Regional Lung Density Changes After Radiation Therapy for Tumors in and Around Thorax

3D deep convolution neural network for radiation pneumonitis prediction following stereotactic body radiotherapy

In this study, we investigated 3D convolutional neural networks (CNNs) with input from radiographic and dosimetric datasets of primary lung tumors and surrounding lung volumes to predict the likelihood of radiation pneumonitis (RP). Pre-treatment, 3- and 6-month follow-up computed tomography (CT) and 3D dose datasets from one hundred and ninety-three NSCLC patients treated with stereotactic body radiotherapy (SBRT) were retrospectively collected and analyzed for this study. DenseNet-121 and ResNet-50 models were selected for this study as they are deep neural networks and have been proven to have high accuracy for complex image classification tasks. Both were modified with 3D convolution and max pooling layers to accept 3D datasets. We used a minority class oversampling approach and data augmentation to address the challenges of data imbalance and data scarcity. We built two sets of models for classification of three (No RP, Grade 1 RP, Grade 2 RP) and two (No RP, Yes RP) classes as outputs. The 3D DenseNet-121 models performed better (F1 score [0.81], AUC [0.91] [three class]; F1 score [0.77], AUC [0.84] [two class]) than the 3D ResNet-50 models (F1 score [0.54], AUC [0.72] [three-class]; F1 score [0.68], AUC [0.71] [two-class]) (p = 0.017 for three class predictions). We also attempted to identify salient regions within the input 3D image dataset via integrated gradient (IG) techniques to assess the relevance of the tumor surrounding volume for RP stratification. These techniques appeared to indicate the significance of the tumor and surrounding regions in the prediction of RP. Overall, 3D CNNs performed well to predict clinical RP in our cohort based on the provided image sets and radiotherapy dose information.

Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis

Objective

The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics.

Materials and Methods

The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD₂) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models.

Results

Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05).

Conclusion

The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

Carbon Monoxide Diffusing Capacity (DLCO) Correlates with CT Morphology after Chemo-Radio-Immunotherapy for Non-Small Cell Lung Cancer Stage III

Introduction: Curatively intended chemo-radio-immunotherapy for non-small cell lung cancer (NSCLC) stage III may lead to post-therapeutic pulmonary function (PF) impairment. We hypothesized that the decrease in global PF corresponds to the increase in tissue density in follow-up CTs. Hence, the study aim was to correlate the dynamics in radiographic alterations to carbon monoxide diffusing capacity (DLCO) and FEV1, which may contribute to a better understanding of radiation-induced lung disease. Methods: Eighty-five patients with NSCLC III were included. All of them received two cycles of platinum-based induction chemotherapy followed by high dose radiation. Thereafter, durvalumab was administered for one year in 63/85 patients (74%). Pulmonary function tests (PFTs) were performed three months and six months after completion of radiotherapy (RT) and compared to baseline. At the same time points, patients underwent diagnostic CT (dCT). These dCTs were matched to the planning CT (pCT) using RayStation® Model Based Segmentation and deformable image registration. Differential volumes defined by specific isodoses were generated to correlate them with the PFTs. Results: In general, significant correlations between PFTs and differential volumes were found in the mid-dose range, especially for the volume of the lungs receiving between 65% and 45% of the dose prescribed (V65−45%) and DLCO (p<0.01). This volume range predicted DLCO after RT (p-value 0.03) as well. In multivariate analysis, DLCO (p-value 0.040) and FEV1 (p-value 0.014) predicted pneumonitis. Conclusions: The current analysis revealed a strong relation between the dynamics of DLCO and CT morphology changes in the mid-dose range, which convincingly indicates the importance of routinely used PFTs in the context of a curative treatment approach.

Evaluation of the relationship between the range of radiation-induced lung injury on CT images after IMRT for stage I lung cancer and dosimetric parameters

BACKGROUND

This study evaluated the correlation between radiation-induced lung injury (RILI) and dosimetric parameters on computed tomography (CT) images of stage I non-small cell lung cancer (NSCLC) patients undergoing intensity-modulated radiotherapy (IMRT).

MATERIALS AND METHODS

Sixty-three stage I NSLC patients who underwent IMRT were enrolled in the study. The patients underwent CT within 6 months (acute phase) and 1.5 years (late phase) after radiotherapy. These were fused with the planned irradiation CT. The range of RILI was measured from 10% to 100%, with an IC in 10% increments.

RESULTS

The median interval from completion of radiotherapy to acute and late phase CT was 92 and 440 days, respectively. The median RILI ranges of the acute and late phases were in the 80% (20-100%) and 70% dose regions (20-100%), respectively. The significantly narrower range of RILI when lung V20 in the acute phase was less than 19.2% and that of V5 in the late phase was less than 27.6% at the time of treatment planning.

CONCLUSIONS

This study showed that RILI occurred in a localized range in stage I NSCLC patients who underwent IMRT. The range of RILI was correlated with V20 in the acute phase and V5 in the late phase. KEY MESSAGES RILI correlated with V20 in acute and V5 in late phase. The shadow of RILI occurred in 80% dose region in acute and 70% in late phase. No relationship exists between radiographic changes in RILI and PTV volume.

Correlation Between Lung Density Changes Under Different Dose Gradients and Radiation Pneumonitis-Based on an Analysis of Computed Tomography Scans During Esophageal Cancer Radiotherapy

Purpose

To assess the relationship between different doses of radiation and lung density changes and to determine the ability of this correlation to identify esophageal cancer (EC) patients who develop radiation pneumonitis (RP) and the occurrence time of RP.

