Regional Lung Density Changes After Radiation Therapy for Tumors in and Around Thorax
To study the temporal nature of regional lung density changes and to assess whether the dose-dependent nature of these changes is associated with patient- and treatment-associated factors.
Between 1991 and 2004, 118 patients with interpretable pre- and post-radiation therapy (RT) chest computed tomography (CT) scans were evaluated. Changes in regional lung density were related to regional dose to define a dose-response curve (DRC) for RT-induced lung injury using three-dimensional planning tools and image fusion. Multiple post-RT follow-up CT scans were evaluated by fitting linear-quadratic models of density changes on dose with time as the covariate. Various patient- and treatment-related factors were examined as well.
There was a dose-dependent increase in regional lung density at nearly all post-RT follow-up intervals. The population volume-weighted changes evolved over the initial 6-month period after RT and reached a plateau thereafter (p < 0.001). On univariate analysis, patient age greater than 65 years (p = 0.003) and/or the use of pre-RT surgery (p < 0.001) were associated with significantly greater changes in CT density at both 6 and 12 months after RT, but the magnitude of this effect was modest.
There appears to be a temporal nature for the dose-dependent increases in lung density. Nondosimetric clinical factors tend to have no, or a modest, impact on these changes.
Available from: Michele Avanzo
- "Patient sex has never been found to be predictive for RRLI  . Age was generally not associated with increased density in CT  , with the exception of one study . Smoke status has been found to be associated with increase on CT density  but this result has not been confirmed by other studies  . "
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ABSTRACT: AbstractPurpose 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.
Available from: Mahmood Albahhar
- "If further future studies can validate the clinical significance of RILD severity scored using our methods, the proposed model can also be used for treatment planning optimization as a cost function to regulate lung dose tolerance. We compared our time-dependence results on the severity of RILD with the reported results from other studies (Geara et al 1998, Stroian et al 2008, Ma et al 2010, Rosen et al 2001, Hof et al 2010). The overall damage was most severe in the first (0–3 months) follow-up period and its partial recovery towards the baseline apparently began at the second follow-up period (3–6 months) and continued till the last follow-up period. "
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ABSTRACT: Knowledge of the dose-response of radiation-induced lung disease (RILD) is necessary for optimization of radiotherapy (RT) treatment plans involving thoracic cavity irradiation. This study models the time-dependent relationship between local radiation dose and post-treatment lung tissue damage measured by computed tomography (CT) imaging. Fifty-eight follow-up diagnostic CT scans from 21 non-small-cell lung cancer patients were examined. The extent of RILD was segmented on the follow-up CT images based on the increase of physical density relative to the pre-treatment CT image. The segmented RILD was locally correlated with dose distribution calculated by analytical anisotropic algorithm and the Monte Carlo method to generate the corresponding dose-response curves. The Lyman-Kutcher-Burman (LKB) model was fit to the dose-response curves at six post-RT time periods, and temporal change in the LKB parameters was recorded. In this study, we observed significant correlation between the probability of lung tissue damage and the local dose for 96% of the follow-up studies. Dose-injury correlation at the first three months after RT was significantly different from later follow-up periods in terms of steepness and threshold dose as estimated from the LKB model. Dependence of dose response on superior-inferior tumour position was also observed. The time-dependent analytical modelling of RILD might provide better understanding of the long-term behaviour of the disease and could potentially be applied to improve inverse treatment planning optimization.
Available from: Egbert F Smit
- "Preclinical studies have shown that radiological lung density (RLD) changes correlate strongly with histopathological radiation damage and physical endpoints . Available data examining the relationship between radiation dose and subsequently lung damage in humans were derived from older studies which did not use high-resolution computed tomography (CT)-scans and acceptable techniques for co-registration of images, and which were not restricted to patients with lung cancer   . Distinguishing between the phases of RP and the subsequent formation of fibrosis on CT-scan can be difficult , and both entities, therefore, are combined in the definition of 'radiation-induced lung disease'  "
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ABSTRACT: Radiation pneumonitis is an important cause of morbidity after concurrent thoracic chemoradiotherapy (CCRT). However, asymptomatic changes in lung density on computed tomography (CT)-scans occur more commonly, and correspond to regions of inflammatory changes. Characterization of dose- and time-related changes in radiological lung density (RLD) may facilitate improved radiation planning, and allow for a more objective measure for assessing damage. We studied changes in RLD following CCRT with cisplatin-etoposide, using deformable registration to co-register follow-up scans. All CT-scans performed for up to 24 months post-treatment were evaluated in 25 patients treated with CCRT for stage III non-small-cell lung cancer. A total of 104 scans (median of 3 per patient) were co-registered with planning scans using a deformable registration tool (VelocityAI, Atlanta, USA). Last follow-up scan was at median 9.4 months (range 3.4-22.6 months). Seven patients developed clinical radiation pneumonitis. RLD changes (in Hounsfield units) were measured in regions receiving 3-66Gy. Linear mixed models were used to study dose-density changes over time. No significant changes in RLD were observed in the first 3 months post-treatment. Increases in RLD were observed at 3-6 months (p<0.0001) and 6-12 months (p=0.006), but stabilized at 1 year. Increases were most evident in regions receiving >30Gy, with only minor density changes at lower dose levels. Planning target volume size was significantly associated with RLD changes (p=0.03). Limiting lung doses to ≤30Gy during CCRT may limit sub-clinical damage, and the time-course of RLD changes may allow for early quantification of pulmonary damage when evaluating novel treatment strategies.
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