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Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiation Therapy
Journal Pre-proof Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy Shannon O’Reilly, PhD, Varsha Jain, MD, PhD, Qijie Huang, PhD, Chingyun Cheng, PhD, Boon-Keng Kevin Teo, PhD, Lingshu Yin, PhD, Miao Zhang, PhD, Eric Diffenderfer, PhD, Taoran Li, PhD, William Levin, MD, Ying Xiao, PhD, Lei Dong, PhD, Steven Feigenberg, MD, Abigail T. Berman, MD, Wei Zou, PhD PII:
S0360-3016(20)30073-0
DOI:
https://doi.org/10.1016/j.ijrobp.2020.01.014
Reference:
ROB 26157
To appear in:
International Journal of Radiation Oncology • Biology • Physics
Received Date: 16 April 2019 Revised Date:
8 December 2019
Accepted Date: 10 January 2020
Please cite this article as: O’Reilly S, Jain V, Huang Q, Cheng C, Kevin Teo B-K, Yin L, Zhang M, Diffenderfer E, Li T, Levin W, Xiao Y, Dong L, Feigenberg S, Berman AT, Zou W, Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy, International Journal of Radiation Oncology • Biology • Physics (2020), doi: https://doi.org/10.1016/j.ijrobp.2020.01.014. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Inc. All rights reserved.
Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy Authors: Shannon O’Reilly, PhD1, Varsha Jain, MD, PhD1, Qijie Huang, PhD2, Chingyun Cheng, PhD1, Boon-Keng Kevin Teo, PhD1, Lingshu Yin, PhD1, Miao Zhang, PhD2, Eric Diffenderfer, PhD1, Taoran Li, PhD1, William Levin, MD1, Ying Xiao, PhD1, Lei Dong, PhD1, Steven Feigenberg, MD1, Abigail T. Berman, MD1, Wei Zou, PhD1 1
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
2
Running title: High Ventilation Dose Predicts Pneumonitis
Corresponding Author: Shannon O’Reilly, PhD Email: Shannon.O'[email protected] Phone: 904-699-2340 Department of Radiation Oncology University of Pennsylvania 3400 Civic Center Blvd Philadelphia, PA 19104 Statistical Analysis: Chingyun Cheng, PhD Conflict of interest: none Financial Disclosure: Dr. Li reports personal fees from Varian Medical Systems, outside the submitted work. Dr. Dong reports personal fees from Varian Medical Systems, outside the submitted work.
Acknowledgement: This study was presented at the 2018 AAPM annual meeting and was selected as the best in physics for the Joint Imaging-Therapy track. A portion of this study was presented at ASTRO 2018. Keywords: Ventilation, Radiation Pneumonitis, Non-Small Cell Lung Cancer
Summary: We studied the relationship between radiation pneumonitis outcome from photon and proton radiotherapy to lung function for patients with locally advanced non-small cell lung cancer. The lung ventilation obtained from 4DCT was mapped with the radiation dose. We found that the dosimetric indexes to the high ventilation zones of the lung has better indication to the radiation pneumonitis compared with that to the low ventilation zones and the traditional whole lung dosimetric indexes.
Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy
Running title: High Ventilation Dose Predicts Pneumonitis
ABSTRACT Purpose: We hypothesize that the radiation dose in high-ventilation portions of the lung better predicts radiation pneumonitis (RP) outcome for patients treated with proton (PR) and photon (PH) radiotherapy. Methods and Materials: 74 patients (38 protons, 36 photons) with locally advanced non-small cell lung (NSCLC) cancer treated with concurrent chemoradiation (CRT) were identified, of whom 24 exhibited RP (graded using CTCAE v4.0) after proton or photon radiotherapy, and 50 were negative controls. The inhale and exhale simulation CT scans were deformed using Advanced Normalization Tools (ANTs). The 3D lung ventilation maps were derived from the deformation matrix and partitioned into low- and highventilation zones for dosimetric analysis. Receiver operating curve analysis was employed to study the power of relationship between RP and ventilation zones to determine an optimal ventilation cutoff. Univariate logistic regression was used to correlate dose in high/low ventilation zones with risk of RP. A non-parametric random forest process was employed for multivariate importance assessment. Results: The optimal high-ventilation zone definition was determined to be the higher 45-60% of the ventilation values. The parameter vV20Gy_high (high ventilation volume receiving ≥ 20Gy) was found to be a significant indicator for RP (PH: p=0.002, PR: p=0.035) with improved AUCs compared with the traditional V20Gy for both photon and proton cohorts. The relationship of RP with dose to the lowventilation zone of the lung was insignificant (PH:p=0.123, PR:p=0.661). Similar trends were observed for ventilation mean lung dose and ventilation V5Gy. Multivariate importance assessment determined vV20Gy_high, vV5_high and MLD were the most significant parameters for the proton cohort with a combined AUC=0.78. Conclusion: Dose to the high-ventilated regions of the lung can improve predictions of RP for both photon and proton radiotherapy.
Introduction Non-small cell lung (NSCLC) cancer is the leading cause of cancer related mortality in the United States. Approximately two thirds of these patients present with locally advanced (LA) disease at time of diagnosis (1,2). These patients are typically treated with definitive chemoradiation and approximately 15-20% of these patients develop significant morbidity associated with radiation pneumonitis (RP) (3). The ability to deliver high radiation doses to the tumor volume is often limited by the normal tissue toxicity due to the radiosensitivity of the lungs. Hence, RP is one of the most challenging and dose limiting toxicities when treating lung tumors to definitive radiation doses (4). The currently accepted normal tissue constraints assume the entire lung works equally well, irrespective of function. However, a number of studies have shown that there is substantial functional heterogeneity within the lung which could be further exacerbated with the presence of disease. This has led to application of functional imaging modalities to identify highly functional regions of the lung and utilization of radiation dose to these regions as a predictor of RP outcome (5,6,7,8,9,10,11). Several studies have recently been carried out aiming to reduce dose to highly functional portions of the lung (12,13,14). Lung ventilation imaging is most commonly performed using single-photon emission computed tomography (SPECT) through the use of radioactive substances (15), which provides regional information but is an invasive modality. Due to the availability and cost, these methods are not readily available in most radiation oncology departments. Another emerging technique for ventilation imaging utilizes phase resolved four-dimensional computed tomography (4DCT) images to calculate spatial maps of pulmonary ventilation (16,17). This is particularly attractive as 4DCT scans have become routinely used for thoracic imaging in radiation oncology as a measure of lung tumor motion. Therefore, the 4DCT derived ventilation images would not result in an increase in radiation dose to the patient or extra financial cost. The ventilation information is generated utilizing the density changes or use of the
Jacobian values from the inhale to exhale CT images (18,19,20). The validity of this technique and comparison with the clinical gold standard has been evaluated in several recent studies (21,22,23,24,25,26). Vinogradskiy et al. validated 4DCT ventilation derived metrics of lung function with nuclear medicine ventilation (21). They found a correlation of 0.68 between the two methods and noted that 4DCT ventilation metrics were able to predict ventilation defects. In this work, we derived the lung ventilation distributions from 4DCT using the Jacobian method and examined the dosimetric parameters in separate lung function regions based on these calculated ventilation values. This study included patients from both photon and proton-based radiotherapy and correlated the calculated regional dosimetric metrics with the RP outcome. We hypothesize that the dosimetric metrics in high ventilation zones are better predictors of RP compared to the traditional clinically used metrics for both modalities.
