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A mathematical model of tumor volume changes during radiotherapy.

Wang P, Feng Y - ScientificWorldJournal (2013)

Bottom Line: The four-level cell population model proposed by Chvetsov et al. was further simplified by removing the initial hypoxic fraction and reoxygenation parameter, which are hard to obtain in routine clinics, such that an easy-to-use model can be developed for clinical applications.The new model was validated with data of nine lung and cervical cancer patients.Out of the nine cases, the new model can predict tumor volume change in six cases with a correlation index R (2) greater than 0.9 and the rest of three with R (2) greater than 0.85.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.

ABSTRACT

Purpose: To develop a clinically viable mathematical model that quantitatively predicts tumor volume change during radiotherapy in order to provide treatment response assessment for prognosis, treatment plan optimization, and adaptation.

Method and materials: The correction factors containing hypoxia, DNA single strand breaks, potentially lethal damage, and other factors were used to develop an improved cell survival model based on the popular linear-quadratic model of cell survival in radiotherapy. The four-level cell population model proposed by Chvetsov et al. was further simplified by removing the initial hypoxic fraction and reoxygenation parameter, which are hard to obtain in routine clinics, such that an easy-to-use model can be developed for clinical applications. The new model was validated with data of nine lung and cervical cancer patients.

Results: Out of the nine cases, the new model can predict tumor volume change in six cases with a correlation index R (2) greater than 0.9 and the rest of three with R (2) greater than 0.85.

Conclusion: Based on a four-level cell population model, a more practical and simplified cell survival curve was proposed to model the tumor volume changes during radiotherapy. Validation study with patient data demonstrated feasibility and clinical usefulness of the new model in predicting tumor volume change in radiotherapy.

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Correlation coefficients of the models: diamonds: R2; triangles: R02; dash lines: mean values. (a) Lung cancer cases, with mean value of 0.94 for R2 and 0.63 for R02, respectively; (b) cervical cancer cases, with mean value of 0.93 for R2 and 0.87 for R02, respectively.
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fig1: Correlation coefficients of the models: diamonds: R2; triangles: R02; dash lines: mean values. (a) Lung cancer cases, with mean value of 0.94 for R2 and 0.63 for R02, respectively; (b) cervical cancer cases, with mean value of 0.93 for R2 and 0.87 for R02, respectively.

Mentions: From Table 2, it can be seen that the new model can predict the tumor volume change in six of the nine cases with correlation index R2 greater than 0.9 and the rest of three cases with R2 greater than 0.85. When comparing the values of R2 with R02 in Figure 1, it is clear that R2 is greater than R02 for all the cases and the mean values of R2 is significantly greater than the ones of R02 (0.94 > 0.63 for lung cancer cases and 0.93 > 0.87 for cervical cancer cases). It is shown that the improved cell survival model yields better prediction for tumor volume change during the radiation treatment.


A mathematical model of tumor volume changes during radiotherapy.

Wang P, Feng Y - ScientificWorldJournal (2013)

Correlation coefficients of the models: diamonds: R2; triangles: R02; dash lines: mean values. (a) Lung cancer cases, with mean value of 0.94 for R2 and 0.63 for R02, respectively; (b) cervical cancer cases, with mean value of 0.93 for R2 and 0.87 for R02, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3814055&req=5

fig1: Correlation coefficients of the models: diamonds: R2; triangles: R02; dash lines: mean values. (a) Lung cancer cases, with mean value of 0.94 for R2 and 0.63 for R02, respectively; (b) cervical cancer cases, with mean value of 0.93 for R2 and 0.87 for R02, respectively.
Mentions: From Table 2, it can be seen that the new model can predict the tumor volume change in six of the nine cases with correlation index R2 greater than 0.9 and the rest of three cases with R2 greater than 0.85. When comparing the values of R2 with R02 in Figure 1, it is clear that R2 is greater than R02 for all the cases and the mean values of R2 is significantly greater than the ones of R02 (0.94 > 0.63 for lung cancer cases and 0.93 > 0.87 for cervical cancer cases). It is shown that the improved cell survival model yields better prediction for tumor volume change during the radiation treatment.

Bottom Line: The four-level cell population model proposed by Chvetsov et al. was further simplified by removing the initial hypoxic fraction and reoxygenation parameter, which are hard to obtain in routine clinics, such that an easy-to-use model can be developed for clinical applications.The new model was validated with data of nine lung and cervical cancer patients.Out of the nine cases, the new model can predict tumor volume change in six cases with a correlation index R (2) greater than 0.9 and the rest of three with R (2) greater than 0.85.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.

ABSTRACT

Purpose: To develop a clinically viable mathematical model that quantitatively predicts tumor volume change during radiotherapy in order to provide treatment response assessment for prognosis, treatment plan optimization, and adaptation.

Method and materials: The correction factors containing hypoxia, DNA single strand breaks, potentially lethal damage, and other factors were used to develop an improved cell survival model based on the popular linear-quadratic model of cell survival in radiotherapy. The four-level cell population model proposed by Chvetsov et al. was further simplified by removing the initial hypoxic fraction and reoxygenation parameter, which are hard to obtain in routine clinics, such that an easy-to-use model can be developed for clinical applications. The new model was validated with data of nine lung and cervical cancer patients.

Results: Out of the nine cases, the new model can predict tumor volume change in six cases with a correlation index R (2) greater than 0.9 and the rest of three with R (2) greater than 0.85.

Conclusion: Based on a four-level cell population model, a more practical and simplified cell survival curve was proposed to model the tumor volume changes during radiotherapy. Validation study with patient data demonstrated feasibility and clinical usefulness of the new model in predicting tumor volume change in radiotherapy.

Show MeSH
Related in: MedlinePlus