NTCP modelling of lung toxicity after SBRT comparing the universal survival curve and the linear quadratic model for fractionation correction

A novel predictor for dosimetry data of lung and the radiation pneumonitis incidence prior to SBRT in lung cancer patients

Normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) in lung cancer patients with stereotactic body radiation therapy (SBRT), which based on dosimetric data from treatment planning, are limited to patients who have already received radiation therapy (RT). This study aims to identify a novel predictive factor for lung dose distribution and RP probability before devising actionable SBRT plans for lung cancer patients. A comprehensive correlation analysis was performed on the clinical and dose parameters of lung cancer patients who underwent SBRT. Linear regression models were utilized to analyze the dosimetric data of lungs. The performance of the regression models was evaluated using mean squared error (MSE) and the coefficient of determination (R2). Correlational analysis revealed that most clinical data exhibited weak correlations with dosimetric data. However, nearly all dosimetric variables showed "strong" or "very strong" correlations with each other, particularly concerning the mean dose of the ipsilateral lung (MI) and the other dosimetric parameters. Further study verified that the lung tumor ratio (LTR) was a significant predictor for MI, which could predict the incidence of RP. As a result, LTR can predict the probability of RP without the need to design an elaborate treatment plan. This study, as the first to offer a comprehensive correlation analysis of dose parameters, explored the specific relationships among them. Significantly, it identified LTR as a novel predictor for both dose parameters and the incidence of RP, without the need to design an elaborate treatment plan.

The potential of integrating stereotactic ablative radiotherapy techniques with hyperfractionation for lung cancer

BACKGROUND

Limited literature exists on the feasibility and effectiveness of integrating stereotactic ablative radiotherapy (SABR) techniques with hyperfractionated regimens for patients with lung cancer. This study aims to assess whether the SABR technique with hyperfractionation can potentially reduce lung toxicity.

METHODS

We utilized the linear-quadratic model to find the optimal fraction to maximize the tumor biological equivalent dose (BED) to normal-tissue BED ratio. Validation was performed by comparing the SABR plans with 50 Gy/5 fractions and hyperfractionationed plans with 88.8 Gy/74 fractions with the same tumor BED and planning criteria for 10 patients with early-stage lung cancer. Mean lung BED, Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP), critical volume (CV) criteria (volume below BED of 22.92 and 25.65 Gy, and mean BED for lowest 1000 and 1500 cc) and the percentage of the lung receiving 20Gy or more (V20) were compared using the Wilcoxon signed-rank test.

RESULTS

The transition point occurs when the tumor-to-normal tissue ratio (TNR) of the physical dose equals the TNR of α/β in the BED dose-volume histogram of the lung. Compared with the hypofractionated regimen, the hyperfractionated regimen is superior in the dose range above but inferior below the transition point. The hyperfractionated regimen showed a lower mean lung BED (6.40 Gy vs. 7.73 Gy) and NTCP (3.50% vs. 4.21%), with inferior results concerning CV criteria and higher V20 (7.37% vs. 7.03%) in comparison with the hypofractionated regimen (p < 0.01 for all).

CONCLUSIONS

The hyperfractionated regimen has an advantage in the high-dose region of the lung but a disadvantage in the low-dose region. Further research is needed to determine the superiority between hypo- and hyperfractionation.

Developing a Prediction Model for Radiation Pneumonitis in Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Combined With Clinical, Dosimetric Factors, and Laboratory Biomarkers

BACKGROUND

The study aims to identify the risk factors and develop a model for predicting grade ≥2 radiation pneumonitis (RP) for lung cancer patients treated with stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

Clinical data, dosimetric data, and laboratory biomarkers from 186 patients treated with lung SBRT were collected. Univariate and multivariate logistic regression were performed to determine the predictive factors for grade ≥2 RP. Three models were developed by using the clinical, dosimetric, and combined factors, respectively.

RESULTS

With a median follow-up of 36 months, grade ≥2 RP was recorded in 13.4% of patients. On univariate logistic regression analysis, clinical factors of age and lung volume, dosimetric factors of treatment durations, fractional dose and V10, and laboratory biomarkers of neutrophil, PLT, PLR, and Hb levels were significantly associated with grade ≥2 RP. However, on multivariate analysis, only age, lung volume, fractional dose, V10, and Hb levels were independent factors. AUC values for the clinical, dosimetric, and combined models were 0.730 (95% CI, 0.660-0.793), 0.711 (95% CI, 0.641-0.775) and 0.830 (95% CI, 0.768-0.881), respectively. The combined model provided superior discriminative ability than the clinical and dosimetric models (P < .05).

