The reduced toxicity and the documented low rate of marginal failures make the adaptive approach a modern option for future randomized studies. The best scenario to confirm its application is probably in neoadjuvant chemoradiation trials.
The primary goal of precision medicine is to minimize side effects and optimize efficacy of treatments. Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of tumor size or radiotracer uptake metrics. The extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity is an interesting process to investigate, in order to provide information that may be useful to guide the therapies and predict survival. This paper discusses the rationale supporting the concept of radiomics and the feasibility of its application to Non-Small Cell Lung Cancer in the field of radiation oncology research. We studied 91 stage III patients treated with concurrent chemoradiation and adaptive approach in case of tumor reduction during treatment. We considered 12 statistics features and 230 textural features extracted from the CT images. In our study, we used an ensemble learning method to classify patients’ data into either the adaptive or non-adaptive group during chemoradiation on the basis of the starting CT simulation. Our data supports the hypothesis that a specific signature can be identified (AUC 0.82). In our experience, a radiomic signature mixing semantic and image-based features has shown promising results for personalized adaptive radiotherapy in non-small cell lung cancer.
The purpose of this study was to evaluate setup uncertainties for brain sites with ExacTrac X‐Ray 6D system and to provide optimal margin guidelines. Fifteen patients with brain tumor were included in this study. Two X‐ray images with ExacTrac X‐Ray 6D system were used to verify patient position and tumor target localization before each treatment. The 6D fusion software first generates various sets of DRRs with position variations in both three translational and three rotational directions (six degrees of freedom) for the CT images. Setup variations (translation and rotation) after correction were recorded and corrected before treatment. The 3D deviations are expressed as mean±standard deviation. The random error false(normalΣfalse(σifalse)false), systematic error false(μifalse), and group systematic error false(Mfalse(μifalse)false) for the different X‐ray were calculated using the definitions of van Herk.
(1)
Mean setup errors were calculated from X‐ray images acquired after all fractions. There is moderate patient‐to‐patient variation in the vertical direction and small variations in systematic errors and magnitudes of random errors are smaller. The global systematic errors were measured to be less than 2.0 mm in each direction. Random component of all patients are smaller ranging from 0.1–0.3 mm small. The safety margin (SM) to the lateral, is 0.5 mm and 2.6 mm for van Herk
(1)
and Stroom et al.,
(2)
respectively, craniocaudal axis is 1.5 mm and 3.4 mm, respectively, and with respect to the antero–posterior axis, 2.3 mm and 3.9 mm. Daily X‐ray imaging is essential to compare and assess the accuracy of treatment delivery to different anatomical locations.PACS number: 87.55.D
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