Skip to main content

Preoperative spirometry and BMI in deep inspiration breath-hold radiotherapy: the early detection of cardiac and lung dose predictors without radiation exposure



This study aimed to investigate preoperative spirometry and BMI as early predictors of the mean heart and lung dose (MHD, MLD) in deep inspiration breath-hold (DIBH) radiotherapy.


Left-sided breast cancer patients underwent breast-conserving surgery followed by DIBH radiotherapy enrolled. Patients who were not available for preoperative spirometry were excluded. One hundred eligible patients were performed free-breathing (FB-) CT and DIBH-CT for plan comparison. We completed the correlative and multivariate analysis to develop the linear regression models for dose prediction. The residuals were calculated to explore the unpreferable subgroups and compare the prediction accuracy.


Among the parameters, vital capacity (VC) and BMI showed the strongest negative correlation with MHD (r = − 0.33) and MLD (r = − 0.34), respectively. They were also significant in multivariate analysis (P < 0.001). Elderly and less VC were independent predictors of increasing absolute residuals (AR). The VC model showed no significant difference in AR compared to the model using the CT parameter of lung volume in FB (LV-FB): median AR of the LV-FB model vs. the VC model was 0.12 vs. 0.11 Gy (P = 0.79). On the other hand, the median AR of the MLD model was 0.38 Gy, showing no specific subgroups of larger AR.


Preoperative spirometry and BMI are significant predictors of MHD and MLD, respectively. Although elderly and low-VC patients may have larger predictive variations, spirometry might be a substitute for LV-FB as a predictor of MHD.


Whole breast radiotherapy (RT) following breast-conserving surgery (BCS) reduces locoregional recurrence and deaths from breast cancer [1]. On the other hand, left-sided breast cancer patients have suffered cardiac toxicity depending on the irradiation dose [2,3,4,5,6,7]. Darby et al. showed that the mean heart dose (MHD) was correlated to the frequency of major coronary events at a rate of 7.4% per Gy with no apparent threshold [6]. Deep inspiration breath-hold (DIBH) is an effective method for reducing cardiac dose compared to free-breathing (FB-) RT [8,9,10,11,12,13]. Several studies have attempted to identify suitable patients or predict MHD in DIBH using anatomical information based on the simulation CT (e.g., maximum cardiac distance, cardiac contact distance) [14,15,16,17,18,19,20,21,22,23,24,25,26]. However, some non-CT parameters such as BMI or pulmonary function test results (PFT) are also reported to correlate to MHD [14, 27,28,29,30,31,32]. Such non-CT parameters may have the advantage of being available earlier without additional burden or radiation exposure to the patient. Hjelstuen et al. demonstrated that PFT could be used to identify the suitable patients for DIBH instead of using the lung volume (LV) on FB-CT (LV-FB): they estimated LV by using multiple PFTs (spirometry, diffusion capacity for carbon monoxide, and whole-body plethysmography), and confirmed a strong correlation of LV-FB with MHD, and the estimated LV with LV-FB. Although the interval between PFTs and simulation CT was shortly 28.5 h in median, it was an important finding that non-CT parameters could be substituted for the CT parameter to select the suitable patients in DIBH [27]. We hypothesized that even preoperative PFTs could be used instead of LV-FB to develop the earlier prediction.

A few studies have examined the relationship between DIBH and the lung dose, which reported that DIBH seemed to have some advantages in decreasing the lung dose [14, 33]. One study achieved to predict mean lung dose (MLD) using the synthetic DIBH-CT generated with a deep learning approach [34]. Lung dose in breast cancer patients has been associated with an increase in radiation-related lung cancer. In a meta-analysis of breast cancer radiotherapy, the risk of radiation-related lung cancer increased by about 11 percent per Gy MLD [7]. Pneumonitis has also been reported to be related to lung dose, and it is essential to keep lung dose as low as possible in breast cancer radiotherapy [35, 36]. Still, unfortunately, no previous studies found promising non-CT parameters related to the lung dose.

