Machine learning in breast MRI

Reig, Beatriu; Heacock, Laura; Geras, Krzysztof J.; Moy, Linda

doi:10.1002/jmri.26852

Cited by 116 publications

(74 citation statements)

References 124 publications

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“…However, the main novelty in that study was to localize the lesion, not to diagnose detected lesions. Two review articles by Reig et al and Sheth et al gave comprehensive information and new research direction about the application of AI and machine learning for analysis of breast MRI.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Zhou

Zhang

Chang

et al. 2019

Magnetic Resonance Imaging

154

123

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Background: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fullyautomatic diagnosis using deep learning is rarely reported. Purpose: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deeplearning methods, by taking peritumor tissues into consideration. Study Type: Retrospective. Population: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). Field Strength/Sequence: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. Assessment: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connectedcomponent labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. Statistical Tests: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. Results: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the perlesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. Data Conclusion: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. Level of Evidence: 3 Technical Efficacy: Stage 2

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Section: Discussionmentioning

confidence: 99%

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Zhou

Zhang

Chang

et al. 2019

Magnetic Resonance Imaging

154

123

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“…There is increasing interest in the use of artificial intelligence in medical imaging. Machine learning is a branch of artificial intelligence that enables computers to learn from existing "training data" and creates complex analytical models [46]. Firstly, this technique can be used for feature selection.…”

Section: Machine Learningmentioning

confidence: 99%

Magnetic Resonance Texture Analysis in Alzheimer's disease

Cai

Zhong

et al. 2020

Academic Radiology

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Texture analysis is an emerging field that allows mathematical detection of changes in MRI signals that are not visible among image pixels. Alzheimer's disease, a progressive neurodegenerative disease, is the most common cause of dementia. Recently, multiple texture analysis studies in patients with Alzheimer's disease have been performed. This review summarizes the main contributors to Alzheimer's disease-associated cognitive decline, presents a brief overview of texture analysis, followed by review of various MR imaging texture analysis applications in Alzheimer's disease. We also discuss the current challenges for widespread clinical utilization. MR texture analysis could potentially be applied to develop neuroimaging biomarkers for use in Alzheimer's disease clinical trials and diagnosis.

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“…Numerous radiomics studies have attempted to predict pCR using features extracted mostly from DCE-MRI, with some studies also evaluating T 2 and DWI parameters. 110 Among the most studied parameters are texture features, 111 which are mathematically extracted quantitative statistical features of an image, as well as morphologic and kinetic features (Fig. 14).…”

Section: Future Directionsmentioning

confidence: 99%

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Reig

Heacock

Lewin

et al. 2020

Magnetic Resonance Imaging

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The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting View this article online at wileyonlinelibrary.com.

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Machine learning in breast MRI

Cited by 116 publications

References 124 publications

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

Magnetic Resonance Texture Analysis in Alzheimer's disease

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

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