Methods

A planning computed tomography (CT) scan and a re-planning CT scan were retrospectively collected under institutional review board approval for each of 103 thoracic segment EC patients who underwent radiotherapy (RT). The isodose curve was established on the planning CT with an interval of 5 Gy, which was used as the standard for dividing different gradient doses. Planning CT and re-planning CT scans were matched and the mean lung CT value (HU) between different doses gradients was automatically obtained by the software system. The density change value (ΔHU) was the difference of CT value between each dose gradient before and after treatment. The correlation between ΔHU and the corresponding dose was calculated, as well as the regression coefficients. Additionally the correlation between ΔHU and the occurrence and time of RP (< 4 weeks, 4-12 weeks, > 12 weeks) was calculated.

Results

The radiation dose and ΔHU was positively correlated, but the correlation coefficient and regression coefficient were lower, 0.261 (P <0.001) and 0.127 (P <0.001), respectively. With the increase of radiation dose gradient, ΔHU in RP≥2 group was higher than that in RP<2 group, and there was significant difference between two groups in ΔHU_20-25, ΔHU_25-30, ΔHU_30-35, ΔHU_35-40, ΔHU_40-45, ΔHU_45-50 (p<0.05). The occurrence time of RP was negatively correlated with the degree of ΔHU (P<0.05), with a high correlation coefficient (Y = week actual value -0.521, P < 0.001) (Y = week grade value -0.381, P = 0.004) and regression coefficient (Y = week actual value -0.503, P<0.001) (Y = week rating value -0.401, P=0.002).

Conclusions

A relationship between radiation dose and lung density changes was observed. For most dose intervals, there was an increase of ΔHU with an increased radiation dose, although low correlation coefficient. ΔHU were obvious after irradiation with dose ≥20 Gy which was closely related to the occurrence of RP. For patients with RP, the more obvious ΔHU, the earlier the occurrence of RP, there was a significant negative correlation between them.

Investigation of the evolution of radiation-induced lung damage using serial CT imaging and pulmonary function tests

BACKGROUND AND PURPOSE

Radiation-induced lung damage (RILD) is a common consequence of lung cancer radiotherapy (RT) with unclear evolution over time. We quantify radiological RILD longitudinally and correlate it with dosimetry and respiratory morbidity.

MATERIALS AND METHODS

CTs were available pre-RT and at 3, 6, 12 and 24-months post-RT for forty-five subjects enrolled in a phase 1/2 clinical trial of isotoxic, dose-escalated chemoradiotherapy for locally advanced non-small cell lung cancer. Fifteen CT-based measures of parenchymal, pleural and lung volume change, and anatomical distortions, were calculated. Respiratory morbidity was assessed with the Medical Research Council (MRC) dyspnoea score and spirometric pulmonary function tests (PFTs): FVC, FEV1, FEV1/FVC and DLCO.

RESULTS

FEV1, FEV1/FVC and MRC scores progressively declined post-RT; FVC decreased by 6-months before partially recovering. Radiologically, an early phase (3-6 months) of acute inflammation was characterised by reversible parenchymal change and non-progressive anatomical distortion. A phase of chronic scarring followed (6-24 months) with irreversible parenchymal change, progressive volume loss and anatomical distortion. Post-RT increase in contralateral lung volume was common. Normal lung volume shrinkage correlated longitudinally with mean lung dose (r = 0.30-0.40, p = 0.01-0.04). Radiological findings allowed separation of patients with predominant acute versus chronic RILD; subjects with predominantly chronic RILD had poorer pre-RT lung function.

CONCLUSIONS

CT-based measures enable detailed quantification of the longitudinal evolution of RILD. The majority of patients developed progressive lung damage, even when the early phase was absent or mild. Pre-RT lung function and RT dosimetry may allow to identify subjects at increased risk of RILD.

Changes of lung parenchyma density following high dose radiation therapy for thoracic carcinomas - an automated analysis of follow up CT scans

Background

An objective way to qualify the effect of radiotherapy (RT) on lung tissue is the analysis of CT scans after RT. In this analysis we focused on the changes in Hounsfield units (ΔHU) and the correlation with the corresponding radiation dose after RT.

Methods

Pre- and post-RT CT scans were matched and ΔHU was calculated using customized research software. ΔHU was calculated in 5-Gy-intervals and the correlation between ΔHU and the corresponding dose was calculated as well as the regression coefficients. Additionally the mean ΔHU and ΔHU in 5-Gy-intervals were calculated for each tumor entity.

Results

The mean density changes at 12 weeks and 6 months post RT were 28,16 HU and 32,83 HU. The correlation coefficient between radiation dose and ΔHU at 12 weeks and 6 months were 0,166 (p = 0,000) and 0,158 (p = 0,000). The resulting regression coefficient were 1439 HU/Gy (p = 0,000) and 1612 HU/Gy (p = 0,000). The individual regression coefficients for each patient range from − 2,23 HU/Gy to 7,46 HU/Gy at 12 weeks and − 0,45 HU/Gy to 10,51 HU/Gy at 6 months. When looking at the three tumor entities individually the highest ΔHU at 12 weeks was seen in patients with SCLC (38,13 HU) and at 6 month in those with esophageal carcinomas (40,98 HU).

Conclusion

For most dose intervals there was an increase of ΔHU with an increased radiation dose. This is reflected by a statistically significant, although low correlation coefficient. The regression coefficients of all patients show large interindividual differences.

The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis

PURPOSE

The purpose of this study was to explore gains in predictive model performance for radiation pneumonitis (RP) using pretreatment CT radiomics features extracted from the normal lung volume.

METHODS

A total of 192 patients treated for nonsmall cell lung cancer with definitive radiotherapy were considered in the current study. In addition to clinical and dosimetric data, CT radiomics features were extracted from the total lung volume defined using the treatment planning scan. A total of 6851 features (15 clinical, 298 total lung and heart dosimetric, and 6538 image features) were gathered and considered candidate predictors for modeling of RP grade ≥3. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors with 50 iterations of tenfold nested cross-validation.

