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Liu Y, Jia X, Zhao J, Peng Y, Yao X, Hu X, Cui J, Chen H, Chen X, Wu J, Hong N, Wang S, Wang Y. A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI. J Magn Reson Imaging 2024; 60:600-612. [PMID: 37933890 DOI: 10.1002/jmri.29111] [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: 08/01/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. PURPOSE To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. STUDY TYPE Retrospective analysis followed by retrospective and prospective cohorts study. POPULATION 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. FIELD STRENGTH/SEQUENCE 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. ASSESSMENT AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. STATISTICAL TESTS Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. RESULTS The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. DATA CONCLUSION An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yulu Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xiaoxuan Jia
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jiaqi Zhao
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, China
| | - Yuan Peng
- Department of Breast Surgery, Peking University People's Hospital, Beijing, China
| | - Xun Yao
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xuege Hu
- Department of Breast Surgery, Peking University People's Hospital, Beijing, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, China
| | - Haoquan Chen
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xiufeng Chen
- Department of General Surgery, Beijing Aerospace General Hospital, Beijing, China
| | - Jing Wu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Shu Wang
- Department of Breast Surgery, Peking University People's Hospital, Beijing, China
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, 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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Faraz K, Dauce G, Bouhamama A, Leporq B, Sasaki H, Bito Y, Beuf O, Pilleul F. Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches. J Pers Med 2023; 13:1062. [PMID: 37511674 PMCID: PMC10382057 DOI: 10.3390/jpm13071062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.
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Affiliation(s)
- Khuram Faraz
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Grégoire Dauce
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
| | - Amine Bouhamama
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Hajime Sasaki
- FUJIFILM Healthcare France S.A.S., 69800 Saint-Priest, France
- FUJIFILM Healthcare Corporation, Tokyo 107-0052, Japan
| | | | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
| | - Frank Pilleul
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, 69621 Lyon, France
- Department of Radiology, Centre Léon Bérard, 69008 Lyon, France
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Anaby D, Shavin D, Zimmerman-Moreno G, Nissan N, Friedman E, Sklair-Levy M. 'Earlier than Early' Detection of Breast Cancer in Israeli BRCA Mutation Carriers Applying AI-Based Analysis to Consecutive MRI Scans. Cancers (Basel) 2023; 15:3120. [PMID: 37370730 DOI: 10.3390/cancers15123120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25-30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate 'earlier than early' BC diagnosis in BRCA PV carriers.
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Affiliation(s)
- Debbie Anaby
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan 52621, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6910201, Israel
| | - David Shavin
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan 52621, Israel
| | | | - Noam Nissan
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan 52621, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6910201, Israel
| | - Eitan Friedman
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6910201, Israel
- Meirav High Risk Center, Sheba Medical Center, Ramat Gan 52621, Israel
| | - Miri Sklair-Levy
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan 52621, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6910201, Israel
- Meirav High Risk Center, Sheba Medical Center, Ramat Gan 52621, Israel
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Miller MM, Rubaiyat AHM, Rohde GK. Predicting Malignancy of Breast Imaging Findings Using Quantitative Analysis of Contrast-Enhanced Mammography (CEM). Diagnostics (Basel) 2023; 13:diagnostics13061129. [PMID: 36980437 PMCID: PMC10047016 DOI: 10.3390/diagnostics13061129] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023] Open
Abstract
We sought to develop new quantitative approaches to characterize the spatial distribution of mammographic density and contrast enhancement of suspicious contrast-enhanced mammography (CEM) findings to improve malignant vs. benign classifications of breast lesions. We retrospectively analyzed all breast lesions that underwent CEM imaging and tissue sampling at our institution from 2014–2020 in this IRB-approved study. A penalized linear discriminant analysis was used to classify lesions based on the averaged histograms of radial distributions of mammographic density and contrast enhancement. T-tests were used to compare the classification accuracies of density, contrast, and concatenated density and contrast histograms. Logistic regression and AUC-ROC analyses were used to assess if adding demographic and clinical data improved the model accuracy. A total of 159 suspicious findings were evaluated. Density histograms were more accurate in classifying lesions as malignant or benign than a random classifier (62.37% vs. 48%; p < 0.001), but the concatenated density and contrast histograms demonstrated a higher accuracy (71.25%; p < 0.001) than the density histograms alone. Including the demographic and clinical data in our models led to a higher AUC-ROC than concatenated density and contrast images (0.81 vs. 0.70; p < 0.001). In the classification of invasive vs. non-invasive malignancy, the concatenated density and contrast histograms demonstrated no significant improvement in accuracy over the density histograms alone (77.63% vs. 78.59%; p = 0.504). Our findings suggest that quantitative differences in the radial distribution of mammographic density could be used to discriminate malignant from benign breast findings; however, classification accuracy was significantly improved with the addition of contrast-enhanced imaging data from CEM. Adding patient demographic and clinical information further improved the classification accuracy.
