Random-Forest Model Prediction of Dose Distribution In InsensityModulated Radiation Therapy (IMRT) Planning for Lung Cancer

Document Type : Original Paper


1 Department of Physics, Faculty of Mathematics and Natural Sciences, Indonesia University, Depok, 16424, West Java, Indonesia

2 Department of Radiotherapy, MRCCC Siloam Hospital Semanggi, Jakarta, 12930, Indonesia


Introduction: Machine-learning models have been widely used to predict dose distribution in therapy planning such as Intensity Modulated Radiation Therapy (IMRT). Random-forest is one of the machine learning models which can reduce output bias by using the average value all of estimators.
Material and Methods: Planning data in Digital Imaging and Communications in Medicine (DICOM) format is exported to Comma Separated Values (CSV). Then, used to random-forest algorithm that will be trained using 7-fold validation and then the model will be evaluated with new data, i.e., data that the model has never seen before. The data evaluated were the parameters to obtain Homogenety Index (HI) for the target organ, whereas the mean and max dose for organs at risk (OARs) were evaluated. Statistical analysis were also carried out to assess the significant difference between the predicted value and the true value.
Results: Random-forest was able to predict the true value with errors evaluated using Mean Absolute Error (MAE) on Planning Target Volume (PTV) features D2 (0.012), D50 (0.015) and D98 (0.018) as well as at OAR features (Dmean and  Dmax) of the right lung (0.104 and 0.228), left lung (0.094 and 0.27), heart (0.088 and 0.267), spinal cord (0.069 and 0.121) and (V95) Body (0.094). Based on the results of statistical tests, p >0.05, there is no significant difference between the two data.
Conclusion: Random-forest regressor is able to predict the dose value with the smallest difference in PTV features.


Main Subjects

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a Cancer Journal for Clinicians [Internet]. 2021 Feb 4;71(3):209– Available from: https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21660
  2. Globocan 2020. Available from: https://gco.iarc.fr/today/data/factsheets/populations/900-world-fact-sheets.pdf
  3. Malicki J, Bly R, Bulot M, Jean-Luc Godet, Jahnen A, Krengli M, et al. Patient safety in external beam radiotherapy, results of the ACCIRAD project: Recommendations for radiotherapy institutions and national authorities on assessing risks and analysing adverse error-events and near misses. Radiotherapy and Oncology. 2018 May 1;127(2):164–
  4. Nashwan KA. Intensity modulated radiation therapy (IMRT) technique for left breast cancer by different numbers of beam fields. International journal of radiation research. 2021 Jan 1;19(1):167–
  5. Roach D, Wortel G, Ochoa CE, Hanne Irene Jensen, Damen E, Vial P, et al. Adapting automated treatment planning configurations across international centres for prostate radiotherapy. Physics and Imaging in Radiation Oncology. 2019 Apr 1;10:7–
  6. Netherton TJ, Cardenas CE, Rhee DJ, Court LE, Beadle BM. The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning. Oncology. 2020 Dec 22;99(2):124–
  7. Issam El Naqa, Murphy MJ. Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer; 2022.
  8. Gabryś HS, Buettner F, Sterzing F, Hauswald H, Bangert M. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia. Frontiers in Oncology. 2018 Mar 5;8.
  9. Khoirunissa HA, Widyaningrum AR, Maharani APA. Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure. Indonesian Journal of Applied Statistics. 2021 May 30;4(1):14.
  10. Li Y, Zou C, Berecibar M, Nanini-Maury E, Chan JCW ., van den Bossche P, et al. Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy. 2018 Dec;232:197–
  11. Wu A, Li Y, Qi M, Lu X, Jia Q, Guo F, et al. Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases. Oral Oncology. 2020 May;104:104625.
  12. Berrar D. Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology. 2019;1:542–
  13. Yang WC, Hsu FM, Yang PC. Precision radiotherapy for non-small cell lung cancer. Journal of Biomedical Science [Internet]. 2020 Jul 22;27:82. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374898/#CR9
  14. Lee SH, Han P, Hales RK, Voong KR, Noro K, Sugiyama S, et al. Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy. Physics in Medicine and Biology [Internet]. 2020 Sep 28 [cited 2022 Jun 4];65(19):195015. Available from: https://pubmed.ncbi.nlm.nih.gov/32235058/
  15. Zhang J, Ge Y, Wu Q. Knowledge-Based Treatment Planning. Springer eBooks. 2022 Jan 1;307–
  16. Sun Y, Li G, Zhang J, Qian D. Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model. Advances in Civil Engineering. 2019 Dec 28;2019:1–
  17. Wang D, BD Xingmin, BD Lu Fu, Gu J, Bai T, Yin Y, et al. The Capabilities and Characteristics of Helical Tomotherapy and Co-Planar Dual Arcs Volumetric-Modulated arc Therapy Associated with Hippocampal Sparing During Prophylactic Cranial Irradiation. Technology in Cancer Research & Treatment. 2021 Jan 1;20:153303382110439-153303382110439.
  18. Ahn SH, Kim E, Kim C, Cheon W, Kim M, Lee SB, et al. Deep learning method for prediction of patient-specific dose distribution in breast cancer. Radiation Oncology. 2021 Aug 17;16(1).
  19. Steurer M, Hill RJ, Pfeifer N. Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research. 2021 Apr 3;38(2):99–
  20. Miyaji I, Fukui H. Change in Knowledge and Awareness in Teacher Education on Satoyama Environmental Learning: Through a Blend of Learning Spaces, Methods and Media. European Journal of Educational Research [Internet]. 2020 Oct 15 [cited 2023 Dec 20];9(4):1663– Available from: https://files.eric.ed.gov/fulltext/EJ1272495.pdf