RESULTS

In the current cohort, 30 of 192 patients (15.6%) presented with RP grade ≥3. Average cross-validated AUC (CV-AUC) using only the clinical and dosimetric parameters was 0.51. CV-AUC was 0.68 when total lung CT radiomics features were added. Analysis with the entire set of available predictors revealed seven different image features selected in at least 40% of the model fits.

CONCLUSIONS

We have successfully incorporated CT radiomics features into a framework for building predictive RP models via LASSO logistic regression. Addition of normal lung image features produced superior model performance relative to traditional dosimetric and clinical predictors of RP, suggesting that pretreatment CT radiomics features should be considered in the context of RP prediction.

Exploration and application of phenomenological RBE models for proton therapy

The relative biological effectiveness (RBE) of protons varies with multiple physical and biological factors. Phenomenological RBE models have been developed to include such factors in the estimation of a variable RBE, in contrast to the clinically applied constant RBE of 1.1. In this study, eleven published phenomenological RBE models and two plan-based models were explored and applied to simulated patient cases. All models were analysed with respect to the distribution and range of linear energy transfer (LET) and reference radiation fractionation sensitivity ((α/β) _x ) of their respective experimental databases. Proton therapy plans for a spread-out Bragg peak in water and three patient cases (prostate adenocarcinoma, pituitary adenoma and thoracic sarcoma) were optimised using an RBE of 1.1 in the Eclipse^™ treatment planning system prior to recalculation and modelling in the FLUKA Monte Carlo code. Model estimated dose-volume parameters for the planning target volumes (PTVs) and organs at risk (OAR) were compared. The experimental in vitro databases for the various models differed greatly in the range of (α/β) _x values and dose-averaged LET (LET_d). There were significant variations between the model estimations, which arose from fundamental differences in the database definitions and model assumptions. The greatest variations appeared in organs with low (α/β) _x and high LET_d, e.g. biological doses given to late responding OARs located distal to the target in the treatment field. In general, the variation in maximum dose (D_2%) was larger than the variation in mean dose and other dose metrics, with D_2% of the left optic nerve ((α/β) _x   =  2.1 Gy) in the pituitary adenoma case showing the greatest discrepancies between models: 28-52 Gy(RBE), while D_2% for RBE_1.1 was 30 Gy(RBE). For all patient cases, the estimated mean RBE to the PTV was in the range 1.09-1.29 ((α/β) _x   =  1.5/3.1/10.6 Gy). There were considerable variations between the estimations of RBE and RBE-weighted doses from the different models. These variations were a consequence of fundamental differences in experimental databases, model assumptions and regression techniques. The results from the implementation of RBE models in dose planning studies should be evaluated in light of these deviations.

Lung density change after SABR: A comparative study between tri-Co-60 magnetic resonance-guided system and linear accelerator

Radiation-induced lung damage is an important treatment-related toxicity after lung stereotactic ablative radiotherapy (SABR). After implementing a tri-⁶⁰Co magnetic-resonance image guided system, ViewRay^TM, we compared the associated early radiological lung density changes to those associated with a linear accelerator (LINAC). Eight patients treated with the tri-⁶⁰Co system were matched 1:1 with patients treated with LINAC. Prescription doses were 52 Gy or 60 Gy in four fractions, and lung dose-volumetric parameters were calculated from each planning system. The first two follow-up computed tomography (CT) were co-registered with the planning CT through deformable registration software, and lung density was measured by isodose levels. Tumor size was matched between the two groups, but the planning target volume of LINAC was larger than that of the tri-⁶⁰Co system (p = 0.036). With regard to clinically relevant dose-volumetric parameters in the lungs, the ipsilateral lung mean dose, V_10Gy and V_20Gy were significantly poorer in tri-⁶⁰Co plans compared to LINAC plans (p = 0.012, 0.036, and 0.017, respectively). Increased lung density was not observed in the first follow-up scan compared to the planning scan. A significant change of lung density was shown in the second follow-up scan and there was no meaningful difference between the tri-⁶⁰Co system and LINAC for all dose regions. In addition, no patient developed clinical radiation pneumonitis until the second follow-up scan. Therefore, there was no significant difference in the early radiological lung damage between the tri-⁶⁰Co system and LINAC for lung SABR despite of the inferior plan quality of the tri-⁶⁰Co system compared to that of LINAC. Further studies with a longer follow-up period are needed to confirm our findings.

Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors

PURPOSE

To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy.

METHODS AND MATERIALS

The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm^-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (P_test).

RESULTS

The best-fit parameters for prediction of RRLI 6months post RT were D₅₀ of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D₅₀ of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the P_test.

CONCLUSIONS

The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.

Imaging Radiation-Induced Normal Tissue Injury to Quantify Regional Dose Response

Noninvasive imaging has and will continue to play a pivotal role in the assessment of radiation-induced normal tissue toxicity. In this review, we will examine key literature regarding the use of anatomic and physiological imaging in relation to radiation-induced normal tissue toxicity. Additionally, this review contains a novel methodology for potentially incorporating dose-response data into treatment planning and normal tissue toxicity modeling.

Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy

BACKGROUND

Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury.

METHODS

A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed. Nine radiomic features (ie, 7 gray level co-occurrence matrix [GLCM] texture features and 2 first-order features) were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship.

RESULTS

The GLCM features (logistic regression P-value range, 0.012-0.262; AUC range, 0.643-0.750) outperformed the first-order features (P-value range, 0.100-0.990; AUC range, 0.543-0.661) in distinguishing lung injury severity levels. Eight of 9 radiomic features demonstrated a significant dose-response relationship at 3, 6, and 9 months post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and the 9-month DRC.

CONCLUSION

Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.

Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography

PURPOSE

To investigate early tumor and normal tissue responses during the course of radiation therapy (RT) for lung cancer using quantitative analysis of daily computed tomography (CT) scans.

