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 D95% 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).

Comparing local control and distant metastasis in NSCLC patients between CyberKnife and conventional SBRT.

BACKGROUND AND PURPOSE: Previous literature suggests that the dose proximally outside the PTV could have an impact on the incidence of distant metastasis (DM) after SBRT in stage I NSCLC patients. We investigated this observation (along with local failure) in deliveries made by different treatment modalities: robotic mounted linac SBRT (CyberKnife) vs conventional SBRT (VMAT/CRT). MATERIALS AND METHODS: This study included 422 stage I NSCLC patients from 2 institutions who received SBRT: 217 treated conventionally and 205 with CyberKnife. The dose behavior outside the PTV of both sub-cohorts were compared by analyzing the mean dose in continuous shells extending 1, 2, 3, …, 100 mm from the PTV. Kaplan-Meier analysis was performed between the two sub-cohorts with respect to DM-free survival and local progression-free survival. A multivariable Cox proportional hazards model was fitted to the combined cohort (n = 422) with respect to DM incidence and local failure. RESULTS: The shell-averaged dose fall-off beyond the PTV was found to be significantly more modest in CyberKnife plans than in conventional SBRT plans. In a 30 mm shell around the PTV, the mean dose delivered with CyberKnife (38.1 Gy) is significantly larger than with VMAT/CRT (22.8 Gy, p<10-8). For 95% of CyberKnife plans, this region receives a mean dose larger than the 21 Gy threshold dose discovered in our previous study. In contrast, this occurs for only 75% of VMAT/CRT plans. The DM-free survival of the entire CyberKnife cohort is superior to that of the 25% of VMAT/CRT patients receiving less than the threshold dose (VMAT/CRT<21Gy), with a hazard ratio of 5.3 (95% CI: 3.0-9.3, p<10-8). The 2 year DM-free survival rates were 87% (95% CI: 81%-91%) and 44% (95% CI: 28%-58%) for CyberKnife and the below-threshold dose conventional cohorts, respectively. A multivariable analysis of the combined cohort resulted in the confirmation that threshold dose was a significant predictor of DM(HR = 0.28, 95% CI: 0.15-0.55, p<10-3) when adjusted for other clinical factors. CyberKnife was also found to be superior to the entire VMAT/CRT with respect to local control (HR = 3.44, CI: 1.6-7.3). The 2-year local progression-free survival rates for the CyberKnife cohort and the VMAT/CRT cohort were 96% (95% CI: 92%-98%) and 88% (95% CI: 82%-92%) respectively. CONCLUSIONS: In standard-of-care CyberKnife treatments, dose distributions that aid distant control are achieved 95% of the time. Although similar doses could be physically achieved by conventional SBRT, this is not always the case with current prescription practices, resulting in worse DM outcomes for 25% of conventional SBRT patients. Furthermore, CyberKnife was found to provide superior local control compared to VMAT/CRT.

Deep learning in head & neck cancer outcome prediction.

Traditional radiomics involves the extraction of quantitative texture features from medical images in an attempt to determine correlations with clinical endpoints. We hypothesize that convolutional neural networks (CNNs) could enhance the performance of traditional radiomics, by detecting image patterns that may not be covered by a traditional radiomic framework. We test this hypothesis by training a CNN to predict treatment outcomes of patients with head and neck squamous cell carcinoma, based solely on their pre-treatment computed tomography image. The training (194 patients) and validation sets (106 patients), which are mutually independent and include 4 institutions, come from The Cancer Imaging Archive. When compared to a traditional radiomic framework applied to the same patient cohort, our method results in a AUC of 0.88 in predicting distant metastasis. When combining our model with the previous model, the AUC improves to 0.92. Our framework yields models that are shown to explicitly recognize traditional radiomic features, be directly visualized and perform accurate outcome prediction.

Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?

BACKGROUND AND PURPOSE: In an era where little is known about the "abscopal" (out-of-the-field) effects of lung SBRT, we investigated correlations between the radiation dose proximally outside the PTV and the risk of cancer recurrence after SBRT in patients with primary stage I non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: This study included 217 stage I NSCLC patients across 2 institutions who received SBRT. Correlations between clinical and dosimetric factors were investigated. The clinical factors considered were distant metastasis (DM), loco-regional control (LRC) and radiation pneumonitis (RP). The dose (converted to EQD2) delivered to regions of varying size directly outside of the PTV was computed. For each feature, area under the curve (AUC) and odds ratios with respect to the outcome parameters DM, LRC and RP were estimated; Kaplan-Meier (KM) analysis was also performed. RESULTS: Thirty-seven (17%) patients developed DM after a median follow-up of 24 months. It was found that the mean dose delivered to a shell-shaped region of thickness 30 mm outside the PTV had an AUC of 0.82. Two years after treatment completion, the rate of DM in patients where the mean dose delivered to this region was higher than 20.8 Gy2 was 5% compared to 60% in those who received a dose lower than 20.8 Gy2. KM analysis resulted in a hazard ratio of 24.2 (95% CI: 10.7, 54.4); p < 10-5. No correlations were found between any factor and either LRC or RP. CONCLUSIONS: The results of this study suggest that the dose received by the region close to the PTV has a significant impact on the risk of distant metastases in stage I NSCLC patients treated with SBRT. If these results are independently confirmed, caution should be taken, particularly when a treatment plan results in a steep dose gradient extending outwards from the PTV.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept.

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice ('span'). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of [Formula: see text] in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters ([Formula: see text]), with an average AUC of [Formula: see text]. Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.