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Affiliation(s)
- Matthew M. Miller
- Department of Radiology and Medical Imaging, University of Virginia Health System, 1215 Lee St., Charlottesville, VA 22903, USA
- Correspondence:
| | - Abu Hasnat Mohammad Rubaiyat
- Department of Electrical and Computer Engineering, University of Virginia, 415 Lane Rd., Charlottesville, VA 22903, USA
| | - Gustavo K. Rohde
- Department of Biomedical Engineering, University of Virginia, 415 Lane Rd., Charlottesville, VA 22903, USA
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Altabella L, Benetti G, Camera L, Cardano G, Montemezzi S, Cavedon C. Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7d8f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/30/2022] [Indexed: 11/11/2022]
Abstract
Abstract
In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.
Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.
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7
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Militello C, Rundo L, Dimarco M, Orlando A, Woitek R, D'Angelo I, Russo G, Bartolotta TV. 3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients. Acad Radiol 2022; 29:830-840. [PMID: 34600805 DOI: 10.1016/j.acra.2021.08.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/26/2021] [Accepted: 08/30/2021] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation). MATERIALS AND METHODS 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An in-depth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data. RESULTS The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725±0.091, sensitivity = 0.709±0.176, specificity = 0.741±0.114, PPV = 0.72±0.093, and NPV = 0.75±0.114. CONCLUSION In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.
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Du Y, Zha HL, Wang H, Liu XP, Pan JZ, Du LW, Cai MJ, Zong M, Li CY. Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma. Br J Radiol 2022; 95:20210598. [PMID: 35138938 PMCID: PMC10993963 DOI: 10.1259/bjr.20210598] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 01/13/2022] [Accepted: 01/20/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma. METHODS A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram. CONCLUSION The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models. ADVANCES IN KNOWLEDGE Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.
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Affiliation(s)
- Yu Du
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Hai-Ling Zha
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Hui Wang
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Xin-Pei Liu
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Jia-Zhen Pan
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Li-Wen Du
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Meng-Jun Cai
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Min Zong
- Department of Radiology, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
| | - Cui-Ying Li
- Department of Ultrasound, The First Affiliated Hospital of
Nanjing Medical University,
Nanjing, China
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Li Z, Ye J, Du H, Cao Y, Wang Y, Liu D, Zhu F, Shen H. Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis. Front Oncol 2022; 12:837257. [PMID: 35299744 PMCID: PMC8920972 DOI: 10.3389/fonc.2022.837257] [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: 12/16/2021] [Accepted: 02/07/2022] [Indexed: 11/25/2022] Open
Abstract
Background To evaluate the preoperative predictive value of radiomics in the diagnosis of breast cancer (BC). Methods By searching PubMed and Embase libraries, our study identified 19 eligible studies. We conducted a meta-analysis to assess the differential value in the preoperative assessment of BC using radiomics methods. Results Nineteen radiomics studies focusing on the diagnostic efficacy of BC and involving 5865 patients were enrolled. The integrated sensitivity and specificity were 0.84 (95% CI: 0.80–0.87, I2 = 76.44%) and 0.83 (95% CI: 0.78–0.87, I2 = 81.79%), respectively. The AUC based on the SROC curve was 0.91, indicating a high diagnostic value. Conclusion Radiomics has shown excellent diagnostic performance in the preoperative prediction of BC and is expected to be a promising method in clinical practice.