The purpose of this study is to find non-CT predictors in preoperative information (BMI, PFT) associated with the cardiac and lung dose advantages in DIBH. We also aimed to develop the linear regression models to predict MHD and MLD and compared the prediction accuracy with the LV-FB model.

Materials and methods

Patient selection and treatment

Our institutional review board approved this study. All participants provided written informed consent and met the following eligibility criteria: histologically proven diagnosis of invasive ductal carcinoma or carcinoma in situ of the left breast, underwent breast-conserving surgery followed by DIBH-RT from June 2018 to September 2021. Patients who do not have available preoperative PFT data were excluded.

Preoperative pulmonary function test

We perform spirometry as preoperative PFT. Spirometry measures the lung capacities and flow speed of breathing [39]. Figure 1 shows the parameters of the lung capacities: Vital capacity (VC), Inspiratory reserve volume (IRV), Tidal volume (TV), Expiratory reserve volume (ERV). Residual volume (RV) is the volume of air remaining in the lungs after a maximal exhalation, which cannot be directly measured by spirometry. The following parameters of flow speed are also obtained in this study: forced vital capacity (FVC), forced expiratory volume in 1s (FEV1), and peak expiratory flow (PEF), which means the highest forced expiratory flow. The preoperative height and weight are measured at the same of spirometry.

Fig. 1
figure 1

The measurements description of spirometry. IRV: inspiratory reserve volume, TV tidal volume, ERV: expiratory reserve volume, VC: vital capacity, RV: reserve volume, FEV1: forced expiratory volume in 1 s, FVC: forced vital capacity

Planning CT simulation

We performed the same DIBH-RT method on all patients by implementing a technique proposed by Bartlett et al. [11]. Patients were trained to inhale, exhale, and hold deep breaths. The breath-hold training was initially for 5–10 s, and then the breath-hold time was increased up to 20 s. The simulation and training time took about 20–30 min per patient. After confirming the respiratory motion, we marked the points along the midline of the chest with black ink, and the extent to which the body moved from the FB position was ascertained. Then, all patients underwent planning CT scans (FB-CT, DIBH-CT) in the supine position on a wing board with their arms stretched overhead with an Aquilion LB CT system (Canon Medical Systems, Tochigi, Japan). The CT slice thickness was 3 mm.

Treatment planning on FB-CT and DIBH-CT

We perform the contouring and dose planning on FB- and DIBH-CT using RayStation version 9 (RaySearch Laboratories AB, Stockholm, Sweden) with the calculation algorithm of Collapsed Cone version 5.1. The prescription dose for the planning target volume (PTV) was 42.56 Gy in 16 fractions using the Varian TrueBeam system (Varian Medical Systems, Palo Alto, USA). We defined clinical target volume (CTV) on each CT, referring to the consensus guidelines [37]. The PTV included the CTV and a 5-mm margin in all directions. The lung contours were automatically created using a model-based segmentation (MBS) function in RayStation. The heart was contoured according to the heart atlas validation study [38]. Three-dimensional conformal RT comprised the treatment planning. All plans consisted of two opposing tangential beams and two additional beams using the field-in-field technique. The two treatment plans were restricted to obtain the beam angle connecting the midline of the patient's chest to the mid-axillary line, and the multileaf collimator (MLC) margin was unified. Figure 2 shows a digitally reconstructed radiograph (DRR) and a dose distribution map of a typical case.

Fig. 2
figure 2

An example of treatment planning. A A digitally reconstructed graph (DRR) and B A dose distribution map of a typically case of this study. This case was treated in two opposing tangential beams (gantry angle: 128 and 305 degree, collimator angle: 0) with field-in-field technique. The 5.0 mm leaf width multileaf collimator was used

Statistical analyses

We defined the mean heart dose (MHD) and the mean lung dose (MLD) as the primary objective variables. The explanatory variables are preoperative and postoperative BMI (pre_BMI, post_BMI) and PFT results (VC, TV, IRV, ERV, FVC, FEV1, PEF). The variance inflation factor (VIF) of each two explanatory variables was calculated to verify the multicollinearity. We remove some variables not to exceed the maximum acceptable level of VIF (= 5).

