Stereotactic body radiation therapy for stage I non-small cell lung cancer: The importance of treatment planning algorithm and evaluation of a tumor control probability model

Predicted Inferior Outcomes for Lung SBRT With Treatment Planning Systems That Fail Independent Phantom-Based Audits

PURPOSE

More than 15% of radiation therapy clinics fail external audits with anthropomorphic phantoms conducted by Imaging and Radiation Oncology Core-Houston (IROC-H) while passing other industry-standard quality assurance (QA) tests. We seek to evaluate the predicted effect of such failed plans on outcomes for patients treated with stereotactic body radiation therapy (SBRT) for lung tumors.

METHODS AND MATERIALS

We conducted a retrospective study of 55 patients treated with SBRT for lung tumors with a prescription biologically equivalent dose (BED)₁₀ ≥100 Gy using a treatment planning system (TPS) that passed IROC-H phantom audits. Sample linear accelerator beam models with introduced errors were commissioned by varying the multileaf collimator leaf-tip offset parameter (ie, dosimetric leaf gap) over the range ±1.0 mm relative to the validated model. These models mimic TPS that pass internal QA measures but fail IROC-H tests. Patient plans were recalculated on sample beam models. The predicted tumor control probability (TCP) and normal tissue complication probability (NTCP) were calculated based on published dose-response models.

RESULTS

A leaf-tip offset value of -1.0 mm decreased the fraction of plans receiving a planning treatment volume of BED₁₀ ≥100 Gy from 95% to 27%. This translated to a significant decrease in 2-year TCP of 4.8% (95% CI: 2.0%-5.5%) with a decrease in TCP up to 21%. Conversely, a leaf-tip offset of +1.0 mm resulted in 36% of patients exceeding previously met organs at risk (OAR) constraints, including 2 instances of spinal cord and brachial plexus overdoses and a small increase in chest wall NTCP of 0.7%, (95% CI: 0.5%-0.8%).

CONCLUSIONS

Simulated treatment plans with modest MLC leaf offsets result in lung SBRT plans that significantly underdose tumor or exceed OAR constraints. These dosimetric endpoints translate to significant detriments in TCP. These simulated plans mimic planning systems that pass internal QA measures but fail independent phantom-based tests, underscoring the need for enhanced quality assurance including external audits of TPS.

Uncertainties in the dosimetric heterogeneity correction and its potential effect on local control in lung SBRT

Objective. Dose calculation in lung stereotactic body radiation therapy (SBRT) is challenging due to the low density of the lungs and small volumes. Here we assess uncertainties associated with tissue heterogeneities using different dose calculation algorithms and quantify potential associations with local failure for lung SBRT.Approach. 164 lung SBRT plans were used. The original plans were prepared using Pencil Beam Convolution (PBC, n = 8) or Anisotropic Analytical Algorithm (AAA, n = 156). Each plan was recalculated with AcurosXB (AXB) leaving all plan parameters unchanged. A subset (n = 89) was calculated with Monte Carlo to verify accuracy. Differences were calculated for the planning target volume (PTV) and internal target volume (ITV) Dmean[Gy], D99%[Gy], D95%[Gy], D1%[Gy], and V100%[%]. Dose metrics were converted to biologically effective doses (BED) usingα/β= 10Gy. Regression analysis was performed for AAA plans investigating the effects of various parameters on the extent of the dosimetric differences. Associations between the magnitude of the differences for all plans and outcome were investigated using sub-distribution hazards analysis.Main results. For AAA cases, higher energies increased the magnitude of the difference (ΔDmean of -3.6%, -5.9%, and -9.1% for 6X, 10X, and 15X, respectively), as did lung volume (ΔD99% of -1.6% per 500cc). Regarding outcome, significant hazard ratios (HR) were observed for the change in the PTV and ITV D1% BEDs upon univariate analysis (p = 0.042, 0.023, respectively). When adjusting for PTV volume and prescription, the HRs for the change in the ITV D1% BED remained significant (p = 0.039, 0.037, respectively).Significance. Large differences in dosimetric indices for lung SBRT can occur when transitioning to advanced algorithms. The majority of the differences were not associated with local failure, although differences in PTV and ITV D1% BEDs were associated upon univariate analysis. This shows uncertainty in near maximal tumor dose to potentially be predictive of treatment outcome.

