Polyak, K. et al. Heterogeneity in breast cancer. The Journal of Clinical Investigation 1213786–3788 (2011).
Marusyk, A. & Polyak, K. Tumor heterogeneity: causes and consequences. Biochimica et Biophysica Acta (BBA)-Cancer journals 1805105-117 (2010).
Gavenonis, SC & Roth, SO Role of magnetic resonance imaging in assessing the extent of disease. Magnetic Resonance Imaging Clinics 18199–206 (2010).
Weinstein, S. & Rosen, M. MRI imaging of the breast: current indications and advanced imaging techniques. Radiology clinics 481013-1042 (2010).
Gillies, RJ, Kinahan, PE & Hricak, H. Radiomics: Images are more than images, they are data. Radiology 278563–577 (2016).
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).
Zwanenburg, A. et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for Image-Based High-Throughput Phenotyping. Radiology 295328–338 (2020).
Valdora, F., Houssami, N., Rossi, F., Calabrese, M. & Tagliafico, AS Quick review: radiomics and breast cancer. Breast cancer research and treatment 169217-229 (2018).
Clark, K. et al. The Cancer Imaging Archive (tcia): maintain and operate a repository of public information. Digital Imaging Review 261045-1057 (2013).
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).
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).
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).
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).
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).
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).
Hylton, NM Assessing the vascularity of breast lesions with gadolinium mr imaging. Magnetic Resonance Imaging Clinics of North America seven411–20 (1999).
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).
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).
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).
Tustison, New Jersey et al. N4itk: Improved bias correction n3. IEEE Transactions on Medical Imaging 291310-1320 (2010).
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).
Yushkevich, Pennsylvania et al. Active user-guided 3D contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimaging 311116-1128 (2006).
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).
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).
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).
Bakas, S. et al. Advance Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomics features. Scientific data 4170117 (2017).
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).
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).