CONCLUSION

Age, lung volume, fractional dose, V10, and Hb levels were demonstrated to be significant factors associated with grade ≥2 RP for lung cancer patients after SBRT. A novel model combining clinical, dosimetric factors, and laboratory biomarkers improved predictive performance compared with the clinical and dosimetric model alone.

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control

Introduction

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.

Methods

In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values.

Results

Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.

Discussion

Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.

Radiotherapy toxicity prediction using knowledge-constrained generalized linear model

Radiation therapy (RT) is a frontline approach to treating cancer. While the target of radiation dose delivery is the tumor, there is an inevitable spill of dose to nearby normal organs causing complications. This phenomenon is known as radiotherapy toxicity. To predict the outcome of the toxicity, statistical models can be built based on dosimetric variables received by the normal organ at risk (OAR), known as Normal Tissue Complication Probability (NTCP) models. To tackle the challenge of the high dimensionality of dosimetric variables and limited clinical sample sizes, statistical models with variable selection techniques are viable choices. However, existing variable selection techniques are data-driven and do not integrate medical domain knowledge into the model formulation. We propose a knowledge-constrained generalized linear model (KC-GLM). KC-GLM includes a new mathematical formulation to translate three pieces of domain knowledge into non-negativity, monotonicity, and adjacent similarity constraints on the model coefficients. We further propose an equivalent transformation of the KC-GLM formulation, which makes it possible to solve the model coefficients using existing optimization solvers. Furthermore, we compare KC-GLM and several well-known variable selection techniques via a simulation study and on two real datasets of prostate cancer and lung cancer, respectively. These experiments show that KC-GLM selects variables with better interpretability, avoids producing counter-intuitive and misleading results, and has better prediction accuracy.

Influence of Using a Contrast-Enhanced CT Image as the Primary Image on CyberKnife Brain Radiosurgery Treatment Plans

Background and Purpose

This study aimed to quantify the differences between pre- and post-contrast agent (CA) CT for CyberKnife brain SRS plans.

Materials and Methods

Twenty-five patients were retrospectively analyzed. They were divided into two categories, inhomogeneous cases (13 patients) and homogeneous cases (12 patients), according to whether the tumor was close to the cavity and inhomogeneous tissues or not. The pre-CA and post-CA plans were designed and calculated using the same monitor unit and paths as those in the ray-tracing algorithm, respectively.

Results

The CT number difference of tumor between pre- and post-CA was significant (on average, 24.78 ± 18.56 HU, P-value < 0.01). The deviation value of the target was the largest at approximately 37 HU (inhomo-) and 13 HU (homo-) (P < 0.01), and the values of the organs at risk (OARs) were not statistically significant (P-value > 0.05). However, it was not statistically significant for the dose difference between the two groups with the injection of CA (P-value > 0.05). The absolute effective depth difference generally remained at a level of 1 mm, but the dose difference was quitely fluctuated sometimes more than 20%. The absolute effective depth difference of the inhomo-case (0.62 mm) was larger than that of the homo-case (0.37 mm) on median, as well as the variation amplitude (P-value < 0.05). Moreover, the relative dose differences between the two cases were 0.38% (inhomo-) and 0.2% (homo-), respectively (P-value < 0.05). At the criterion of 1 mm/1%, the gamma pass rate of the homo-case (95.89%) was larger than that of the inhomo-case (93.79%). For the OARs, except for the cochlea, the two cases were almost the same (>98.85%). The tumor control probability of the target was over 99.99% before and after injection of a CA, as well as the results for the homo-case and inhomo-case.

Conclusions

Considering the difference of evaluation indexes between pre- and post-CA images, we recommended plain CT to be employed as the primary image for improving the CK treatment accuracy of brain SRS, especially when the target was close to CA-sensitive OARs and cavity.