METHODS AND MATERIALS

Daily diagnostic-quality CT scans acquired using CT-on-rails during CT-guided RT for 20 lung cancer patients were quantitatively analyzed. On each daily CT set, the contours of the gross tumor volume (GTV) and lungs were generated and the radiation dose delivered was reconstructed. The changes in CT image intensity (Hounsfield unit [HU]) features in the GTV and the multiple normal lung tissue shells around the GTV were extracted from the daily CT scans. The associations between the changes in the mean HUs, GTV, accumulated dose during RT delivery, and patient survival rate were analyzed.

RESULTS

During the RT course, radiation can induce substantial changes in the HU histogram features on the daily CT scans, with reductions in the GTV mean HUs (dH) observed in the range of 11 to 48 HU (median 30). The dH is statistically related to the accumulated GTV dose (R² > 0.99) and correlates weakly with the change in GTV (R² = 0.3481). Statistically significant increases in patient survival rates (P=.038) were observed for patients with a higher dH in the GTV. In the normal lung, the 4 regions proximal to the GTV showed statistically significant (P<.001) HU reductions from the first to last fraction.

CONCLUSION

Quantitative analysis of the daily CT scans indicated that the mean HUs in lung tumor and surrounding normal tissue were reduced during RT delivery. This reduction was observed in the early phase of the treatment, is patient specific, and correlated with the delivered dose. A larger HU reduction in the GTV correlated significantly with greater patient survival. The changes in daily CT features, such as the mean HU, can be used for early assessment of the radiation response during RT delivery for lung cancer.

Lung deformations and radiation-induced regional lung collapse in patients treated with stereotactic body radiation therapy

PURPOSE

To differentiate radiation-induced fibrosis from regional lung collapse outside of the high dose region in patients treated with stereotactic body radiation therapy (SBRT) for lung tumors.

METHODS

Lung deformation maps were computed from pre-treatment and post-treatment computed tomography (CT) scans using a point-to-point translation method. Fifty anatomical landmarks inside the lung (vessel or airway branches) were matched on planning and follow-up scans for the computation process. Two methods using the deformation maps were developed to differentiate regional lung collapse from fibrosis: vector field and Jacobian methods. A total of 40 planning and follow-ups CT scans were analyzed for 20 lung SBRT patients.

RESULTS

Regional lung collapse was detected in 15 patients (75%) using the vector field method, in ten patients (50%) using the Jacobian method, and in 12 patients (60%) by radiologists. In terms of sensitivity and specificity the Jacobian method performed better. Only weak correlations were observed between the dose to the proximal airways and the occurrence of regional lung collapse.

CONCLUSIONS

The authors presented and evaluated two novel methods using anatomical lung deformations to investigate lung collapse and fibrosis caused by SBRT treatment. Differentiation of these distinct physiological mechanisms beyond what is usually labeled "fibrosis" is necessary for accurate modeling of lung SBRT-induced injuries. With the help of better models, it becomes possible to expand the therapeutic benefits of SBRT to a larger population of lung patients with large or centrally located tumors that were previously considered ineligible.

The use of isodose levels to interpret radiation induced lung injury: a quantitative analysis of computed tomography changes

BACKGROUND

Patients treated with stereotactic body radiation therapy (SBRT) for lung cancer are often found to have radiation-induced lung injury (RILI) surrounding the treated tumor. We investigated whether treatment isodose levels could predict RILI.

METHODS

Thirty-seven lung lesions in 32 patients were treated with SBRT and received post-treatment follow up (FU) computed tomography (CT). Each CT was fused with the original simulation CT and treatment isodose levels were overlaid. The RILI surrounding the treated lesion was contoured. The RILI extension index [fibrosis extension index (FEI)] was defined as the volume of RILI extending outside a given isodose level relative to the total volume of RILI and was expressed as a percentage.

RESULTS

Univariate analysis revealed that the planning target volume (PTV) was positively correlated with RILI volume at FU: correlation coefficient (CC) =0.628 and P<0.0001 at 1(st) FU; CE =0.401 and P=0.021 at 2(nd) FU; CE =0.265 and P=0.306 at 3(rd) FU. FEI -40 Gy at 1(st) FU was significantly positively correlated with FEI -40 Gy at subsequent FU's (CC =0.689 and P=6.5×10(-5) comparing 1(st) and 2(nd) FU; 0.901 and P=0.020 comparing 2(nd) and 3(rd) FU. Ninety-six percent of the RILI was found within the 20 Gy isodose line. Sixty-five percent of patients were found to have a decrease in RILI on the second 2(nd) CT.

CONCLUSIONS

We have shown that RILI evolves over time and 1(st) CT correlates well with subsequent CTs. Ninety-six percent of the RILI can be found to occur within the 20 Gy isodose lines, which may prove beneficial to radiologists attempting to distinguish recurrence vs. RILI.

Quantification of CT-assessed radiation-induced lung damage in lung cancer patients treated with or without chemotherapy and cetuximab

BACKGROUND AND PURPOSE

Prediction models for radiation-induced lung damage (RILD) are still unsatisfactory, with clinical toxicity endpoints that are difficult to quantify objectively. We therefore evaluated RILD more objectively, quantitatively and on a continuous scale measuring the lung tissue density changes per voxel.

MATERIAL AND METHODS

Patients treated with radiotherapy (RT) alone, sequential and concurrent chemo-RT with and without the addition of cetuximab were studied. Follow-up computed tomography (CT) scans were co-registered using deformable registration to baseline CT scans. CT density changes were correlated to the RT dose delivered in every part of the lungs.

