References
- Baumann M, Krause M, Overgaard J, Debus J, Bentzen SM, Daartz J, et al. Radiation oncology in the era of precision medicine. Nat Rev Cancer. 2016;16:234-249. https://doi.org/10.1038/nrc.2016.18
- Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793-795. https://doi.org/10.1056/NEJMp1500523
- Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883-892. Erratum in: N Engl J Med. 2012;367:976. https://doi.org/10.1056/NEJMoa1113205
- Mayerhoefer ME, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P, et al. Introduction to radiomics. J Nucl Med. 2020;61:488-495. https://doi.org/10.2967/jnumed.118.222893
- Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res. 2017;6:86-91. https://doi.org/10.21037/tlcr.2017.01.04
- Brancato V, Cerrone M, Lavitrano M, Salvatore M, Cavaliere C. A systematic review of the current status and quality of radiomics for glioma differential diagnosis. Cancers (Basel). 2022;14:2731.
- Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun. 2020;11:1236. Erratum in: Nat Commun. 2021;12:4370.
- Li G, Li L, Li Y, Qian Z, Wu F, He Y, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain. 2022;145:1151-1161. https://doi.org/10.1093/brain/awab340
- Yu Y, Tan Y, Xie C, Hu Q, Ouyang J, Chen Y, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer. JAMA Netw Open. 2020;3:e2028086.
- Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30:1323-1341. https://doi.org/10.1016/j.mri.2012.05.001
- National Cancer Institute (NCI). Cancer imaging archive. Bethesda (MD): NCI, 2011 [cited 2023 Jan 11]. Available from: https://www.cancerimagingarchive.net/
- American Joint Committee on Cancer (AJCC). AJCC cancer staging manual. 7th ed. New York: Springer; 2010:253-270.
- Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44:1050-1062. https://doi.org/10.1002/mp.12123
- Larue RTHM, van Timmeren JE, de Jong EEC, Feliciani G, Leijenaar RTH, Schreurs WMJ, 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. 2017;56:1544-1553. https://doi.org/10.1080/0284186X.2017.1351624
- Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep. 2013. 3:3529.
- van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017. 77:e104-e107. https://doi.org/10.1158/0008-5472.CAN-17-0339
- Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: the Lasso and generalizations. New York: CRC Press; 2015:2-10.
- Shao J. Linear model selection by cross-validation. J Am Stat Assoc. 1993;88:486-494. https://doi.org/10.1080/01621459.1993.10476299
- Li R, Xing L, Napel S, Rubin DL. Radiomics and radiogenomics: technical basis and clinical applications. Boca Raton: CRC Press; 2019.
- Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610-621. https://doi.org/10.1109/TSMC.1973.4309314
- Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975;4:172-179. https://doi.org/10.1016/S0146-664X(75)80008-6
- Zwanenburg A, Vallieres M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328-338. https://doi.org/10.1148/radiol.2020191145
- Yan F, Kittler J, Mikolajczyk K, Tahir A. Non-sparse multiple kernel Fisher discriminant analysis. J Mach Learn Res. 2012;13:607-642.
- Park SH, Goo JM, Jo CH. Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol. 2004;5:11-18. https://doi.org/10.3348/kjr.2004.5.1.11
- DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837-845. https://doi.org/10.2307/2531595
- MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284:228-243. https://doi.org/10.1148/radiol.2017161659
- Honda O, Tsubamoto M, Inoue A, Johkoh T, Tomiyama N, Hamada S, et al. Pulmonary cavitary nodules on computed tomography: differentiation of malignancy and benignancy. J Comput Assist Tomogr. 2007;31:943-949. https://doi.org/10.1097/RCT.0b013e3180415e20
- Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol. 2011;6:244-285. https://doi.org/10.1097/JTO.0b013e318206a221
- Lim HJ, Ahn S, Lee KS, Han J, Shim YM, Woo S, et al. Persistent pure ground-glass opacity lung nodules ≥ 10 mm in diameter at CT scan: histopathologic comparisons and prognostic implications. Chest. 2013;144:1291-1299. https://doi.org/10.1378/chest.12-2987
- Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90:20160665.