Calculating the individualized fraction regime in stereotactic body radiotherapy for non-small cell lung cancer based on uncomplicated tumor control probability function

BACKGROUND

To calculate the individualized fraction regime (IFR) in stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC) patients using the uncomplicated tumor control probability (UTCP, P⁺) function.

METHODS

Thirty-three patients with peripheral lung cancer or lung metastases who had undergone SBRT were analyzed. Treatment planning was performed using the dose regime of 48 Gy in 4 fractions. Dose volume histogram (DVH) data for the gross tumor volume (GTV), lung, chest wall (CW) and rib were exported and the dose bin was multiplied by a certain percentage of the dose in that bin which ranged from 1 to 200% in steps of 1%. For each dose fraction, P⁺ values were calculated by considering the tumor control probability (TCP), radiation-induced pneumonitis (RIP), chest wall pain (CWP) and radiation-induced rib fracture (RIRF). UTCP values as a function of physical dose were plotted and the maximum P⁺ values corresponded to the optimal therapeutic gain. The IFR in 3 fractions was also calculated with the same method by converting the dose using the linear quadratic (LQ) model.

RESULTS

Thirty-three patients attained an IFR using the introduced methods. All the patients achieved a TCP value higher than 92.0%. The IFR ranged from 3 × 10.8 Gy to 3 × 12.5 Gy for 3 fraction regimes and from 4 × 9.2 Gy to 4 × 10.7 Gy for 4 fraction regimes. Four patients with typical tumor characteristics demonstrated that the IFR was patient-specific and could maximize the therapeutic gain. Patients with a large tumor had a lower TCP and UTCP and a smaller fractional dose than patients with a small tumor. Patients with a tumor adjacent to the organ at risk (OAR) or at a high risk of RIP had a lower UTCP and a smaller fractional dose compared with patients with a tumor located distant from the OAR.

CONCLUSIONS

The proposed method is capable of predicting the IFR for NSCLC patients undergoing SBRT. Further validation in clinical samples is required.

Influence of respiratory motion management technique on radiation pneumonitis risk with robotic stereotactic body radiation therapy

PURPOSE/OBJECTIVES

For lung stereotactic body radiation therapy (SBRT), real-time tumor tracking (RTT) allows for less radiation to normal lung compared to the internal target volume (ITV) method of respiratory motion management. To quantify the advantage of RTT, we examined the difference in radiation pneumonitis risk between these two techniques using a normal tissue complication probability (NTCP) model.

MATERIALS/METHOD

20 lung SBRT treatment plans using RTT were replanned with the ITV method using respiratory motion information from a 4D-CT image acquired at the original simulation. Risk of symptomatic radiation pneumonitis was calculated for both plans using a previously derived NTCP model. Features available before treatment planning that identified significant increase in NTCP with ITV versus RTT plans were identified.

RESULTS

Prescription dose to the planning target volume (PTV) ranged from 22 to 60 Gy in 1-5 fractions. The median tumor diameter was 3.5 cm (range 2.1-5.5 cm) with a median volume of 14.5 mL (range 3.6-59.9 mL). The median increase in PTV volume from RTT to ITV plans was 17.1 mL (range 3.5-72.4 mL), and the median increase in PTV/lung volume ratio was 0.46% (range 0.13-1.98%). Mean lung dose and percentage dose-volumes were significantly higher in ITV plans at all levels tested. The median NTCP was 5.1% for RTT plans and 8.9% for ITV plans, with a median difference of 1.9% (range 0.4-25.5%, pairwise P < 0.001). Increases in NTCP between plans were best predicted by increases in PTV volume and PTV/lung volume ratio.

CONCLUSIONS

The use of RTT decreased the risk of radiation pneumonitis in all plans. However, for most patients the risk reduction was minimal. Differences in plan PTV volume and PTV/lung volume ratio may identify patients who would benefit from RTT technique before completing treatment planning.

Intensity-modulated radiation therapy versus volumetric-modulated arc therapy in non-small cell lung cancer: assessing the risk of radiation pneumonitis

Purpose

This study aimed to compare intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) regarding plan quality and healthy lung sparing, in stage III non-small cell lung cancer (NSCLC) patients.