RESULTS

One hundred and seventeen lung cancer patients were included. Mean dose to tumor was 60 Gy (range 45-79.2 Gy). Dose response curves showed a linear increase in the dose region between 0 and 65 Gy having a slope (based on coefficients of the multilevel model) expressed as a lung density increase per dose of 0.86 (95% CI 0.73-0.99), 1.31 (95% CI 1.19-1.43), 1.39 (95% CI 1.28-1.50) and 2.07 (95% CI 1.93-2.21) for patients treated only with RT (N=19), sequential chemo-RT (N=30), concurrent chemo-RT (N=49), and concurrent chemo-RT with cetuximab (N=19), respectively.

CONCLUSIONS

CT density changes allow quantitative assessment of lung damage after fractionated RT, giving complementary information to standard used clinical endpoints. Patients receiving cetuximab showed a significantly larger dose response compared with other treatments.

Effect of deformable registration on the dose calculated in radiation therapy planning CT scans of lung cancer patients

PURPOSE

To characterize the effects of deformable image registration of serial computed tomography (CT) scans on the radiation dose calculated from a treatment planning scan.

METHODS

Eighteen patients who received curative doses (≥ 60 Gy, 2 Gy/fraction) of photon radiation therapy for lung cancer treatment were retrospectively identified. For each patient, a diagnostic-quality pretherapy (4-75 days) CT scan and a treatment planning scan with an associated dose map were collected. To establish correspondence between scan pairs, a researcher manually identified anatomically corresponding landmark point pairs between the two scans. Pretherapy scans then were coregistered with planning scans (and associated dose maps) using the demons deformable registration algorithm and two variants of the Fraunhofer MEVIS algorithm ("Fast" and "EMPIRE10"). Landmark points in each pretherapy scan were automatically mapped to the planning scan using the displacement vector field output from each of the three algorithms. The Euclidean distance between manually and automatically mapped landmark points (dE) and the absolute difference in planned dose (|ΔD|) were calculated. Using regression modeling, |ΔD| was modeled as a function of dE, dose (D), dose standard deviation (SD(dose)) in an eight-pixel neighborhood, and the registration algorithm used.

RESULTS

Over 1400 landmark point pairs were identified, with 58-93 (median: 84) points identified per patient. Average |ΔD| across patients was 3.5 Gy (range: 0.9-10.6 Gy). Registration accuracy was highest using the Fraunhofer MEVIS EMPIRE10 algorithm, with an average dE across patients of 5.2 mm (compared with >7 mm for the other two algorithms). Consequently, average |ΔD| was also lowest using the Fraunhofer MEVIS EMPIRE10 algorithm. |ΔD| increased significantly as a function of dE (0.42 Gy/mm), D (0.05 Gy/Gy), SD(dose) (1.4 Gy/Gy), and the algorithm used (≤ 1 Gy).

CONCLUSIONS

An average error of <4 Gy in radiation dose was introduced when points were mapped between CT scan pairs using deformable registration, with the majority of points yielding dose-mapping error <2 Gy (approximately 3% of the total prescribed dose). Registration accuracy was highest using the Fraunhofer MEVIS EMPIRE10 algorithm, resulting in the smallest errors in mapped dose. Dose differences following registration increased significantly with increasing spatial registration errors, dose, and dose gradient (i.e., SDdose). This model provides a measurement of the uncertainty in the radiation dose when points are mapped between serial CT scans through deformable registration.

Computed tomography number changes observed during computed tomography-guided radiation therapy for head and neck cancer

PURPOSE

To investigate CT number (CTN) changes in gross tumor volume (GTV) and organ at risk (OAR) according to daily diagnostic-quality CT acquired during CT-guided intensity modulated radiation therapy for head and neck cancer (HNC) patients.

METHODS AND MATERIALS

Computed tomography scans acquired using a CT-on-rails during daily CT-guided intensity modulated radiation therapy for 15 patients with stage II to IVa squamous cell carcinoma of the head and neck were analyzed. The GTV, parotid glands, spinal cord, and nonspecified tissue were generated on each selected daily CT. The changes in CTN distributions and the mean and mode values were collected. Pearson analysis was used to assess the correlation between the CTN change, organ volume reduction, and delivered radiation dose.

RESULTS

Volume and CTN changes for GTV and parotid glands can be observed during radiation therapy delivery for HNC. The mean (±SD) CTNs in GTV and ipsi- and contralateral parotid glands were reduced by 6 ± 10, 8 ± 7, and 11 ± 10 Hounsfield units, respectively, for all patients studied. The mean CTN changes in both spinal cord and nonspecified tissue were almost invisible (<2 Hounsfield units). For 2 patients studied, the absolute mean CTN changes in GTV and parotid glands were strongly correlated with the dose delivered (P<.001 and P<.05, respectively). For the correlation between CTN reductions and delivered isodose bins for parotid glands, the Pearson coefficient varied from -0.98 (P<.001) in regions with low-dose bins to 0.96 (P<.001) in high-dose bins and were patient specific.

CONCLUSIONS

The CTN can be reduced in tumor and parotid glands during the course of radiation therapy for HNC. There was a fair correlation between CTN reduction and radiation doses for a subset of patients, whereas the correlation between CTN reductions and volume reductions in GTV and parotid glands were weak. More studies are needed to understand the mechanism for the radiation-induced CTN changes.

The use of strain tensor to estimate thoracic tumors deformation

PURPOSE

Respiration-induced kinematics of thoracic tumors suggests a simple analogy with elasticity, where a strain tensor is used to characterize the volume of interests. The application of the biomechanical framework allows for the objective determination of tumor characteristics.

METHODS

Four-dimensional computed tomography provides the snapshots of the patient's anatomy at the end of inspiration and expiration. Image registration was used to obtain the displacement vector fields and deformation fields, which allows one for the determination of the strain tensor. Its departure from the identity matrix gauges the departure of the medium from rigidity. The tensorial characteristic of each GTV voxel was determined and averaged. To this end, the standard Euclidean matrix norm as well as the Log-Euclidean norm were employed. Tensorial anisotropy was gauged with the fractional anisotropy measure which is based on the normalized variance of the tensors eigenvalues. Anisotropy was also evaluated with the geodesic distance in the Log-Euclidean framework of a given strain tensor to its closest isotropic counterpart.

