Details

Autor(en) / Beteiligte

• Avanzo, M
• Trovo, M
• Furlan, C
• Barresi, L
• Linda, A
• Stancanello, J
• Andreon, L
• Minatel, E
• Bazzocchi, M
• Trovo, M.G
• Capra, E

Titel

Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer

Ist Teil von

• Physica medica, 2015-02, Vol.31 (1), p.1-8

Ort / Verlag

Italy: Elsevier Ltd

Erscheinungsjahr

2015

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Abstract Purpose To derive Normal Tissue Complication Probability (NTCP) models for severe patterns of early radiological radiation-induced lung injury (RRLI) in patients treated with radiotherapy (RT) for lung tumors. Second, derive threshold doses and optimal doses for prediction of RRLI to be used in differential diagnosis of tumor recurrence from RRLI during follow-up. Methods and materials Lyman-EUD (LEUD), Logit-EUD (LogEUD), relative seriality (RS) and critical volume (CV) NTCP models, with DVH corrected for fraction size, were used to model the presence of severe early RRLI in follow-up CTs. The models parameters, including α/β, were determined by fitting data from forty-five patients treated with IMRT for lung cancer. Models were assessed using Akaike information criterion (AIC) and area under receiver operating characteristic curve (AUC). Threshold doses for risk of RRLI and doses corresponding to the optimal point of the receiver operating characteristic (ROC) curve were determined. Results The α/βs obtained with different models were 2.7–3.2 Gy. The thresholds and optimal doses curves were EUDs of 3.2–7.8 Gy and 15.2–18.1 Gy with LEUD, LogEUD and RS models, and μd of 0.013 and 0.071 with the CV model. NTCP models had AUCs significantly higher than 0.5. Occurrence and severity of RRLI were correlated with patients’ values of EUD and μd. Conclusions The models and dose levels derived can be used in differential diagnosis of tumor recurrence from RRLI in patients treated with RT. Cross validation is needed to prove prediction performance of the model outside the dataset from which it was derived.