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Wang G, Guo Q, Shi D, Zhai H, Luo W, Zhang H, Ren Z, Yan G, Ren K. Clinical Breast MRI-based Radiomics for Distinguishing Benign and Malignant Lesions: An Analysis of Sequences and Enhanced Phases. J Magn Reson Imaging 2024; 60:1178-1189. [PMID: 38006286 DOI: 10.1002/jmri.29150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Previous studies have used different imaging sequences and different enhanced phases for breast lesion calsification in radiomics. The optimal sequence and contrast enhanced phase is unclear. PURPOSE To identify the optimal magnetic resonance imaging (MRI) radiomics model for lesion clarification, and to simulate its incremental value for multiparametric MRI (mpMRI)-guided biopsy. STUDY TYPE Retrospective. POPULATION 329 female patients (138 malignant, 191 benign), divided into a training set (first site, n = 192) and an independent test set (second site, n = 137). FIELD STRENGTH/SEQUENCE 3.0-T, fast spoiled gradient-echo and fast spin-echo T1-weighted imaging (T1WI), fast spin-echo T2-weighted imaging (T2WI), echo-planar diffusion-weighted imaging (DWI), and fast spoiled gradient-echo contrast-enhanced MRI (CE-MRI). ASSESSMENT Two breast radiologists with 3 and 10 years' experience developed radiomics model on CE-MRI, CE-MRI + DWI, CE-MRI + DWI + T2WI, CE-MRI + DWI + T2WI + T1WI at each individual phase (P) and for multiple combinations of phases. The optimal radiomics model (Rad-score) was identified as having the highest area under the receiver operating characteristic curve (AUC) in the test set. Specificity was compared between a traditional mpMRI model and an integrated model (mpMRI + Rad-score) at sensitivity >98%. STATISTICAL TESTS Wilcoxon paired-samples signed rank test, Delong test, McNemar test. Significance level was 0.05 and Bonferroni method was used for multiple comparisons (P = 0.007, 0.05/7). RESULTS For radiomics models, CE-MRI/P3 + DWI + T2WI achieved the highest performance in the test set (AUC = 0.888, 95% confidence interval: 0.833-0.944). The integrated model had significantly higher specificity (55.3%) than the mpMRI model (31.6%) in the test set with a sensitivity of 98.4%. DATA CONCLUSION The CE-MRI/P3 + DWI + T2WI model is the optimized choice for breast lesion classification in radiomics, and has potential to reduce benign biopsies (100%-specificity) from 68.4% to 44.7% while retaining sensitivity >98%. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Qiu Guo
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Huige Zhai
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Wenbin Luo
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Zhendong Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Gen Yan
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen university, Xiamen, Fujian, China
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Wang J, Wang L, Yang Z, Tan W, Liu Y. Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study. Eur J Cancer Prev 2024:00008469-990000000-00145. [PMID: 38743632 DOI: 10.1097/cej.0000000000000892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
OBJECTIVE The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer. METHODS The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism. RESULTS The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% ( P = 0.027); stage II, 72% ( P = 0.014); stage III, 50% ( P = 0.032); and stage IV, 45% ( P = 0.041). CONCLUSIONS The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.
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Affiliation(s)
- Jinhua Wang
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University
| | - Liang Wang
- Interventional Department, The University of Hong Kong-Shenzhen Hospital, Shenzhen
| | - Zhongxian Yang
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University
| | - Wanchang Tan
- Department of Radiology, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, China
| | - Yubao Liu
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- The Third of Clinical Medicine, Southern Medical University
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Liu L, He S, Niu Z, Yin R, Guo Y, Dou Z, Ma W, Ye Z, Lu H. Preoperative magnetic resonance imaging identify feasibility of breast-conserving surgery for breast cancer patients. Gland Surg 2024; 13:640-653. [PMID: 38845837 PMCID: PMC11150189 DOI: 10.21037/gs-23-509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/28/2024] [Indexed: 06/09/2024]
Abstract
Background Breast-conserving surgery (BCS) stands as the favored modality for treating early-stage breast cancer. Accurately forecasting the feasibility of BCS preoperatively can aid in surgical planning and reduce the rate of switching of surgical methods and reoperation. The objective of this study is to identify the radiomics features and preoperative breast magnetic resonance imaging (MRI) characteristics that are linked with positive margins following BCS in patients with breast cancer, with the ultimate aim of creating a predictive model for the feasibility of BCS. Methods This study included a cohort of 221 pretreatment MRI images obtained from patients with breast cancer. A total of seven MRI semantic features and 1,561 radiomics features of lesions were extracted. The feature subset was determined by eliminating redundancy and correlation based on the features of the training set. The least absolute shrinkage and selection operator (LASSO) logistic regression was then trained with this subset to classify the final BCS positive and negative margins and subsequently validated using the test set. Results Seven features were significant in the discrimination of cases achieving positive and negative margins. The radiomics signature achieved area under the curve (AUC), accuracy, sensitivity, and specificity of 0.760 [95% confidence interval (CI): 0.630, 0.891], 0.712 (95% CI: 0.569, 0.829), 0.882 (95% CI: 0.623, 0.979) and 0.629 (95% CI: 0.449, 0.780) in the test set, respectively. The combined model of radiomics signature and background parenchymal enhancement (BPE) demonstrated an AUC, accuracy, sensitivity, and specificity of 0.759 (95% CI: 0.628, 0.890), 0.654 (95% CI: 0.509, 0.780), 0.679 (95% CI: 0.476, 0.834) and 0.625 (95% CI: 0.408, 0.804). Conclusions The combination of preoperative MRI radiomics features can well predict the success of breast conserving surgery.
