Applying deep generative model in plan review of intensity modulated radiotherapy

Background Plan review is critical for safely delivering radiation dose to a patient under radiotherapy and mainly performed by medical physicist in routine clinical practice. Recently, the deep‐learning models have been used to assist this manual process. As black‐box models the reason for their pr...

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Vydáno v:	Medical physics (Lancaster) Ročník 52; číslo 6; s. 4630 - 4641
Hlavní autoři:	Huang, Peng, Shang, Jiawen, Fan, Yuhan, Chang, Zhixing, Xu, Yingjie, Zhang, Ke, Hu, Zhihui, Dai, Jianrong, Yan, Hui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 01.06.2025
Témata:	adversarial autoencoder networks anomaly detection Deep Learning Humans plan review radiotherapy Radiotherapy Planning, Computer-Assisted - methods Radiotherapy, Intensity-Modulated plan review radiotherapy deep learning anomaly detection adversarial autoencoder networks
ISSN:	0094-2405, 2473-4209, 2473-4209
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Background Plan review is critical for safely delivering radiation dose to a patient under radiotherapy and mainly performed by medical physicist in routine clinical practice. Recently, the deep‐learning models have been used to assist this manual process. As black‐box models the reason for their predictions are unknown. Thus, it is important to improve the model interpretability to make them more reliable for clinical deployment. Purpose To alleviate this issue, a deep generative model, adversarial autoencoder networks (AAE), was employed to automatically detect anomalies in intensity‐modulated radiotherapy plans. Methods The typical plan parameters (collimator position, gantry angle, monitor unit, etc.) were collected to form a feature vector for the training sample. The reconstruction error was the difference between the output and input of the model. Based on the distribution of reconstruction errors of the training samples, a detection threshold was determined. For a test plan, its reconstruction error obtained by the learned model was compared with the threshold to determine its category (anomaly or regular). The model was tested with four network settings. It was also compared with the vanilla AE and the other six classic models. The area under receiver operating characteristic curve (AUC) along with other statistical metrics was employed for evaluation. Results The AAE model achieved the highest accuracy (AUC = 0.997). The AUCs of the other seven classic methods are 0.935 (AE), 0.981 (K‐means), 0.896 (principle component analysis), 0.978 (one‐class support vector machine), 0.934 (local outlier factor), and 0.944 (hierarchical density‐based spatial clustering of applications with noise), and 0.882 (isolation forest). This indicates that AAE model could detect more anomalous plans with less false positive rate. Conclusions The AAE model can effectively detect anomaly in radiotherapy plans for lung cancer patients. Comparing with the vanialla AE and other classic detection models, the AAE model is more accurate and transparent. The proposed AAE model can improve the interpretability of the results for radiotherapy plan review.
Bibliografie:	Peng Huanga and Jiawen Shang are co‐first authors and they contributed equally to this work. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0094-2405 2473-4209 2473-4209
DOI:	10.1002/mp.17704