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. 2020 Dec 28;15(12):e0244143. doi: 10.1371/journal.pone.0244143

Prediction of radiation pneumonitis using dose-volume histogram parameters with high attenuation in two types of cancer: A retrospective study

Yasuki Uchida 1, Takuya Tsugawa 2, Sachiko Tanaka-Mizuno 3,4, Kazuo Noma 5, Ken Aoki 2, Kentaro Fukunaga 1, Hiroaki Nakagawa 1, Daisuke Kinose 1, Masafumi Yamaguchi 1, Makoto Osawa 1,6, Taishi Nagao 1, Emiko Ogawa 1,7, Yasutaka Nakano 1,*
Editor: Dandan Zheng8
PMCID: PMC7769248  PMID: 33370345

Abstract

The constraint values of dose-volume histogram (DVH) parameters for radiation pneumonitis (RP) prediction have not been uniform in previous studies. We compared the differences between conventional DVH parameters and DVH parameters with high attenuation volume (HAV) in CT imaging in both esophageal cancer and lung cancer patients to determine the most suitable DVH parameters in predicting RP onset. Seventy-seven and 72 patients who underwent radiation therapy for lung cancer and esophageal cancer, respectively, were retrospectively assessed. RP was valued according to the Common Terminology Criteria for Adverse Events. We quantified HAV with quantitative computed tomography analysis. We compared conventional DVH parameters and DVH parameters with HAV in both groups of patients. Then, the thresholds of DVH parameters that predicted symptomatic RP and the differences in threshold of DVH parameters between lung cancer and esophageal cancer patient groups were compared. The predictive performance of DVH parameters for symptomatic RP was compared using the area under the receiver operating characteristic curve. Mean lung dose, HAV30% (the proportion of the lung with HAV receiving ≥30 Gy), and HAV20% were the top three parameters in lung cancer, while HAV10%, HAV5%, and V10 (the percentage of lung volume receiving 10 Gy or more) were the top three in esophageal cancer. By comparing the differences in the threshold for parameters predicting RP between the two cancers, we saw that HAV30% retained the same value in both cancers. DVH parameters with HAV showed narrow differences in the threshold between the two cancer patient groups compared to conventional DVH parameters. DVH parameters with HAV may have higher commonality than conventional DVH parameters in both patient groups tested.

Introduction

Radiation pneumonitis (RP) is a serious adverse effect of radiation therapy; symptomatic pneumonitis particularly negatively influences chemotherapy after radiotherapy. Therefore, the prediction and prevention of symptomatic RP are very significant. Even among lung cancer patients alone, various threshold values of DVH parameters have been reported for the prediction of RP [1–3]. Thus, guidelines for thoracic radiotherapy have set constraint values for traditional DVH parameters such as the mean lung dose (MLD) (20−23 Gy) and the percentage of lung volume receiving 20 Gy or more (V20) (30−40%) to reduce the risk of RP [4–8]. We recently showed that DVH parameters calculated using only volumes of high attenuation in CT imaging by excluding emphysematous lesions were better predictors of RP than traditional parameters [9]. However, little has been reported on the threshold values of DVH parameters for the prediction of RP in different types of cancers. Threshold values that are closer or the same in different types of cancers would be more reliable and versatile. We compared the performances of our new DVH parameters with traditional parameters in predicting RP in both esophageal cancer patients and lung cancer patients to evaluate whether our new DVH parameters could predict RP more accurately, without varying threshold values among different types of cancer.

Methods and materials

Study design

This study was a single-center, retrospective, observational study. The endpoint of this analysis was to evaluate the performance of DVH parameters for the prediction of symptomatic RP of grade 2 or worse (Common Terminology Criteria for Adverse Events, version 5.0).

Selection of study participants

Patients who received radiotherapy for lung cancer (n = 77) or esophageal cancer (n = 72) at our institution between June 2010 and July 2017 were selected retrospectively. The inclusion criteria were as follows: first time receiving radiotherapy, total irradiation dose >30 Gy (fraction dose is 1.8–3.0 Gy), pneumonectomy not performed within 5 months after radiotherapy or before the occurrence of symptomatic RP, follow-up period >5 months if symptomatic RP did not occur, and entire lung fields scanned using computed tomography (CT) before radiotherapy. We excluded patients who underwent stereotactic body radiotherapy. Patients were treated with either curative or palliative intent with radiotherapy alone or with concurrent chemoradiation.

Radiotherapy planning and image analysis

Radiotherapy planning was performed as 3D treatment planning using the EclipseTM software (Varian Medical Systems, Palo Alto, CA, USA) with an analytical anisotropic algorithm, and the calculation grid was 2.5 mm for the lung. The treatment planning was based on a 1.25-mm thick CT scans obtained in the treatment position. The resolution of CT scans was 1.25 mm×1.25 mm (field of view = 64 cm, matrix = 512×512 pixels). The distribution of the radiation dose was calculated using lung heterogeneity corrections. The breathing phase of the CT scan was free breathing. Gating and breath-hold were not used. We evaluated DVH parameters without emphysematous lesions, as previously described [5, 9]. Low attenuation volume (LAV), which expresses emphysematous lesions in the lung, was assessed using the upper threshold limit of −856 HU. To further verify the validity of this constraint value, we previously validated the association between the CT under free-breathing and the inspiratory CT performed within 45 days after free-breathing CT [10]. Inspiratory CT was performed using Toshiba Aquillion ONE (Toshiba Medical Systems Corp., Otawara, Tochigi, Japan), and LAV was analyzed using Aquarius iNtuitionTM software ver.4.4.12 (TeraRecon Inc., San Mateo, Calif) and evaluated using the threshold limit of -950 HU. The LAV in inspiratory CT was highly correlated with the LAV in CT under free breathing. Regarding relative electron density, −856HU was 0.1488. Since “total lung volume (TLV)–LAV” is equal to high attenuation volume (HAV) ≥−856 HU in the lung, we described “TLV–LAV” as HAV in this report (Fig 1). We also defined HAV irradiated at ≥30 Gy as HAV30 and “HAV30 / TLV” as HAV30%. We evaluated the DVH parameters that predicted RP more accurately in our previous study (See S1 Table). The mean high attenuation lung dose (MHALD) was defined as the mean dose irradiated on a high attenuation lung field.