Statistical analysis was performed using R version 3.6.1 (The R Foundation for Statistical Computing, Vienna, Austria). The correlation between the covariates and the objective variables (MHD or MLD) was tested using Pearson's s correlation coefficient; r. The required sample size was calculated as follows: Past studies showed r = − 0.25 to − 0.81 between covariates and MHD [14, 27,28,29,30,31,32], and if this study shows the minimum r = − 0.25, ninety-seven patients were required with alpha = 0.05 and power = 0.80. The significantly correlated factors with MHD or MLD will be examined using multivariable analyses. A linear regression model was created using the variable with the highest absolute value of the correlation coefficient. Residuals of each regression model were calculated to evaluate and compare the prediction performance. We also looked for a specific patient group with high variability from the scatter plot and compared the absolute errors between patient groups using the Mann–Whitney U test. P < 0.05 was considered statistically significant.



In one hundred and thirteen patients enrolled in the study, 100 patients were eligible with available PFT results. Table 1 shows the characteristics of the eligible patients. The median age was 52 years (range: 30 to 76 years, 25% were > 60 years), and the median pre_BMI was 21.6 (range: 16.7 to 39.1). All patients underwent BCS followed by DIBH-RT. Spirometry was performed before BCS, and the interval between spirometry and a simulation CT was a median of 85 days (range: 25 to 258 days). Fifteen patients received preoperative chemotherapy, all of whom underwent spirometry after chemotherapy but before surgery. Ten patients who received postoperative chemotherapy had significantly longer intervals between the spirometry and the simulation CT (mean: 85 vs. 189 days, P < 0.001).

Table 1 Patient characteristics

Since the pairs of pre_BMI vs. post_BMI (VIF = 125), FEV1 vs. FVC (VIF = 6.02), and VC vs. FEV1 (VIF = 5.95) exceeded the maximum acceptance VIF level, post_BMI, FVC, and FEV1 were excluded from the following statistical analysis. The remaining explanatory variables are Age, pre_BMI, VC, TV, IRV, ERV, and PEF.

Statistical results for the mean heart dose

The results of the correlation analysis with MHD and MLD as the objective variables are shown in Table 2. Among the remaining explanatory variables, VC showed the strongest correlation with MHD (r = − 0.33, P < 0.001, Fig. 3a) and was also an independent and significant predictor in multivariate analysis (P < 0.001). All other variables were not statistically significant. The following specifies the linear regression fitting model of MHD.

Table 2 The correlation analysis between the doses and preoperative parameters
Fig. 3
figure 3

The scatter plot of variables and a regression line. a The mean heart dose (MHD) and the vital capacity in spirometry (VC), b MHD and the lung volume on free-breathing CT (LV), c The mean lung dose (MLD) and preoperative BMI. r: correlation coefficient

$$MHD \left(Gy\right)=-0.190\times VC (L)+1.317$$

The median (range) absolute residuals were 0.10 (0.0090–1.27) Gy: older age and less VC were independent predictors with increasing the absolute residuals (P < 0.001). When differentiated by age, patients older than 60 years had significantly greater variability: Median value (range) of the residuals in patients > 60 vs. < 60 years were 0.21 (0.0090–1.27) Gy vs. 0.10 (0.00090–0.46) Gy (P < 0.001).

LV-FB was significantly correlated to MHD (r = − 0.33, P < 0.001, Fig. 3b). However, the following LV-FB linear regression model did not show significantly different residuals compared to the VC model: median values (range) of the absolute residuals were 0.12 (0.0023–1.37) Gy vs. 0.11 (0.00090–1.27) Gy (LV-FB vs. VC, P = 0.79).

$$MHD \left(Gy\right)=-0.180\times LVFB (L)+1.190$$

Statistical results for the mean lung dose

On the other hand, pre_BMI showed the strongest correlation with MLD (r = − 0.34, P < 0.001, Fig. 3c) and was also a significant independent predictor in multivariate analysis (P < 0.001). All other variables were not statistically significant. The linear regression model for MLD is specified below.

$$MLD (Gy)=-0.050\times pre\_BMI (kg/{m}^{2})+3.141$$

The median (range) absolute residuals of the pre_BMI model was 0.38 (0.0023–2.49) Gy. In contrast to MHD, there was no specific groups with larger residuals: the variability between the elderly and non-elderly in MLD: Median value (range) of the absolute residuals in patients > 60 vs. < 60 years were 0.39 (0.0023–2.49) Gy vs. 0.37 (0.0036–1.19) Gy.