Methods and Materials Patient selection This study was approved by the Institutional Review Board of the XXXX. We retrospectively identified 194 patients (103 photon and 91 proton patients) treated for LA-NSCLC between 2011 and 2016 with chemoradiation. The proton patients were all treated to a dose of 66.6 Gy in 1.8 Gy fractions. The photon patient prescriptions varied from 64.8 Gy to 72 Gy in 1.8 Gy fractions. RP was scored using the Common Toxicity Criteria (CTCAE version 4.0) at 3 and 6 months post radiation therapy (27). The endpoint used in this study was RP grade ≥ 2 which represents clinical symptoms leading to limitation of instrumental activities of daily living and requiring medical intervention such as steroids. Our exclusion criteria included patients with prior thoracic treatments, those with poor quality 4DCT scans, patients treated with combination therapy (photon and proton in same course of treatment) and/or patients that received adaptive re-planning during treatment. After these exclusion criteria were applied, 12
photon and 12 proton patients who developed grade 2 or higher RP were identified; 50 patients (24 photon and 26 proton) were randomly selected for the non-RP group. In total, 74 patients were selected for this study. The demographic information of the cohort was listed in Table 1 together with their tumor staging.
4DCT acquisition and ventilation map generation The Siemens Sensation Open CT scanner (Siemens Medical Solutions USA, Inc. Malvern, PA) or the Philips Gemini Big Bore PET/CT scanner (Philips Healthcare, Andover, MA) were used to obtain the CT images of these patients, which included a 4DCT image set of eight phases and a free-breathing CT. The 4DCT scans were acquired using a Varian Real-time Position Management (RPM) system (Varian Medical Systems, Palo Alto, CA). Motion mitigation was not employed during this study period. The RPM signal during the breathing cycles was reviewed to ensure regularity so that reliable 4DCT images are collected in repeated phases with the lung breathing motion. The 3D ventilation matrix was generated utilizing deformable image registration of the inhale images to the exhale images. The inhale and exhale phases of the 4DCT DICOM images were exported to the clinically validated deformable image registration software Advanced Normalization Tools (ANTs) (26,28,29). The deformation process was performed as a two-step, multi-resolution process. First an affine registration was performed. This was followed by a diffeomorphic deformable transformation. The 3D deformation on the voxel base was recorded and the Jacobian determinant was calculated based on Equation 1. A Jacobian determinant of 1 represents no volume change during the breathing motion. The voxelized ventilation was defined as Jacobian determinant minus 1 (Equation 2) with the assumption that the ventilation is indicated by the power of volume changes in the voxels.
(,,)
1 + (,,) (, , ) = (,,)
(,,)
1 +
(,,)
(,,)
(,,)
(,,) 1 + (,,)
= (, , ) − 1
[Eqn. 1]
[Eqn. 2]
ROC analysis Based on the voxelized ventilation values, the lung was separated into high and low ventilation zones using a threshold. Each lung voxel was also registered to a dose value which was extracted from the 3D dose map of the patients’ treatment plan. The dose-volume parameters in the ventilation zones were obtained in similar fashion to the clinical dosimetric metrics V5Gy, V20Gy and mean lung dose (MLD). The cumulative lung volume that received higher than 20Gy in the high ventilation region was obtained and normalized to the total healthy lung volume to derive the vV20Gy_high. vV5Gy_high for the volume fraction that received higher than 5Gy and vMLD_high for mean lung dose (MLD) in the high ventilation region were also derived in this manner. This process was repeated to obtain analogous dosimetric parameters in the low ventilation region (vV20Gy_low, vV5Gy_low and vMLD_low). In order to determine the optimal threshold to define the high ventilation zone based on the voxel ventilation values, receiver operating curve (ROC) analysis was employed to study the power of relationship between the RP and the vV20Gy_high under such threshold. The absolute ventilation values of the voxels were sorted in a descending order (high to low). The highest N% of the total voxels were deemed as the high ventilation region. The receiver operating characteristic (ROC) curves were obtained by varying N% between 0%-100% by which the defined vV20Gy_high was used to classify the pneumonitis and non-pneumonitis cases. When N%=100% (i.e. the entire normal lung volume is defined as high ventilation), vV20Gy_high is equivalent to the traditional lung dose-volume parameter V20Gy.
The area under the curve (AUC) values were calculated and compared to using traditional V20Gy in order to determine the optimal ventilation threshold that yields the highest AUC value. The robustness of the identified optimal threshold was tested using the same process with 5, 10, 30, 40, 50Gy and MLD dose-volume metrics in the high ventilation zones.
Correlation of the dosimetric metrics in ventilation zones with the incidence of RP For each dosimetric parameter (V5Gy, V25Gy and MLD) in the entire lung and high/low ventilation zones, univariate logistic regression was used to study its relationship with RP. This analysis was conducted for patients treated with protons and photons separately. The statistical package SPSS 21.0 (International Business Machines, Armonk, NY) was used and a p-value <0.05 was considered statistically significant. For each parameter, ROC analysis was performed to derive its AUC value with 95% confidence intervals (CIs) using non-parametric bootstrap methods (30) with 1000 random samplings. Spearman’s correlation coefficients for the high ventilation dosimetric parameters were computed for both photon and proton cohort. For the importance assessment and combined capacity of these functional and dosimetric parameters in predicting RP for both photon and proton patients, a non-parametric classification random forest (31) process was applied to the photon and proton cohort separately. The process generated an ensemble of standard classification and regression trees (CART) with all the dosimetric parameters V5Gy, V20Gy and MLD in the entire lung and high/low ventilation zones. Given the limited size of the photon and proton cohort, 100 random forest ensembles of 200 trees each were generated. The importance of each parameter was ranked based on their frequency of importance ranking in the 100 random forest ensembles. This analysis was implemented in Matlab R2019a (Natick, MA).