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Affiliation(s)
- Zhenkai Li
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Juan Ye
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Hongdi Du
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Ying Cao
- Department of Radiotherapy, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Ying Wang
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Desen Liu
- Department of Thoracic Surgery, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Feng Zhu
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
| | - Hailin Shen
- Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Suzhou, China
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Xia B, Wang H, Wang Z, Qian Z, Xiao Q, Liu Y, Shao Z, Zhou S, Chai W, You C, Gu Y. A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy. Front Genet 2021; 12:783513. [PMID: 34868273 PMCID: PMC8632946 DOI: 10.3389/fgene.2021.783513] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS. Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation. Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761-0.907) and 0.868 (95% CI, 0.787-949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709-0.743; validation group: C-index = 0.774,95% CI = 0.743-0.805). Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.
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Affiliation(s)
- Bingqing Xia
- International Peace Maternity and Child Health Hospital, Shanghai, China.,Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhe Wang
- Shanghai United Imaging Medical Technology Co., Ltd., Shanghai, China
| | - Zhaoxia Qian
- International Peace Maternity and Child Health Hospital, Shanghai, China
| | - Qin Xiao
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yin Liu
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhimin Shao
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shuling Zhou
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weimin Chai
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chao You
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yajia Gu
- Shanghai Cancer Center, Fudan University, Shanghai, China
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Lee HJ, Nguyen AT, Ki SY, Lee JE, Do LN, Park MH, Lee JS, Kim HJ, Park I, Lim HS. Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning. Front Oncol 2021; 11:744460. [PMID: 34926256 PMCID: PMC8679659 DOI: 10.3389/fonc.2021.744460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/08/2021] [Indexed: 01/02/2023] Open
Abstract
ObjectiveThis study was conducted in order to investigate the feasibility of using radiomics analysis (RA) with machine learning algorithms based on breast magnetic resonance (MR) images for discriminating malignant from benign MR-detected additional lesions in patients with primary breast cancer.Materials and MethodsOne hundred seventy-four MR-detected additional lesions (benign, n = 86; malignancy, n = 88) from 158 patients with ipsilateral primary breast cancer from a tertiary medical center were included in this retrospective study. The entire data were randomly split to training (80%) and independent test sets (20%). In addition, 25 patients (benign, n = 21; malignancy, n = 15) from another tertiary medical center were included for the external test. Radiomics features that were extracted from three regions-of-interest (ROIs; intratumor, peritumor, combined) using fat-saturated T1-weighted images obtained by subtracting pre- from postcontrast images (SUB) and T2-weighted image (T2) were utilized to train the support vector machine for the binary classification. A decision tree method was utilized to build a classifier model using clinical imaging interpretation (CII) features assessed by radiologists. Area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity were used to compare the diagnostic performance.ResultsThe RA models trained using radiomics features from the intratumor-ROI showed comparable performance to the CII model (accuracy, AUROC: 73.3%, 69.6% for the SUB RA model; 70.0%, 75.1% for the T2 RA model; 73.3%, 72.0% for the CII model). The diagnostic performance increased when the radiomics and CII features were combined to build a fusion model. The fusion model that combines the CII features and radiomics features from multiparametric MRI data demonstrated the highest performance with an accuracy of 86.7% and an AUROC of 91.1%. The external test showed a similar pattern where the fusion models demonstrated higher levels of performance compared with the RA- or CII-only models. The accuracy and AUROC of the SUB+T2 RA+CII model in the external test were 80.6% and 91.4%, respectively.ConclusionOur study demonstrated the feasibility of using RA with machine learning approach based on multiparametric MRI for quantitatively characterizing MR-detected additional lesions. The fusion model demonstrated an improved diagnostic performance over the models trained with either RA or CII alone.
