Validation of an elastic registration technique to estimate anatomical lung modification in Non-Small-Cell Lung Cancer Tomotherapy

<p>Abstract</p> <p>Background</p> <p>The study of lung parenchyma anatomical modification is useful to estimate dose discrepancies during the radiation treatment of Non-Small-Cell Lung Cancer (NSCLC) patients. We propose and validate a method, based on free-form deformation and mutual information, to elastically register planning kVCT with daily MVCT images, to estimate lung parenchyma modification during Tomotherapy.</p> <p>Methods</p> <p>We analyzed 15 registrations between the planning kVCT and 3 MVCT images for each of the 5 NSCLC patients. Image registration accuracy was evaluated by visual inspection and, quantitatively, by Correlation Coefficients (CC) and Target Registration Errors (TRE). Finally, a lung volume correspondence analysis was performed to specifically evaluate registration accuracy in lungs.</p> <p>Results</p> <p>Results showed that elastic registration was always satisfactory, both qualitatively and quantitatively: TRE after elastic registration (average value of 3.6 mm) remained comparable and often smaller than voxel resolution. Lung volume variations were well estimated by elastic registration (average volume and centroid errors of 1.78% and 0.87 mm, respectively).</p> <p>Conclusions</p> <p>Our results demonstrate that this method is able to estimate lung deformations in thorax MVCT, with an accuracy within 3.6 mm comparable or smaller than the voxel dimension of the kVCT and MVCT images. It could be used to estimate lung parenchyma dose variations in thoracic Tomotherapy.</p

Skin dose calculation during radiotherapy of head and neck cancer using deformable image registration of planning and mega-voltage computed tomography scans

Background and Purpose: Head-Neck (HN) patients may experience severe acute skin complications that can cause treatment interruption and increase the risk of late fibrosis. This study assessed a method for accurately monitoring skin dose changes during helical tomotherapy for HN cancer based on deformable image registration of planning computed tomography (CT) and mega-voltage CT (MVCT). Materials and Methods: Planning CTs of nine patients were deformably registered to mid-treatment MVCT (MV15) images resulting in CTdef images. The original plans were recalculated on both CTdef and mid-treatment kilo-voltage CT (CT15) taken as ground truth. Superficial layers (SL) of the body with thicknesses of 2, 3 and 5 mm (SL2, SL3, SL5) were considered as derma surrogates. SL V95%, V97%, V98%, V100%, V102%, V105% and V107% of the prescribed PTV dose were extracted for CT15/CTdef and compared (considering patients with skin dose > 95%). For comparison, doses were calculated directly on the calibrated MVCT and analyzed in the same way. Results: Differences between SL2/SL3/SL5 V95%-V107% in CT15/CTdef were very small: for eight of nine patients the difference between the considered SL2 Vd% computed on CTdef and CT15 was less than 1.4 cm3 for all d%. A larger value was found when using MVCT for skin dose calculation (4.8 cm3 for SL2), although CTdef body contour matched CT15 body with accuracy similar to that of MV15. Conclusions: Deforming the planning CT-to-MVCT was shown to be accurate considering external body contours and skin DVHs. The method was able to accurately identify superficial overdosing. Keywords: Skin dose, DIR, MVCT, Dose of the day, Tomotherap

External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer

Purpose/objective The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. Material/methods Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66–69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. Results Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62–0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6–11.4). The 5-year CSS for R were 97% (95% CI: 93–100%) vs 74% (56–92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89–100%) vs 65% (36–94%) and 97% (95% CI: 88–100%) vs 77% (58–93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. Conclusion A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy