Error detection model developed using a multi-task convolutional neural network in patient-specific quality assurance for volumetric-modulated arc therapy.
convolutional neural network
deep learning
patient-specific QA
radiotherapy
volumetric-modulated radiation therapy
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Sep 2021
Sep 2021
Historique:
revised:
29
05
2021
received:
26
02
2021
accepted:
02
06
2021
pubmed:
9
6
2021
medline:
23
9
2021
entrez:
8
6
2021
Statut:
ppublish
Résumé
In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA. A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error-free plan, we simulated 12 types of errors: two types of multileaf collimator positional errors (systematic or random leaf offset of 2 mm), two types of monitor unit (MU) scaling errors (±3%), two types of gantry rotation errors (±2° in clockwise and counterclockwise direction), and six types of phantom setup errors (±1 mm in lateral, longitudinal, and vertical directions). The error-introduced predicted dose distributions were created by editing the calculated dose distributions using a TPS with in-house software. Those 13 types of dose difference maps, consisting of an error-free map and 12 error maps, were created from the measured and predicted dose distributions and were used to train the convolutional neural network (CNN) model. Our model was a multi-task model that individually detected each of the 12 types of errors. Two datasets, Test sets 1 and 2, were prepared to evaluate the performance of the model. Test set 1 consisted of 13 types of dose maps used for training, whereas Test set 2 included the dose maps with 25 types of errors in addition to the error-free dose map. The dose map, which introduced 25 types of errors, was generated by combining two of the 12 types of simulated errors. For comparison with the performance of our model, gamma analysis was performed for Test sets 1 and 2 with the criteria set to 3%/2 mm and 2%/1 mm (dose difference/distance to agreement). For Test set 1, the overall accuracy of our CNN model, gamma analysis with the criteria set to 3%/2 mm, and gamma analysis with the criteria set to 2%/1 mm was 0.92, 0.19, and 0.81, respectively. Similarly, for Test set 2, the overall accuracy was 0.44, 0.42, and 0.95, respectively. Our model outperformed gamma analysis in the classification of dose maps containing a single type error, and the performance of our model was inferior in the classification of dose maps containing compound errors. A multi-task CNN model for detecting errors in patient-specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective in identifying the error type in the dose map for VMAT QA.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4769-4783Informations de copyright
© 2021 American Association of Physicists in Medicine.
Références
Ezzell GA, Galvin JM, Low D, et al. Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee. Med Phys. 2003;30:2089-2115.
Ezzell GA, Burmeister JW, Dogan N, et al. IMRT commissioning: multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119. Med Phys. 2009;36:5359-5373.
Low DA, Harms WB, Mutic S, et al. A technique for the quantitative evaluation of dose distributions. Med Phys. 1998;25:656-661.
Kalet AM, Luk SMH, Phillips MH. Radiation therapy quality assurance tasks and tools: the many roles of machine learning. Med Phys. 2020;47:e168-e177.
Chan MF, Witztum A, Valdes G. Integration of AI and machine learning in radiotherapy QA. Front Artif Intell. 2020;3:577620.
Valdes G, Scheuermann R, Hung CY, et al. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016;43:4323-4334.
Interian Y, Rideout V, Kearney VP, et al. Deep nets vs expert designed features in medical physics: an IMRT QA case study. Med Phys. 2018;45:2672-2680.
Tomori S, Kadoya N, Takayama Y, et al. A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance. Med Phys. 2018;45:4055-4065.
Hirashima H, Ono T, Nakamura M, et al. Improvement of prediction and classification performance for gamma passing rate by using plan complexity and dosiomics features. Radiother Oncol. 2020;153:250-257.
Tomori S, Kadoya N, Kajikawa T, et al. Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy. Med Phys. 2021;48:1003-1018.
Lam D, Zhang X, Li H, et al. Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning. Med Phys. 2019;46:4666-4675.
Granville DA, Sutherland JG, Belec JG, La Russa DJ. Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics. Phys Med Biol. 2019;64:095017.
Mahdavi SR, Tavakol A, Sanei M, et al. Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields. Br J Radiol. 2019;92:20190355.
Li J, Wang LE, Zhang X, et al. Machine learning for patient-specific quality assurance of VMAT: prediction and classification accuracy. Int J Radiat Oncol Biol Phys. 2019;105:893-902.
Wang LE, Li J, Zhang S, et al. Multi-task autoencoder based classification-regression model for patient-specific VMAT QA. Phys Med Biol. 2020;65:235023.
Kruse JJ. On the insensitivity of single field planar dosimetry to IMRT inaccuracies. Med Phys. 2010;37:2516-2524.
Nelms BE, Zhen H, Tome WA. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors. Med Phys. 2011;38:1037-1044.
Kry SF, Molineu A, Kerns JR, et al. Institutional patient-specific IMRT QA does not predict unacceptable plan delivery. Int J Radiat Oncol Biol Phys. 2014;90:1195-1201.
Yan G, Liu C, Simon TA, et al. On the sensitivity of patient-specific IMRT QA to MLC positioning errors. J Appl Clin Med Phys. 2009;10:120-128.
Mijnheer B, Jomehzadeh A, González P, et al. Error detection during VMAT delivery using EPID-based 3D transit dosimetry. Phys Med. 2018;54:137-145.
Scaggion A, Negri A, Rossato MA, et al. Delivering RapidArc(R): A comprehensive study on accuracy and long term stability. Phys Med. 2016;32:866-873.
Wootton LS, Nyflot MJ, Chaovalitwongse WA, et al. Error detection in intensity-modulated radiation therapy quality assurance using radiomic analysis of gamma distributions. Int J Radiat Oncol Biol Phys. 2018;102:219-228.
Nyflot MJ, Thammasorn P, Wootton LS, et al. Deep learning for patient-specific quality assurance: identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks. Med Phys. 2019;46:456-464.
Kimura Y, Kadoya N, Tomori S, et al. Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy. Phys Med. 2020;73:57-64.
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441-446.
Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563-577.
Avanzo M, Stancanello J, El Naqa I. Beyond imaging: the promise of radiomics. Phys Med. 2017;38:122-139.
Potter NJ, Mund K, Andreozzi JM, et al. Error detection and classification in patient-specific IMRT QA with dual neural networks. Med Phys. 2020;47:4711-4720.
Ma C, Wang R, Zhou S, et al. The structural similarity index for IMRT quality assurance: radiomics-based error classification. Med Phys. 2021;48:80-93.
Sakai M, Nakano H, Kawahara D, et al. Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy. Med Phys. 2021;48:991-1002.
Wolfs CJA, Canters RAM, Verhaegen F. Identification of treatment error types for lung cancer patients using convolutional neural networks and EPID dosimetry. Radiother Oncol. 2020;153:243-249.
Masi L, Doro R, Favuzza V, et al. Impact of plan parameters on the dosimetric accuracy of volumetric modulated arc therapy. Med Phys. 2013;40:071718.
Scandidos. Delta4PT user's guide. Uppsala, Sweden: Scandidos. 2013.
Bedford JL, Lee YK, Wai P, et al. Evaluation of the Delta4 phantom for IMRT and VMAT verification. Phys Med Biol. 2009;54:N167-176.
Srivastava RP, De Wagter C. Clinical experience using Delta 4 phantom for pretreatment patient-specific quality assurance in modern radiotherapy. J Radiother Pract. 2018;18:210-214.
Klein EE, Hanley J, Bayouth J, et al. Task Group 142 report: quality assurance of medical accelerators. Med Phys. 2009;36:4197-4212.
Miften M, Olch A, Mihailidis D, et al. Tolerance limits and methodologies for IMRT measurement-based verification QA: recommendations of AAPM task group no. 218. Med Phys. 2018;45:e53-e83.
Godbole S, Sarawagi S. Discriminative Methods for Multi-labeled Classification. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004), 2004;22-30.