Results Voxelized ventilation values in patients
The voxel-based lung ventilation distribution for each patient was obtained from the Jacobian values in Equation 1 and 2. Dose was co-registered with the ventilation value for the same voxel. In the example shown in Figure 1, for a patient who received 66.6 Gy of proton treatment to his right lung, the axial lung dose distribution (1a) and the ventilation map (1b) were plotted side by side. Figure 1c presents the dose volume histograms for the entire lung as well as high and low ventilation regions of the lung. Determining optimal ventilation threshold Representative plots for ROC curves using three different ventilation thresholds (6%, 45%, 60%) for the definition of high ventilation zone are demonstrated in Figure 2a and compared with the ROC curve of the traditional V20Gy for the entire lung (N%=100%). While the AUCs from the vV20_high with 45% and 60% threshold are similar, they both showed better predictive power than V20Gy from entire lung or with a 6% threshold. The AUC values were calculated using 6%-100% threshold and plotted in Figure 2b. It is observed that the optimal threshold falls between 45% and 60% where AUC>0.78 while the AUC with V20Gy was 0.75. We adopted the threshold value of 60% for the definition of high ventilation zone and studied its robustness. The robustness of the threshold for the ventilation zone definition was analyzed with multiple dosevolume parameters (V5Gy, V10Gy, V20Gy, V30Gy, V40Gy, V50Gy and MLD). Compared with the AUCs for these nominal dosimetric parameters for the entire lung which were 0.65, 0.72, 0.75, 0.77, 0.77, 0.72, 0.77, the AUCs derived from using dosimetric parameters for high ventilation lung with a 60% threshold were 0.72, 0.75, 0.79, 0.80, 0.82, 0.75, 0.79. The 60% threshold for the high ventilation zone definition is therefore considered robust as it demonstrated consistent superior predictive power compared with using the nominal dose-volume metrics.
Correlation of the dosimetric metrics in ventilation zones with the incidence of RP
Box plots representing the RP and non-RP group dosimetric parameters (V20Gy, V5Gy and MLD) for the entire lung as well as the high and low ventilation regions are shown in Figure 3. The photon (left) and proton (right) patients were presented as separate groups. The magnitude of differences in all three dosimetric parameters was larger in high ventilation zones as compared to low ventilation zones or entire lung. These findings remained consistent between proton and photon patients. The proton patients received a smaller amount of low lung dose than the photon patients. The significance of the differences in the dosimetric parameters for the entire lung, high and low ventilations were studied using univariate logistic regression (Table 2). The traditional dosimetric parameter V20Gy for pneumonitis and non-pneumonitis for both treatment modalities are: photon RP 31.5±4.2% vs. non-RP 25.60±6.7% with p=0.012, and proton RP 30.7±6.3% and non-RP 27.7±5.5% with p=0.112. V20Gy was not found to be statistically significant for prediction of radiation pneumonitis for the proton patients, while the vV20Gy_high was significant (p=0.035). A greater separation between the RP and non-RP cohort was observed with vV20Gy_high where p=0.002 for photon patients. But such differentiation was not observed in vV20Gy_low (p=0.123 and p=0.661 for photon and proton cohort), as also shown in Figure 3. Furthermore, the AUC for V20Gy was less than the AUC for vV20Gy_high: 0.76 (CI: 0.59-0.90) vs. 0.89 (CI: 0.76-0.98) for photon and 0.69 (CI: 0.50-0.87) vs. 0.73 (CI: 0.54-0.89) for proton. The observations were similar for V5Gy and MLD as listed in Table 2. Out of the same patient cohort, the differences in the dosimetric parameters to the low-ventilation zones were not significant for both photon and proton patients, while the differences to the high-ventilation zones were significant. The dosimetric parameters from the high ventilation zones yielded better AUC values compared with the traditional V20Gy, V5Gy and MLD. The findings indicate that the dose to the high ventilation zone could be a better predictor to pneumonitis outcome.
Spearman’s correlation coefficients were computed to assess the correlation between vMLD_high, vV20Gy_high and vV5Gy_high. For photon cohort, vMLD_high and vV20Gy_high are strongly correlated (r = 0.961, p <0.001). vMLD_high and vV5Gy_high, vV20Gy_high and vV5Gy_high are also highly correlated (with r = 0.864, p <0.001 and r = 0.837, p <0.001 respectively). For the proton arm, all three dose-volume metrics are strongly correlated (r=0.953 p<0.001 for vMLD_high and vV20Gy_high, r=0.917 p<0.001 for vMLD_high and vV5Gy_high, r=0.948 p<0.001 for vV20Gy_high and vV5Gy_high). From the random forest analysis, the three most important parameters were determined to be vV20Gy_high, vMLD_high and V5Gy for the photon cohort, and vV20Gy_high, vV5_high and MLD for the proton cohort. The low ventilation parameters vV20Gy_low, vV5_low and vMLD_low were ranked lowest importance for both photon and proton cohort. When combining the three most important parameters for each cohort, the AUCs generated by random forest were 0.91 for photon and 0.78 for proton.
Discussion The univariate logistic regression and ROC analysis (Table 2) revealed a general improvement in p-value and AUCs when using the dosimetric parameters in the high ventilation portion of the lung compared to using the traditional V5, V20 and MLD. For photon patients, although clinical V5, V20 and MLD had pvalue <0.05, the high ventilation vV5_high, vV20_high and vMLD_high all showed an improved significance level of p<0.01. Our results show that the dose to the highly ventilated lung zone could improve the prediction to pneumonitis outcome compared to the traditional V5Gy, V20Gy and MLD, while there are insignificant inferences from the dose in the low ventilation zone between pneumonitis and non-pneumonitis patients. This conclusion agrees with multiple photon-based publications [8,10,14,21]. Faught et al. found better AUCs of 0.71 and 0.67 when predicting grade 2 and 3 radiation pneumonitis (respectively) with a fitted NTCP model and functional fV20Gy, as compared to the AUCs of 0.55 and 0.52 derived using the V20Gy parameter (10). The observed increase in the AUCs were
comparable with our results (0.89 vs. 0.76 in Table 2) when using vV20Gy_high in the high ventilation zone for photon patients. For proton patients, the MLD was statistically significant (p<0.05) while the V5Gy and V20Gy were not. This is also consistent with the parameter importance assessment that MLD is among the top three important parameters for the indication of RP for proton patients. While using the dosimetric parameters from the high ventilation portion of the lung, all three parameters vV5_high, vV20_high and vMLD_high were statistically significant. On the contrary, the dosimetric indices derived from the low ventilation portion of lung were statistically insignificant for both the photon and proton cohort indicating their remote relationship with RP. The traditional constraint of V20Gy was significant for the photon cohort (p=0.012) but was not found to be statistically significant for the proton cohort (p=0.112). The clinically used parameters are derived from photon data and may not be as relevant to proton patients (32). Further work is needed to evaluate if additional dosimetric metrics more accurately predict for radiation pneumonitis in proton patients. From the univariate AUC study, the AUC values for individual high ventilation dosimetric parameters were greater compared with their counterparts in the entire lung. All the high ventilation dosimetric parameters yielded AUCs >0.81 for photon patients and >0.73 for proton patients, although marginal improvements were observed for proton cohort when considering the 95% CI. The AUCs from the low ventilation dosimetric parameters were generally much lower. These findings are consistent with the results from univariate regression analysis. In our study, the combination of three most important parameters (vV20Gy high, vMLD_high and V5Gy) for the photon cohort generated an AUC value of 0.91, which was comparable with the largest AUCs using just vV20Gy_high or vMLD_high. For the proton cohort, the combination of the three parameters vV20Gy_high, vV5_high and MLD yielded an improved AUC of 0.78 against the best AUC of 0.75 derived from a single parameter (vV5_high). As indicated by Vinogradskiy et al (8), the combination of the
