Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.
Abdominal CT scanning
Patient-specific modeling
Radiation dosage
Radiomics
Support vector regression
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 08 2024
20 08 2024
Historique:
received:
13
02
2024
accepted:
14
08
2024
medline:
22
8
2024
pubmed:
22
8
2024
entrez:
21
8
2024
Statut:
epublish
Résumé
The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT examinations using minimized computational resources at a fast speed. The CT data of 247 abdominal patients were selected and exported to the auto-segmentation software named DeepViewer to generate abdominal regions of interest (ROIs). Radiomics feature were extracted based on the selected CT data and ROIs. Reference organ doses were obtained by GPU-based Monte Carlo simulations. The support vector regression (SVR) model was trained based on the radiomics features and reference organ doses to predict abdominal organ doses from CT examinations. The prediction performance of the SVR model was tested and verified by changing the abdominal patients of the train and test sets randomly. For the abdominal organs, the maximal difference between the reference and the predicted dose was less than 1 mGy. For the body and bowel, the organ doses were predicted with a percentage error of less than 5.2%, and the coefficient of determination (R
Identifiants
pubmed: 39169118
doi: 10.1038/s41598-024-70316-7
pii: 10.1038/s41598-024-70316-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
19393Subventions
Organisme : National Natural Science Foundation of China
ID : 12075064
Organisme : National Key R&D Program of China
ID : 2019YFC0117304
Informations de copyright
© 2024. The Author(s).
Références
Sarin, S. K. et al. Liver diseases in the Asia-Pacific region: A lancet gastroenterology & hepatology commission. Lancet Gastroenterol. Hepatol. 5, 167–228. https://doi.org/10.1016/S2468-1253(19)30342-5 (2020).
pubmed: 31852635
De Bortoli, N. et al. Gastroesophageal reflux disease, functional dyspepsia and irritable bowel syndrome: Common overlapping gastrointestinal disorders. Ann. Gastroenterol. 31, 639–648. https://doi.org/10.20524/aog.2018.0314 (2018).
pubmed: 30386113
pmcid: 6191868
Lee, S. Y. et al. Prevalence and risk factors for overlaps between gastroesophageal reflux disease, dyspepsia, and irritable bowel syndrome: A population-based study. Digestion 79, 196–201. https://doi.org/10.1159/000212077 (2009).
pubmed: 19342860
Tam, C. C. et al. Longitudinal study of infectious intestinal disease in the UK (IID2 study): Incidence in the community and presenting to general practice. Gut 61, 69–77. https://doi.org/10.1136/gut.2011.238386 (2012).
pubmed: 21708822
Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249. https://doi.org/10.3322/caac.21660 (2021).
pubmed: 33538338
Nan, Y. et al. Role of CT Images in the diagnosis of common acute abdominal diseases in general surgery. J. Healthc. Eng. https://doi.org/10.1155/2022/5732357 (2022).
pubmed: 36147870
pmcid: 9489389
Cartwright, S. L. & Knudson, M. P. Evaluation of acute abdominal pain in adults. Am. Fam. Physician 91, 452–459 (2015).
pubmed: 25884745
American Cancer Society. Understanding radiation risk from imaging tests. https://www.cancer.org/cancer/diagnosis-staging/tests/imaging-tests/understanding-radiation-risk-from-imaging-tests.html . Accessed 28 Jan 2024
Mathews, J. D. et al. Cancer risk in 680000 people exposed to computed tomography scans in childhood or adolescence: Data linkage study of 11 million Australians. BMJ. 346, f2360. https://doi.org/10.1136/bmj.f2360 (2013).
pubmed: 23694687
pmcid: 3660619
Rajaraman, V., Ponnusamy, M. & Halanaik, D. Size specific dose estimate (SSDE) for estimating patient dose from CT used in myocardial perfusion SPECT/CT. Asia Ocean J. Nucl. Med. Biol. 8, 58–63. https://doi.org/10.22038/aojnmb.2019.40863.1276 (2020).
pubmed: 32064284
pmcid: 6994783
Wang, J. et al. Personalized organ dose estimation for chest CT based on deep learning segmentation techniques. Phys. Med. Biol. 68, 035006. https://doi.org/10.1088/1361-6560/ac0e7a (2023).
