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.

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.

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.

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.

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.