Materials and methods

The plans of 60 patients were allocated either to the IMRT (n=30) or the VMAT (n=30) group. The dose prescribed to the planning target volume (PTV) was evaluated at the 95% level and the mean lung dose (MLD) and the healthy lung receiving 5, 10 and 20 Gy (V5, V10and V20, respectively) were analysed. The normal tissue complication probability (NTCP) for radiation pneumonitis was calculated with the Lyman–Kutcher–Burman model.

Results

Both techniques achieved comparable results for target coverage (V95%=97·87 versus 97·18%,p>0·05) and homogeneity. The MLD (15·57 versus 16·98 Gy,p>0·05), V5(60·35 versus 67·25%,p>0·05) and V10(45·22 versus 53·14%,p=0·011) were lower for IMRT, whereas VMAT reduced V20(26·44 versus 25·90%,p>0·05). The NTCP for radiation pneumonitis was higher for VMAT, but no statistical significance was observed (11·07 versus 12·75,p>0·05).

Conclusion

Both techniques seemed suitable for NSCLC treatment, but IMRT presented better results regarding lung sparing thus being beneficial in reducing the risk of radiation-induced pneumonitis.

Radiobiological Optimization in Lung Stereotactic Body Radiation Therapy: Are We Ready to Apply Radiobiological Models?

Lung tumors are often associated with a poor prognosis although different schedules and treatment modalities have been extensively tested in the clinical practice. The complexity of this disease and the use of combined therapeutic approaches have been investigated and the use of high dose-rates is emerging as effective strategy. Technological improvements of clinical linear accelerators allow combining high dose-rate and a more conformal dose delivery with accurate imaging modalities pre- and during therapy. This paper aims at reporting the state of the art and future direction in the use of radiobiological models and radiobiological-based optimizations in the clinical practice for the treatment of lung cancer. To address this issue, a search was carried out on PubMed database to identify potential papers reporting tumor control probability and normal tissue complication probability for lung tumors. Full articles were retrieved when the abstract was considered relevant, and only papers published in English language were considered. The bibliographies of retrieved papers were also searched and relevant articles included. At the state of the art, dose–response relationships have been reported in literature for local tumor control and survival in stage III non-small cell lung cancer. Due to the lack of published radiobiological models for SBRT, several authors used dose constraints and models derived for conventional fractionation schemes. Recently, several radiobiological models and parameters for SBRT have been published and could be used in prospective trials although external validations are recommended to improve the robustness of model predictive capability. Moreover, radiobiological-based functions have been used within treatment planning systems for plan optimization but the advantages of using this strategy in the clinical practice are still under discussion. Future research should be directed toward combined regimens, in order to potentially improve both local tumor control and survival. Indeed, accurate knowledge of the relevant parameters describing tumor biology and normal tissue response is mandatory to correctly address this issue. In this context, the role of medical physicists and the AAPM in the development of radiobiological models is crucial for the progress of developing specific tool for radiobiological-based optimization treatment planning.

A Unified Dose Response Relationship to Predict High Dose Fractionation Response in the Lung Cancer Stereotactic Body Radiation Therapy

AIM

This study is designed to investigate the superiority and applicability of the model among the linear-quadratic (LQ), linear-quadratic-linear (LQ-L) and universal-survival-curve (USC) models by fitting published radiation cell survival data of lung cancer cell lines.

MATERIALS AND METHOD

The radiation cell survival data for small cell (SC) and non-small cell (NSC) lung cancer cell lines were obtained from published reports, and were used to determine the LQ and cell survival curve parameters, which ultimately were used in the curve fitting of the LQ, LQ-L and USC models.

RESULTS

The results of this study demonstrate that the LQ-L(Dt-mt) model, compared with the LQ and USC models, provides best fit with smooth and gradual transition to the linear portion of the curve at transition dose Dt-mt, where the LQ model loses its validity, and the LQ-L(Dt-2α/β) and USC(Dt-mt) models do not transition smoothly to the linear portion of the survival curve.

CONCLUSION

The LQ-L(Dt-mt) model is able to fit wide variety of cell survival data over a very wide dose range, and retains the strength of the LQ model in the low-dose range.

Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors

PURPOSE

To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy.

METHODS AND MATERIALS

The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm^-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (P_test).

RESULTS

The best-fit parameters for prediction of RRLI 6months post RT were D₅₀ of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D₅₀ of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the P_test.