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Affiliation(s)
- Liangsheng Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Shanshan He
- Department of Breast Reconstruction, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Zhenhua Niu
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China
| | - Rui Yin
- School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China
| | - Yijun Guo
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Zhaoxiang Dou
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Wenjuan Ma
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Hong Lu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
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Guo Y, Zhang H, Yuan L, Chen W, Zhao H, Yu QQ, Shi W. Machine learning and new insights for breast cancer diagnosis. J Int Med Res 2024; 52:3000605241237867. [PMID: 38663911 PMCID: PMC11047257 DOI: 10.1177/03000605241237867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 02/21/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.
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Affiliation(s)
- Ya Guo
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Heng Zhang
- Department of Laboratory Medicine, Shandong Daizhuang Hospital, Jining, Shandong Province, China
| | - Leilei Yuan
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Weidong Chen
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Haibo Zhao
- Department of Oncology, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Qing-Qing Yu
- Phase I Clinical Research Centre, Jining No.1 People’s Hospital, Shandong First Medical University, Jining, Shandong Province, China
| | - Wenjie Shi
- Molecular and Experimental Surgery, University Clinic for General-, Visceral-, Vascular- and Trans-Plantation Surgery, Medical Faculty University Hospital Magdeburg, Otto-von Guericke University, Magdeburg, Germany
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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Zhou C, Xie H, Zhu F, Yan W, Yu R, Wang Y. Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters. Clin Exp Med 2023; 23:2357-2368. [PMID: 36413273 DOI: 10.1007/s10238-022-00944-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/05/2022] [Indexed: 11/23/2022]
Abstract
Radiomics has been a promising imaging biomarker for many malignant diseases. We developed a novel radiomics strategy that incorporating radiomics features extracted from dual-view mammograms and clinical parameters for identifying benign and malignant breast lesions, and validated whether the radiomics assessment could improve the accurate diagnosis of breast cancer. A total of 380 patients (mean age, 52 ± 7 years) with 621 breast lesions utilizing mammograms on craniocaudal (CC) and mediolateral oblique (MLO) views were randomly allocated into the training (n = 486) and testing (n = 135) sets in this retrospective study. A total of 1184 and 2368 radiomics features were extracted from single-position region of interest (ROI) and position-paired ROI, separately. Clinical parameters were then combined for better prediction. Recursive feature elimination and least absolute shrinkage and selection operator methods were applied to select optimal predictive features. Random forest was used to conduct the predictive model. Intraclass correlation coefficient test was used to assess repeatability and reproducibility of features. After preprocessing, 467 radiomics features and clinical parameters remained in the single-view and dual-view models. The performance and significance of models were quantified by the area under the curve (AUC), sensitivity, specificity, and accuracy. The correlation analysis between variables was evaluated using the correlation ratio and Pearson correlation coefficient. The model using a combination of dual-view radiomics and clinical parameters achieved a favorable performance (AUC: 0.804, 95% CI: 0.668-0.916), outperformed single-view model and model without clinical parameters. Incorporating with radiomics features of dual-view (CC&MLO) mammogram, age, breast density, and type of suspicious lesions can provide a noninvasive approach to evaluate the malignancy of breast lesions and facilitate clinical decision-making.