Fig 1. Area inside the purple line shows the high attenuation lung using a threshold of –856 HU.

Fig 1

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The colorful area represents the irradiated area (red indicates the area with the highest dose, and blue represents the area with the lowest dose), and the overlaps were calculated.

Clinical toxicity

The severity of RP was assessed retrospectively using the Common Terminology Criteria for Adverse Events, version 5.0 [11]. Patients were generally followed up for 3 to 6 weeks after the completion of radiotherapy, and at 3- to 6-month intervals thereafter. The diagnosis of RP and the evaluation of its severity was performed based on radiographic images, laboratory test results, physical examination findings, and clinical symptoms, by reviewing medical records.

The study protocol was approved by the Institutional Review Board of the Shiga University of Medical Science (IRB no. R2014-236).

Statistical analyses

We used summary statistics to analyze clinical factors such as age, sex, disease stage, histologic type, type of radiotherapy methods, chemotherapy, smoking history, smoking index, body mass index, and interstitial lung disease (ILD) for all patients. Continuous variables are presented as medians and ranges, and categorical variables as percentages. We also compared clinical factors between RP ≥grade 2 and RP ≤grade 1 using Wilcoxon’s rank-sum test or Fisher’s exact test, as appropriate.

Conventional DVH parameters, including MLD, V2, V5, V10, V20, and V30, and other DVH parameters of high lung attenuation, are described as medians and interquartile ranges.

Univariate logistic regression analysis was performed to evaluate the association between each DVH parameter and the onset of symptomatic RP. The predictive performance of each DVH parameter for RP was compared using the area under the receiver operating characteristic curve (AUC). We identified the optimal decision threshold value of each DVH parameter with the highest sensitivity and specificity. Statistical analyses were performed using JMP version 11 (SAS Institute Inc., Cary, NC, USA) and R version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria) [12]. Analyzed items with P<0.05 were considered statistically significant.

Results

Clinical parameters

RP was observed in 43 out of 77 lung cancer patients (grade 1, n = 14; grade 2, n = 13; grade 3, n = 13; grade 4, n = 1; and grade 5, n = 2) and in 27 out of 72 esophageal cancer patients (grade 1, n = 19; grade 2, n = 3; grade 3, n = 4; grade 4, n = 0; and grade 5, n = 1).

In the univariate analysis of lung cancer patients, concurrent chemotherapy, pre-existing ILD, MLD, and V20 were significantly correlated with the occurrence of symptomatic RP (Table 1). In the univariate analysis of esophageal cancer patients, smoking history, MLD, and V20 were significantly correlated with the occurrence of symptomatic RP (Table 2). Chemotherapeutic agents are summarized in S2 Table.

Table 1. Clinical parameters in lung cancer patients with symptomatic and asymptomatic radiation pneumonitis.

Characteristic Symptomatic Patients (N = 29) Asymptomatic Patients (N = 48) P-value
Median age (range), year 69 (57–82) 67 (39–89) 0.678
Male sex 26 (89.6) 40 (83.3) 0.520
Disease stage 0.104
1 0 (0) 5 (10.4)
2 2 (6.9) 3 (6.3)
3 24 (82.8) 29 (60.4)
4 3 (10.3) 11 (22.9)
*Histology type 0.494
SqCC 13 (44.8) 15 (31.2)
Adenocarcinoma 7 (24.1) 13 (27.1)
SCC 7 (24.1) 8 (16.7)
NSCC 2 (6.9) 6 (12.5)
Unknown 0 (0) 4 (8.3)
Others 0 (0) 2 (4.2)
Treatment type 1.000
IMRT 1 (3.5) 3 (6.3)
3D Conformal 28 (96.6) 45 (93.8)
Chemotherapy 0.002
     Yes 23 (79.3) 20 (41.7)
    No 6 (20.7) 28 (58.3)
Smoking history 0.561
Current 12 (41.4) 14 (29.2)
Former 14 (48.2) 28 (58.3)
Never 3 (10.3) 6 (12.5)
Smoking (range), pack-years 45 (0–120) 42 (0–180) 0.709
Median BMI (range), kg/m2 20.55 (16.19–24.83) 19.47 (14.98–25.42) 0.091
ILD 0.004
Yes 4 (13.8) 0 (0)
No 25 (86.2) 48 (100)
Median MLD (IQR), Gy 13.307 (8.89–17.006) 7.056 (4.123–9.367) <0.0001
Median V20 (IQR) 23.227 (18.047–32.352) 13.554 (7.526–14.423) <0.0001
Median LAV% (IQR) 8.5 (3.6–24.3) 11.1 (2.6–28.5) 0.801
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*Percentages in this column may not add up to exactly 100% because of rounding.