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Affiliation(s)
- Hyo-jae Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
| | - Anh-Tien Nguyen
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
| | - So Yeon Ki
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun-gun, South Korea
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
| | - Luu-Ngoc Do
- Department of Radiology, Chonnam National University, Gwangju, South Korea
| | - Min Ho Park
- Department of Radiology, Chonnam National University, Gwangju, South Korea
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun-gun, South Korea
| | - Ji Shin Lee
- Department of Radiology, Chonnam National University, Gwangju, South Korea
- Department of Pathology, Chonnam National University Hwasun Hospital, Hwasun-gun, South Korea
| | - Hye Jung Kim
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, South Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
- Department of Radiology, Chonnam National University, Gwangju, South Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea
- *Correspondence: Ilwoo Park, ; Hyo Soon Lim,
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun-gun, South Korea
- Department of Radiology, Chonnam National University, Gwangju, South Korea
- *Correspondence: Ilwoo Park, ; Hyo Soon Lim,
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12
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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13
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Whitney HM, Drukker K, Edwards A, Papaioannou J, Medved M, Karczmar G, Giger ML. Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths. Magn Reson Imaging 2021; 82:111-121. [PMID: 34174331 PMCID: PMC8386988 DOI: 10.1016/j.mri.2021.06.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 06/19/2021] [Accepted: 06/21/2021] [Indexed: 02/07/2023]
Abstract
Radiomic features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As medical centers transition from 1.5 T to 3.0 T magnetic resonance (MR) imaging, it is beneficial to identify potentially robust radiomic features across field strengths because images acquired at different field strengths could be used in machine learning models. Dynamic contrast-enhanced MR images of benign breast lesions and hormone receptor positive/HER2-negative (HR+/HER2-) breast cancers were acquired retrospectively, yielding 612 unique cases: 150 and 99 benign lesions imaged at 1.5 T and 3.0 T, and 223 and 140 HR+/HER2- cancerous lesions imaged at 1.5 T and 3.0 T, respectively. In addition, an independent set of seven lesions imaged at both field strengths, three benign lesions and four HR+/HER2- cancers, was analyzed separately. Lesions were automatically segmented using a 4D fuzzy c-means method; thirty-eight radiomic features were extracted. Feature value distributions were compared by cancer status and imaging field strength using the Kolmogorov-Smirnov test. Features that did not demonstrate a statistically significant difference were considered to be potentially robust. The area under the receiver operating characteristic curve (AUC), for the task of classifying lesions as benign or HR+/HER2- cancer, was determined for each feature at each field strength. Three features were found to be both potentially robust across field strength and of high classification performance, i.e., AUCs statistically greater than 0.5 in the classification task: one shape feature (irregularity), one texture feature (sum average) and one enhancement variance kinetics features (enhancement variance increasing rate). In the demonstration set of lesions imaged at both field strengths, two of the three potentially robust features showed qualitative agreement across field strength. These findings may contribute to the development of computer-aided diagnosis models that are robust across field strength for this classification task.
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Affiliation(s)
- Heather M Whitney
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America; Department of Physics, Wheaton College, Wheaton, IL 60187, United States of America.
| | - Karen Drukker
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America
| | - Alexandra Edwards
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America
| | - John Papaioannou
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America
| | - Milica Medved
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America
| | - Gregory Karczmar
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America
| | - Maryellen L Giger
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States of America.
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14
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Xie T, Zhao Q, Fu C, Grimm R, Gu Y, Peng W. Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm). Eur Radiol 2021; 32:1634-1643. [PMID: 34505195 DOI: 10.1007/s00330-021-08244-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/21/2021] [Accepted: 08/03/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVES To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm). METHODS This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis. RESULTS For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001). CONCLUSIONS Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI. KEY POINTS • For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.
