• Polyak, K. et al. Heterogeneity in breast cancer. The Journal of Clinical Investigation 1213786–3788 (2011).

    CASE
    Article

    Google Scholar

  • Marusyk, A. & Polyak, K. Tumor heterogeneity: causes and consequences. Biochimica et Biophysica Acta (BBA)-Cancer journals 1805105-117 (2010).

    CASE
    Article

    Google Scholar

  • Gavenonis, SC & Roth, SO Role of magnetic resonance imaging in assessing the extent of disease. Magnetic Resonance Imaging Clinics 18199-206 (2010).

    Article

    Google Scholar

  • Weinstein, S. & Rosen, M. MRI imaging of the breast: current indications and advanced imaging techniques. Radiology clinics 481013-1042 (2010).

    Article

    Google Scholar

  • Gillies, RJ, Kinahan, PE & Hricak, H. Radiomics: Images are more than images, they are data. Radiology 278563-577 (2016).

    Article

    Google Scholar

  • McNitt-Gray, M. et al. Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Characteristics of Different Software Packages on Digital Reference Objects and Patient Datasets. Tomography 6118-128 (2020).

    CASE
    Article

    Google Scholar

  • Zwanenburg, A. et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for Image-Based High-Throughput Phenotyping. Radiology 295328–338 (2020).

    Article

    Google Scholar

  • Valdora, F., Houssami, N., Rossi, F., Calabrese, M. & Tagliafico, AS Quick review: radiomics and breast cancer. Breast cancer research and treatment 169217-229 (2018).

    Article

    Google Scholar

  • Clark, K. et al. The Cancer Imaging Archive (tcia): maintain and operate a repository of public information. Digital Imaging Review 261045-1057 (2013).

    Article

    Google Scholar

  • Saha, A. et al. A machine learning approach to breast cancer radiogenomics: a study of 922 subjects and 529 dce-mri features. British Journal of Cancer 119508-516 (2018).

    CASE
    Article

    Google Scholar

  • Saha, A. et al. Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor localizations. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.e3sv-re93 (2021).

  • Lehman, C. et al. Group of investigators of the Acrin 6667 trial. MRI evaluation of the contralateral breast in women with newly diagnosed breast cancer. N English J med 3561295–303 (2007).

    CASE
    Article

    Google Scholar

  • Kinahan, P., Muzi, M., Bialecki, B., Herman, B. & Coombs, L. Acrin-contralateral-breast-mr (acrin 6667). The Cancer Imaging Archive. https://doi.org/10.7937/Q1EE-J082 (2021).

  • Castaldo, R., Pane, K., Nicolai, E., Salvatore, M. & Franzese, M. The impact of normalization approaches to automatically detect radiogenomic phenotypes characterizing breast cancer receptor status. Cancer 12518 (2020).

    CASE
    Article

    Google Scholar

  • Pati, S. et al. Reproducibility analysis of multi-institutional paired expert annotations and radiomic characteristics of the Ivy Glioblastoma Atlas Project dataset (Ivy Gap). medical physics 476039–6052 (2020).

    ADS
    Article

    Google Scholar

  • Saint-Martin, M.-J. et al. A radiomics pipeline dedicated to breast MRI: validation on a multi-scanner phantom study. Magnetic resonance materials in physics, biology and medicine 34355–366 (2021).

    Article

    Google Scholar

  • Newitt, D. et al. Multicenter breast MRI data and patient segmentations from the i-spy 1/acrin 6657 trials. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.HdHpgJLK (2016).

  • Hylton, New Mexico et al. Neoadjuvant chemotherapy for breast cancer: functional tumor volume on MRI predicts disease-free survival – results from the acrin 6657/calgb 150007 i-spy 1 trial. Radiology 27944–55 (2016).

    Article

    Google Scholar

  • Hylton, NM Assessing the vascularity of breast lesions with gadolinium mr imaging. Magnetic Resonance Imaging Clinics of North America seven411–20 (1999).

    CASE
    Article

    Google Scholar

  • Chitalia, R. et al. Radiomic tumor phenotypes may augment molecular profiling to predict survival after neoadjuvant breast chemotherapy: results from acrin 6657/i-spy 1. In the study (2021).

  • Chitalia, DR et al. Imaging phenotypes of breast cancer heterogeneity in preoperative dynamic contrast magnetic resonance imaging (dce-mri) examinations of the breast predict recurrence at 10 years. Clinical cancer research 26862–869 (2020).