Unless otherwise specified, data are expressed as numbers of patients, and numbers in parentheses are percentages. RP = radiation pneumonitis; SqCC = squamous cell carcinoma; SCC = small cell carcinoma; NSCC = non-small cell carcinoma; IMRT = intensity-modulated radiation therapy; BMI = body mass index; ILD = interstitial lung disease; MLD = mean lung dose; IQR = interquartile range; V20 = percentage of lung volume irradiated ≥ 20 Gy; LAV% = ratio of low attenuation volume to the lung volume.

Table 2. Clinical parameters in symptomatic radiation pneumonitis patients and asymptomatic esophageal cancer patients.

Characteristic Symptomatic Patients (N = 8) Asymptomatic Patients (N = 64) P-value
Median age (range), year 69.5 (64–80) 71 (48–89) 0.907
Male sex 7 (87.5) 56 (87.5) 1.000
Disease stage 0.905
0 0 (0) 1 (1.6)
1 1 (12.5) 7 (10.9)
2 0 (0) 8 (12.5)
3 4 (50.0) 30 (46.9)
4 3 (37.5) 18 (28.1)
*Histology type 0.325
SqCC 7 (87.5) 59 (92.2)
Adenocarcinoma 0 (0) 3 (4.7)
Unknown 1 (12.5) 1 (1.6)
Others 0 (0) 1 (1.6)
Chemotherapy 0.585
    Yes 8 (100) 56 (87.5)
    No 0 (0) 8 (12.5)
Smoking history 0.036
Current 0 (0) 22 (34.4)
Former 8 (100) 34 (53.1)
Never 0 (0) 8 (12.5)
Smoking (range), pack-years 44.5 (35–60) 39 (0–159) 0.337
Median BMI (range), kg/m2 21.99 (15.63–25.37) 19.59 (14.19–28.56) 0.282
ILD 1.000
Yes 1 (12.5) 9 (14.1)
No 7 (87.5) 55 (85.9)
Median MLD (IQR), Gy 14.43 (10.039–19.258) 9.637 (7.096–11.884) 0.007
Median V20 (IQR) 26.868 (15.349–37.984) 14.911 (10.150–24.238) 0.029
Median LAV% (IQR) 4.6 (0.598–9.032) 10.781 (2.989–22.868) 0.082
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*Percentages in this column may not add up to exactly 100% because of rounding.

Unless otherwise specified, data are expressed as numbers of patients, and numbers in parentheses are percentages. RP = radiation pneumonitis; SqCC = squamous cell carcinoma; SCC = small cell carcinoma; NSCC = non-small cell carcinoma; BMI = body mass index; ILD = interstitial lung disease; MLD = mean lung dose; IQR = interquartile range; V20 = percentage of lung volume irradiated ≥ 20 Gy; LAV% = ratio of low attenuation volume to the lung volume.

DVH parameters

In both lung cancer and esophageal cancer patients, univariate logistic regression analysis for symptomatic RP (≥grade 2) showed that all DVH parameters were significantly related to symptomatic RP (See S3 Table). When the predictive performances of DVH parameters for symptomatic RP were compared using the AUC, MLD, HAV30%, and HAV20% were the three best parameters in lung cancer and HAV10%, HAV5%, and V10 were the three best in esophageal cancer (Fig 2). As there were no overlaps between lung cancer and esophageal cancer, we compared the thresholds of these parameters for the prediction of RP between the two forms of cancer (Fig 3). When the differences in threshold of parameters between the two cancers were compared, threshold values of HAV30% were found to be almost identical in these cancers. For all DVH parameters, differences in the threshold between the two cancers were smaller when considering non-emphysematous (MLHAD, HAV30%, HAV20%, HAV10%, and HAV5%) than conventional (MLD, V30, V20, V10, and V5) parameters (Fig 4). Boxplots for each DVH parameter are shown in S1 Fig.

Fig 2. Comparison of the AUC of dose-volume histogram parameters and RP in lung cancer and esophageal cancer.

Fig 2

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Black bars indicate the three best DVH parameters. MLD, HAV30%, and HAV20% were the best three parameters in lung cancer, and HAV10%, HAV5%, and V10 were the three best in esophageal cancer.

Fig 3. Threshold of each DVH parameter for the prediction of symptomatic RP.

Fig 3

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Black bars represent esophageal cancer, and gray bars represent lung cancer. The same scores were obtained for HAV30% in both esophageal cancer and lung cancer.

Fig 4. Comparison of the differences in threshold of each DVH parameter predicting symptomatic RP between lung cancer and esophageal cancer.

Fig 4

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Gray bars indicate DVH parameters associated with high attenuation lung volume, and black bars indicate traditional DVH parameters. All DVH parameters associated with high attenuation lung volume (gray bars) were smaller than their counterpart traditional DVH parameters (black bars). The difference in HAV30% was zero.