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Affiliation(s)
- Tianwen Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qiufeng Zhao
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Caixia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, People's Republic of China
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare, Erlangen, Germany
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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15
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Ohyu S, Tozaki M, Sasaki M, Chiba H, Xiao Q, Fujisawa Y, Sagara Y. Combined Use of Texture Features and Morphological Classification Based on Dynamic Contrast-enhanced MR Imaging: Differentiating Benign and Malignant Breast Masses with High Negative Predictive Value. Magn Reson Med Sci 2021; 21:485-498. [PMID: 34176860 PMCID: PMC9316135 DOI: 10.2463/mrms.mp.2020-0160] [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] [Indexed: 11/26/2022] Open
Abstract
Purpose: We evaluated the diagnostic performance of the texture features of dynamic contrast-enhanced (DCE) MRI for breast cancer diagnosis in which the discriminator was optimized, so that the specificity was maximized via the restriction of the negative predictive value (NPV) to greater than 98%. Methods: Histologically proven benign and malignant mass lesions of DCE MRI were enrolled retrospectively. Training and testing sets consist of 166 masses (49 benign, 117 malignant) and 50 masses (15 benign, 35 malignant), respectively. Lesions were classified via MRI review by a radiologist into 4 shape types: smooth (S-type, 34 masses in training set and 8 masses in testing set), irregular without rim-enhancement (I-type, 60 in training and 14 in testing), irregular with rim-enhancement (R-type, 56 in training and 22 in testing), and spicula (16 in training and 6 in testing). Spicula were immediately classified as malignant. For the remaining masses, 298 texture features were calculated using a parametric map of DCE MRI in 3D mass regions. Masses were classified into malignant or benign using two thresholds on a feature pair. On the training set, several feature pairs and their thresholds were selected and optimized for each mass shape type to maximize specificity with the restriction of NPV > 98%. NPV and specificity were computed using the testing set by comparison with histopathologic results and averaged on the selected feature pairs. Results: In the training set, 27, 12, and 15 texture feature pairs are selected for S-type, I-type, and R-type masses, respectively, and thresholds are determined. In the testing set, average NPV and specificity using the selected texture features were 99.0% and 45.2%, respectively, compared to the NPV (85.7%) and specificity (40.0%) in visually assessed MRI category-based diagnosis. Conclusion: We, therefore, suggest that the NPV of our texture-based features method described performs similarly to or greater than the NPV of the MRI category-based diagnosis.
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Affiliation(s)
- Shigeharu Ohyu
- Research and Development Center, Canon Medical Systems Corporation
| | | | | | - Hisae Chiba
- MRI Sales Department, Canon Medical Systems Corporation
| | - Qilin Xiao
- Research & Development Center, Canon Medical Systems (China) Co., Ltd
| | - Yasuko Fujisawa
- Research and Development Center, Canon Medical Systems Corporation
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16
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Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Sooknanan C, Thakur SB, Jochelson MS, Sevilimedu V, Morris EA, Baltzer PAT, Helbich TH, Pinker K. Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics (Basel) 2021; 11:diagnostics11060919. [PMID: 34063774 PMCID: PMC8223779 DOI: 10.3390/diagnostics11060919] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/11/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
- Correspondence: (I.D.N.); (P.G.)
| | - Peter Gibbs
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Correspondence: (I.D.N.); (P.G.)
| | - Jeffrey S. Reiner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Caleb Sooknanan
- Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, New York, NY 10065, USA;
| | - Sunitha B. Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA;
| | - Elizabeth A. Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
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17
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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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18
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Radiomische Analyse kleiner Brusttumoren. ROFO-FORTSCHR RONTG 2021. [DOI: 10.1055/a-1151-9740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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19
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Lin F, Wang Z, Zhang K, Yang P, Ma H, Shi Y, Liu M, Wang Q, Cui J, Mao N, Xie H. Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for Identifying Benign and Malignant Breast Lesions of Sub-1 cm. Front Oncol 2020; 10:573630. [PMID: 33194677 PMCID: PMC7662120 DOI: 10.3389/fonc.2020.573630] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/12/2020] [Indexed: 12/16/2022] Open
Abstract
Objectives To develop a radiomics nomogram that incorporates contrast-enhanced spectral mammography (CESM)-based radiomics features and clinico-radiological variables for identifying benign and malignant breast lesions of sub-1 cm. Methods This retrospective study included 139 patients with the diameter of sub-1 cm on cranial caudal (CC) position of recombined images. Radiomics features were extracted from low-energy and recombined images on CC position. The variance threshold, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select optimal predictive features. Radiomics signature (Rad-score) was calculated by a linear combination of selected features. The independent predictive factors were identified by ANOVA and multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability of lesions. The performance and clinical utility of the nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results Nineteen radiomics features were selected to calculate Rad-score. Breast imaging reporting and data system (BI-RADS) category and age were identified as predictive factors. The radiomics nomogram combined with Rad-score, BI-RADS category, and age showed better performance (area under curves [AUC]: 0.940, 95% confidence interval [CI]: 0.804-0.992) than Rad-score (AUC: 0.868, 95% CI: 0.711-0.958) and clinico-radiological model (AUC: 0.864, 95% CI: 0.706-0.956) in the validation cohort. The calibration curve and DCA showed that the radiomics nomogram had good consistency and clinical utility. Conclusions The radiomics nomogram incorporated with CESM-based radiomics features, BI-RADS category and age could identify benign and malignant breast lesions of sub-1 cm.
