Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).
Giger, M. L. Update on the potential of computer-aided diagnosis for breast cancer. Fut. Oncol. 6, 1–4 (2010).
Doi, K. Computer-aided diagnosis in medical imaging: historical review, current status, and future potential. Comput. Med. Imaging Graph. 31, 198–211 (2007).
Article PubMed PubMed Central Google Scholar
Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).
Article PubMed PubMed Central Google Scholar
FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource (Food and Drug Administration and National Institutes of Health, 2016).
FDA. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Devices https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices. (2022).
Fornacon-Wood, I. M. et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur. Radiol. 30, 6241–6250 (2020).
Article PubMed PubMed Central Google Scholar
Radiomics. Radiomics Quality Score – RQS 2.0 https://www.radiomics.world/rqs2 (2022).
Zwanenburg, A. et al. The image biomarker standardization initiative: standardized quantitative radiomics for high throughput image-based phenotyping. Radiology 295, 328–338 (2020).
Kumar, V. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging 30, 1234–1248 (2012).
Article PubMed PubMed Central Google Scholar
Fournier, L. et al. Incorporating radiomics into clinical trials: expert consensus endorsed by the European society of radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur. Radiol. 31, 6001–6012 (2021).
Article PubMed PubMed Central Google Scholar
McShane, L. M. et al. Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration. BMC Med. 11, 220 (2013).
Article PubMed PubMed Central Google Scholar
Jiang, Y., Edwards, A. V. & Newstead, G. M. Artificial intelligence applied to breast MRI for improved diagnosis. Radiology 298, 39–46 (2021).
Data Science Institute, American College of Radiology. FDA Cleared AI Algorithms https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms, (2022).
Clark, G. M. Prognostic factors versus predictive factors: examples from a clinical trial of erlotinib. Mol. Oncol. 1, 406–412 (2008).
Aerts, H. J. W. L. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).
Article CAS PubMed Google Scholar
Li, H. et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer 2, 16012 (2016).
Article PubMed PubMed Central Google Scholar
Li, H. et al. MRI radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of gene assays of MammaPrint, Oncotype DX, and PAM50. Radiology 281, 382–391 (2016).
Cha, K. H. et al. Bladder cancer treatment response assessment in CT using radiomics with deep learning. Nat. Sci. Rep. 7, 8738 (2017).
Drukker, K. et al. Most-enhancing tumor volume by mri radiomics predicts recurrence-free survival “Early On” in neoadjuvant treatment of breast cancer. Cancer Imaging 18, 12 (2018).
Article PubMed PubMed Central Google Scholar
Huang, E. P., Lin, F. I. & Shankar, L. K. Beyond correlations, sensitivities, and specificities: a roadmap for demonstrating utility of advanced imaging in oncology treatment and clinical trial design. Acad. Radiol. 24, 1036–1049 (2017).
Article PubMed PubMed Central Google Scholar
Subramanian, J. & Simon, R. What should physicians look for in evaluating prognostic gene-expression signatures? Nat. Rev. Clin. Oncol. 7, 327–334 (2010).
Shafiq-Ul-Hassan, M. et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med. Phys. 44, 1050–1062 (2017).
Article CAS PubMed PubMed Central Google Scholar
Berenguer, R. et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288, 407–415 (2018).
American College of Radiology. ACR Appropriateness Criteria https://www.acr.org/Clinical-Resources/ACR-Appropriateness-Criteria (2022).
Society of Nuclear Medicine and Medical Imaging. Procedure Standards https://www.snmmi.org/ClinicalPractice/content.aspx?ItemNumber=6414. (2022).
European Association of Nuclear Medicine. Guidelines https://www.eanm.org/publications/guidelines/ (2022).
QIBQ Wiki. Profiles http://qibawiki.rsna.org/index.php/Profiles (2022).
Fass, L. Imaging and cancer: a review. Mol. Oncol. 2, 115–152 (2008).
Article PubMed PubMed Central Google Scholar
Zhao, B. et al. Exploring intra- and inter-reader variability in unidimensional, bidimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals. Eur. J. Radiol. 82, 959–968 (2013).
O’Connor, J. P. B., Jackson, A., Parker, G. J. M., Roberts, C. & Jayson, G. C. Dynamic contrast-enhanced MRI in clinical trials of anti-vascular therapies. Nat. Rev. Clin. Oncol. 9, 167–177 (2012).
Tudorica, L. A. et al. QIN: a feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings. Magn. Reson. Imaging 30, 1257–1267 (2012).
Article PubMed PubMed Central Google Scholar
Nardone, V. et al. Delta radiomics: a systematic review. Radiol. Med. 126, 1571–1583 (2021).
Pinker, K., Riedl, C. & Weber, W. A. Evaluating tumor response with FDG-PET: updates on PERCIST, comparison with EORTC criteria and clues to future development. Eur. J. Nucl. Med. Mol. Imaging 44, 55–66 (2017).
Article PubMed PubMed Central Google Scholar
Mackin, D. et al. Harmonizing the pixel size in retrospective computed tomography radiomics studies. PLoS ONE 12, e0178524 (2017).
Article PubMed PubMed Central Google Scholar
Madabhushi, A., Udupa, J. K. & Souza, A. Generalized scale: theory, algorithms, and application to image inhomogeneity correction. Comput. Image Vis. Underst. 101, 100–121 (2006).
Madabhushi, A. & Udupa, J. K. New methods of MR image intensity standardization via generalized scale. Med. Phys. 33, 3426–3434 (2006).
Whitney, H. M. et al. Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J. Med. Imaging 7, 012707 (2020).
Duron, L. et al. Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS ONE 14, e0213459 (2019).
Article CAS PubMed PubMed Central Google Scholar
Larue, R. T. H. M. et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents, and slice thicknesses: a comprehensive phantom study. Acta Oncol. 56, 1544–1553 (2017).
Leijenaar, R. T. et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Nat. Sci. Rep. 5, 11075 (2015).
Willemink, M. J. et al. Preparing medical imaging data for machine learning. Radiology 295, 4–15 (2020).
Mali, S. A. et al. Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods. J. Per. Med. 11, 842 (2021).
Lin, Y. et al. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Eur. Radiol. 30, 1297–1305 (2020).
Parmar, C., Grossman, P., Bussink, J., Lambin, P. & Aerts, H. J. W. L. Machine learning methods for quantitative radiomic biomarkers. Nat. Sci. Rep. 5, 13087 (2015).
Primakov, S. P. et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat. Commun. 13, 3423 (2022).
Article CAS PubMed PubMed Central Google Scholar
Gilhuijs, K. G. A., Giger, M. L. & Bick, U. Automated analysis of breast lesions in three dimensions using dynamic magnetic resonance imaging. Med. Phys. 25, 1647–1654 (1998).
Article CAS PubMed Google Scholar
Chen, W., Giger, M. L., Lan, L. & Bick, U. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med. Phys. 31, 1076–1082 (2004).
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