RESULTS

The averaged strain tensor was determined for each of the 15 retrospectively analyzed thoracic GTVs. The amplitude of GTV motion varied from 0.64 to 4.21 with the average of 1.20 cm. The GTV size ranged from 5.16 to 149.99 cc with the average of 43.19 cc. The tensorial analysis shows that deformation is inconsiderable and that the tensorial anisotropy is small. The Log-Euclidean distance of averaged strain tensors from the identity matrix ranged from 0.06 to 0.31 with the average of 0.19. The Frobenius distance from the identity matrix is similar and ranged from 0.06 to 0.35 with the average of 0.21. Their fractional anisotropy ranged from 0.02 to 0.12 with the average of 0.07. Their geodesic anisotropy ranged from 0.03 to 0.16 with the average of 0.09. These values also indicate insignificant deformation.

CONCLUSIONS

The tensorial framework allows for direct measurements of tissue deformation. It goes beyond the evaluation of deformation via comparison of shapes. It is an independent and objective determination of tissue properties. This methodology can be used to determine possible changes in lung properties due to radiation therapy and possible toxicities.

Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development

PURPOSE

To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP).

METHODS AND MATERIALS

A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction.

RESULTS

For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84).

CONCLUSIONS

A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.

Dose impact in radiographic lung injury following lung SBRT: Statistical analysis and geometric interpretation

PURPOSE

To demonstrate a new method of evaluating dose response of treatment-induced lung radiographic injury post-SBRT (stereotactic body radiotherapy) treatment and the discovery of bimodal dose behavior within clinically identified injury volumes.

METHODS

Follow-up CT scans at 3, 6, and 12 months were acquired from 24 patients treated with SBRT for stage-1 primary lung cancers or oligometastic lesions. Injury regions in these scans were propagated to the planning CT coordinates by performing deformable registration of the follow-ups to the planning CTs. A bimodal behavior was repeatedly observed from the probability distribution for dose values within the deformed injury regions. Based on a mixture-Gaussian assumption, an Expectation-Maximization (EM) algorithm was used to obtain characteristic parameters for such distribution. Geometric analysis was performed to interpret such parameters and infer the critical dose level that is potentially inductive of post-SBRT lung injury.

RESULTS

The Gaussian mixture obtained from the EM algorithm closely approximates the empirical dose histogram within the injury volume with good consistency. The average Kullback-Leibler divergence values between the empirical differential dose volume histogram and the EM-obtained Gaussian mixture distribution were calculated to be 0.069, 0.063, and 0.092 for the 3, 6, and 12 month follow-up groups, respectively. The lower Gaussian component was located at approximately 70% prescription dose (35 Gy) for all three follow-up time points. The higher Gaussian component, contributed by the dose received by planning target volume, was located at around 107% of the prescription dose. Geometrical analysis suggests the mean of the lower Gaussian component, located at 35 Gy, as a possible indicator for a critical dose that induces lung injury after SBRT.

CONCLUSIONS

An innovative and improved method for analyzing the correspondence between lung radiographic injury and SBRT treatment dose has been demonstrated. Bimodal behavior was observed in the dose distribution of lung injury after SBRT. Novel statistical and geometrical analysis has shown that the systematically quantified low-dose peak at approximately 35 Gy, or 70% prescription dose, is a good indication of a critical dose for injury. The determined critical dose of 35 Gy resembles the critical dose volume limit of 30 Gy for ipsilateral bronchus in RTOG 0618 and results from previous studies. The authors seek to further extend this improved analysis method to a larger cohort to better understand the interpatient variation in radiographic lung injury dose response post-SBRT.

Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy

This study examines the correlation between the radiologist-defined severity of normal tissue damage following radiation therapy (RT) for lung cancer treatment and a set of mathematical descriptors of computed tomography (CT) scan texture ('texture features'). A pre-therapy CT scan and a post-therapy CT scan were retrospectively collected under IRB approval for each of the 25 patients who underwent definitive RT (median dose: 66 Gy). Sixty regions of interest (ROIs) were automatically identified in the non-cancerous lung tissue of each post-therapy scan. A radiologist compared post-therapy scan ROIs with pre-therapy scans and categorized each as containing no abnormality, mild abnormality, moderate abnormality, or severe abnormality. Twenty texture features that characterize gray-level intensity, region morphology, and gray-level distribution were calculated in post-therapy scan ROIs and compared with anatomically matched ROIs in the pre-therapy scan. Linear regression and receiver operating characteristic (ROC) analysis were used to compare the percent feature value change (ΔFV) between ROIs at each category of visible radiation damage. Most ROIs contained no (65%) or mild abnormality (30%). ROIs with moderate (3%) or severe (2%) abnormalities were observed in 9 patients. For 19 of 20 features, ΔFV was significantly different among severity levels. For 12 features, significant differences were observed at every level. Compared with regions with no abnormalities, ΔFV for these 12 features increased, on average, by 1.5%, 12%, and 30%, respectively, for mild, moderate, and severe abnormalitites. Area under the ROC curve was largest when comparing ΔFV in the highest severity level with the remaining three categories (mean AUC across features: 0.84). In conclusion, 19 features that characterized the severity of radiologic changes from pre-therapy scans were identified. These features may be used in future studies to quantify acute normal lung tissue damage following RT.

Time evolution of regional CT density changes in normal lung after IMRT for NSCLC

PURPOSE

This study investigates the clinical radiobiology of radiation induced lung disease in terms of regional computed tomography (CT) density changes following intensity modulated radiotherapy (IMRT) for non-small-cell lung cancer (NSCLC).