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Affiliation(s)
- Fan Lin
- School of Medical Imaging, Binzhou Medical University, Yantai, China.,Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Zhongyi Wang
- School of Medical Imaging, Binzhou Medical University, Yantai, China.,Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Meijie Liu
- School of Medical Imaging, Binzhou Medical University, Yantai, China.,Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Qinglin Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Jingjing Cui
- Collaboration Department, Huiying Medical Technology, Beijing, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
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Yin R, Jiang M, Lv WZ, Jiang F, Li J, Hu B, Cui XW, Dietrich CF. Study Processes and Applications of Ultrasomics in Precision Medicine. Front Oncol 2020; 10:1736. [PMID: 33014858 PMCID: PMC7494734 DOI: 10.3389/fonc.2020.01736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 08/04/2020] [Indexed: 12/12/2022] Open
Abstract
Ultrasomics is the science of transforming digitally encrypted medical ultrasound images that hold information related to tumor pathophysiology into mineable high-dimensional data. Ultrasomics data have the potential to uncover disease characteristics that are not found with the naked eye. The task of ultrasomics is to quantify the state of diseases using distinctive imaging algorithms and thereby provide valuable information for personalized medicine. Ultrasomics is a powerful tool in oncology but can also be applied to other medical problems for which a disease is imaged. To date there is no comprehensive review focusing on ultrasomics. Here, we describe how ultrasomics works and its capability in diagnosing disease in different organs, including breast, liver, and thyroid. Its pitfalls, challenges and opportunities are also discussed.
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Affiliation(s)
- Rui Yin
- Department of Ultrasound, Affiliated Renhe Hospital of China Three Gorges University, Yichang, China
| | - Meng Jiang
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Fan Jiang
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jun Li
- Department of Ultrasound, The First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Bing Hu
- Department of Ultrasound, Affiliated Renhe Hospital of China Three Gorges University, Yichang, China
| | - Xin-Wu Cui
- Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Orlando A, Dimarco M, Cannella R, Bartolotta TV. Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art. Artif Intell Med Imaging 2020; 1:6-18. [DOI: 10.35711/aimi.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 06/17/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.
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Affiliation(s)
- Alessia Orlando
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Mariangela Dimarco
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Roberto Cannella
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Palermo 90015, Italy
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22
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Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers. Eur Radiol 2020; 30:6721-6731. [PMID: 32594207 PMCID: PMC7599163 DOI: 10.1007/s00330-020-06991-7] [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: 01/20/2020] [Revised: 04/09/2020] [Accepted: 05/28/2020] [Indexed: 01/21/2023]
Abstract
Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone. Electronic supplementary material The online version of this article (10.1007/s00330-020-06991-7) contains supplementary material, which is available to authorized users.
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23
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Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics. J Clin Med 2020; 9:jcm9061853. [PMID: 32545851 PMCID: PMC7356091 DOI: 10.3390/jcm9061853] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/06/2020] [Accepted: 06/09/2020] [Indexed: 12/20/2022] Open
Abstract
We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.