CONCLUSIONS

The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.

Optimization of the fractionated irradiation scheme considering physical doses to tumor and organ at risk based on dose-volume histograms

PURPOSE

Radiotherapy of solid tumors has been performed with various fractionation regimens such as multi- and hypofractionations. However, the ability to optimize the fractionation regimen considering the physical dose distribution remains insufficient. This study aims to optimize the fractionation regimen, in which the authors propose a graphical method for selecting the optimal number of fractions (n) and dose per fraction (d) based on dose-volume histograms for tumor and normal tissues of organs around the tumor.

METHODS

Modified linear-quadratic models were employed to estimate the radiation effects on the tumor and an organ at risk (OAR), where the repopulation of the tumor cells and the linearity of the dose-response curve in the high dose range of the surviving fraction were considered. The minimization problem for the damage effect on the OAR was solved under the constraint that the radiation effect on the tumor is fixed by a graphical method. Here, the damage effect on the OAR was estimated based on the dose-volume histogram.

RESULTS

It was found that the optimization of fractionation scheme incorporating the dose-volume histogram is possible by employing appropriate cell surviving models. The graphical method considering the repopulation of tumor cells and a rectilinear response in the high dose range enables them to derive the optimal number of fractions and dose per fraction. For example, in the treatment of prostate cancer, the optimal fractionation was suggested to lie in the range of 8-32 fractions with a daily dose of 2.2-6.3 Gy.

CONCLUSIONS

It is possible to optimize the number of fractions and dose per fraction based on the physical dose distribution (i.e., dose-volume histogram) by the graphical method considering the effects on tumor and OARs around the tumor. This method may stipulate a new guideline to optimize the fractionation regimen for physics-guided fractionation.

Extracting the normal lung dose-response curve from clinical DVH data: a possible role for low dose hyper-radiosensitivity, increased radioresistance

In conventionally fractionated radiation therapy for lung cancer, radiation pneumonitis' (RP) dependence on the normal lung dose-volume histogram (DVH) is not well understood. Complication models alternatively make RP a function of a summary statistic, such as mean lung dose (MLD). This work searches over damage profiles, which quantify sub-volume damage as a function of dose. Profiles that achieve best RP predictive accuracy on a clinical dataset are hypothesized to approximate DVH dependence.Step function damage rate profiles R(D) are generated, having discrete steps at several dose points. A range of profiles is sampled by varying the step heights and dose point locations. Normal lung damage is the integral of R(D) with the cumulative DVH. Each profile is used in conjunction with a damage cutoff to predict grade 2 plus (G2+) RP for DVHs from a University of Michigan clinical trial dataset consisting of 89 CFRT patients, of which 17 were diagnosed with G2+ RP.Optimal profiles achieve a modest increase in predictive accuracy--erroneous RP predictions are reduced from 11 (using MLD) to 8. A novel result is that optimal profiles have a similar distinctive shape: enhanced damage contribution from low doses (<20 Gy), a flat contribution from doses in the range ~20-40 Gy, then a further enhanced contribution from doses above 40 Gy. These features resemble the hyper-radiosensitivity / increased radioresistance (HRS/IRR) observed in some cell survival curves, which can be modeled using Joiner's induced repair model.A novel search strategy is employed, which has the potential to estimate RP dependence on the normal lung DVH. When applied to a clinical dataset, identified profiles share a characteristic shape, which resembles HRS/IRR. This suggests that normal lung may have enhanced sensitivity to low doses, and that this sensitivity can affect RP risk.

Survival and tumour control probability in tumours with heterogeneous oxygenation: a comparison between the linear-quadratic and the universal survival curve models for high doses

BACKGROUND

The validity of the linear-quadratic (LQ) model at high doses has been questioned due to a decreasing agreement between predicted survival and experimental cell survival data. A frequently proposed alternative is the universal survival curve (USC) model, thought to provide a better fit in the high-dose region. The comparison between the predictions of the models has mostly been performed for uniform populations of cells with respect to sensitivity to radiation. This study aimed to compare the two models in terms of cell survival and tumour control probability (TCP) for cell populations with mixed sensitivities related to their oxygenation.

