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Background Large variation regarding prescription and dose inhomogeneity exists in stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer. The aim of this modeling study was to identify which dose metric correlates best with local tumor control probability to make recommendations regarding SBRT prescription. Methods and Materials We combined 2 retrospective databases of patients with non-small cell lung cancer, yielding 1500 SBRT treatments for analysis. Three dose parameters were converted to biologically effective doses (BEDs): (1) the (near-minimum) dose prescribed to the planning target volume (PTV) periphery (yielding BEDmin); (2) the (near-maximum) dose absorbed by 1% of the PTV (yielding BEDmax); and (3) the average between near-minimum and near-maximum doses (yielding BEDave). These BED parameters were then correlated to the risk of local recurrence through Cox regression. Furthermore, BED-based prediction of local recurrence was attempted by logistic regression and fast and frugal trees. Models were compared using the Akaike information criterion. Results There were 1500 treatments in 1434 patients; 117 tumors recurred locally. Actuarial local control rates at 12 and 36 months were 96.8% (95% confidence interval, 95.8%-97.8%) and 89.0% (87.0%-91.1%), respectively. In univariable Cox regression, BEDave was the best predictor of risk of local recurrence, and a model based on BEDmin had substantially less evidential support. In univariable logistic regression, the model based on BEDave also performed best. Multivariable classification using fast and frugal trees revealed BEDmax to be the most important predictor, followed by BEDave. Conclusions BEDave was generally better correlated with tumor control probability than either BEDmax or BEDmin. Because the average between near-minimum and near-maximum doses was highly correlated to the mean gross tumor volume dose, the latter may be used as a prescription target. More emphasis could be placed on achieving sufficiently high mean doses within the gross tumor volume rather than the PTV covering dose, a concept needing further validation.

Item does not contain fulltext Introduction: Inter-observer variability (IOV) in target volume delineation is a well-documented source of geometric uncertainty in radiotherapy. Such variability has not yet been explored in the context of adaptive re-delineation based on imaging data acquired during treatment. We compared IOV in the pre- and mid-treatment setting using expert primary gross tumour volume (GTV) and clinical target volume (CTV) delineations in locoregionally advanced head-and-neck squamous cell carcinoma (HNSCC) and (non-)small cell lung cancer [(N)SCLC]. Material and methods: Five and six observers participated in the HNSCC and (N)SCLC arm, respectively, and provided delineations for five cases each. Imaging data consisted of CT studies partly complemented by FDG-PET and was provided in two separate phases for pre- and mid-treatment. Global delineation compatibility was assessed with a volume overlap metric (the Generalised Conformity Index), while local extremes of IOV were identified through the standard deviation of surface distances from observer delineations to a median consensus delineation. Details of delineation procedures, in particular, GTV to CTV expansion and adaptation strategies, were collected through a questionnaire. Results: Volume overlap analysis revealed a worsening of IOV in all but one case per disease site, which failed to reach significance in this small sample (p-value range .063-.125). Changes in agreement were propagated from GTV to CTV delineations, but correlation could not be formally demonstrated. Surface distance based analysis identified longitudinal target extent as a pervasive source of disagreement for HNSCC. High variability in (N)SCLC was often associated with tumours abutting consolidated lung tissue or potentially invading the mediastinum. Adaptation practices were variable between observers with fewer than half stating that they consistently adapted pre-treatment delineations during treatment. Conclusion: IOV in target volume delineation increases during treatment, where a disparity in institutional adaptation practices adds to the conventional causes of IOV. Consensus guidelines are urgently needed.

Highlights • Tumour volume, p16 status and N stage could be validated regarding LRC and OS. • Addition of cancer stem cell markers can further improve the baseline model. • Prognostic value of tumour hypoxia warrants analysis in a larger cohort., Objective To independently validate the impact of tumour volume, p16 status, cancer stem cell (CSC) marker expression and hypoxia-associated gene signatures as potential prognostic biomarkers for patients with locally advanced head and neck squamous cell carcinoma (HNSCC), who underwent primary radiotherapy or radiochemotherapy (RCTx). These markers have previously been reported in a study of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) (Linge et al., 2016). Materials and methods In this retrospective monocentric study, 92 patients with locally advanced HNSCC were included. Univariable and multivariable logistic regressions and Cox models presented in the study of the DKTK-ROG were validated using the area under the curve (AUC) and the concordance index (ci), respectively. The primary endpoint of this study was loco-regional tumour control (LRC) after primary RCTx. Results Although both cohorts significantly differed in the proportion of the tumour subsites, the parameters tumour volume, p16 status and N stage could be validated regarding LRC and overall survival (OS) using multivariable Cox regression (LRC ci: 0.59, OS ci: 0.63). These models were slightly improved by combination with the putative CSC marker CD44 (LRC ci: 0.61, OS ci: 0.69). The logistic regression model for 2-year LRC based on tumour volume, p16 status and CD44 protein was validated with an AUC of 0.64. The patient stratification based on hypoxia-associated gene signatures status was similar to the original study but without significant differences in LRC and OS. Conclusions In this validation study, the inclusion of the putative CSC marker CD44 slightly improved the prognostic performance of the baseline parameters tumour volume, p16 status and N stage. No improvement was observed when including expressions of the hypoxia-associated gene signatures. Prospective validation on a larger cohort is warranted to assess the clinical relevance of these markers.

Purpose: The aim of this study was to identify and independently validate a novel gene signature predicting locoregional tumor control (LRC) for treatment individualization of patients with locally advanced HPV-negative head and neck squamous cell carcinomas (HNSCC) who are treated with postoperative radio(chemo)therapy (PORT-C). Experimental Design: Gene expression analyses were performed using NanoString technology on a multicenter training cohort of 130 patients and an independent validation cohort of 121 patients. The analyzed gene set was composed of genes with a previously reported association with radio(chemo)sensitivity or resistance to radio(chemo)therapy. Gene selection and model building were performed comparing several machine-learning algorithms. Results: We identified a 7-gene signature consisting of the three individual genes HILPDA, CD24, TCF3, and one metagene combining the highly correlated genes SERPINE1, INHBA, P4HA2, and ACTN1. The 7-gene signature was used, in combination with clinical parameters, to fit a multivariable Cox model to the training data (concordance index, ci = 0.82), which was successfully validated (ci = 0.71). The signature showed improved performance compared with clinical parameters alone (ci = 0.66) and with a previously published model including hypoxia-associated genes and cancer stem cell markers (ci = 0.65). It was used to stratify patients into groups with low and high risk of recurrence, leading to significant differences in LRC in training and validation (P < 0.001). Conclusions: We have identified and validated the first hypothesis-based gene signature for HPV-negative HNSCC treated by PORT-C including genes related to several radiobiological aspects. A prospective validation is planned in an ongoing prospective clinical trial before potential application in clinical trials for patient stratification. Clin Cancer Res; 24(6); 1364–74. ©2018 AACR.

To compare 4 different proton pencil beam scanning (PBS) treatment approaches for unilateral head and neck cancer (HNC) targets in terms of robustness, including anatomical changes during the treatment course.

Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control.We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection.Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED(ISO)) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED(ISO) and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED(ISO), age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively.These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are expected through a nonlinear combination of multiple features, eventually helping in the task of personalized treatment planning.