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Onishi N, Sadinski M, Hughes MC, Ko ES, Gibbs P, Gallagher KM, Fung MM, Hunt TJ, Martinez DF, Shukla-Dave A, Morris EA, Sutton EJ. Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer. Breast Cancer Res 2020; 22:58. [PMID: 32466799 PMCID: PMC7254650 DOI: 10.1186/s13058-020-01292-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 05/10/2020] [Indexed: 01/17/2023] Open
Abstract
Background Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4–6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7–4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant. Results Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1–2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001). Conclusions Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.
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Affiliation(s)
- Natsuko Onishi
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meredith Sadinski
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mary C Hughes
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eun Sook Ko
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter Gibbs
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katherine M Gallagher
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Theodore J Hunt
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Danny F Martinez
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amita Shukla-Dave
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth A Morris
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth J Sutton
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Chhetri A, Li X, Rispoli JV. Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer. Front Med (Lausanne) 2020; 7:175. [PMID: 32478083 PMCID: PMC7235971 DOI: 10.3389/fmed.2020.00175] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 04/15/2020] [Indexed: 01/10/2023] Open
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.
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Affiliation(s)
- Apekshya Chhetri
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Xin Li
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - Joseph V. Rispoli
- Magnetic Resonance Biomedical Engineering Laboratory, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
- Center for Cancer Research, Purdue University, West Lafayette, IN, United States
- School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, United States
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26
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Jiang Z, Yin J. Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE-MRI in classifying benign from malignant lesions. J Surg Oncol 2020; 121:1181-1190. [PMID: 32167588 DOI: 10.1002/jso.25901] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/02/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND OBJECTIVES To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors. METHODS A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SIslope ), initial percentage of peak enhancement (Einitial ), percentage of peak enhancement (Epeak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out cross-validation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. RESULTS The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SIslope , 0.756 for Einitial , 0.923 for Epeak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between Einitial vs MSI, Einitial vs Epeak and Einitial vs SEP (P < .05). There were no significant differences in other pairwise comparisons. CONCLUSION Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis.
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Affiliation(s)
- Zejun Jiang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.,Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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27
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Kim S, Kim MJ, Kim EK, Yoon JH, Park VY. MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer. Sci Rep 2020; 10:3750. [PMID: 32111957 PMCID: PMC7048756 DOI: 10.1038/s41598-020-60822-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 02/15/2020] [Indexed: 12/21/2022] Open
Abstract
Radiomic features hold potential to improve prediction of disease-free survival (DFS) in triple-negative breast cancer (TNBC) and may show better performance if developed from TNBC patients. We aimed to develop a radiomics score based on MRI features to estimate DFS in patients with TNBC. A total of 228 TNBC patients who underwent preoperative MRI and surgery between April 2012 and December 2016 were included. Patients were temporally divided into the training (n = 169) and validation (n = 59) set. Radiomic features of the tumor were extracted from T2-weighted and contrast-enhanced T1- weighted MRI. Then a radiomics score was constructed with the least absolute shrinkage and selection operator regression in the training set. Univariate and multivariate Cox proportional hazards models were used to determine what associations the radiomics score and clinicopathologic variables had with DFS. A combined clinicopathologic-radiomic (CCR) model was constructed based on multivariate Cox analysis. The incremental values of the radiomics score were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC) and bootstrapping (n = 1000). The radiomics score, which consisted of 5 selected MRI features, was significantly associated with worse DFS in both the training and validation sets (p = 0.002, p = 0.033, respectively). In both the training and validation set, the radiomics score showed comparable performance with the clinicopathologic model. The CCR model demonstrated better performance than the clinicopathologic model in the training set (iAUC, 0.844; difference in iAUC, p < 0.001) and validation set (iAUC, 0.765, difference in iAUC, p < 0.001). In conclusion, MRI-based radiomic features can improve the prediction of DFS when integrated with clinicopathologic data in patients with TNBC.