Relationship between Dose Prescription Methods and Local Control Rate in Stereotactic Body Radiotherapy for Early Stage Non-Small-Cell Lung Cancer: Systematic Review and Meta-Analysis

Variations in dose prescription methods in stereotactic body radiotherapy (SBRT) for early stage non-small-cell lung cancer (ES-NSCLC) make it difficult to properly compare the outcomes of published studies. We conducted a comprehensive search of the published literature to summarize the outcomes by discerning the relationship between local control (LC) and dose prescription sites. We systematically searched PubMed to identify observational studies reporting LC after SBRT for peripheral ES-NSCLC. The correlations between LC and four types of biologically effective doses (BED) were evaluated, which were calculated from nominal, central, and peripheral prescription points and, from those, the average BED. To evaluate information on SBRT for peripheral ES-NSCLC, 188 studies were analyzed. The number of relevant articles increased over time. The use of an inhomogeneity correction was mentioned in less than half of the articles, even among the most recent. To evaluate the relationship between the four BEDs and LC, 33 studies were analyzed. Univariate meta-regression revealed that only the central BED significantly correlated with the 3-year LC of SBRT for ES-NSCLC (p = 0.03). As a limitation, tumor volume, which might affect the results of this study, could not be considered due to a lack of data. In conclusion, the central dose prescription is appropriate for evaluating the correlation between the dose and LC of SBRT for ES-NSCLC. The standardization of SBRT dose prescriptions is desirable.

Does HCC Etiology Impact the Efficacy of Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma? An Asian Liver Radiation Therapy Group Study

Background/Purpose

The Asian Liver Radiation Therapy Study Group has formed a large and detailed multinational database of outcomes following stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma (HCC). Here, we explored the potential impact of HCC etiology on SBRT efficacy. Tumor control probability (TCP) models were established to estimate the likelihood of local control (LC).

Methods

Data from 415 patients who were treated with SBRT for HCC were reviewed. Cox proportional hazards models were used to identify key predictors of LC. TCP models accounting for biologic effective dose (BED) and tumor diameter were generated to quantify associations between etiology and LC.

Results

Cox models demonstrated that hepatitis C virus (HCV) infection was associated with favorable LC following SBRT (HR=0.52, 95% CI 0.04–0.96, p=0.036). The 2-year LC rate for patients with HCV etiology was 88%, compared to 78% for other patients. Small tumor and high BED were also associated with favorable LC. TCP models demonstrated a 10–20% absolute increase in predicted LC across the range of SBRT doses and tumor sizes.

Conclusion

We found a novel association between HCV status and LC after SBRT for HCC that warrants further exploration. If validated in other datasets, our findings could help clinicians tailor SBRT schedules.

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

PURPOSE

The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution.

MATERIALS AND METHODS

Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (Rad_CT ), radiomics CT and BED (Rad_CT,BED ), deep learning (DL) CT (DL_CT ), and DL CT and BED (DL_CT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5.

RESULTS

Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a Rad_CT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DL_CT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867).

CONCLUSION

According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

Large-scale dosimetric assessment of Monte Carlo recalculated doses for lung robotic stereotactic body radiation therapy

Owing to its short computation time and simplicity, the Ray-Tracing algorithm (RAT) has long been used to calculate dose distributions for the CyberKnife system. However, it is known that RAT fails to fully account for tissue heterogeneity and is therefore inaccurate in the lung. The aim of this study is to make a dosimetric assessment of 219 non-small cell lung cancer CyberKnife plans by recalculating their dose distributions using an independent Monte Carlo (MC) method. For plans initially calculated by RAT without heterogeneity corrections, target coverage was found to be significantly compromised when considering MC doses. Only 35.4% of plans were found to comply to their prescription doses. If the normal tissue dose limits were respected in the treatment planning dose, the MC recalculated dose did not exceed these limits in over 97% of the plans. Comparison of RAT and recalculated-MC doses confirmed the overestimation of RAT doses observed in previous studies. An inverse correlation between the RAT/MC dose ratio and the target size was also found to be statistically significant (p<10^-4), consistent with other studies. In addition, the inaccuracy and variability in target coverage incurred from dose calculations using RAT without heterogeneity corrections was demonstrated. On average, no clinically relevant differences were observed between MC-calculated dose-to-water and dose-to-medium for all tissues investigated (⩽1%). Patients receiving a dose D_95% larger than 119 Gy in EQD2₁₀ (or ≈52 Gy in 3 fractions) as recalculated by MC were observed to have significantly superior loco-regional progression-free survival rates (p=0.02) with a hazard ratio of 3.45 (95%CI: 1.14-10.5).

