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Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model

  • Authors:
    • Fangfen Dong
    • Jing Chen
    • Feiyu Liu
    • Zhiyu Yang
    • Yimin Wu
    • Xiaobo Li
  • View Affiliations / Copyright

    Affiliations: Department of Radiation Oncology, Fujian Medical University Union Hospital/Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors/Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Hematological and Breast Malignancies), Fuzhou, Fujian 350001, P.R. China, School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, P.R. China
    Copyright: © Dong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 573
    |
    Published online on: September 30, 2024
       https://doi.org/10.3892/ol.2024.14706
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Abstract

The aim of the present study was to develop a prediction model for set‑up error distribution in breast cancer image‑guided radiotherapy (IGRT) using a Gaussian mixture model (GMM). To achieve this, the image‑guided set‑up errors data of 80 patients with breast cancer were selected, and the GMM was used to develop the set‑up errors distribution prediction model. The predicted error center points, covariance and probability were calculated and compared with the planning target volume (PTV) margin formula. A total of 1,200 sets of set‑up errors in IGRT for breast cancer were collected. The results of the Gaussian model parameters showed that the set‑up errors were mainly in the direction of µ1‑µ4 center points. All the raw errors in the lateral, longitudinal and vertical directions were ‑6.30‑4.60, ‑5.40‑1.47 and ‑2.70‑1.70 mm, respectively. According to the probability of each center, the set‑up error was most likely to shift in the µ1 direction, reaching 0.53. The set‑up errors of the other three centers, µ2, µ3 and µ4, were 0.11, 0.34 and 0.12, respectively. According to the covariance parameters of the GMM, the maximum statistical standard deviation of the set‑up errors reached 29.06. In conclusion, the results of the present study demonstrated that the GMM can be used to quantitatively describe and predict the distribution of set‑up errors in IGRT for breast cancer, and these findings could be useful as a reference for set‑up error control and tumor PTV expansion in breast cancer radiotherapy without routine, daily IGRT.
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Copy and paste a formatted citation
Spandidos Publications style
Dong F, Chen J, Liu F, Yang Z, Wu Y and Li X: Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model. Oncol Lett 28: 573, 2024.
APA
Dong, F., Chen, J., Liu, F., Yang, Z., Wu, Y., & Li, X. (2024). Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model. Oncology Letters, 28, 573. https://doi.org/10.3892/ol.2024.14706
MLA
Dong, F., Chen, J., Liu, F., Yang, Z., Wu, Y., Li, X."Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model". Oncology Letters 28.6 (2024): 573.
Chicago
Dong, F., Chen, J., Liu, F., Yang, Z., Wu, Y., Li, X."Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model". Oncology Letters 28, no. 6 (2024): 573. https://doi.org/10.3892/ol.2024.14706
Copy and paste a formatted citation
x
Spandidos Publications style
Dong F, Chen J, Liu F, Yang Z, Wu Y and Li X: Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model. Oncol Lett 28: 573, 2024.
APA
Dong, F., Chen, J., Liu, F., Yang, Z., Wu, Y., & Li, X. (2024). Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model. Oncology Letters, 28, 573. https://doi.org/10.3892/ol.2024.14706
MLA
Dong, F., Chen, J., Liu, F., Yang, Z., Wu, Y., Li, X."Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model". Oncology Letters 28.6 (2024): 573.
Chicago
Dong, F., Chen, J., Liu, F., Yang, Z., Wu, Y., Li, X."Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model". Oncology Letters 28, no. 6 (2024): 573. https://doi.org/10.3892/ol.2024.14706
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