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Affiliation(s)
- Sungwon Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Min Jung Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Eun-Kyung Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jung Hyun Yoon
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Vivian Youngjean Park
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
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Whitney HM, Li H, Ji Y, Liu P, Giger ML. Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:163-177. [PMID: 34045769 PMCID: PMC8152568 DOI: 10.1109/jproc.2019.2950187] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Digital image-based signatures of breast tumors may ultimately contribute to the design of patient-specific breast cancer diagnostics and treatments. Beyond traditional human-engineered computer vision methods, tumor classification methods using transfer learning from deep convolutional neural networks (CNNs) are actively under development. This article will first discuss our progress in using CNN-based transfer learning to characterize breast tumors for various diagnostic, prognostic, or predictive image-based tasks across multiple imaging modalities, including mammography, digital breast tomosynthesis, ultrasound (US), and magnetic resonance imaging (MRI), compared to both human-engineered feature-based radiomics and fusion classifiers created through combination of such features. Second, a new study is presented that reports on a comprehensive comparison of the classification performances of features derived from human-engineered radiomic features, CNN transfer learning, and fusion classifiers for breast lesions imaged with MRI. These studies demonstrate the utility of transfer learning for computer-aided diagnosis and highlight the synergistic improvement in classification performance using fusion classifiers.
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Affiliation(s)
- Heather M Whitney
- Department of Radiology, The University of Chicago, Chicago, IL 60637 USA, and also with the Department of Physics, Wheaton College, Wheaton, IL 60187 USA
| | - Hui Li
- Department of Radiology, The University of Chicago, Chicago, IL 60637 USA
| | - Yu Ji
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University, Tianjin 30060, China
| | - Peifang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Medical University, Tianjin 30060, China
| | - Maryellen L Giger
- Department of Radiology, The University of Chicago, Chicago, IL 60637 USA
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29
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Marino MA, Gucalp A, Leithner D, Keating D, Avendano D, Bernard-Davila B, Morris EA, Pinker K, Jochelson MS. Mammographic screening in male patients at high risk for breast cancer: is it worth it? Breast Cancer Res Treat 2019; 177:705-711. [PMID: 31280425 PMCID: PMC6745275 DOI: 10.1007/s10549-019-05338-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 06/26/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE To investigate the utility of mammography for breast cancer screening in a population of males at increased risk for breast cancer. METHODS In this HIPAA-compliant institutional review board-approved single-institution study, mammography records and clinical data of 827 male patients who underwent digital mammography from September 2011-July 2018 were analyzed via the electronic medical record. 664 of these men presented with masses, pain, or nipple discharge and were excluded from this study. The remaining 163 asymptomatic men with familial and/or personal history of breast cancer, or with a known germline mutation in BRCA, underwent screening mammography and were included in this analysis. RESULTS 163 asymptomatic men (age: mean 63 years, range 24-87 years) underwent 806 screening mammograms. 125/163 (77%) had a personal history of breast cancer and 72/163 (44%) had a family history of breast cancer. 24/163 (15%) were known mutation carriers: 4/24 (17%) BRCA1 and 20/24 (83%) BRCA2. 792/806 (98%) of the screening mammograms were negative (BI-RADS 1 or 2); 10/806 (1.2%) were classified as BI-RADS 3, all of which were eventually downgraded to BI-RADS 2 on follow-up. 4/806 (0.4%) mammograms were abnormal (BI-RADS 4/5): all were malignant. The cancer detection rate in this cohort was 4.9 cancers/1000 examinations. CONCLUSIONS In our cohort, screening mammography yielded a cancer detection rate of 4.9 cancers/1000 examinations which is like the detection rate of screening mammography in a population of women at average risk, indicating that screening mammography is of value in male patients at high risk for breast cancer.
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Affiliation(s)
- Maria Adele Marino
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Ayca Gucalp
- Breast Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Doris Leithner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Delia Keating
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Daly Avendano
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Department Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Nuevo Leon, Mexico
| | - Blanca Bernard-Davila
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Maxine S Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
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30
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Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019; 52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022] Open
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
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Affiliation(s)
- Beatriu Reig
- The Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krzysztof J Geras
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
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