Discussion

V20 or MLD has been commonly used as an index for the prevention of severe RP in practice. In various clinical applications of radiotherapy, the limit of MLD or V20 has been described [13–16]. These DVH parameters are simple to calculate and accurate enough for the prediction of RP but have never been compared with other DVH parameters in this regard. In addition, the reported threshold values of dose-volume histograms for the prediction of RP have only been discussed for lung cancer patients [1–3]. We previously showed that DVH parameters representing irradiated non-emphysematous lung volume were better predictors of RP than conventional DVH parameters [9]. Little has been reported on the comparison of thresholds of DVH parameters associated with the onset of RP among different malignant populations. In this study, we assessed the performance of conventional DVH parameters and some DVH parameters calculated by excluding emphysematous lesions reported in our previous study and compared the accuracy using the AUC in esophageal cancer and lung cancer populations. We found that in each population, DVH parameters with HAV predicted the onset of symptomatic RP more accurately than traditional DVH parameters. In addition, the differences in the DVH parameters of the two populations with HAV were smaller than those of traditional DVH parameters. Due to the identical threshold values of HAV30% in these two cancer types, this one threshold value could be used in common, at least in lung cancer and esophageal cancer. Based on the results presented in this study, we cannot affirm that HAV30% is the best predictor of RP. Indeed, HAV30% was not among the three parameters when the predictive performances of DVH parameters for symptomatic RP were compared using the AUC in esophageal cancer. Moreover, we cannot compare between MLD, MLHAD, and HAV30%, due to the unit differences. However, HAV might be a better predictor than the traditional DVH parameters. The frequency of RP was different in the two groups, but the threshold values were not so different between the groups. This may be because fewer patients with esophageal cancer received irradiation at high doses than did lung cancer patients. Some studies showed that chronic obstructive pulmonary disease (COPD) is a risk factor for RP [17–20], while others showed that RP was less severe in patients with more serious COPD than in patients with normal lung function [21, 22]. Our study is in line with the latter. Some previous studies assessing risk factors for RP [9, 23, 24] showed that emphysematous lesions decreased the risk of RP, which is in line with our results. While we used high attenuation area in the present study, these previous studies considered the low attenuation area; these have almost the same meaning because the threshold limit of CT value (−856 HU) is the same. The measurement of dose volume using high attenuation area is a very easy and convenient way to predict RP and may be widely applicable. In clinical trials, traditional DVH parameters excluding high attenuation from consideration were used as the standard to avoid RP without doubting their predictive accuracy for RP. To make every possible effort to avoid RP, DVH parameters with high attenuation might be used better when treating lung cancer and esophageal cancer patients.

We used –856 HU as the threshold [10, 25–28]. CT scans were performed under free breathing, meaning they are almost equal to expiratory CT scans. We previously validated that the LAV in inspiratory CT was highly associated with the LAV in CT under free breathing [9].

In our study, four lung cancer patients were treated with intensity-modulated radiotherapy. The type of irradiation used for treatment was not significantly associated with the onset of RP. Whether intensity-modulated radiotherapy increases or decreases the risk of RP has not been clarified. Immune checkpoint inhibitor treatment used during or after chemotherapy increases the risk of RP [16]; therefore, this problem must be verified in the future.

This study has several limitations that require further evaluation. First, our sample sizes were relatively small in both groups, and the subjects were enrolled from a single institution. This was also a retrospective study. A prospective multicenter study is needed to confirm the presented results. Second, in our previous study [9], we adjusted our analysis for ILD and chemotherapy, but we did not in this study. The purpose of this study was to evaluate the best DVH parameters that can be used in the real-world setting; therefore, we simply compared the threshold of DVH parameters for the prediction of RP. Another clinical element separate from DVH parameters may affect the onset of RP. For example, chemotherapy and ILD were significantly associated with the onset of symptomatic RP in lung cancer (Table 1). We also believe that it is necessary to evaluate these parameters in other malignancies, such as breast cancer and mediastinal tumors. Inspiration-breath-hold scans commonly used for SBRT lung radiotherapy treatments would have larger LAV%, and hence, larger differentials between the segmented and non-segmented metrics. The number of patients who have undergone IMRT is small. This can potentially introduce bias since static IMRT or VMAT plans can have higher, if not much higher, low dose contribution to the treatment. In other words, if the portion of the IMRT plan increases, we may see the optimal dose evaluation criteria shift toward a low dose. The 75% percentiles of HAV20% and V20 and V5 in esophageal cancer were lower than the threshold; therefore, comparing these may not be of much significance.

Conclusions

DVH parameters with a high attenuation area may have higher commonality than conventional DVH parameters in lung cancer and in esophageal cancer populations. HAV30% may be a better DVH parameter for predicting RP than other conventional parameters.

Supporting information

S1 Table. The following dosimetric parameters were evaluated.

(DOCX)

Click here for additional data file. (18.9KB, docx)
S2 Table. Chemotherapy regimens used in the study.

(DOCX)

Click here for additional data file. (16.6KB, docx)
S3 Table. Univariate logistic regression analysis of dosimetric parameters for symptomatic radiation pneumonitis (≥ Grade 2) in lung cancer and esophageal cancer.

