Radiobiological Models of Tissue Response to Radiation in Treatment Planning Systems

Comparative analysis of dosimetric parameters of three different radiation techniques for patients with Graves' ophthalmopathy treated with retro-orbital irradiation

Background

We would like to investigate the if IMRT produced better target coverage and dose sparing to adjacent normal structures as compared with 3-dimensional conformal radiotherapy (3DCRT) and lateral opposing fields (LOF) for patients with Graves’ ophthalmopathy treated with retro-orbital irradiation.

Methods

Ten consecutive patients diagnosed with Graves’ ophthalmopathy were prospectively recruited into this study. An individual IMRT, 3DCRT and LOF plan was created for each patient. Conformity index (CI), homogeneity index (HI) and other dosimetric parameters of the targets and organs-at-risk (OAR) generated by IMRT were compared with the other two techniques.

Results

Mann–Whitney U test demonstrated that CI generated by IMRT was superior to that produced by 3DCRT and LOF (p=0.005 for both respectively). Similarly HI with IMRT was proven better than 3DCRT (p=0.007) and LOF (p=0.005). IMRT gave rise to better dose sparing to some OARs including globes, lenses and optic nerves as compared with 3DCRT but not with LOF.

Conclusions

IMRT, as compared with 3DCRT and LOF, was found to have a better target coverage, conformity and homogeneity and dose sparing to some surrounding structures, despite a slight increase but clinically negligible dose to other structures. Dosimetrically it might be a preferred treatment technique and a longer follow up is warranted to establish its role in routine clinical use.

Integrated-boost IMRT or 3-D-CRT using FET-PET based auto-contoured target volume delineation for glioblastoma multiforme--a dosimetric comparison

Background

Biological brain tumor imaging using O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET)-PET combined with inverse treatment planning for locally restricted dose escalation in patients with glioblastoma multiforme seems to be a promising approach.

The aim of this study was to compare inverse with forward treatment planning for an integrated boost dose application in patients suffering from a glioblastoma multiforme, while biological target volumes are based on FET-PET and MRI data sets.

Methods

In 16 glioblastoma patients an intensity-modulated radiotherapy technique comprising an integrated boost (IB-IMRT) and a 3-dimensional conventional radiotherapy (3D-CRT) technique were generated for dosimetric comparison. FET-PET, MRI and treatment planning CT (P-CT) were co-registrated. The integrated boost volume (PTV1) was auto-contoured using a cut-off tumor-to-brain ratio (TBR) of ≥ 1.6 from FET-PET. PTV2 delineation was MRI-based. The total dose was prescribed to 72 and 60 Gy for PTV1 and PTV2, using daily fractions of 2.4 and 2 Gy.

Results

After auto-contouring of PTV1 a marked target shape complexity had an impact on the dosimetric outcome. Patients with 3-4 PTV1 subvolumes vs. a single volume revealed a significant decrease in mean dose (67.7 vs. 70.6 Gy). From convex to complex shaped PTV1 mean doses decreased from 71.3 Gy to 67.7 Gy. The homogeneity and conformity for PTV1 and PTV2 was significantly improved with IB-IMRT. With the use of IB-IMRT the minimum dose within PTV1 (61.1 vs. 57.4 Gy) and PTV2 (51.4 vs. 40.9 Gy) increased significantly, and the mean EUD for PTV2 was improved (59.9 vs. 55.3 Gy, p < 0.01). The EUD for PTV1 was only slightly improved (68.3 vs. 67.3 Gy). The EUD for the brain was equal with both planning techniques.

Conclusion

In the presented planning study the integrated boost concept based on inversely planned IB-IMRT is feasible. The FET-PET-based automatically contoured PTV1 can lead to very complex geometric configurations, limiting the achievable mean dose in the boost volume. With IB-IMRT a better homogeneity and conformity, compared to 3D-CRT, could be achieved.

