Criteria for the translation of radiomics into clinically useful tests

Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).

Article  PubMed  Google Scholar 

Giger, M. L. Update on the potential of computer-aided diagnosis for breast cancer. Fut. Oncol. 6, 1–4 (2010).

Article  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

Cha, K. H. et al. Bladder cancer treatment response assessment in CT using radiomics with deep learning. Nat. Sci. Rep. 7, 8738 (2017).

Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

Article  Google Scholar 

Madabhushi, A. & Udupa, J. K. New methods of MR image intensity standardization via generalized scale. Med. Phys. 33, 3426–3434 (2006).

Article  PubMed  Google Scholar 

Whitney, H. M. et al. Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J. Med. Imaging 7, 012707 (2020).

Article  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

CAS  Google Scholar 

Willemink, M. J. et al. Preparing medical imaging data for machine learning. Radiology 295, 4–15 (2020).

Article  PubMed  Google Scholar 

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).

Article  Google Scholar 

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).

Article  PubMed  Google Scholar 

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).

CAS  Google Scholar 

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).

Article 

留言 (0)

沒有登入
gif