Radiobiological effect based treatment plan optimization with the linear quadratic model

Geant4 Modeling of Cellular Dosimetry of 188Re: Comparison between Geant4 Predicted Surviving Fraction and Experimentally Surviving Fraction Determined by MTT Assay

BACKGROUND

The importance of cellular dosimetry in both diagnostic and radiation therapy is becoming increasingly recognized.

OBJECTIVE

This study aims to compare surviving fractions, which were predicted using Geant4 and contained three types of cancer cell lines exposed to 188Re with the experimentally surviving fraction determined by MTT assay.

MATERIAL AND METHODS

In this comparative study, Geant4 was used to simulate the transport of electrons emitted by 188Re from the cell surface, cytoplasm, nucleus or medium around the cells. The nucleus dose per decay (S-value) was computed for models of single cell and random monolayer cell. Geant4-computed survival fraction (SF) of cancer cells exposed to 188Re was compared with the experimental SF values of MTT assay.

RESULTS

For single cell model, Geant4 S-values of nucleus-to-nucleus were consistent with values reported by Goddu et al. (ratio of S-values by analytical techniques vs. Geant4 = 0.811-0.975). Geant4 S-values of cytoplasm and cell surface to nucleus were relatively comparable to the reported values (ratio =0.914-1.21). For monolayer model, the values of SCy→N and SCS→N, were greater compared to those for model of single cell (2%-25% and 4%-38% were larger than single cell, respectively). The Geant4 predicted SF for monolayer MCF7, HeLa and A549 cells was in agreement with the experimental data in 10μCi activity (relative error of 2.29%, 2.69% and 2.99%, respectively).

CONCLUSION

Geant4 simulation with monolayer cell model showed the highest accuracy in predicting the SF of cancer cells exposed to homogeneous distribution of 188Re in the medium.

Isobio software: biological dose distribution and biological dose volume histogram from physical dose conversion using linear-quadratic-linear model

PURPOSE

To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL) model.

MATERIAL AND METHODS

The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR), and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD₂) was calculated using biological effective dose (BED) based on the LQL model. The software calculation and the manual calculation were compared for EQD₂ verification with pair t-test statistical analysis using IBM SPSS Statistics version 22 (64-bit).

RESULTS

Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS) in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV) determined by D_90%, 0.56% in the bladder, 1.74% in the rectum when determined by D_2cc, and less than 1% in Pinnacle. The difference in the EQD₂ between the software calculation and the manual calculation was not significantly different with 0.00% at p-values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT) and 0.240, 0.320, and 0.849 for brachytherapy (BT) in HR-CTV, bladder, and rectum, respectively.

CONCLUSIONS

The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.

Multivariable normal tissue complication probability model-based treatment plan optimization for grade 2-4 dysphagia and tube feeding dependence in head and neck radiotherapy

BACKGROUND AND PURPOSE

Radiotherapy of the head and neck is challenged by the relatively large number of organs-at-risk close to the tumor. Biologically-oriented objective functions (OF) could optimally distribute the dose among the organs-at-risk. We aimed to explore OFs based on multivariable normal tissue complication probability (NTCP) models for grade 2-4 dysphagia (DYS) and tube feeding dependence (TFD).

MATERIALS AND METHODS

One hundred head and neck cancer patients were studied. Additional to the clinical plan, two more plans (an OF_DYS and OF_TFD-plan) were optimized per patient. The NTCP models included up to four dose-volume parameters and other non-dosimetric factors. A fully automatic plan optimization framework was used to optimize the OF_NTCP-based plans.

RESULTS

All OF_NTCP-based plans were reviewed and classified as clinically acceptable. On average, the Δdose and ΔNTCP were small comparing the OF_DYS-plan, OF_TFD-plan, and clinical plan. For 5% of patients NTCP_TFD reduced >5% using OF_TFD-based planning compared to the OF_DYS-plans.

CONCLUSIONS

Plan optimization using NTCP_DYS- and NTCP_TFD-based objective functions resulted in clinically acceptable plans. For patients with considerable risk factors of TFD, the OF_TFD steered the optimizer to dose distributions which directly led to slightly lower predicted NTCP_TFD values as compared to the other studied plans.

A bio-mathematical model for parallel organs and its use in ranking radiation treatment plans

To develop a new bio-mathematical model, named LQ-based parallel-organ model, that can overcome the limitation of interpreting the simple dose-volume information so as to rank the radio- toxicity of parallel organs in the same patient. A parallel organ consists of Function Subunits (FSUs), with each FSU being equal and representative in functional status. Based on the Linear-Quadratic model (LQ model), we had derived a bio-mathematical model to calculate the survival cell number for radiation dose response. We then compared the cell survival number for the ranking of treatment plans for the same patient. Ninety 3D plans from forty-five randomly selected lung cancer patients were generated using the ELEKTA precise 2.12 treatment planning system. The LQ-based parallel-organ model was tested against the widely used Lyman-Kutcher-Burman model (LKB model). There was no distinct statistical difference in plan ranking between using the LQ-based parallel-organ model and the LKB model (P = 0.475). Ranking plans by the V(x), Mean Lung Dose (MLD) and the LQ-based parallel-organ model shows that there was no distinct statistical difference between V(5), V(10), V(20), MLD and the LQ-based parallel-organ model, respectively (all Ps > 0.05). The proposed LQ-based parallel-organ model was found to be efficient and reliable for ranking treatment plans for the same patient.