dosimetric parameters with functional parameters yielded higher AUCs for photon patients. Our results indicated that this still holds true for the proton cohort. We also observed the effect of V5Gy in high ventilation zones of proton cohort has certain indication to pneumonitis outcome (p=0.019). Our random forest results indicated vV5Gy_high in the proton cohort was among the most important parameters for RP. In photon radiotherapy, V5Gy was found to be correlated with the V20Gy and MLD (33) and clinically it was given less priority in planning (3). For proton treatments the low dose is distributed to smaller proportions of lung volume compared with photon treatments (Figure 3) and its spatial distribution is different; it could be worthwhile to further study the correlation of RP to the low dose bath. In our study, the follow-up period for the diagnosis of RP is limited to 6 months within which the onset of RP typically occurs. It is notable that a small percentage of radiation pneumonitis cases occurs more than 6 months after treatment (34). Further study on the relationship of the functional dosimetric indices to the time to RP could be of interest. The sample size is another limitation in our study. The RP sample size was limited to 12 patients each for the photon and proton cohorts. In our analysis, initial functional dosimetric parameters of the RP and non-RP group (using 12 patients per group per cohort) were obtained. Based on their high ventilation dosimetric values, an acceptable significance level of alpha=0.05 and power of 80% would require a nonRP control group size of 24 (35), which was employed in our study. In the analysis of random forest importance ranking and the AUC calculations, bootstrapping methods were implemented to reduce the effect of the small sample size. Further studies should be conducted including more patients in each cohort.
Conclusion There is accruing data demonstrating that radiation dose to the functional high ventilated lung might serve as an improved predictor of radiation pneumonitis compared to the traditional dosimetric
parameters such as V5Gy, V20Gy and MLD. Our study indicates that quantifying dosimetric parameters in high ventilation zones with a threshold yields better indication to the incidence of the radiation pneumonitis, not only for photon patients but also for a proton cohort. Reducing the dose to highly ventilated regions, rather than to the entire lung volume as a whole could potentially reduce radiation pneumonitis outcome. This gives rise to the idea that more targeted planning strategies could be implemented which specifically aim to reduce the dose to the highly ventilated portions of the lung and result in more patient specific photon and proton radiotherapy. Using this concept of functional heterogeneity in lung ventilation zones would enable better design of treatment plans to avoid high doses to the high ventilation regions and result in better patient toxicity outcomes.
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34. Tucker SL, Liu HH, Liao ZX, et al. Analysis of radiation pneumonitis risk using a generalized lyman model. Int J Radiat Oncol Biol Phys 2008;72(2):568-574. [doi: 10.1016/j.ijrobp.2008.04.053] 35. Hulley SB, Cummings SR, Browner WS, et al. Designing Clinical Research. 4th ed. Philadelphia, PA: Wolters Kluwer/Lippincott Williams & Wilkins; 2013.
Figure Legends Figure 1. Axial plots representing a) ventilation map, and b) dose distribution for a patient treated with protons to a dose of 66.6 Gy. c) Dose volume histograms for the total lung, as well as high and low ventilation regions of the lung. Figure 2. a) The receiver operating characteristic (ROC) curves for vV20Gy_high at various high ventilation thresholds (6%, 45%, 60% and 100%). Notice here the vV20Gy_high with 100% threshold equals to V20Gy. b) The AUC values changes with threshold. Figure 3. The box-plots of the differences in dosimetric parameters V20Gy, V5Gy and MLD for the entire lung, high-ventilation lung and low-ventilation lung. In each subplot, the left portion represents photon and right represents proton plans. Here the high and low ventilation were defined using 60% ventilation value threshold. The median is represented by the central line and the edges of the box represent the 25th and 75th percentile.
Table 1. Patient characteristics with Fisher’s exact test p-values. Pneumonitis Modality
Stage
Tumor Location
Proton
12
26
Photon
12
24
IIIA
14
36
IIIB
10
14
RUL
7
17
RML
5
4
RLL
6
7
LUL
3
19
LLL
2
1
Hilum
1
2
14
31
cisplatin/etoposide
5
16
Other
5
3
Male
11
23
Female
13
27
Mean
71
68
Std
8
9
Never
4
3
Former
20
47
carboplatin/taxol Chemo type
Gender
Age (yrs)
Smoking Status
Non-Pneumonitis
p-value
0.240
0.084
0.147
0.989
0.051
0.207
Table 2. The mean and standard deviation of the dosimetric parameters for entire lung, high ventilation zone, low ventilation zone of the pneumonitis and non-pneumonitis patients for both modalities. p-values were calculated using univariate logistic regression. Pneumonitis
Non-
p-value
AUC (95% CI)
Pneumonitis V20Gy
vV20Gy_high
vV20Gy_low
MLD
vMLD_high
vMLD_low
V5Gy
vV5Gy_high
vV5Gy_low
Photon 31.5±4.2 %
25.6±6.7 %
0.012
0.76 (0.59-0.90)
Proton
30.7±6.3 %
27.5±5.5 %
0.112
0.69 (0.50-0.87)
Photon 20.6±2.7 %
14.5±5.6 %
0.002
0.89 (0.76-0.98)
Proton
18.4±3.7 %
15.0±4.6 %
0.035
0.73 (0.54-0.89)
Photon 13.8±2.2 %
11.7±4.4 %
0.123
0.74 (0.62-0.93)
Proton
11.6±2.9 %
0.661
0.57 (0.38-0.78)
Photon 18.2±2.2 Gy
15.3±3.8 Gy
0.020
0.73 (0.53-0.88)
Proton
17.1±3.7 Gy
14.9±2.9 Gy
0.033
0.73 (0.48-0.88)
Photon 20.0±1.6 Gy
14.6±5.0 Gy
0.002
0.90 (0.74-0.98)
Proton
17.3±3.8 Gy
13.5±4.3 Gy
0.013
0.74 (0.53-0.89)
Photon 20.5±3.0 Gy
17.7±6.7 Gy
0.172
0.67 (0.47-0.84)
Proton
15.6±4.1 Gy
0.274
0.61 (0.37-0.81)
Photon 54.8±6.9 %
45.3±12.7 %
0.023
0.76 (0.59-0.90)
Proton
38.2±7.4 %
33.6±6.8 %
0.067
0.69 (0.46-0.86)
Photon 33.9±4.6 %
25.7±9.4 %
0.007
0.81 (0.65-0.93)
Proton
22.9±4.0 %
18.7±5.1 %
0.019
0.75 (0.57-0.89)
Photon 22.7±2.5 %
19.2±7.0 %
0.101
0.68 (0.50-0.85)
Proton
14.2±3.2 %
0.559
0.56 (0.34-0.79)
12.1±3.3 %
17.3±4.9 Gy
14.9±3.9 %
S0360-3016(20)30073-0
DOI:
https://doi.org/10.1016/j.ijrobp.2020.01.014
Reference:
ROB 26157
To appear in:
International Journal of Radiation Oncology • Biology • Physics
Received Date: 16 April 2019 Revised Date:
8 December 2019
Accepted Date: 10 January 2020
Please cite this article as: O’Reilly S, Jain V, Huang Q, Cheng C, Kevin Teo B-K, Yin L, Zhang M, Diffenderfer E, Li T, Levin W, Xiao Y, Dong L, Feigenberg S, Berman AT, Zou W, Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy, International Journal of Radiation Oncology • Biology • Physics (2020), doi: https://doi.org/10.1016/j.ijrobp.2020.01.014. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Inc. All rights reserved.
Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy Authors: Shannon O’Reilly, PhD1, Varsha Jain, MD, PhD1, Qijie Huang, PhD2, Chingyun Cheng, PhD1, Boon-Keng Kevin Teo, PhD1, Lingshu Yin, PhD1, Miao Zhang, PhD2, Eric Diffenderfer, PhD1, Taoran Li, PhD1, William Levin, MD1, Ying Xiao, PhD1, Lei Dong, PhD1, Steven Feigenberg, MD1, Abigail T. Berman, MD1, Wei Zou, PhD1 1
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
2
Running title: High Ventilation Dose Predicts Pneumonitis
Corresponding Author: Shannon O’Reilly, PhD Email: Shannon.O'[email protected] Phone: 904-699-2340 Department of Radiation Oncology University of Pennsylvania 3400 Civic Center Blvd Philadelphia, PA 19104 Statistical Analysis: Chingyun Cheng, PhD Conflict of interest: none Financial Disclosure: Dr. Li reports personal fees from Varian Medical Systems, outside the submitted work. Dr. Dong reports personal fees from Varian Medical Systems, outside the submitted work.
Acknowledgement: This study was presented at the 2018 AAPM annual meeting and was selected as the best in physics for the Joint Imaging-Therapy track. A portion of this study was presented at ASTRO 2018. Keywords: Ventilation, Radiation Pneumonitis, Non-Small Cell Lung Cancer
Summary: We studied the relationship between radiation pneumonitis outcome from photon and proton radiotherapy to lung function for patients with locally advanced non-small cell lung cancer. The lung ventilation obtained from 4DCT was mapped with the radiation dose. We found that the dosimetric indexes to the high ventilation zones of the lung has better indication to the radiation pneumonitis compared with that to the low ventilation zones and the traditional whole lung dosimetric indexes.
Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiotherapy
Running title: High Ventilation Dose Predicts Pneumonitis
ABSTRACT Purpose: We hypothesize that the radiation dose in high-ventilation portions of the lung better predicts radiation pneumonitis (RP) outcome for patients treated with proton (PR) and photon (PH) radiotherapy. Methods and Materials: 74 patients (38 protons, 36 photons) with locally advanced non-small cell lung (NSCLC) cancer treated with concurrent chemoradiation (CRT) were identified, of whom 24 exhibited RP (graded using CTCAE v4.0) after proton or photon radiotherapy, and 50 were negative controls. The inhale and exhale simulation CT scans were deformed using Advanced Normalization Tools (ANTs). The 3D lung ventilation maps were derived from the deformation matrix and partitioned into low- and highventilation zones for dosimetric analysis. Receiver operating curve analysis was employed to study the power of relationship between RP and ventilation zones to determine an optimal ventilation cutoff. Univariate logistic regression was used to correlate dose in high/low ventilation zones with risk of RP. A non-parametric random forest process was employed for multivariate importance assessment. Results: The optimal high-ventilation zone definition was determined to be the higher 45-60% of the ventilation values. The parameter vV20Gy_high (high ventilation volume receiving ≥ 20Gy) was found to be a significant indicator for RP (PH: p=0.002, PR: p=0.035) with improved AUCs compared with the traditional V20Gy for both photon and proton cohorts. The relationship of RP with dose to the lowventilation zone of the lung was insignificant (PH:p=0.123, PR:p=0.661). Similar trends were observed for ventilation mean lung dose and ventilation V5Gy. Multivariate importance assessment determined vV20Gy_high, vV5_high and MLD were the most significant parameters for the proton cohort with a combined AUC=0.78. Conclusion: Dose to the high-ventilated regions of the lung can improve predictions of RP for both photon and proton radiotherapy.
Introduction Non-small cell lung (NSCLC) cancer is the leading cause of cancer related mortality in the United States. Approximately two thirds of these patients present with locally advanced (LA) disease at time of diagnosis (1,2). These patients are typically treated with definitive chemoradiation and approximately 15-20% of these patients develop significant morbidity associated with radiation pneumonitis (RP) (3). The ability to deliver high radiation doses to the tumor volume is often limited by the normal tissue toxicity due to the radiosensitivity of the lungs. Hence, RP is one of the most challenging and dose limiting toxicities when treating lung tumors to definitive radiation doses (4). The currently accepted normal tissue constraints assume the entire lung works equally well, irrespective of function. However, a number of studies have shown that there is substantial functional heterogeneity within the lung which could be further exacerbated with the presence of disease. This has led to application of functional imaging modalities to identify highly functional regions of the lung and utilization of radiation dose to these regions as a predictor of RP outcome (5,6,7,8,9,10,11). Several studies have recently been carried out aiming to reduce dose to highly functional portions of the lung (12,13,14). Lung ventilation imaging is most commonly performed using single-photon emission computed tomography (SPECT) through the use of radioactive substances (15), which provides regional information but is an invasive modality. Due to the availability and cost, these methods are not readily available in most radiation oncology departments. Another emerging technique for ventilation imaging utilizes phase resolved four-dimensional computed tomography (4DCT) images to calculate spatial maps of pulmonary ventilation (16,17). This is particularly attractive as 4DCT scans have become routinely used for thoracic imaging in radiation oncology as a measure of lung tumor motion. Therefore, the 4DCT derived ventilation images would not result in an increase in radiation dose to the patient or extra financial cost. The ventilation information is generated utilizing the density changes or use of the
Jacobian values from the inhale to exhale CT images (18,19,20). The validity of this technique and comparison with the clinical gold standard has been evaluated in several recent studies (21,22,23,24,25,26). Vinogradskiy et al. validated 4DCT ventilation derived metrics of lung function with nuclear medicine ventilation (21). They found a correlation of 0.68 between the two methods and noted that 4DCT ventilation metrics were able to predict ventilation defects. In this work, we derived the lung ventilation distributions from 4DCT using the Jacobian method and examined the dosimetric parameters in separate lung function regions based on these calculated ventilation values. This study included patients from both photon and proton-based radiotherapy and correlated the calculated regional dosimetric metrics with the RP outcome. We hypothesize that the dosimetric metrics in high ventilation zones are better predictors of RP compared to the traditional clinically used metrics for both modalities.
