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Evidence Based Radiology : Published Articles on Statistics in Radiology

This pilot guide lists information resources on a radiology and radiological sciences. 

Articles on Statistic Methods in Radiology

  • Peskin, A. , Saiprasad, G. , Filliben, J. and Dima, A. (2015), Evaluation of Low-Contrast Detectability of Iterative Reconstruction across Multiple Institutions, CT Scanner Manufacturers, and Radiation Exposure Levels, Radiology, https://doi.org/10.1148/radiol.2015141260 
     
  • Chen-Mayer, H. and Obuchowski, N. (2016), Statistical Issues in Testing Conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Profile Claims, Academic Radiology 
     
  • Fong, J. , Heckert, N. , Filliben, J. , Ma, L. , Stupic, K. , Keenan, K. and Russek, S. (2014), A Design-of-Experiments Approach to FEM Uncertainty Analysis for Optimizing Magnetic Resonance Imaging RF Coil Design, Proc. 2014 International COMSOL Users' Conference, Boston, MA, U.S.A., Boston, MA
     
  • Ahn, S., Park, S. H., & Lee, K. H. (2013). How to demonstrate similarity by using noninferiority and equivalence statistical testing in radiology research. Radiology, 267(2), 328-338. doi:10.1148/radiol.12120725
     
  • Anvari, A., Halpern, E. F., & Samir, A. E. (2015). Statistics 101 for Radiologists. Radiographics, 35(6), 1789-1801. doi:10.1148/rg.2015150112
     
  • Benelli, M., Barucci, A., Zoppetti, N., Calusi, S., Redapi, L., Della Gala, G., . . . Pallotta, S. (2020). Comprehensive Analysis of Radiomic Datasets by RadAR. Cancer Res, 80(15), 3170-3174. doi:10.1158/0008-5472.Can-20-0332
     
  • Demircioğlu, A. (2019). Radiomics-AI-based image analysis. Pathologe, 40(Suppl 3), 271-276. doi:10.1007/s00292-019-00704-8
     
  • Dipnall, J. F., Lu, J., Gabbe, B. J., Cosic, F., Edwards, E., Page, R., & Du, L. (2022). Comparison of state-of-the-art machine and deep learning algorithms to classify proximal humeral fractures using radiology text. Eur J Radiol, 153, 110366. doi:10.1016/j.ejrad.2022.110366
     
  • Dreyer, K. J., & Geis, J. R. (2017). When Machines Think: Radiology's Next Frontier. Radiology, 285(3), 713-718. doi:10.1148/radiol.2017171183
     
  • Nakajima, Y., Yamada, K., Imamura, K., & Kobayashi, K. (2008). Radiologist supply and workload: international comparison--Working Group of Japanese College of Radiology. Radiat Med, 26(8), 455-465. doi:10.1007/s11604-008-0259-2
     
  • Obuchowski, N. A., Subhas, N., & Polster, J. (2017). Statistics for Radiology Research. Semin Musculoskelet Radiol, 21(1), 23-31. doi:10.1055/s-0036-1597252
     
  • Park, J. E., Park, S. Y., Kim, H. J., & Kim, H. S. (2019). Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. Korean J Radiol, 20(7), 1124-1137. doi:10.3348/kjr.2018.0070
     
  • Psoter, K. J., Roudsari, B. S., Dighe, M. K., Richardson, M. L., Katz, D. S., & Bhargava, P. (2014). Biostatistics primer for the radiologist. AJR Am J Roentgenol, 202(4), W365-375. doi:10.2214/ajr.13.11657
     
  • Varriano, G., Guerriero, P., Santone, A., Mercaldo, F., & Brunese, L. (2022). Explainability of radiomics through formal methods. Comput Methods Programs Biomed, 220, 106824. doi:10.1016/j.cmpb.2022.106824

Articles on Statistic Theories, Methods, Applications

  • Croarkin, C. (2001), Statistics and Measurements, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD 
     
  • Wu, J. and Kacker, R. (2021), Standard Errors and Significance Testing in Data Analysis for Testing Classifiers, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, https://doi.org/10.6028/NIST.IR.8383, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932649 
     
  • Yen, J. , Leber, D. and Pibida, L. (2020), Comparing Instruments, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, https://doi.org/10.6028/NIST.TN.2106 
     
  • Reiman, A. , Cerfon, A. and McFadden, G. (2015), Tokamak Plasma High Field Side Response to an n = 3 Magnetic Perturbation: A Comparison of 3D Equilibrium Solutions from Seven Different Codes, Nuclear Fusion,  https://doi.org/10.1088/0029-5515/55/6/063026, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=917763 

  • Curtin, F., & Schulz, P. (1998). Multiple correlations and Bonferroni's correction. Biol Psychiatry, 44(8), 775-777. doi:10.1016/s0006-3223(98)00043-2

  • Francis, G. (2017). Equivalent statistics and data interpretation. Behav Res Methods, 49(4), 1524-1538. doi:10.3758/s13428-016-0812-3

  • Hou, W., & Carden, D. (2012). Statistics for the nonstatistician: Part II. South Med J, 105(3), 131-135. doi:10.1097/SMJ.0b013e31824b2b69

  • Mishra, P., Pandey, C. M., Singh, U., Keshri, A., & Sabaretnam, M. (2019). Selection of appropriate statistical methods for data analysis. Ann Card Anaesth, 22(3), 297-301. doi:10.4103/aca.ACA_248_18

  • Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J, 24(3), 69-71.

  • Savalei, V., & Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. Br J Math Stat Psychol, 66(2), 201-223. doi:10.1111/j.2044-8317.2012.02049.x

  • Yuan, K. H., Gomer, B., & Marcoulides, K. M. (2022). Smoothed Quantiles for χ2 Type Test Statistics with Applications. Multivariate Behav Res, 57(2-3), 223-242. doi:10.1080/00273171.2020.1858018

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