METHODS

A total of 387 follow-up CT scans in 131 NSCLC patients receiving IMRT to a prescribed dose of 60 or 66 Gy in 2 Gy fractions were analyzed. The dose-dependent temporal evolution of the density change was analyzed using a two-component model, a superposition of an early, transient component and a late, persistent component.

RESULTS

The CT density of healthy lung tissue was observed to increase significantly (p<0.0001) for all dose levels after IMRT. The time evolution and the size of the density signal depend on the local delivered dose. The transient component of the density signal was found to peak in the range of 3-4 months, while the density tends to stabilize at times >12 months.

CONCLUSIONS

The radiobiology of lung injury may be analyzed in terms of CT density change. The initial transient change in density is consistent with radiation pneumonitis, while the subsequent stabilization of the density is consistent with pulmonary fibrosis.

Overexpression of TOB1 confers radioprotection to bronchial epithelial cells through the MAPK/ERK pathway

The aim of this study was to investigate the effects and mechanisms of antiproliferative transducer of erbB2, 1 (TOB1) on the radiosensitivity of the normal human bronchial epithelial cell line HBE. After exposure to different doses of irradiation or a certain dose for different time intervals, the expression of TOB1 mRNA and protein in HBE cells was determined by semi-quantitative RT-PCR and western blot analysis. Liposome-induced recombinant plasmid transfection and G418 selection were performed to establish a stably transfected TOB1-overexpressing HBE cell line. A clonogenic assay was used to determine the radiosensitivity of the HBE cells with different TOB1 expression statuses. The cell cycle distribution was detected by flow cytometry. The ionizing radiation (IR)-induced γ-H2AX foci formation was detected by immunofluorescence assay. The related mechanism was explored by western blot analysis. TOB1 expression in the HBE cells was not induced by IR, neither dose-dependently nor time-dependently. Compared to the parental or 'mock' transfected HBE cells, the radiosensitivity of HBE cells overexpressing TOB1 was significantly decreased (P<0.05). Exogenous TOB1 prevented HBE cells from apoptosis after IR, in contrast to the control cells (P<0.05), and significantly decreased the IR-induced γ-H2AX foci formation. After IR, the expression of DNA damage repair proteins such as XRCC1, MRE11, FEN1 and ATM was increased in the TOB1‑overexpressing HBE cells when compared with the expression levels in the control cells. HBE/TOB1 cells presented a much higher phosphorylated ERK1/2 and phosphorylated p53 when compared with the levels in the control cell lines when receiving 6 Gy of X-rays. Notably, the increased expression of phosphorylated p53 in HBE/TOB1 cells after IR was sufficiently blocked by U0126, a specific inhibitor of MEK1/2. Different from its functions in several lung cancer cell lines, TOB1 demonstrated a radioprotective function in the immortalized normal human bronchial epithelial cell line HBE via the MAPK/ERK signaling pathway.

Assessing the impact of radiation-induced changes in soft tissue density ∕ thickness on the study of radiation-induced perfusion changes in the lung and heart

PURPOSE

Abnormalities in single photon emission computed tomography (SPECT) perfusion within the lung and heart are often detected following radiation for tumors in∕around the thorax (e.g., lung cancer or left-sided breast cancer). The presence of SPECT perfusion defects is determined by comparing pre- and post-RT SPECT images. However, RT may increase the density of the soft tissue surrounding the lung∕heart (e.g., chest wall∕breast) that could possibly lead to an "apparent" SPECT perfusion defect due to increased attenuation of emitted photons. Further, increases in tissue effective depth will also increase SPECT photon attenuation and may lead to "apparent" SPECT perfusion defects. The authors herein quantitatively assess the degree of density changes and effective depth in soft tissues following radiation in a series of patients on a prospective clinical study.

METHODS

Patients receiving thoracic RT were enrolled on a prospective clinical study including pre- and post-RT thoracic computed tomography (CT) scans. Using image registration, changes in tissue density and effective depth within the soft tissues were quantified (as absolute change in average CT Hounsfield units, HU, or tissue thickness, cm). Changes in HU and tissue effective depth were considered as a continuous variable. The potential impact of these tissue changes on SPECT images was estimated using simulation data from a female SPECT thorax phantom with varying tissue densities.

RESULTS

Pre- and serial post-RT CT images were quantitatively studied in 23 patients (4 breast cancer, 19 lung cancer). Data were generated from soft tissue regions receiving doses of 20-50 Gy. The average increase in density of the chest was 5 HU (range 46 to -69). The average change in breast density was a decrease of -1 HU (range 13 to -13). There was no apparent dose response in neither the dichotomous nor the continuous analysis. Seventy seven soft tissue contours were created for 19 lung cancer patients. The average change in tissue effective depth was +0.2 cm (range -1.9 to 2.2 cm). The changes in HU represent a <2% average change in tissue density. Based on simulation, the small degree of density and tissue effective depth change is unlikely to yield meaningful changes in either SPECT lung or heart perfusion.

CONCLUSIONS

RT doses of 20-50 Gy can cause up to a 46 HU increase in soft tissue density 6 months post-RT. Post-RT soft tissue effective depth may increase by 2.0 cm. These modest increases in soft tissue density and effective depth are unlikely to be responsible for the perfusion changes seen on post-RT SPECT lung or heart scans. Further, there was no clear dose response of the soft tissue density changes. Ultimately, the authors findings suggest that prior perfusion reports do reflect changes in the physiology of the lungs and heart.