Methods and Materials Patient selection This study was approved by the Institutional Review Board of the XXXX. We retrospectively identified 194 patients (103 photon and 91 proton patients) treated for LA-NSCLC between 2011 and 2016 with chemoradiation. The proton patients were all treated to a dose of 66.6 Gy in 1.8 Gy fractions. The photon patient prescriptions varied from 64.8 Gy to 72 Gy in 1.8 Gy fractions. RP was scored using the Common Toxicity Criteria (CTCAE version 4.0) at 3 and 6 months post radiation therapy (27). The endpoint used in this study was RP grade ≥ 2 which represents clinical symptoms leading to limitation of instrumental activities of daily living and requiring medical intervention such as steroids. Our exclusion criteria included patients with prior thoracic treatments, those with poor quality 4DCT scans, patients treated with combination therapy (photon and proton in same course of treatment) and/or patients that received adaptive re-planning during treatment. After these exclusion criteria were applied, 12
photon and 12 proton patients who developed grade 2 or higher RP were identified; 50 patients (24 photon and 26 proton) were randomly selected for the non-RP group. In total, 74 patients were selected for this study. The demographic information of the cohort was listed in Table 1 together with their tumor staging.
4DCT acquisition and ventilation map generation The Siemens Sensation Open CT scanner (Siemens Medical Solutions USA, Inc. Malvern, PA) or the Philips Gemini Big Bore PET/CT scanner (Philips Healthcare, Andover, MA) were used to obtain the CT images of these patients, which included a 4DCT image set of eight phases and a free-breathing CT. The 4DCT scans were acquired using a Varian Real-time Position Management (RPM) system (Varian Medical Systems, Palo Alto, CA). Motion mitigation was not employed during this study period. The RPM signal during the breathing cycles was reviewed to ensure regularity so that reliable 4DCT images are collected in repeated phases with the lung breathing motion. The 3D ventilation matrix was generated utilizing deformable image registration of the inhale images to the exhale images. The inhale and exhale phases of the 4DCT DICOM images were exported to the clinically validated deformable image registration software Advanced Normalization Tools (ANTs) (26,28,29). The deformation process was performed as a two-step, multi-resolution process. First an affine registration was performed. This was followed by a diffeomorphic deformable transformation. The 3D deformation on the voxel base was recorded and the Jacobian determinant was calculated based on Equation 1. A Jacobian determinant of 1 represents no volume change during the breathing motion. The voxelized ventilation was defined as Jacobian determinant minus 1 (Equation 2) with the assumption that the ventilation is indicated by the power of volume changes in the voxels.
(,,)
1 + (,,) (, , ) = (,,)
(,,)
1 +
(,,)
(,,)
(,,)
(,,) 1 + (,,)
= (, , ) − 1
[Eqn. 1]
[Eqn. 2]
ROC analysis Based on the voxelized ventilation values, the lung was separated into high and low ventilation zones using a threshold. Each lung voxel was also registered to a dose value which was extracted from the 3D dose map of the patients’ treatment plan. The dose-volume parameters in the ventilation zones were obtained in similar fashion to the clinical dosimetric metrics V5Gy, V20Gy and mean lung dose (MLD). The cumulative lung volume that received higher than 20Gy in the high ventilation region was obtained and normalized to the total healthy lung volume to derive the vV20Gy_high. vV5Gy_high for the volume fraction that received higher than 5Gy and vMLD_high for mean lung dose (MLD) in the high ventilation region were also derived in this manner. This process was repeated to obtain analogous dosimetric parameters in the low ventilation region (vV20Gy_low, vV5Gy_low and vMLD_low). In order to determine the optimal threshold to define the high ventilation zone based on the voxel ventilation values, receiver operating curve (ROC) analysis was employed to study the power of relationship between the RP and the vV20Gy_high under such threshold. The absolute ventilation values of the voxels were sorted in a descending order (high to low). The highest N% of the total voxels were deemed as the high ventilation region. The receiver operating characteristic (ROC) curves were obtained by varying N% between 0%-100% by which the defined vV20Gy_high was used to classify the pneumonitis and non-pneumonitis cases. When N%=100% (i.e. the entire normal lung volume is defined as high ventilation), vV20Gy_high is equivalent to the traditional lung dose-volume parameter V20Gy.
The area under the curve (AUC) values were calculated and compared to using traditional V20Gy in order to determine the optimal ventilation threshold that yields the highest AUC value. The robustness of the identified optimal threshold was tested using the same process with 5, 10, 30, 40, 50Gy and MLD dose-volume metrics in the high ventilation zones.
Correlation of the dosimetric metrics in ventilation zones with the incidence of RP For each dosimetric parameter (V5Gy, V25Gy and MLD) in the entire lung and high/low ventilation zones, univariate logistic regression was used to study its relationship with RP. This analysis was conducted for patients treated with protons and photons separately. The statistical package SPSS 21.0 (International Business Machines, Armonk, NY) was used and a p-value <0.05 was considered statistically significant. For each parameter, ROC analysis was performed to derive its AUC value with 95% confidence intervals (CIs) using non-parametric bootstrap methods (30) with 1000 random samplings. Spearman’s correlation coefficients for the high ventilation dosimetric parameters were computed for both photon and proton cohort. For the importance assessment and combined capacity of these functional and dosimetric parameters in predicting RP for both photon and proton patients, a non-parametric classification random forest (31) process was applied to the photon and proton cohort separately. The process generated an ensemble of standard classification and regression trees (CART) with all the dosimetric parameters V5Gy, V20Gy and MLD in the entire lung and high/low ventilation zones. Given the limited size of the photon and proton cohort, 100 random forest ensembles of 200 trees each were generated. The importance of each parameter was ranked based on their frequency of importance ranking in the 100 random forest ensembles. This analysis was implemented in Matlab R2019a (Natick, MA).