METHODS

The study was performed in two parts. For the first part, cell survival curves were calculated with both models assuming various homogeneous populations of cells irradiated with uniform doses. For the second part, a realistic three-dimensional (3D) model of complex tumour oxygenation was used to study the impact of the differences in cell survival on the modelled TCP. Cellular response was assessed with the LQ and USC models at voxel level and a Poisson TCP model at tumour level.

RESULTS

For hypoxic tumours, the disputed continuous bend of the LQ survival curve was counteracted by the increased radioresistance of the hypoxic cells and the survival curves started to diverge only at much higher doses than for oxic tumours. This was also reflected by the TCP curves for hypoxic tumours for which the difference in D50 values for the LQ and USC models was reduced from 5.4 to 0.2 Gy for 1 and 3 fractions, respectively, in a tumour with only 1.1% hypoxia and from 9.5 to 0.4 Gy in a tumour with 11.1% hypoxia.

CONCLUSIONS

For a large range of fractional doses including hypofractionated schemes, the difference in predicted survival and TCP between the LQ and USC models for tumours with heterogeneous oxygenation was found to be negligible.

Treating patients with real-time tumor tracking using the Vero gimbaled linac system: implementation and first review

PURPOSE

To report on the first clinical application of a real-time tumor tracking (RTTT) solution based on the Vero SBRT gimbaled linac system for treatment of moving tumors.

METHODS AND MATERIALS

A first group of 10 SBRT patients diagnosed with NSCLC or oligometastatic disease in lung or liver was treated with the RTTT technique. The PTV volumes and OAR exposure were benchmarked against the widely used ITV approach. Based on data acquired during execution of RTTT treatments, a first review was performed of the process.

RESULTS

The 35% PTV volume reduction with RTTT of the studied single lesions SBRT irradiations of small target volumes is expected to result in a small (<1%) reduction of lung or liver NTCP. A GTV-PTV margin of 5.0mm was applied for treatment planning of RTTT. From patient data on residual geometric uncertainties, a CTV-PTV margin of 3.2mm was calculated. Reduction of the GTV-PTV margin below 5.0mm without better understanding of biological definition of tumor boundaries was discouraged. Total treatment times were reduced to 34.4 min on average.

CONCLUSION

A considerable PTV volume reduction was achieved applying RTTT and time efficiency for respiratory correlated SBRT was reestablished with Vero RTTT.

Significance of low-dose radiation distribution in development of radiation pneumonitis after helical-tomotherapy-based hypofractionated radiotherapy for pulmonary metastases

Hypofractionated radiotherapy (HRT) is now commonly used for pulmonary malignancies, since a tumoricidal dose can be accurately delivered to the target without a consequential dose to adjacent normal tissues. However, radiation pneumonitis (RP) is still a major problem after HRT. To determine the significant parameters associated with developing RP, we retrospectively investigated data from patients with lung metastases treated with HRT using helical tomotherapy. A total of 45 patients were included in the study and the median age was 53 years old. The median prescriptive doses were 50 Gy to the internal target volume and 40 Gy to the planning target volume in 10 fractions over 2 weeks. RP was diagnosed by chest X-ray or computed tomography after HRT, and its severity was determined by CTCAE version 4.0. The incidence of symptomatic RP was 26.6%. Univariate analysis indicated that mean lung doses, V5, V10, V15, V20 and V25 were associated with the development of symptomatic RP (P < 0.05). However, multivariate analysis indicated that only V5 was associated with the development of symptomatic RP (P = 0.019). From the ROC curve, V5 was the most powerful predictor of symptomatic RP, and its AUC (area under curve) was 0.780 (P = 0.004). In addition, the threshold value of V5 for the development of symptomatic RP was 65%. A large distribution of low-dose radiation resulted in a higher risk of lung toxicity. So, to prevent symptomatic RP, it is recommended that the V5 be limited to <65%, in addition to considering conventional dosimetric factors. However, further clinical study must be undertaken in order to confirm this result.

Optimization of normalized prescription isodose selection for stereotactic body radiation therapy: conventional vs robotic linac

PURPOSE

Although modern technology has allowed for target dose escalation by minimizing normal tissue dose, the dose delivered to a tumor and surrounding tissues still depends largely on the inherent characteristics of the radiation delivery platform. This work aims to determine the optimal prescription isodose line that minimizes normal tissue irradiation for stereotactic body radiation therapy (SBRT) for a conventional linear accelerator and a robotic delivery platform.