(DOCX)

Click here for additional data file. (17.1KB, docx)
S1 Fig

(TIF)

Click here for additional data file. (887.2KB, TIF)

Abbreviations

RP

Radiation pneumonitis

DVH

dose-volume histogram

HAV

high attenuation volume

HAVx%

the proportion of the lung with HAV receiving x Gy or more

MLD

mean lung dose

Vx

the percentage of lung volume receiving x Gy or more

CT

computed tomograph

LAV

Low attenuation volume

TLV

total lung volume

ILD

interstitial lung disease

AUC

the area under the receiver operating characteristic curve

COPD

chronic obstructive pulmonary disease

MHALD

mean high attenuation lung dose

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported by Daiichi-Sankyo Company, Limited [grant number A19-1287], (YU), https://www.daiichisankyo.co.jp/corporate/ds-shougakukifu/, Ono Pharmaceutical Company, Limited [grant number ONOS20180618011], (NY), https://kifu-shinsei.jp/kifu-entry/cmn/doc/index_N1gsZhJaOt.html, Pfizer Inc [grant number 54334665], (NY), https://pfizer-ac-web.pfizer.co.jp/detail.html, Bayer Yakuhin, Limited [BASJ20180409030], https://byl.bayer.co.jp/researchers/, Boehringer Ingelheim GmbH [grant number RS2019A00769379], (NY), https://www.boehringer-ingelheim.jp/Research_support_2019, and Astelas Pharma [grant number RS2019A001313], (NY), https://www.astellas.com/jp/ja/responsibility/astellas-foundations, and had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

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  • 24.Saito T, Nakayama H, Yamada T, Shiraishi S, Tokuuye K. Is severe emphysema, as defined by quantitative CT measurement, a negative risk factor of radiation fibrosis? Br J Radiol. 2018; 91: 20170921 10.1259/bjr.20170921 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Newman KB, Lynch DA, Newman LS, Ellegood D, Newell JD Jr. Quantitative computed tomography detects air trapping due to asthma. Chest. 1994; 106: 105–109. 10.1378/chest.106.1.105 [DOI] [PubMed] [Google Scholar]
  • 26.Mets OM, van Hulst RA, Jacobs C, van Ginneken B, de Jong PA. Normal range of emphysema and air trapping on CT in young men. AJR Am J Roentgenol. 2012; 199: 336–340. 10.2214/AJR.11.7808 [DOI] [PubMed] [Google Scholar]
  • 27.Schroeder JD, McKenzie AS, Zach JA, Wilson CG, Curran-Everett D, Stinson DS, et al. Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease. AJR Am J Roentgenol. 2013; 201: W460–W470. 10.2214/AJR.12.10102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Regan EA, Hokanson JE, Murphy JR, Make B, Lynch DA, Beaty TH, et al. Genetic epidemiology of COPD (COPDGene) study design. Copd. 2010; 7: 32–43. 10.3109/15412550903499522 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Dandan Zheng

18 Aug 2020

PONE-D-20-14886

Prediction of radiation pneumonitis using dose-volume histogram parameters with high attenuation in two types of cancer: A retrospective study

PLOS ONE

Dear Dr. Nakano,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

Kind regards,

Dandan Zheng, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: General comments

The concept of segmenting the lung volume into low density and high density regions, and ignoring the low density is interesting. It is reasonable to expect a differential in the metrics vs clinical outcomes after subtracting out the low density volumes. It does seem, however, that this is a small improvement on current metrics. The paper requires several major additions to more clearly and definitively show that these differences are meaningful.

Major things

Choice of CT number

Please expand on the choice of -856 HU, why this value? could other choices be made?

Ideally one would perform your LAV segmented analysis for a range HU and find the HU number cut-off with the largest improvement in over the non-segmented metrics.

Direct comparison of the of new segmented HAV metrics to corresponding traditional V metrics

The data suggests that the percentage of low attenuation volume is roughly 10% or less of the total lung volume. Doesn’t this mean that the segmentation is only a small (roughly 10% or less) correction compared to standard analyses without the LAV segmentation?

Please directly compare and comment on the statistical significance of the differences between segmented and non-segmented metrics g.g. HAV20% = 33.7 and V20% = 35.2 for lung cancer. Total patients is 77. Is this segmented HAV20% really a significant improvement on the V20%? My quick visual comparison suggests that most the “V” metrics give very similar results to their HAV counterparts.

Same also for the ROC results: e.g HAV10% = 0.869 vs V10%=0.849 in the esophageal ROC figure. Is this difference significant?

Important missing technical details

If possible, also give the mass density or electron densities that the -856 HU threshold value corresponds to for your CT scanner (or at least give a density range).

Please specify the dose calculation algorithms used? This may be particularly significant as simpler algorithms such as Varian’s AAA algorithm, is known to overestimate dose to low density lung volumes compared to Boltsmann-solver or Monte-Carlo algorithms such as accuros.

Please specify the breathing phase of the CT scan (inspiration breathhold , free breathing,4$D CT at a given phase etc). Note that you may want to add to the discussion that for inspiration-breathold scans which are commonly used for SBRT lung radiotherapy treatments would have larger LAV% and hence larger differential between the segmented and non-segmented metrics.

Please also discuss the margins and motion management during treatment (presumably no gating, no breathold was used on these treatments?).

Smaller things

line 49

“have set vague cut-off values for”

strongly suggest to remove word “vague” as the metrics themselves are not vague.

Use of “cut-off” is unusual. Suggest to replace “cut-off” with “constraints” as the constraints can be one of a number of metrics such as: volume that gets at least a given dose, mean dose, max dose.

Line 50-51

“lung volume receiving 20 Gy or more (V20%) (30−40%) to reduce the risk of RP”

Terminology as given is confusing: We need both Dose in Gy and volume in % to define the “V” metrics. Normally the first number (eg V20) is the dose in Gy, not % dose nor % volume. E.g. V20 < 30% means the volume that receives at least 20 Gy should be less than 30% of the lung volume. Here by writing V20% you are implying that the 20 refers to a %, where it normally refers to the dose in Gy. Please correct the your “V20%” notation to the more standard notation “V20 <20%” and complete notation defining both the dose and the volume.

