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

作者：Avanzo M^*; Trovo M; Furlan C; Barresi L; Linda A; Stancanello J; Andreon L; Minatel E; Bazzocchi M; Trovo M G; Capra E

来源：Physica Medica, 2015, 31(1): 1-8.

DOI：10.1016/j.ejmp.2014.10.006

• Lung tumors
• IMRT
• Lung injury
• NTCP
• Tomotherapy

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 alpha/beta, 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 alpha/beta s 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 mu(d) 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 md.
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.

• 出版日期2015-2