1. National Cancer Institute, Surveillance Epidemiology, and End Results Program. Cancer of the prostate - cancer stat facts.

2. Otto, K. Volumetric modulated arc therapy: IMRT in a single gantry arc. Med. Phys. 35, 310–317 (2008).

3. Palma, D. A., Verbakel, W. F. A. R., Otto, K. & Senan, S. New developments in arc radiation therapy: A review. Cancer Treat. Rev. 36, 393–399 (2010).

4. Jacobs, B. L. et al. Use of advanced treatment technologies among men at low risk of dying from prostate cancer. JAMA 309, 2587–2595 (2013).

5. Dawson, L. A. & Jaffray, D. A. Advances in image-guided radiation therapy. J. Clin. Oncol. 25, 938–946 (2007).

6. Bujold, A., Craig, T., Jaffray, D. & Dawson, L. A. Image-guided radiotherapy: Has it influenced patient outcomes? Semin. Radiat. Oncol. 22, 50–61 (2012).

7. Yu, C. X. Intensity-modulated arc therapy with dynamic multileaf collimation: An alternative to tomotherapy. Phys. Med. Biol. 40, 1435–1449 (1995).

8. Mackie, T. R. et al. Tomotherapy: A new concept for the delivery of dynamic conformal radiotherapy. Med. Phys. 20, 1709–1719 (1993).

9. Faiz M. Khan and Bruce J. Gerbi. Treatment planning in radiation oncology. (Wolters Kluwer Health, 2012).

10. Gramacy, R. B. & Lee, H. K. H. Cases for the nugget in modeling computer experiments. Stat. Comput. 22, 713–722 (2012).

11. Kessler, M. L. et al. Costlets: A generalized approach to cost functions for automated optimization of IMRT treatment plans. Optim. Eng. 6, 421–448 (2005).

12. Shepard, D. M., Earl, M. A., Li, X. A., Naqvi, S. & Yu, C. Direct aperture optimization: A turnkey solution for step-and-shoot IMRT. Med. Phys. 29, 1007–1018 (2002).

13. Kamperis, E., Kodona, C., Hatziioannou, K. & Giannouzakos, V. Complexity in radiation therapy: It’s complicated. Int. J. Radiat. Oncol. Biol. Phys. 106, 182–184 (2020).

14. Craft, D., Süss, P. & Bortfeld, T. The tradeoff between treatment plan quality and required number of monitor units in intensity-modulated radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 67, 1596–1605 (2007).

15. Mu, X., Löfroth, P.-O., Karlsson, M. & Zackrisson, B. The effect of fraction time in intensity modulated radiotherapy: Theoretical and experimental evaluation of an optimisation problem. Radiother. Oncol. 68, 181–187 (2003).

16. Wang, J. Z., Li, X. A., D’Souza, W. D. & Stewart, R. D. Impact of prolonged fraction delivery times on tumor control: A note of caution for intensity-modulated radiation therapy (IMRT). Int. J. Radiat. Oncol. Biol. Phys. 57, 543–552 (2003).

17. Fowler, J. F., Welsh, J. S. & Howard, S. P. Loss of biological effect in prolonged fraction delivery. Int. J. Radiat. Oncol. Biol. Phys. 59, 242–249 (2004).

18. Paganetti, H. Changes in tumor cell response due to prolonged dose delivery times in fractionated radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 63, 892–900 (2005).

19. Yang, W. et al. Tumor cell survival dependence on helical tomotherapy, continuous arc and segmented dose delivery. Phys. Med. Biol. 54, 6635–6643 (2009).

20. Götstedt, J., Karlsson Hauer, A. & Bäck, A. Development and evaluation of aperture-based complexity metrics using film and EPID measurements of static MLC openings. Med. Phys. 42, 3911–3921 (2015).

21. Matuszak, M. M., Larsen, E. W. & Fraass, B. A. Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function. Med. Phys. 34, 507–520 (2007).

22. Craft, D., Süss, P. & Bortfeld, T. The tradeoff between treatment plan quality and required number of monitor units in intensity-modulated radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 67, 1596–1605 (2007).

23. Du, W., Cho, S. H., Zhang, X., Hoffman, K. E. & Kudchadker, R. J. Quantification of beam complexity in intensity-modulated radiation therapy treatment plans. Med. Phys. 41, 021716 (2014).

24. Hernandez, V., Saez, J., Pasler, M., Jurado-Bruggeman, D. & Jornet, N. Comparison of complexity metrics for multi-institutional evaluations of treatment plans in radiotherapy. Physics and Imaging in Radiation Oncology 5, 37–43 (2018).

25. Chiavassa, S., Bessieres, I., Edouard, M., Mathot, M. & Moignier, A. Complexity metrics for IMRT and VMAT plans: A review of current literature and applications. Br. J. Radiol. 92, 20190270 (2019).

26. Younge, K. C. et al. Penalization of aperture complexity in inversely planned volumetric modulated arc therapy. Med. Phys. 39, 7160–7170 (2012).

27. Crowe, S. B. et al. Treatment plan complexity metrics for predicting IMRT pre-treatment quality assurance results. Australas. Phys. Eng. Sci. Med. 37, 475–482 (2014).