For example see the RTOG 0937 protocol where this constraint originated: https://www.rtog.org/ClinicalTrials/ProtocolTable/StudyDetails.aspx?action=openFile&FileID=13697

Line 52

For clarity suggest to explain or expand on “low attenuation” and “high attenuation” straightaway eg consider “volumes of low attenuation in CT imaging” Suggest to also apply to abstract.

Line 52

It is technically wrong to say “areas of low attenuation” you mean “volumes of low attenuation”

Line 80

“using the threshold limit of −856 HU”

Replace with

“using the upper threshold limit of −856 HU”

Line 106 please specify more clearly and consistent with standard notation

Replace “V2%, V5%, V10%, V20%, and V30%,”

with presumed meaning: “V20 Gy < 2%, 5%, 10%, 20%, 30%”

Please also clearly specify that it is the volume of both lungs if that is the case.

Tables

I find the extreme-spaced tables impossible to digest. E.g table 1 is only 3 data columns but spans 3 pages.

data too spread out to meaningfully digest.

Please specify the resolution, or at least range of resolutions / slice thickness settings used for the CT scans. E.g. voxel size can effect the segmented volumes if the resolution is too course. e.g a voxel that is 4/5 with air 1/5 dense tumor tissue will represented by approx -800HU

Reviewer #2: The manuscript was well written and the analysis was properly done to support the conclusion.

There are a few suggestions for author to consider:

1. It's not very clear in the manuscript how the MU threshold of “- 856” was determined. Was there any systematic analysis done? As you have presented in the manuscript, by setting such a threshold it will impact the selection of optimal dose evaluation criteria and corresponding volume threshold. It's probably a good idea to address this issue to ensure that the difference in dose evaluation was not caused by the uncertainty of the chosen threshold.

2. As what I have noticed that only a small portion of plans were done with IMRT technique, this can potentially introduce bias since static IMRT or VMAT plans can have higher, if not much higher, low dose contribution to the treatment. That means if the portion of the IMRT plan increases, one may see the optimal dose evaluation criteria shift towards low dose.

3. For conventional lung radiation therapy, I can imagine the fractional dose would be similar. However, it would be clearer to include the fractional dose information rather than just the total dose > 30Gy when demonstrating the patient selection criteria.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2020 Dec 28;15(12):e0244143. doi: 10.1371/journal.pone.0244143.r002

Author response to Decision Letter 0

2 Oct 2020

Response to Reviewer #1:

Thank you very much for providing us with important insights. We are delighted to hear that you think our work will spark debate in our field. In the following sections, you will find our responses to each of your points and suggestions. We are grateful for the time and energy you expended in reviewing this work.

Major things

Choice of CT number

Please expand on the choice of -856 HU, why this value? could other choices be made?

Ideally one would perform your LAV segmented analysis for a range HU and find the HU number cut-off with the largest improvement in over the non-segmented metrics.

Response:

In general, most studies on COPD have used a CT cut-off value of -950 HU for inspiration and -856 HU for expiration as the cut-off value for emphysematous lesions. Because the regions suggesting an emphysematous lesion in the free-breathing CT images used in this study is close to the images taken during expiration, we used -856HU, which is commonly used during expiration.

To further verify the validity of this cutoff value, we previously validated the association between CT under free breathing and the inspiratory CT performed within 45 days after free breathing CT. Inspiratory CT was performed using Toshiba Aquillion ONE (Toshiba Medical Systems Corp., Otawara, Tochigi, Japan) and LAV was analyzed using the Aquarius iNtuitionTM software ver.4.4.12 (TeraRecon Inc., San Mateo, Calif) and evaluated using the threshold limit of -950 HU. We found that the LAV in inspiratory CT was highly correlated with the LAV in CT under free breathing. This detail can be found in reference #9. We've added these details on lines 87-91.

Direct comparison of the of new segmented HAV metrics to corresponding traditional V metrics

The data suggests that the percentage of low attenuation volume is roughly 10% or less of the total lung volume. Doesn’t this mean that the segmentation is only a small (roughly 10% or less) correction compared to standard analyses without the LAV segmentation?

Response:

This correction may seem trivial. However, there were differences between individual patients, even if the overall rate was 10%. Some patients had more emphysematous lesions and others had less, and differences appeared between those with larger and smaller corrections.

An error in the unit of values for the lung cancer population has been corrected.

Please directly compare and comment on the statistical significance of the differences between segmented and non-segmented metrics g.g. HAV20% = 33.7 and V20% = 35.2 for lung cancer. Total patients is 77. Is this segmented HAV20% really a significant improvement on the V20%? My quick visual comparison suggests that most the “V” metrics give very similar results to their HAV counterparts.

Same also for the ROC results: e.g HAV10% = 0.869 vs V10%=0.849 in the esophageal ROC figure. Is this difference significant?

Response:

We agree that you are making a fair point. The AUC was used as a measure of accuracy in this study. In our previous study, we have assessed the AUC, as well as other indices such as NRI (net reclassification improvement) and IDI (integrated discrimination improvement). Although we did not present significant differences in individual values, the main purpose of this study was to compare cut-off values from a highly accurate index to ascertain universality. The purpose was not to compare significant differences in individual values.

Important missing technical details

If possible, also give the mass density or electron densities that the -856 HU threshold value corresponds to for your CT scanner (or at least give a density range).