METHODS

Spherical targets with diameters of 10, 20, and 30 mm were constructed in the lungs and liver of a computer based digital torso phantom which simulates respiratory and cardiac motion. Normal tissue contours included normal lung, normal liver, and a concentric 10 mm shell of normal tissue extending from the spherical target surface. For linac planning, noncoplanar, nonopposing three dimensional (3D) conformal beams were designed, and variable prescription isodose lines were achieved by varying the MLC block margin. For CyberKnife planning, variable prescription isodose lines were achieved by inverse planning. True 4D dose calculations were used for the moving target and surrounding tissue based on each of ten phases of a 4D CT dataset. Doses of 60 Gy in three fractions were prescribed to cover 95% of the target tumor. Commonly used conformality, dosimetric, and radiobiological indices for lung and liver SBRT were used to compare different plans and determine the optimally prescribed isodose line for each treatment platform.

RESULTS

For linac plans, the average optimal prescription isodose line based on all indices evaluated occurred between 59% and 69% for lung tumors and between 67% and 77% for liver tumors depending on the tumor size. CyberKnife plans had average optimal prescription isodose lines occurring between 40% and 48% for lung tumors and between 41% and 42% depending on the tumor size. However, prescription isodose lines under 50% are not advised to prevent large heterogeneous dose distributions within the target.

CONCLUSIONS

The choice of prescription isodose line was shown to have a significant impact on parameters commonly used as constraints for lung and liver SBRT treatment planning for both linac-based and CyberKnife delivery platforms. By methodically choosing the prescription isodose line, normal tissue toxicities from SBRT may be reduced.

The impact of fractionation in SBRT: analysis with the linear quadratic model and the universal survival curve model

BACKGROUND

Since the impact of fractionation in SBRT has not been systematically investigated, this modelling study was performed to see how the therapeutic window is affected for a range of fractions at target doses commonly administered in SBRT.

MATERIAL AND METHODS

Isoeffective tumour-doses (constant cell survival) were calculated with the linear quadratic (LQ) and the universal survival curve (USC) models for 2-20 fractions. The isoeffective tumour-regimes (with α/β = 10 Gy) were used to calculate the sparing of normal tissues (with α/β = 3 Gy) for an increasing number of fractions. Sparing was calculated as an increase in cell survival and decrease in normal tissue complication probability (NTCP) as compared to a common scheme with 3 fractions of 22 Gy to the centre of the target [(15 Gy to the periphery of the planning target volume (PTV)].

RESULTS

At a high dose per fraction, above about 15 Gy, the USC model predicted much lower fractionation sensitivity than the LQ model. This holds true for both tumour and normal tissues. The USC model also predicted greater sparing of normal tissues outside the PTV as compared to the LQ model. Especially at dose levels of the order of 30-50% to that in the centre of the target. The decrease in NTCP predicted by the USC model was of the order of 30% for 10 fractions as compared to the NTCP for 3 fractions. With the LQ model the corresponding decrease was of the order of 10%.

CONCLUSION

The USC model generally predicts a larger therapeutic window than the LQ model for an increasing number of fractions than today's practice in SBRT.

Graphical representation of the effects on tumor and OAR for determining the appropriate fractionation regimen in radiation therapy planning

PURPOSE

The authors propose a graphical representation of the relation between the effect on the tumor and the damage effect on an organ at risk (OAR) against the irradiation dose, as an aid for choosing an appropriate fractionation regimen.

METHODS

The graphical relation is depicted by the radiation effect on the tumor E(1) versus that on an OAR E(0). By observing the features of the E(1) vs E(0) relation curve, i.e., convex or concave shape, one can judge whether multifractionation is better or not. This method is applied to the linear-quadratic model (with α and β parameters) as an example. Further, the method is extended to the general case for nonuniform dose distribution to the OAR, which is frequently seen in clinical situations.

RESULTS

The criterion for selecting multi- or hypofractionation is based on the relation between the dose for the OAR and the α∕β ratio of the OAR to the tumor. It is also shown that the graphical relation enables us to estimate the final effect after multifractionated treatment by plotting a tangent line on the curve.

