yielded promising performance in distinguishing between malignant and benign breast lesions.
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Affiliation(s)
- Yu Ji
- Department of Breast Imaging, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University, Tianjin, 30060, China.,Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL, 60637, USA
| | - Hui Li
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL, 60637, USA
| | - Alexandra V Edwards
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL, 60637, USA
| | - John Papaioannou
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL, 60637, USA
| | - Wenjuan Ma
- Department of Breast Imaging, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University, Tianjin, 30060, China
| | - Peifang Liu
- Department of Breast Imaging, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University, Tianjin, 30060, China
| | - Maryellen L Giger
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL, 60637, USA.
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