Response:

Regarding relative electron density, −856HU was 0.1488. We have added these details on lines 92-93.

Please specify the dose calculation algorithms used? This may be particularly significant as simpler algorithms such as Varian’s AAA algorithm, is known to overestimate dose to low density lung volumes compared to Boltsmann-solver or Monte-Carlo algorithms such as accuros.

Response:

correction-based algorithm AAA (version 13.6.23) and the calculation grid was 2.5 mm for the lung. (lines 80-81)

Please specify the breathing phase of the CT scan (inspiration breathhold , free breathing,4$D CT at a given phase etc). Note that you may want to add to the discussion that for inspiration-breathold scans which are commonly used for SBRT lung radiotherapy treatments would have larger LAV% and hence larger differential between the segmented and non-segmented metrics.

Response:

We have added it on line 84 and 244-246.

Please also discuss the margins and motion management during treatment (presumably no gating, no breathold was used on these treatments?).

Response:

Gating and breath-hold were not used, as you pointed out. We have included this information in the manuscript.

Smaller things

line 49

“have set vague cut-off values for”

strongly suggest to remove word “vague” as the metrics themselves are not vague.

Use of “cut-off” is unusual. Suggest to replace “cut-off” with “constraints” as the constraints can be one of a number of metrics such as: volume that gets at least a given dose, mean dose, max dose.

Response:

We have removed the word “vague” and replaced “cut-off” with “constraints.”

Line 50-51

“lung volume receiving 20 Gy or more (V20%) (30−40%) to reduce the risk of RP”

Terminology as given is confusing: We need both Dose in Gy and volume in % to define the “V” metrics. Normally the first number (eg V20) is the dose in Gy, not % dose nor % volume. E.g. V20 < 30% means the volume that receives at least 20 Gy should be less than 30% of the lung volume. Here by writing V20% you are implying that the 20 refers to a %, where it normally refers to the dose in Gy. Please correct the your “V20%” notation to the more standard notation “V20 <20%” and complete notation defining both the dose and the volume.

For example see the RTOG 0937 protocol where this constraint originated: https://www.rtog.org/ClinicalTrials/ProtocolTable/StudyDetails.aspx?action=openFile&FileID=13697

Response:

Thank you for your comment. We have removed "%."

Line 52

For clarity suggest to explain or expand on “low attenuation” and “high attenuation” straightaway eg consider “volumes of low attenuation in CT imaging” Suggest to also apply to abstract.

Response:

Thank you for pointing this out. Your recommendation has been followed, the abstract inclusive.

Line 52

It is technically wrong to say “areas of low attenuation” you mean “volumes of low attenuation”

Response:

We have replaced with "volumes," as suggested.

Line 80

“using the threshold limit of −856 HU”

Replace with

“using the upper threshold limit of −856 HU”

Response:

Following your recommendation, we have added "upper" to the phrase.

Line 106 please specify more clearly and consistent with standard notation

Replace “V2%, V5%, V10%, V20%, and V30%,”

with presumed meaning: “V20 Gy < 2%, 5%, 10%, 20%, 30%”

Please also clearly specify that it is the volume of both lungs if that is the case.

Response:

Thank you for your comment. We have removed "%."

Tables

I find the extreme-spaced tables impossible to digest. E.g table 1 is only 3 data columns but spans 3 pages.

data too spread out to meaningfully digest.

Response:

Thank you for your comment. We have revised the tables for clarity.

Please specify the resolution, or at least range of resolutions / slice thickness settings used for the CT scans. E.g. voxel size can effect the segmented volumes if the resolution is too course. e.g a voxel that is 4/5 with air 1/5 dense tumor tissue will represented by approx -800HU

Response:

Thank you for your comment. We have included this information on lines 82-82.

Reviewer #2: The manuscript was well written and the analysis was properly done to support the conclusion.

There are a few suggestions for author to consider:

1. It's not very clear in the manuscript how the MU threshold of “- 856” was determined. Was there any systematic analysis done? As you have presented in the manuscript, by setting such a threshold it will impact the selection of optimal dose evaluation criteria and corresponding volume threshold. It's probably a good idea to address this issue to ensure that the difference in dose evaluation was not caused by the uncertainty of the chosen threshold.

Response:

In general, most studies on COPD have used a CT cut-off value of -950 HU for inspiration and -856 HU for expiration as the cut-off value for emphysematous lesions. Because the regions suggesting an emphysematous lesion in the free-breathing CT images used in this study is close to the images taken during expiration, we used -856HU, which is commonly used during expiration.

To further verify the validity of this cutoff value, we previously validated the association between CT under free breathing and the inspiratory CT performed within 45 days after free breathing CT. Inspiratory CT was performed using Toshiba Aquillion ONE (Toshiba Medical Systems Corp., Otawara, Tochigi, Japan) and LAV was analyzed using the Aquarius iNtuitionTM software ver.4.4.12 (TeraRecon Inc., San Mateo, Calif) and evaluated using the threshold limit of -950 HU. We found that the LAV in inspiratory CT was highly correlated with the LAV in CT under free breathing. This detail can be found in reference #9.

2. As what I have noticed that only a small portion of plans were done with IMRT technique, this can potentially introduce bias since static IMRT or VMAT plans can have higher, if not much higher, low dose contribution to the treatment. That means if the portion of the IMRT plan increases, one may see the optimal dose evaluation criteria shift towards low dose.

