ArticleMedical physics2020
Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.European radiology · 2025Pooled it
- Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy.BMC neurology · 2020Trial
- AdaptiveInvolutionNet: Spatially-adaptive involution with channel-wise attention for breast MRI tumor classification.PloS one · 2026Article
- Optimizing YOLOv11 for automated classification of breast cancer in medical images.Scientific reports · 2025Article
- Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review.Cureus · 2024Review
- Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.European radiology · 2024Article
- Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.Radiology. Imaging cancer · 2024Article
- Deep learning in breast imaging.BJR open · 2022Review
- Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans.Radiology. Artificial intelligence · 2022Article
- Review
- Deep learning in breast radiology: current progress and future directions.European radiology · 2021Review
- Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML).Diagnostics (Basel, Switzerland) · 2021Review
- Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.Contrast media & molecular imaging · 2020Review
- Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results.Medical physics · 2020Article
- Deep learning and radiomics in precision medicine.Expert review of precision medicine and drug development · 2019Article
- Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.International journal of radiation oncology, biology, physics · 2018Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
purposeDeep learning is emerging in radiology due to the increased computational capabilities available to reading rooms. These computational developments have the ability to mimic the radiologist and may allow for more accurate tissue characterization of normal and pathological lesion tissue to assist radiologists in defining different diseases. We introduce a novel tissue signature model based on tissue characteristics in breast tissue from multiparametric magnetic resonance imaging (mpMRI). The breast tissue signatures are used as inputs in a stacked sparse autoencoder (SSAE) multiparametric deep learning (MPDL) network for segmentation of breast mpMRI.
methodsWe constructed the MPDL network from SSAE with 5 layers with 10 nodes at each layer. A total cohort of 195 breast cancer subjects were used for training and testing of the MPDL network. The cohort consisted of a training dataset of 145 subjects and an independent validation set of 50 subjects. After segmentation, we used a combined SAE-support vector machine (SAE-SVM) learning method for classification. Dice similarity (DS) metrics were calculated between the segmented MPDL and dynamic contrast enhancement (DCE) MRI-defined lesions. Sensitivity, specificity, and area under the curve (AUC) metrics were used to classify benign from malignant lesions.
resultsThe MPDL segmentation resulted in a high DS of 0.87 ± 0.05 for malignant lesions and 0.84 ± 0.07 for benign lesions. The MPDL had excellent sensitivity and specificity of 86% and 86% with positive predictive and negative predictive values of 92% and 73%, respectively, and an AUC of 0.90.
conclusionsUsing a new tissue signature model as inputs into the MPDL algorithm, we have successfully validated MPDL in a large cohort of subjects and achieved results similar to radiologists.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.