Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?

Abstract

Purpose: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatingemcitabine, followed by two to three cycles of cisplatinetoposide concurrently with involved-field thoracic radiotherapy (4666 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density changebased algorithm with mass correction. In addition to the conventional dosevolume metrics (V20, V30, V40, and mean lung dose), dosefunction metrics (fV20, fV30, fV40, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dosesubvolume metrics (sV20, sV30, sV40, and subvolume mean lung dose); these were defined as the conventional dosevolume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). Results: AUC values for the dosevolume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.650.69, 0.570.70, and 0.690.76, respectively. The respective ranges for dosefunction metrics were 0.630.66, 0.610.71, and 0.720.80 and for dosesubvolume metrics were 0.500.65, 0.650.75, and 0.730.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dosevolume, dosefunction, dosesubvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dosefunction and dosesubvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dosefunction and/or dosesubvolume metrics with dosevolume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). Conclusions: The authors results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dosesubvolume metrics provided a promising method for preventing radiation-induced pulmonary complications.