Results Voxelized ventilation values in patients
The voxel-based lung ventilation distribution for each patient was obtained from the Jacobian values in Equation 1 and 2. Dose was co-registered with the ventilation value for the same voxel. In the example shown in Figure 1, for a patient who received 66.6 Gy of proton treatment to his right lung, the axial lung dose distribution (1a) and the ventilation map (1b) were plotted side by side. Figure 1c presents the dose volume histograms for the entire lung as well as high and low ventilation regions of the lung. Determining optimal ventilation threshold Representative plots for ROC curves using three different ventilation thresholds (6%, 45%, 60%) for the definition of high ventilation zone are demonstrated in Figure 2a and compared with the ROC curve of the traditional V20Gy for the entire lung (N%=100%). While the AUCs from the vV20_high with 45% and 60% threshold are similar, they both showed better predictive power than V20Gy from entire lung or with a 6% threshold. The AUC values were calculated using 6%-100% threshold and plotted in Figure 2b. It is observed that the optimal threshold falls between 45% and 60% where AUC>0.78 while the AUC with V20Gy was 0.75. We adopted the threshold value of 60% for the definition of high ventilation zone and studied its robustness. The robustness of the threshold for the ventilation zone definition was analyzed with multiple dosevolume parameters (V5Gy, V10Gy, V20Gy, V30Gy, V40Gy, V50Gy and MLD). Compared with the AUCs for these nominal dosimetric parameters for the entire lung which were 0.65, 0.72, 0.75, 0.77, 0.77, 0.72, 0.77, the AUCs derived from using dosimetric parameters for high ventilation lung with a 60% threshold were 0.72, 0.75, 0.79, 0.80, 0.82, 0.75, 0.79. The 60% threshold for the high ventilation zone definition is therefore considered robust as it demonstrated consistent superior predictive power compared with using the nominal dose-volume metrics.
Correlation of the dosimetric metrics in ventilation zones with the incidence of RP
Box plots representing the RP and non-RP group dosimetric parameters (V20Gy, V5Gy and MLD) for the entire lung as well as the high and low ventilation regions are shown in Figure 3. The photon (left) and proton (right) patients were presented as separate groups. The magnitude of differences in all three dosimetric parameters was larger in high ventilation zones as compared to low ventilation zones or entire lung. These findings remained consistent between proton and photon patients. The proton patients received a smaller amount of low lung dose than the photon patients. The significance of the differences in the dosimetric parameters for the entire lung, high and low ventilations were studied using univariate logistic regression (Table 2). The traditional dosimetric parameter V20Gy for pneumonitis and non-pneumonitis for both treatment modalities are: photon RP 31.5±4.2% vs. non-RP 25.60±6.7% with p=0.012, and proton RP 30.7±6.3% and non-RP 27.7±5.5% with p=0.112. V20Gy was not found to be statistically significant for prediction of radiation pneumonitis for the proton patients, while the vV20Gy_high was significant (p=0.035). A greater separation between the RP and non-RP cohort was observed with vV20Gy_high where p=0.002 for photon patients. But such differentiation was not observed in vV20Gy_low (p=0.123 and p=0.661 for photon and proton cohort), as also shown in Figure 3. Furthermore, the AUC for V20Gy was less than the AUC for vV20Gy_high: 0.76 (CI: 0.59-0.90) vs. 0.89 (CI: 0.76-0.98) for photon and 0.69 (CI: 0.50-0.87) vs. 0.73 (CI: 0.54-0.89) for proton. The observations were similar for V5Gy and MLD as listed in Table 2. Out of the same patient cohort, the differences in the dosimetric parameters to the low-ventilation zones were not significant for both photon and proton patients, while the differences to the high-ventilation zones were significant. The dosimetric parameters from the high ventilation zones yielded better AUC values compared with the traditional V20Gy, V5Gy and MLD. The findings indicate that the dose to the high ventilation zone could be a better predictor to pneumonitis outcome.
Spearman’s correlation coefficients were computed to assess the correlation between vMLD_high, vV20Gy_high and vV5Gy_high. For photon cohort, vMLD_high and vV20Gy_high are strongly correlated (r = 0.961, p <0.001). vMLD_high and vV5Gy_high, vV20Gy_high and vV5Gy_high are also highly correlated (with r = 0.864, p <0.001 and r = 0.837, p <0.001 respectively). For the proton arm, all three dose-volume metrics are strongly correlated (r=0.953 p<0.001 for vMLD_high and vV20Gy_high, r=0.917 p<0.001 for vMLD_high and vV5Gy_high, r=0.948 p<0.001 for vV20Gy_high and vV5Gy_high). From the random forest analysis, the three most important parameters were determined to be vV20Gy_high, vMLD_high and V5Gy for the photon cohort, and vV20Gy_high, vV5_high and MLD for the proton cohort. The low ventilation parameters vV20Gy_low, vV5_low and vMLD_low were ranked lowest importance for both photon and proton cohort. When combining the three most important parameters for each cohort, the AUCs generated by random forest were 0.91 for photon and 0.78 for proton.
Discussion The univariate logistic regression and ROC analysis (Table 2) revealed a general improvement in p-value and AUCs when using the dosimetric parameters in the high ventilation portion of the lung compared to using the traditional V5, V20 and MLD. For photon patients, although clinical V5, V20 and MLD had pvalue <0.05, the high ventilation vV5_high, vV20_high and vMLD_high all showed an improved significance level of p<0.01. Our results show that the dose to the highly ventilated lung zone could improve the prediction to pneumonitis outcome compared to the traditional V5Gy, V20Gy and MLD, while there are insignificant inferences from the dose in the low ventilation zone between pneumonitis and non-pneumonitis patients. This conclusion agrees with multiple photon-based publications [8,10,14,21]. Faught et al. found better AUCs of 0.71 and 0.67 when predicting grade 2 and 3 radiation pneumonitis (respectively) with a fitted NTCP model and functional fV20Gy, as compared to the AUCs of 0.55 and 0.52 derived using the V20Gy parameter (10). The observed increase in the AUCs were
comparable with our results (0.89 vs. 0.76 in Table 2) when using vV20Gy_high in the high ventilation zone for photon patients. For proton patients, the MLD was statistically significant (p<0.05) while the V5Gy and V20Gy were not. This is also consistent with the parameter importance assessment that MLD is among the top three important parameters for the indication of RP for proton patients. While using the dosimetric parameters from the high ventilation portion of the lung, all three parameters vV5_high, vV20_high and vMLD_high were statistically significant. On the contrary, the dosimetric indices derived from the low ventilation portion of lung were statistically insignificant for both the photon and proton cohort indicating their remote relationship with RP. The traditional constraint of V20Gy was significant for the photon cohort (p=0.012) but was not found to be statistically significant for the proton cohort (p=0.112). The clinically used parameters are derived from photon data and may not be as relevant to proton patients (32). Further work is needed to evaluate if additional dosimetric metrics more accurately predict for radiation pneumonitis in proton patients. From the univariate AUC study, the AUC values for individual high ventilation dosimetric parameters were greater compared with their counterparts in the entire lung. All the high ventilation dosimetric parameters yielded AUCs >0.81 for photon patients and >0.73 for proton patients, although marginal improvements were observed for proton cohort when considering the 95% CI. The AUCs from the low ventilation dosimetric parameters were generally much lower. These findings are consistent with the results from univariate regression analysis. In our study, the combination of three most important parameters (vV20Gy high, vMLD_high and V5Gy) for the photon cohort generated an AUC value of 0.91, which was comparable with the largest AUCs using just vV20Gy_high or vMLD_high. For the proton cohort, the combination of the three parameters vV20Gy_high, vV5_high and MLD yielded an improved AUC of 0.78 against the best AUC of 0.75 derived from a single parameter (vV5_high). As indicated by Vinogradskiy et al (8), the combination of the
dosimetric parameters with functional parameters yielded higher AUCs for photon patients. Our results indicated that this still holds true for the proton cohort. We also observed the effect of V5Gy in high ventilation zones of proton cohort has certain indication to pneumonitis outcome (p=0.019). Our random forest results indicated vV5Gy_high in the proton cohort was among the most important parameters for RP. In photon radiotherapy, V5Gy was found to be correlated with the V20Gy and MLD (33) and clinically it was given less priority in planning (3). For proton treatments the low dose is distributed to smaller proportions of lung volume compared with photon treatments (Figure 3) and its spatial distribution is different; it could be worthwhile to further study the correlation of RP to the low dose bath. In our study, the follow-up period for the diagnosis of RP is limited to 6 months within which the onset of RP typically occurs. It is notable that a small percentage of radiation pneumonitis cases occurs more than 6 months after treatment (34). Further study on the relationship of the functional dosimetric indices to the time to RP could be of interest. The sample size is another limitation in our study. The RP sample size was limited to 12 patients each for the photon and proton cohorts. In our analysis, initial functional dosimetric parameters of the RP and non-RP group (using 12 patients per group per cohort) were obtained. Based on their high ventilation dosimetric values, an acceptable significance level of alpha=0.05 and power of 80% would require a nonRP control group size of 24 (35), which was employed in our study. In the analysis of random forest importance ranking and the AUC calculations, bootstrapping methods were implemented to reduce the effect of the small sample size. Further studies should be conducted including more patients in each cohort.
