Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

Cited by (149)

Items 101–149 of 149

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3. Assessment of Concurrent Stereotactic Radiosurgery and Bevacizumab Treatment of Recurrent Malignant Gliomas Using Multiparametric MRI Imaging and Radiomics Analysis
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5. Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer
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7. Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers
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8. Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis
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10. Integrative Bayesian Models Using Post-Selective Inference: A Case Study in Radiogenomics
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11. A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer
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13. The use of radiomics in magnetic resonance imaging for the pre‐treatment characterisation of breast cancers: A scoping review
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14. The role of the quality control system for diagnostics of oncological diseases in radiomics: a review
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15. Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches
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16. Breast cancer molecular subtype classification using deep features: preliminary results
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17. Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology
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19. Advances in Pediatric Liver Tumors
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23. Cross-Modality Calibration in Multi-Input Network for Axillary Lymph Node Metastasis Evaluation
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24. High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma
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25. Decision support systems in breast cancer
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26. A Methodological Research on Radiogenomics: Combining Radiomics and Genomics for Classification
    Merve Kaşıkçı, Erdal Coşgun, Erdem Karabulut · 2024 · Turkiye Klinikleri Journal of Biostatistics
27. DCE-MRI radiomics of primary breast lesions combined with ipsilateral axillary lymph nodes for predicting efficacy of NAT
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29. Advances in Artificial Intelligence-Based Radiomultiomics for Assisting Cancer Diagnosis and Treatment
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30. The Role of Computed Tomography-Determined Total Tumor Volume at Baseline in Predicting Outcomes of Patients with Locally Advanced Unresectable or Metastatic Pancreatic Ductal Adenocarcinoma
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31. Application and Research of Convolution Neural Network in MRI Image Classification and Recognition
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32. Deep Learning in Mammography Breast Cancer Detection
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33. Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan.
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34. Emerging techniques in breast MRI
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36. Combining data science and medical imaging
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37. Comparing Low and High-Temporal Resolution DCE-MRI Texture Analysis for Discrimination of Breast Lesions from Background Enhancement
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38. Combination of CT Imaging-based Radiomics and Clinicopathological Characteristics for Predicting the Clinical Benefit of Immune Checkpoint Inhibitors in Lung Cancer
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39. 13 Future Applications: Radiomics and Deep Learning on Breast MRI
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43. Radiomics for the radiologist: opportunities and challenges
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44. Radiomics: Principles and Applications in Oncology
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45. Additional file 2 of “Real-world” radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues
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46. Additional file 1 of Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer
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48. Contributions of radiomics in ¹⁸F-FDG PET/CT and in MRI in breast cancer
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