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Affiliation(s)
- Chenyi Zhou
- Department of Radiology, The People's Hospital of Suzhou New District, Suzhou, 215129, Jiangsu, China
| | - Hui Xie
- Department of Radiology, The People's Hospital of Suzhou New District, Suzhou, 215129, Jiangsu, China
| | - Fanglian Zhu
- Department of Radiology, The People's Hospital of Suzhou New District, Suzhou, 215129, Jiangsu, China
| | - Wanying Yan
- Beijing Infervision Technology Co. Ltd., Beijing, 100025, Beijing, China
| | - Ruize Yu
- Beijing Infervision Technology Co. Ltd., Beijing, 100025, Beijing, China
| | - Yanling Wang
- Department of Radiology, The People's Hospital of Suzhou New District, Suzhou, 215129, Jiangsu, China.
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Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions. Diagnostics (Basel) 2023; 13:diagnostics13061007. [PMID: 36980315 PMCID: PMC10047866 DOI: 10.3390/diagnostics13061007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/28/2023] [Accepted: 03/05/2023] [Indexed: 03/09/2023] Open
Abstract
Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.
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Madani M, Behzadi MM, Nabavi S. The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review. Cancers (Basel) 2022; 14:5334. [PMID: 36358753 PMCID: PMC9655692 DOI: 10.3390/cancers14215334] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 12/02/2022] Open
Abstract
Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.
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Affiliation(s)
- Mohammad Madani
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Mohammad Mahdi Behzadi
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Sheida Nabavi
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
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10
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Li C, Li W, Liu C, Zheng H, Cai J, Wang S. Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Med Phys 2022; 49:e1024-e1054. [PMID: 35980348 DOI: 10.1002/mp.15936] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Peng Cheng Laboratory, Shenzhen, 518066, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
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Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA, Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms. Cancers (Basel) 2022; 14:3442. [PMID: 35884503 PMCID: PMC9322973 DOI: 10.3390/cancers14143442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/02/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
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Affiliation(s)
- Jesus A. Basurto-Hurtado
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Irving A. Cruz-Albarran
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
| | - Manuel Toledano-Ayala
- División de Investigación y Posgrado de la Facultad de Ingeniería (DIPFI), Universidad Autónoma de Querétaro, Cerro de las Campanas S/N Las Campanas, Santiago de Querétaro 76010, Mexico;
| | - Mario Alberto Ibarra-Manzano
- Laboratorio de Procesamiento Digital de Señales, Departamento de Ingeniería Electrónica, Division de Ingenierias Campus Irapuato-Salamanca (DICIS), Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico;
| | - Luis A. Morales-Hernandez
- C.A. Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Rio Moctezuma 249, San Cayetano, San Juan del Rio 76807, Mexico; (J.A.B.-H.); (I.A.C.-A.)
| | - Carlos A. Perez-Ramirez
- Laboratorio de Dispositivos Médicos, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Carretera a Chichimequillas S/N, Ejido Bolaños, Santiago de Querétaro 76140, Mexico
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The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
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Tumakov D, Kayumov Z, Zhumaniezov A, Chikrin D, Galimyanov D. Elimination of Defects in Mammograms Caused by a Malfunction of the Device Matrix. J Imaging 2022; 8:jimaging8050128. [PMID: 35621892 PMCID: PMC9143204 DOI: 10.3390/jimaging8050128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 11/22/2022] Open
Abstract
Today, the processing and analysis of mammograms is quite an important field of medical image processing. Small defects in images can lead to false conclusions. This is especially true when the distortion occurs due to minor malfunctions in the equipment. In the present work, an algorithm for eliminating a defect is proposed, which includes a change in intensity on a mammogram and deteriorations in the contrast of individual areas. The algorithm consists of three stages. The first is the defect identification stage. The second involves improvement and equalization of the contrasts of different parts of the image outside the defect. The third involves restoration of the defect area via a combination of interpolation and an artificial neural network. The mammogram obtained as a result of applying the algorithm shows significantly better image quality and does not contain distortions caused by changes in brightness of the pixels. The resulting images are evaluated using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Naturalness Image Quality Evaluator (NIQE) metrics. In total, 98 radiomics features are extracted from the original and obtained images, and conclusions are drawn about the minimum changes in features between the original image and the image obtained by the proposed algorithm.
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Affiliation(s)
- Dmitrii Tumakov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
- Correspondence:
| | - Zufar Kayumov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
| | - Alisher Zhumaniezov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
| | - Dmitry Chikrin
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia; (Z.K.); (A.Z.); (D.C.)
| | - Diaz Galimyanov
- Medical Unit, Department of Radiology, Kazan Federal University, 420008 Kazan, Russia;
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