Response:

Thank you for pointing this out. We have included information regarding this on lines 246-249

3. For conventional lung radiation therapy, I can imagine the fractional dose would be similar. However, it would be clearer to include the fractional dose information rather than just the total dose > 30Gy when demonstrating the patient selection criteria.

Response:

The fraction dose is 1.8–3.0 Gy. We have included this in the manuscri

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (25.9KB, docx)

Decision Letter 1

Dandan Zheng

29 Oct 2020

PONE-D-20-14886R1

Prediction of radiation pneumonitis using dose-volume histogram parameters with high attenuation in two types of cancer: A retrospective study

PLOS ONE

Dear Dr. Nakano,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: The revision is much improved with only minor issues remaining. Please revise accordingly.

==============================

Please submit your revised manuscript by Dec 13 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Dandan Zheng, PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The revised version of the manuscript fully addresses all my concerns. Thank you for your corrections.

Reviewer #2: Line 225 - This suggestion may not be appropriate for two reasons: 1. As you have mentioned HAV30% is not the most sensitive predictor of RP for esophagus cases, therefore it’s necessary to have multiple/other predictors in order to reach a useful sensitivity. 2. HAV30% is not the most sensitive predictor for a reason. It’s unlikely that HAV30%/V30 from esophagus will be as high as that in lung cases simply because of the anatomies and beam arrangements (i.e. unlikely you will see an APPA treatment for esophagus as what you have shown as an example for lung). Therefore, the onset of RP could be dominated by the low dose to lung for esophagus cases, which was suggested by the shift of most sensitive predictors from HAV30% in lung to HAV10% and HAV5% in esophagus cases. Therefore, suggesting using HAV30% for both sites can be misleading and may not be clinically useful.

I would also suggest author to generate bar plots of each DVH parameter side by side for the two sites so that it’s clear how the distribution of those parameters different from each other, and compare the calculated thresholds with the 75% percentile of each parameter to see if using that specific predictor is meaningful. For example if the 75% percentile of HAV30% for esophagus cases is 5% and the HAV30% threshold is 11%, it would not be useful to suggest HAV30% as a RP predictor for esophagus cases.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2020 Dec 28;15(12):e0244143. doi: 10.1371/journal.pone.0244143.r004

Author response to Decision Letter 1

24 Nov 2020

Response to Reviewer #2:

Thank you very much for providing us with important insights. We are delighted to hear that you think our work will spark debate in our field. In the following sections, you will find our responses to each of your points and suggestions. We are grateful for the time and energy you expended in reviewing this work.

Line 225 - This suggestion may not be appropriate for two reasons: 1. As you have mentioned HAV30% is not the most sensitive predictor of RP for esophagus cases, therefore it’s necessary to have multiple/other predictors in order to reach a useful sensitivity. 2. HAV30% is not the most sensitive predictor for a reason. It’s unlikely that

HAV30%/V30 from esophagus will be as high as that in lung cases simply because of the anatomies and beam arrangements (i.e. unlikely you will see an APPA treatment for esophagus as what you have shown as an example for lung). Therefore, the onset of RP could be dominated by the low dose to lung for esophagus cases, which was suggested by the shift of most sensitive predictors from HAV30% in lung to HAV10% and HAV5% in esophagus cases. Therefore, suggesting using HAV30% for both sites can be misleading and may not be clinically useful.

Response:

We deleted this sentence.

I would also suggest author to generate bar plots of each DVH parameter side by side for the two sites so that it’s clear how the distribution of those parameters different from each other, and compare the calculated thresholds with the 75% percentile of each parameter to see if using that specific predictor is meaningful. For example if the 75% percentile of

HAV30% for esophagus cases is 5% and the HAV30% threshold is 11%, it would not be useful to suggest HAV30% as a RP predictor for esophagus cases.

Response:

We created box plots instead of bar blots to clarify the 75% percentile (S4 Figure). As you pointed out, the 75% percentile of HAV20% and V20 and V5 in esophageal cancer were lower than threshold. Therefor this point was added as the Limitation to Discussion.

Attachment

Submitted filename: Response to Reviwers.docx

Click here for additional data file. (16.7KB, docx)

Decision Letter 2

Dandan Zheng

4 Dec 2020

Prediction of radiation pneumonitis using dose-volume histogram parameters with high attenuation in two types of cancer: A retrospective study

PONE-D-20-14886R2

Dear Dr. Nakano,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Dandan Zheng, PhD

Academic Editor

PLOS ONE

Acceptance letter

Dandan Zheng

8 Dec 2020

PONE-D-20-14886R2

Prediction of radiation pneumonitis using dose-volume histogram parameters with high attenuation in two types of cancer: A retrospective study

Dear Dr. Nakano:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Dandan Zheng

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. The following dosimetric parameters were evaluated.

    (DOCX)

    Click here for additional data file. (18.9KB, docx)
    S2 Table. Chemotherapy regimens used in the study.

    (DOCX)

    Click here for additional data file. (16.6KB, docx)
    S3 Table. Univariate logistic regression analysis of dosimetric parameters for symptomatic radiation pneumonitis (≥ Grade 2) in lung cancer and esophageal cancer.

    (DOCX)

    Click here for additional data file. (17.1KB, docx)
    S1 Fig

    (TIF)

    Click here for additional data file. (887.2KB, TIF)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (25.9KB, docx)
    Attachment

    Submitted filename: Response to Reviwers.docx

    Click here for additional data file. (16.7KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

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