Cagni, E. et al. Personalized Monte Carlo-based organ dose estimates in spiral CT examinations using a portable optical scanner. Phys. Med. Biol. 68, 045003. https://doi.org/10.1088/1361-6560/ac0f5a (2023).
Myronakis, M., Stratakis, J. & Damilakis, J. Rapid estimation of personalized organ doses using a deep learning network. Med. Phys. 50(11), 7236–7244. https://doi.org/10.1002/mp.16356 (2023).
pubmed: 36918360
Salimi, Y., Akhavanallaf, A., Mansouri, Z., Shiri, I. & Zaidi, H. Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks. Eur Radiol. 33(12), 9411–9424. https://doi.org/10.1007/s00330-023-09839-y (2023).
pubmed: 37368113
pmcid: 10667156
Tzanis, E. & Damilakis, J. A novel methodology to train and deploy a machine learning model for personalized dose assessment in head CT. Eur Radiol 32(9), 6418–6426. https://doi.org/10.1007/s00330-022-08756-w (2022).
pubmed: 35384458
Maier, J., Klein, L., Eulig, E., Sawall, S. & Kachelrieß, M. Real-time estimation of patient-specific dose distributions for medical CT using the deep dose estimation. Med. Phys. 49(4), 2259–2269. https://doi.org/10.1002/mp.15488 (2022).
pubmed: 35107176
Myronakis, M., Stratakis, J. & Damilakis, J. Rapid estimation of patient-specific organ doses using a deep learning network. Med. Phys. 50(11), 7236–7244. https://doi.org/10.1002/mp.16356 (2023).
pubmed: 36918360
Tzanis, E., Stratakis, J., Myronakis, M. & Damilakis, J. A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT. Phys Med. 117, 103195. https://doi.org/10.1016/j.ejmp.2023.103195 (2023).
pubmed: 38048731
Principi, S. et al. Deterministic linear Boltzmann transport equation solver for patient-specific CT dose estimation: Comparison against a Monte Carlo benchmark for realistic scanner configurations and patient models. Med. Phys. 47, 6470–6483 (2020).
pubmed: 32981038
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (2016). https://doi.org/10.1145/2939672.2939785
Maffei, N. et al. Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning. Phys. Med. 83, 278–286. https://doi.org/10.1016/j.ejmp.2021.05.009 (2021).
pubmed: 33992865
Peng, Z., et al. Validation and clinical application of DL-based automatic target and OAR segmentation software, DeepViewer. In Proceedings of the American Association of Physicists in Medicine Annual Meeting (Vancouver, BC, July 12–16) 123–124 (2020).
Li, X. et al. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47–56. https://doi.org/10.1016/j.jneumeth.2016.03.001 (2016).
pubmed: 26945974
van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339 (2017).
pubmed: 29092951
pmcid: 5672828
Rebuffi, S. A., et al. Data augmentation can improve robustness. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (Neural Information Processing Systems Foundation).
Dwibedi, D., Misra, I. & Hebert, M. Cut, paste and learn: Surprisingly easy synthesis for instance detection. In Proceedings of the IEEE International Conference on Computer Vision 1301–1310 (2017). https://doi.org/10.1109/ICCV.2017.144
Zhang, Z., et al. A Robustness-oriented data augmentation method for DNN. In 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) 1–6 (2021).
Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).
Anaconda. The World’s Most Popular Data Science Platform. https://www.anaconda.com . Accessed 28 Jan 2024.
Bert, J., et al. GGEMS: GPU GEant4-based Monte Carlo Simulation platform. In 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD) 1–4 (2016). https://doi.org/10.1109/NSSMIC.2016.8069508
Smola, A. J. & Schölkopf, B. A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004).
Vapnik, V. N. The Nature of Statistical Learning Theory 2nd edn. (Springer, New York, 2000).
Drucker, H. et al. Support vector regression. In Advances in Neural Information Processing Systems 9 (NIPS 1996) (eds Mozer, M. C. et al.) 155–161 (MIT Press, Cambridge, 1997).