Correlation of normal lung density changes with dose after stereotactic body radiotherapy (SBRT) for early stage lung cancer

BACKGROUND AND PURPOSE

To investigate the correlation between normal lung CT density changes with dose accuracy and outcome after stereotactic body radiation therapy (SBRT) for patients with early stage non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

Thirty-one patients (with a total of 33 lesions) with non-small cell lung cancer were selected out of 270 patients treated with SBRT at a single institution between 2003 and 2009. Out of these 31 patients, 10 patients had developed radiation pneumonitis (RP). Dose distributions originally planned using a 1-D pencil beam-based dose algorithm were retrospectively recomputed using different algorithms. Prescription dose was 48 Gy in 4 fractions in most patients. Planning CT images were rigidly registered to follow-up CT datasets at 3-9 months after treatment. Corresponding dose distributions were mapped from planning to follow-up CT images. Hounsfield Unit (HU) changes in lung density in individual, 5 Gy, dose bins from 5 to 45 Gy were assessed in the peri-tumoral region. Correlations between HU changes in various normal lung regions, dose indices (V20, MLD, generalized equivalent uniform dose (gEUD)), and RP grade were investigated.

RESULTS

Strong positive correlation was found between HU changes in the peri-tumoral region and RP grade (Spearman's r = 0.760; p < 0.001). Positive correlation was also observed between RP and HU changes in the region covered by V20 for all algorithms (Spearman's r ≥ 0.738; p < 0.001). Additionally, V20, MLD, and gEUD were significantly correlated with RP grade (p < 0.01). MLD in the peri-tumoral region computed with model-based algorithms was 5-7% lower than the PB-based methods.

CONCLUSION

Changes of lung density in the peri-tumoral lung and in the region covered by V20 were strongly associated with RP grade. Relative to model-based methods, PB algorithms over-estimated mean peri-tumoral dose and showed displacement of the high-dose region, which correlated with HU changes on follow-up CT scans.

Biologically Effective Dose in Stereotactic Body Radiotherapy and Survival for Patients With Early-Stage NSCLC

INTRODUCTION

Stereotactic body radiotherapy (SBRT) results in excellent local control of stage I NSCLC. Radiobiology models predict greater tumor response when higher biologically effective doses (BED₁₀) are given. Prior studies support a BED₁₀ greater than or equal to 100 Gy with SBRT; however, data are limited comparing outcomes after various SBRT regimens. We therefore sought to evaluate national trends and the effect of using "low" versus "high" BED₁₀ SBRT courses on overall survival (OS).

METHODS

This retrospective study used the National Cancer Data Base to identify patients diagnosed with clinical stage I (cT1-2aN0M0) NSCLC from 2004 to 2014 treated with SBRT. Patients were categorized into LowBED (100-129 Gy) or HighBED (≥130 Gy) groups. A 1:1 matched analysis based on patient and tumor characteristics was used to compare OS by BED₁₀ group. Tumor centrality was not assessed.

RESULTS

O 25,039 patients treated with LowBED (n = 14,756; 59%) or HighBED (n = 10,283; 41%) SBRT, 20,542 were matched. Shifts in HighBED to LowBED SBRT regimen use correlated with key publications in the literature. In the matched cohort, 5-year OS rates were 26% for LowBED and 34% for HighBED groups (p = 0.039). On multivariate analysis, receipt of LowBED was associated with significantly worse survival (hazard ratio = 1.046, 95% confidence interval: 1.004-1.090, p = 0.032).

CONCLUSIONS

LowBED SBRT for treating stage I NSCLC is becoming more common. However, our findings suggest SBRT regimens with BED₁₀ greater than or equal to 130 Gy may confer an additional survival benefit. Additional studies are required to evaluate the dose-response relationship and toxicities associated with modern HighBED SBRT.