28. Kairn, T., Crowe, S. B., Kenny, J., Knight, R. T. & Trapp, J. V. Predicting the likelihood of QA failure using treatment plan accuracy metrics. J. Phys. Conf. Ser. 489, 012051 (2014).

29. McNiven, A. L., Sharpe, M. B. & Purdie, T. G. A new metric for assessing IMRT modulation complexity and plan deliverability. Med. Phys. 37, 505–515 (2010).

30. Sumida, I. et al. Organ-specific modulation complexity score for the evaluation of dose delivery. J. Radiat. Res. 58, 675–684 (2017).

31. Masi, L., Doro, R., Favuzza, V., Cipressi, S. & Livi, L. Impact of plan parameters on the dosimetric accuracy of volumetric modulated arc therapy. Med. Phys. 40, 071718 (2013).

32. Park, J. M. et al. Modulation indices for volumetric modulated arc therapy. Phys. Med. Biol. 59, 7315–7340 (2014).

33. Webb, S. Use of a quantitative index of beam modulation to characterize dose conformality: Illustration by a comparison of full beamlet IMRT, few-segment IMRT (fsIMRT) and conformal unmodulated radiotherapy. Phys. Med. Biol. 48, 2051–2062 (2003).

34. Alexidis, P. et al. The role of hypofractionated radiotherapy for the definitive treatment of localized prostate cancer: Early results of a randomized trial. J. Cancer 10, 6217–6224 (2019).

35. Alexidis, P. et al. Late results of a randomized trial on the role of mild hypofractionated radiotherapy for the treatment of localized prostate cancer. J. Cancer 11, 1008–1016 (2020).

36. Bethesda. ICRU 50: Prescribing, recording and reporting photon beam therapy. (1993).

37. Bethesda. ICRU 62: Prescribing, recording, and reporting photon beam therapy (supplement to ICRU report 50). (1999).

38. Riet, A. van’t, Mak, A. C., Moerland, M. A., Elders, L. H. & Zee, W. van der. A conformation number to quantify the degree of conformality in brachytherapy and external beam irradiation: Application to the prostate. Int. J. Radiat. Oncol. Biol. Phys. 37, 731–736 (1997).

39. Mukherjee, S., Hong, L., Deasy, J. O. & Zarepisheh, M. Integrating soft and hard dose-volume constraints into hierarchical constrained IMRT optimization. Med. Phys. 47, 414–421 (2020).

40. R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).

41. Xie, Y. Bookdown: Authoring books and technical documents with r markdown. (2020).

42. Xie, Y. Bookdown: Authoring books and technical documents with R markdown. (Chapman; Hall/CRC, 2016).

43. Kaiser, H. F. & Rice, J. Little jiffy, mark IV. Educational and psychological measurement. Educ. Psychol. Meas. 34, 111–117 (1974).

44. Riley, R. D. et al. Minimum sample size for developing a multivariable prediction model: Part I - continuous outcomes. Stat. Med. 38, 1262–1275 (2019).

45. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An introduction to statistical learning with applications in R. (Springer, 2017).

46. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1–22 (2010).

47. Kaiser, H. F. An index of factorial simplicity. Psychometrika 39, 31–36 (1974).

48. Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).

49. Haykin, S. Neural networks and learning machines. (Pearson Education, 2010).

50. Moran, J. M. et al. Safety considerations for IMRT: Executive summary. Pract. Radiat. Oncol. 1, 190–195 (2011).

51. Glenn, M. C. et al. Treatment plan complexity does not predict IROC houston anthropomorphic head and neck phantom performance. Phys. Med. Biol. 63, 205015 (2018).

52. Tomori, S. et al. A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance. Med. Phys. (2018).

53. Granville, D. A., Sutherland, J. G., Belec, J. G. & La Russa, D. J. Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics. Phys. Med. Biol. 64, 095017 (2019).

54. Lam, D. et al. Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning. Med. Phys. (2019).

55. Li, J. et al. Machine learning for Patient-Specific quality assurance of VMAT: Prediction and classification accuracy. Int. J. Radiat. Oncol. Biol. Phys. (2019).

56. Ono, T. et al. Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning. Med. Phys. 46, 3823–3832 (2019).

57. Wang, L. et al. Multi-task autoencoder based classification-regression model for patient-specific VMAT QA. Phys. Med. Biol. 65, 235023 (2020).

58. Gilpin, L. H. et al. Explaining explanations: An overview of interpretability of machine learning. (2018).

59. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An introduction to statistical learning with applications in R. (Springer, 2017).

60. Santos, T., Ventura, T. & Lopes, M. do C. Evaluation of the complexity of treatment plans from a national IMRT/VMAT audit - towards a plan complexity score. Phys. Med. 70, 75–84 (2020).

61. Low, D. A. & Dempsey, J. F. Evaluation of the gamma dose distribution comparison method. Med. Phys. 30, 2455–2464 (2003).

62. Ezzell, G. A. et al. IMRT commissioning: Multiple institution planning and dosimetry comparisons, a report from AAPM task group 119. Med. Phys. 36, 5359–5373 (2009).