Giuseppe Palma1, Alberto Taffelli2, Francesco Fellin3, Vittoria D'Avino1, Daniele Scartoni3, Francesco Tommasino4, Emanuele Scifoni2, Marco Durante5, Maurizio Amichetti3, Marco Schwarz6, Dante Amelio3, Laura Cella7. 1. National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy. 2. Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics and Applications, Trento, Italy. 3. Trento Proton Therapy Center, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy. 4. Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics and Applications, Trento, Italy; University of Trento, Physics Department, Trento, Italy. 5. GSI Helmholtzzentrum für Schwerionenforschung, Biophysics Department, Darmstadt, Germany; Technische Universität Darmstadt, Institut für Festkörperphysik, Darmstadt, Germany. 6. Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics and Applications, Trento, Italy; Trento Proton Therapy Center, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy. 7. National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy. Electronic address: laura.cella@cnr.it.
Abstract
PURPOSE: To develop normal tissue complication probability (NTCP) models for radiation-induced alopecia (RIA) in brain tumor patients treated with proton therapy (PT). METHODS AND MATERIALS: We analyzed 116 brain tumor adult patients undergoing scanning beam PT (median dose 54 GyRBE; range 36-72) for CTCAE v.4 grade 2 (G2) acute (≤90 days), late (>90 days) and permanent (>12 months) RIA. The relative dose-surface histogram (DSH) of the scalp was extracted and used for Lyman-Kutcher-Burman (LKB) modelling. Moreover, DSH metrics (Sx: the surface receiving ≥ X Gy, D2%: near maximum dose, Dmean: mean dose) and non-dosimetric variables were included in a multivariable logistic regression NTCP model. Model performances were evaluated by the cross-validated area under the receiver operator curve (ROC-AUC). RESULTS: Acute, late and permanent G2-RIA was observed in 52%, 35% and 19% of the patients, respectively. The LKB models showed a weak dose-surface effect (0.09 ≤ n ≤ 0.19) with relative steepness 0.29 ≤ m ≤ 0.56, and increasing tolerance dose values when moving from acute and late (22 and 24 GyRBE) to permanent RIA (44 GyRBE). Multivariable modelling selected S21Gy for acute and S25Gy, for late G2-RIA as the most predictive DSH factors. Younger age was selected as risk factor for acute G2-RIA while surgery as risk factor for late G2-RIA. D2% was the only variable selected for permanent G2-RIA. Both LKB and logistic models exhibited high predictive performances (ROC-AUCs range 0.86-0.90). CONCLUSION: We derived NTCP models to predict G2-RIA after PT, providing a comprehensive modelling framework for acute, late and permanent occurrences that, once externally validated, could be exploited for individualized scalp sparing treatment planning strategies in brain tumor patients.
PURPOSE: To develop normal tissue complication probability (NTCP) models for radiation-induced alopecia (RIA) in brain tumorpatients treated with proton therapy (PT). METHODS AND MATERIALS: We analyzed 116 brain tumor adult patients undergoing scanning beam PT (median dose 54 GyRBE; range 36-72) for CTCAE v.4 grade 2 (G2) acute (≤90 days), late (>90 days) and permanent (>12 months) RIA. The relative dose-surface histogram (DSH) of the scalp was extracted and used for Lyman-Kutcher-Burman (LKB) modelling. Moreover, DSH metrics (Sx: the surface receiving ≥ X Gy, D2%: near maximum dose, Dmean: mean dose) and non-dosimetric variables were included in a multivariable logistic regression NTCP model. Model performances were evaluated by the cross-validated area under the receiver operator curve (ROC-AUC). RESULTS: Acute, late and permanent G2-RIA was observed in 52%, 35% and 19% of the patients, respectively. The LKB models showed a weak dose-surface effect (0.09 ≤ n ≤ 0.19) with relative steepness 0.29 ≤ m ≤ 0.56, and increasing tolerance dose values when moving from acute and late (22 and 24 GyRBE) to permanent RIA (44 GyRBE). Multivariable modelling selected S21Gy for acute and S25Gy, for late G2-RIA as the most predictive DSH factors. Younger age was selected as risk factor for acute G2-RIA while surgery as risk factor for late G2-RIA. D2% was the only variable selected for permanent G2-RIA. Both LKB and logistic models exhibited high predictive performances (ROC-AUCs range 0.86-0.90). CONCLUSION: We derived NTCP models to predict G2-RIA after PT, providing a comprehensive modelling framework for acute, late and permanent occurrences that, once externally validated, could be exploited for individualized scalp sparing treatment planning strategies in brain tumorpatients.
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