Conclusion There is accruing data demonstrating that radiation dose to the functional high ventilated lung might serve as an improved predictor of radiation pneumonitis compared to the traditional dosimetric
parameters such as V5Gy, V20Gy and MLD. Our study indicates that quantifying dosimetric parameters in high ventilation zones with a threshold yields better indication to the incidence of the radiation pneumonitis, not only for photon patients but also for a proton cohort. Reducing the dose to highly ventilated regions, rather than to the entire lung volume as a whole could potentially reduce radiation pneumonitis outcome. This gives rise to the idea that more targeted planning strategies could be implemented which specifically aim to reduce the dose to the highly ventilated portions of the lung and result in more patient specific photon and proton radiotherapy. Using this concept of functional heterogeneity in lung ventilation zones would enable better design of treatment plans to avoid high doses to the high ventilation regions and result in better patient toxicity outcomes.
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Figure Legends Figure 1. Axial plots representing a) ventilation map, and b) dose distribution for a patient treated with protons to a dose of 66.6 Gy. c) Dose volume histograms for the total lung, as well as high and low ventilation regions of the lung. Figure 2. a) The receiver operating characteristic (ROC) curves for vV20Gy_high at various high ventilation thresholds (6%, 45%, 60% and 100%). Notice here the vV20Gy_high with 100% threshold equals to V20Gy. b) The AUC values changes with threshold. Figure 3. The box-plots of the differences in dosimetric parameters V20Gy, V5Gy and MLD for the entire lung, high-ventilation lung and low-ventilation lung. In each subplot, the left portion represents photon and right represents proton plans. Here the high and low ventilation were defined using 60% ventilation value threshold. The median is represented by the central line and the edges of the box represent the 25th and 75th percentile.
Table 1. Patient characteristics with Fisher’s exact test p-values. Pneumonitis Modality
Stage
Tumor Location
Proton
12
26
Photon
12
24
IIIA
14
36
IIIB
10
14
RUL
7
17
RML
5
4
RLL
6
7
LUL
3
19
LLL
2
1
Hilum
1
2
14
31
cisplatin/etoposide
5
16
Other
5
3
Male
11
23
Female
13
27
Mean
71
68
Std
8
9
Never
4
3
Former
20
47
carboplatin/taxol Chemo type
Gender
Age (yrs)
Smoking Status
Non-Pneumonitis
p-value
0.240
0.084
0.147
0.989
0.051
0.207
Table 2. The mean and standard deviation of the dosimetric parameters for entire lung, high ventilation zone, low ventilation zone of the pneumonitis and non-pneumonitis patients for both modalities. p-values were calculated using univariate logistic regression. Pneumonitis
Non-
p-value
AUC (95% CI)
Pneumonitis V20Gy
vV20Gy_high
vV20Gy_low
MLD
vMLD_high
vMLD_low
V5Gy
vV5Gy_high
vV5Gy_low
Photon 31.5±4.2 %
25.6±6.7 %
0.012
0.76 (0.59-0.90)
Proton
30.7±6.3 %
27.5±5.5 %
0.112
0.69 (0.50-0.87)
Photon 20.6±2.7 %
14.5±5.6 %
0.002
0.89 (0.76-0.98)
Proton
18.4±3.7 %
15.0±4.6 %
0.035
0.73 (0.54-0.89)
Photon 13.8±2.2 %
11.7±4.4 %
0.123
0.74 (0.62-0.93)
Proton
11.6±2.9 %
0.661
0.57 (0.38-0.78)
Photon 18.2±2.2 Gy
15.3±3.8 Gy
0.020
0.73 (0.53-0.88)
Proton
17.1±3.7 Gy
14.9±2.9 Gy
0.033
0.73 (0.48-0.88)
Photon 20.0±1.6 Gy
14.6±5.0 Gy
0.002
0.90 (0.74-0.98)
Proton
17.3±3.8 Gy
13.5±4.3 Gy
0.013
0.74 (0.53-0.89)
Photon 20.5±3.0 Gy
17.7±6.7 Gy
0.172
0.67 (0.47-0.84)
Proton
15.6±4.1 Gy
0.274
0.61 (0.37-0.81)
Photon 54.8±6.9 %
45.3±12.7 %
0.023
0.76 (0.59-0.90)
Proton
38.2±7.4 %
33.6±6.8 %
0.067
0.69 (0.46-0.86)
Photon 33.9±4.6 %
25.7±9.4 %
0.007
0.81 (0.65-0.93)
Proton
22.9±4.0 %
18.7±5.1 %
0.019
0.75 (0.57-0.89)
Photon 22.7±2.5 %
19.2±7.0 %
0.101
0.68 (0.50-0.85)
Proton
14.2±3.2 %
0.559
0.56 (0.34-0.79)
12.1±3.3 %
17.3±4.9 Gy
14.9±3.9 %