CONCLUSIONS

The graphical representation method is of use for improving planning in radiotherapy by determining the effective fractionation scheme.

(Radio)biological optimization of external-beam radiotherapy

“Biological optimization” (BIOP) means planning treatments using (radio)biological criteria and models, that is, tumour control probability and normal-tissue complication probability. Four different levels of BIOP are identified: Level I is “isotoxic” individualization of prescription dose D_presc at fixed fraction number. D_presc is varied to keep the NTCP of the organ at risk constant. Significant improvements in local control are expected for non-small-cell lung tumours. Level II involves the determination of an individualized isotoxic combination of D_presc and fractionation scheme. This approach is appropriate for “parallel” OARs (lung, parotids). Examples are given using our BioSuite software. Hypofractionated SABR for early-stage NSCLC is effectively Level-II BIOP. Level-III BIOP uses radiobiological functions as part of the inverse planning of IMRT, for example, maximizing TCP whilst not exceeding a given NTCP. This results in non-uniform target doses. The NTCP model parameters (reflecting tissue “architecture”) drive the optimizer to emphasize different regions of the DVH, for example, penalising high doses for quasi-serial OARs such as rectum. Level-IV BIOP adds functional imaging information, for example, hypoxia or clonogen location, to Level III; examples are given of our prostate “dose painting” protocol, BioProp. The limitations of and uncertainties inherent in the radiobiological models are emphasized.

Results of stereotactic radiotherapy for stage I non-small-cell lung cancer: is there a need for image guidance and highly sophisticated devices?

SUMMARY In stage I non-small-cell lung cancer, stereotactic body radiation therapy achieves a local control of 90%, by accurate dose delivery with stereotactic beam navigation and/or image-guided techniques, and extremely dose-escalated hypofractionated radiotherapy. Three-to-ten fractions over 1–2 weeks or one single fraction as radiosurgery are used. A broad spectrum of different techniques have also been introduced, some encouraged by electric companies, and heavily commercialized by institutions and physicians. Although a direct comparison of these techniques has been carried out only in technical and not within clinical trials; clinical data from the few prospective Phase I and II trials and the majority of retrospective evaluations have not shown superiority of either technique. Based on personal experiences, there are nearly no limitations for the use of very simple and cheap techniques, and the broad and increasing disposition of dedicated systems is questionable.

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.

Predicting radiation pneumonitis after stereotactic ablative radiation therapy in patients previously treated with conventional thoracic radiation therapy

PURPOSE

To determine the incidence of and risk factors for radiation pneumonitis (RP) after stereotactic ablative radiation therapy (SABR) to the lung in patients who had previously undergone conventional thoracic radiation therapy.

METHODS AND MATERIALS

Seventy-two patients who had previously received conventionally fractionated radiation therapy to the thorax were treated with SABR (50 Gy in 4 fractions) for recurrent disease or secondary parenchymal lung cancer (T<4 cm, N0, M0, or Mx). Severe (grade≥3) RP and potential predictive factors were analyzed by univariate and multivariate logistic regression analyses. A scoring system was established to predict the risk of RP.

RESULTS

At a median follow-up time of 16 months after SABR (range, 4-56 months), 15 patients had severe RP (14 [18.9%] grade 3 and 1 [1.4%] grade 5) and 1 patient (1.4%) had a local recurrence. In univariate analyses, Eastern Cooperative Oncology Group performance status (ECOG PS) before SABR, forced expiratory volume in 1 second (FEV1), and previous planning target volume (PTV) location were associated with the incidence of severe RP. The V10 and mean lung dose (MLD) of the previous plan and the V10-V40 and MLD of the composite plan were also related to RP. Multivariate analysis revealed that ECOG PS scores of 2-3 before SABR (P=.009), FEV1≤65% before SABR (P=.012), V20≥30% of the composite plan (P=.021), and an initial PTV in the bilateral mediastinum (P=.025) were all associated with RP.

CONCLUSIONS

We found that severe RP was relatively common, occurring in 20.8% of patients, and could be predicted by an ECOG PS score of 2-3, an FEV1≤65%, a previous PTV spanning the bilateral mediastinum, and V20≥30% on composite (previous RT+SABR) plans. Prospective studies are needed to validate these predictors and the scoring system on which they are based.