Self-consistent tumor control probability and normal tissue complication probability models based on generalized EUDa)

Traditional methods to compute the tumor control probability (TCP) or normal tissue complication probability (NTCP) typically require a heterogeneous radiation dose distribution to be converted into a simple uniform dose distribution with an equivalent biological effect. Several power-law type dose-volume-histogram reduction schemes, particularly Niemierko's generalized equivalent uniform dose model [Med. Phys. 26, 1000 (1999)], have been proposed to achieve this goal. In this study, we carefully examine the mathematical outcome of these schemes. We demonstrate that (1) for tumors, with each tumor cell independently responding to local radiation dose, a closed-form analytical solution for tumor survival fraction and TCP can be obtained; (2) for serial structured normal tissues, an exponential power-law form relating survival to functional sub-unit (FSU) radiation is required, and a closed-form analytical solution for the related NTCP is provided; (3) in the case of a parallel structured normal tissue, when NTCP is determined solely by the number of the surviving FSUs, a mathematical solution is available only when there is a non-zero threshold dose and/or a finite critical dose defining the radiotherapy response. Some discussion is offered for the partial irradiation effect on normal tissues in this category; (4) for normal tissues with alternative architectures, where the radiation response of FSU is inhomogeneous, there is no exact global mathematical solution for SF or NTCP within the available schemes. Finally, numerical fits of our models to some experimental data are also presented.

Multivariable modeling of radiotherapy outcomes, including dose–volume and clinical factors

PURPOSE

The probability of a specific radiotherapy outcome is typically a complex, unknown function of dosimetric and clinical factors. Current models are usually oversimplified. We describe alternative methods for building multivariable dose-response models.

METHODS

Representative data sets of esophagitis and xerostomia are used. We use a logistic regression framework to approximate the treatment-response function. Bootstrap replications are performed to explore variable selection stability. To guard against under/overfitting, we compare several analytical and data-driven methods for model-order estimation. Spearman's coefficient is used to evaluate performance robustness. Novel graphical displays of variable cross correlations and bootstrap selection are demonstrated.

RESULTS

Bootstrap variable selection techniques improve model building by reducing sample size effects and unveiling variable cross correlations. Inference by resampling and Bayesian approaches produced generally consistent guidance for model order estimation. The optimal esophagitis model consisted of 5 dosimetric/clinical variables. Although the xerostomia model could be improved by combining clinical and dose-volume factors, the improvement would be small.

CONCLUSIONS

Prediction of treatment response can be improved by mixing clinical and dose-volume factors. Graphical tools can mitigate the inherent complexity of multivariable modeling. Bootstrap-based variable selection analysis increases the reliability of reported models. Statistical inference methods combined with Spearman's coefficient provide an efficient approach to estimating optimal model order.

Direct aperture optimization: a turnkey solution for step-and-shoot IMRT

IMRT treatment plans for step-and-shoot delivery have traditionally been produced through the optimization of intensity distributions (or maps) for each beam angle. The optimization step is followed by the application of a leaf-sequencing algorithm that translates each intensity map into a set of deliverable aperture shapes. In this article, we introduce an automated planning system in which we bypass the traditional intensity optimization, and instead directly optimize the shapes and the weights of the apertures. We call this approach "direct aperture optimization." This technique allows the user to specify the maximum number of apertures per beam direction, and hence provides significant control over the complexity of the treatment delivery. This is possible because the machine dependent delivery constraints imposed by the MLC are enforced within the aperture optimization algorithm rather than in a separate leaf-sequencing step. The leaf settings and the aperture intensities are optimized simultaneously using a simulated annealing algorithm. We have tested direct aperture optimization on a variety of patient cases using the EGS4/BEAM Monte Carlo package for our dose calculation engine. The results demonstrate that direct aperture optimization can produce highly conformal step-and-shoot treatment plans using only three to five apertures per beam direction. As compared with traditional optimization strategies, our studies demonstrate that direct aperture optimization can result in a significant reduction in both the number of beam segments and the number of monitor units. Direct aperture optimization therefore produces highly efficient treatment deliveries that maintain the full dosimetric benefits of IMRT.