    Article

    Google Scholar

  • Davatzikos, C. et al. Cancer Phenomics Imaging Toolkit: Quantitative Imaging Analysis for Precision Diagnoses and Predictive Modeling of Clinical Outcomes. Medical Imaging Review 5011018 (2018).

    Article

    Google Scholar

  • Pati, S. et al. The phenomic cancer imaging toolbox (captk): technical overview. In MICCAI International Workshop on Brain Injury380–394 (Springer, 2019).

  • Rathore, S. et al. Brain Cancer Imaging Phenomics Toolkit (brain-captk): an interactive platform for the quantitative analysis of glioblastoma. In MICCAI International Workshop on Brain Injury133–145 (Springer, 2017).

  • Cox, R. et al. A (sort of) new image data format standard: Nifti-1: We 150. Neuroimaging 22 (2004).

  • Sled, JG, Zijdenbos, AP & Evans, AC A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 1787–97 (1998).

    CASE
    Article

    Google Scholar

  • Tustison, New Jersey et al. N4itk: Improved bias correction n3. IEEE Transactions on Medical Imaging 291310-1320 (2010).

    Article

    Google Scholar

  • Al Shalabi, L. & Shaaban, Z. Normalization as a preprocessing engine for data mining and the preference matrix approach. In 2006 International Conference on the Reliability of Computing Systems207–214 (IEEE, 2006).

  • Ribaric, S. & Fratric, I. Experimental evaluation of match score normalization techniques on different multimodal biometric systems. In MELECON 2006-2006 IEEE Mediterranean Electrotechnical Conference498–501 (IEEE, 2006).

  • Bakas, S. et al. Identify the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXivpreprintarXiv:1811.02629 (2018)..

  • Abdi, H. et al. Data normalization. Research Design Encyclopedia 1 (2010).

  • Jafri, NF et al. Optimization of functional tumor volume from breast MRI as a biomarker of disease-free survival after neoadjuvant chemotherapy. Magnetic Resonance Imaging Journal 40476–482 (2014).

    Article

    Google Scholar

  • Yushkevich, Pennsylvania et al. Active user-guided 3D contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimaging 311116-1128 (2006).

    Article

    Google Scholar

  • Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Informatics and Computer Assisted Intervention234–241 (Springer, 2015).

  • Thakur, S. et al. Brain extraction on MRI in the presence of diffuse glioma: multi-institutional performance evaluation of deep learning methods and robust modality-independent training. NeuroImage 220117081 (2020).

    Article

    Google Scholar

  • Zijdenbos, AP, Dawant, BM, Margolin, RA & Palmer, AC Morphometric analysis of white matter lesions in MRI images: method and validation. IEEE Transactions on Medical Imaging 13716–724 (1994).

    CASE
    Article

    Google Scholar

  • Sudre, CH, Li, W., Vercauteren, T., Ourselin, S. & Cardoso, MJ Generalized Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. In Deep learning in medical image analysis and multimodal learning for clinical decision support240-248 (Springer, 2017).

  • Pati, S. et al. Gandlf: A generally nuanced deep learning framework for scalable end-to-end clinical workflows in medical imaging. preprint arXiv arXiv:2103.01006 (2021).

  • Macyszyn, L. et al. Imaging models predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro-oncology 18417–425 (2015).

    Article

    Google Scholar

  • Bakas, S. et al. Advance Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomics features. Scientific data 4170117 (2017).

    Article

    Google Scholar

  • Fathi Kazerooni, A. et al. Cancer imaging phenomics via captk: multi-institutional prediction of progression-free survival and recurrence pattern of glioblastoma. JCO Cancer Clinical Informatics 4234-244 (2020).

    Article

    Google Scholar

  • Bakas, S. et al. Integrative radiomic analysis for pre-surgical prognostic stratification of patients with glioblastoma: from advanced MRI protocols to basic protocols. In Medical Imaging 2020: Image Guided Procedures, Robotic Interventions and Modeling, flight. 11315113151S (International Society of Optics and Photonics, 2020).

  • Thakur, SP et al. Skull stripping of glioblastoma MRIs using 3D deep learning. In MICCAI International Workshop on Brain Injury57–68 (Springer, 2019).

  • Chitalia, R. et al. Expert annotations of tumors and radiomic features for data collection from the ispy1/acrin 6657 trial. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.XC7A-QT20 (2022).

  • Wilkinson, MD et al. Equitable guiding principles for the management and stewardship of scientific data. Scientific data 31–9 (2016).

    Article

    Google Scholar