Delivery parameter variations and early clinical outcomes of volumetric modulated arc therapy for 31 prostate cancer patients: an intercomparison of three treatment planning systems

We created volumetric modulated arc therapy (VMAT) plans for 31 prostate cancer patients using one of three treatment planning systems (TPSs)—ERGO++, Monaco, or Pinnacle—and then treated those patients. A dose of 74 Gy was prescribed to the planning target volume (PTV). The rectum, bladder, and femur were chosen as organs at risk (OARs) with specified dose-volume constraints. Dose volume histograms (DVHs), the mean dose rate, the beam-on time, and early treatment outcomes were evaluated and compared. The DVHs calculated for the three TPSs were comparable. The mean dose rates and beam-on times for Ergo++, Monaco, and SmartArc were, respectively, 174.3 ± 17.7, 149.7 ± 8.4, and 185.8 ± 15.6 MU/min and 132.7 ± 8.4, 217.6 ± 13.1, and 127.5 ± 27.1 sec. During a follow-up period of 486.2 ± 289.9 days, local recurrence was not observed, but distant metastasis was observed in a single patient. Adverse events of grade 3 to grade 4 were not observed. The mean dose rate for Monaco was significantly lower than that for ERGO++ and SmartArc (P < 0.0001), and the beam-on time for Monaco was significantly longer than that for ERGO++ and SmartArc (P < 0.0001). Each TPS was successfully used for prostate VMAT planning without significant differences in early clinical outcomes despite significant TPS-specific delivery parameter variations.

Analysis of single nucleotide polymorphisms and radiation sensitivity of the lung assessed with an objective radiologic endpoin

BACKGROUND

The primary objective of this study was to evaluate the association between radiation sensitivity of the lungs and candidate single nucleotide polymorphisms (SNP) in genes implicated in radiation-induced toxicity.

METHODS

Patients with lung cancer who received radiation therapy (RT) had pre-RT and serial post-RT single photon emission computed tomography (SPECT) lung perfusion scans. RT-induced changes in regional perfusion were related to regional dose, which generated patient-specific dose-response curves (DRC). The slope of the DRC is independent of total dose and the irradiated volume, and is taken as a reflection of the patient's inherent sensitivity to RT. DNA was extracted from blood samples obtained at baseline. SNPs were determined by using a combination of high-resolution melting, TaqMan assays, and direct sequencing. Genotypes from 33 SNPs in 22 genes were compared against the slope of the DRC by using the Kruskal-Wallis test for ordered alternatives.

RESULTS

Thirty-nine self-reported Caucasian patients with pre-RT and ≥6 month post-RT SPECTs, and blood samples were identified. An association between genotype and increasing slope of the DRC was noted in G(1301) A in XRCC1 (rs25487) (P = .01) and G(3748) A in BRCA1 (rs16942) (P = .03).

CONCLUSIONS

By using an objective radiologic assessment, polymorphisms within genes involved in repair of DNA damage (XRCC1 and BRCA1) were associated with radiation sensitivity of the lungs.

[18F]-FDG uptake dose-response correlates with radiation pneumonitis in lung cancer patients

PURPOSE

To quantify the post-radiotherapy 2-[(18)F]-fluoro-2-deoxyglucose (FDG) pulmonary uptake dose-response in lung cancer patients and determine its relationship with radiation pneumonitis symptoms.

METHODS AND MATERIALS

The data from 24 patients treated for lung cancer with thoracic radiotherapy who received restaging PET/CT imaging between 4 and 12 weeks after radiotherapy completion were evaluated. Their radiation dose distribution was registered with the post-treatment restaging PET/CT. Using histogram analysis, the voxel average FDG-PET uptake vs. radiation dose was obtained for each case and linear regression was performed. The resulting slope, the pulmonary metabolic radiation response (PMRR), was used to characterize the dose-response. The Common Toxicity Criteria version 3 was used to score clinical pulmonary toxicity symptoms. Receiver operating characteristic (ROC) curves were used to determine the level of FDG uptake vs. dose, MLD, V(5), V(10), V(20), and V(30) that can best predict symptomatic and asymptomatic patients.

RESULTS

The median time between radiotherapy completion and FDG-PET imaging was 59 days (range, 26-70 days). The median of the mean SUV from lung that received 0-5 Gy was 1.00 (range, 0.37-1.48), 5-10 Gy was 1.01 (range, 0.37-1.77), 10-20 Gy was 1.04 (0.42-1.53), and >20 Gy was 1.29 (range, 0.41-8.01). Using the dose range of 0 Gy to the maximum dose minus 10 Gy, hierarchical linear regression model of the radiation dose and normalized FDG uptake per case found an adequate fit with the linear model. Pneumonitis scores were: Grade 0 for 13, Grade 1 for 5, Grade 2 for 6, and Grade 3, 4 or 5 for none. Using a PMRR threshold of 0.017 yields an associated true positive rate of 0.67 and false positive rate of 0.15 with average error of 30%. A V(5) threshold of 57.6 gives an associated true positive rate of 0.67 and false positive rate of 0.05 with a 20% average error.

CONCLUSION

The metabolic radiation pneumonitis dose-response was evaluated from post-treatment FDG-PET/CT imaging. Statistical modeling found a linear relationship. The FDG uptake dose-response and V(5) correlated with symptomatic radiation pneumonitis.

Radiation-related treatment effects across the age spectrum: differences and similarities or what the old and young can learn from each other

Radiation related effects in children and adults limit the delivery of effective radiation doses and result in long-term morbidity affecting function and quality of life. Improvements in our understanding of the etiology and biology of these effects, including the influence of clinical variables, dosimetric factors, and the underlying biological processes have made treatment safer and more efficacious. However, the approach to studying and understanding these effects differs between children and adults. Using the pulmonary and skeletal organ systems as examples, comparisons are made across the age spectrum for radiation related effects, including pneumonitis, pulmonary fibrosis, osteonecrosis, and fracture. Methods for dosimetric analysis, incorporation of imaging and biology as well a length of follow-up are compared, contrasted, and discussed for both organ systems in children and adults. Better understanding of each age specific approach and how it differs may improve our ability to study late effects of radiation across the ages.