ArticleTomography (Ann Arbor, Mich.)2022
Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer.
Article in Tomography (Ann Arbor, Mich.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- The Impact of CT Reconstruction Parameters on Emphysema Index Quantification, HU-Based Measurements, and Goddard Score in COPD Assessment: A Prospective Study.International journal of biomedical imaging · 2026Article
- Enhanced maximum intensity projection (eMIP) for improving the fidelity of optoacoustic images.Npj imaging · 2025Article
- Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer.Frontiers in oncology · 2025Review
- Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [European radiology · 2024Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound.Journal of medical ultrasonics (2001) · 2023Article
- Leveraging open dataset and transfer learning for accurate recognition of chronic pulmonary embolism from CT angiogram maximum intensity projection images.European radiology experimental · 2023Article
- Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- State of the Art in 2022 PET/CT in Breast Cancer: A Review.Journal of clinical medicine · 2023Review
- Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
- A Feasibility Study on Proton Range Monitoring UsingTomography (Ann Arbor, Mich.) · 2022Article
- Review
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13 authors.
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Abstract
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80-98% and a specificity of 80%, 88%, and 76-92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872-0.967,
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