Predictive models for pulmonary function changes after radiotherapy for breast cancer and lymphoma.

PURPOSE: To propose multivariate predictive models for changes in pulmonary function tests (ΔPFTs) with respect to preradiotherapy (pre-RT) values in patients undergoing RT for breast cancer and lymphoma.

METHODS AND MATERIALS: A prospective study was designed to measure ΔPFTs of patients undergoing RT. Sixty-six patients were included. Spirometry, lung capacity (measured by helium dilution), and diffusing capacity of carbon monoxide tests were used to measure lung function. Two lung definitions were considered: paired lung vs. irradiated lung (IL). Correlation analysis of dosimetric parameters (mean lung dose and the percentage of lung volume receiving more than a threshold dose) and ΔPFTs was carried out to find the best dosimetric predictor. Chemotherapy, age, smoking, and the selected dose-volume parameter were considered as single and interaction terms in a multivariate analysis. Stability of results was checked by bootstrapping.

RESULTS: Both lung definitions proved to be similar. Modeling was carried out for IL. Acute and late damage showed the highest correlations with volumes irradiated above ~20 Gy (maximum R(2) = 0.28) and ~40 Gy (maximum R(2) = 0.21), respectively. RT alone induced a minor and transitory restrictive defect (p = 0.013). Doxorubicin-cyclophosphamide-paclitaxel (Taxol), when administered pre-RT, induced a late, large restrictive effect, independent of RT (p = 0.031). Bootstrap values confirmed the results.

CONCLUSIONS: None of the dose-volume parameters was a perfect predictor of outcome. Thus, different predictor models for ΔPFTs were derived for the IL, which incorporated other nondosimetric parameters mainly through interaction terms. Late ΔPFTs seem to behave more serially than early ones. Large restrictive defects were demonstrated in patients pretreated with doxorubicin-cyclophosphamide-paclitaxel.