In this study, we investigated the relationship of non-CT parameters to MHD and MLD in DIBH. Although BMI and PFT have also been used as exploring parameters in previous studies [14, 27,28,29,30,31,32], this study used only preoperative information for the first time. Thus, the prediction results can be extrapolated earlier, which may help determine the initial surgical treatment or postoperative radiotherapy. Also, the parameters of this study were obtained as a routine workup, so additional patient burden or radiation exposure would not occur. Compared to the previous studies that used non-CT parameters as described in Table 3 [14, 27,28,29,30,31,32], this study has some strong points: a large sample size, investigating the relationship to MLD as well as MHD.

Table 3 Reference of past similar studies

VC was negatively correlated with MHD (r = − 0.34), the strongest correlation among the preoperative factors; this correlation value was unexpectedly low since Hjelstuen et al. showed a strong correlation of 0.8 or higher [27]. One of the possible reasons for this gap could be the small sample size (16–18 patients) in their study. Small sample studies may have a significant sampling error, so appropriate sample sizes should be prepared based on calculations: if ten times of random subsampling of 16 patients, our cohort showed r in the range of –0.77 to 0.44 (median: –0.31). Another possible reason is that our cohort has different patient characteristics from theirs. A systematic review shows that the cardiac dose from DIBH has tended to be lower in recent years [40], with a maximum cardiac dose of about 4 Gy in this study and 10–14 Gy in theirs. Furthermore, we found that older age and lower VC were associated with larger residuals in the predicted results, so the inclusion of many elderly patients may be one reason why the correlation values were lower than expected. If patients aged > 60 were excluded, the correlation values slightly improved (r = − 0.36). Spirometry cannot measure RV, so the difference between VC and LV-FB might be more significant in elderly or low VC patients expected to have a large RV.

Previous studies showed the correlation between LV-FB and MHD [14, 27, 28], and Hjelstuen et al. confirmed that PFT could be used as a surrogate for LV-FB to select suitable patients of DIBH. However, their PFT must be performed additionally according to the planned CT schedule, and multiple types of PFT are necessary. Furthermore, compared to their study, the interval between CT and PFT in our study was significantly different, with a median of 28.5 h (range: 0.5–528 h) vs. 85 days (25–258 days). Our study showed that preoperative spirometry alone is also a substitute for LV-FB as a predictor of MHD.

Although three studies have reported a negative correlation between BMI and MHD [29,30,31], this study did not show a significant correlation between them. On the other hand, BMI negatively correlated with MLD (r = − 0.33). To investigate the reason for the negative correlation, we focused on the LV change from FB to DIBH, which correlated with BMI (r = 0.31, P < 0.001). Thus, the lungs of high BMI patients expand greater than those of low-BMI patients, possibly resulting in the higher dose sparing benefit.

There are several limitations to this study. First, the patients in this study were mainly those who underwent BCS with good pulmonary function. All patients were performed DIBH-CT simulations regardless of their pulmonary function or BMI results. Therefore, it is still unclear whether our results can be extrapolated to patients undergoing chest wall or lymph node irradiation. Second, although the correlation coefficients obtained in this study showed statistical significance with an appropriate sample size, the correlation value is not strong, so further exploration of other preoperative parameters would be desirable for more accurate prediction. Third, there is a large variability in the interval between the preoperative PFT examination and the simulation CT.

In conclusion, preoperative spirometry and BMI are significant predictors of MHD and MLD, respectively. Although the elderly and low-VC patients may have larger predictive variations, spirometry might be a substitute for LV-FB as a predictor of MHD.

Availability of data and materials

Research data are stored in an institutional repository and anonymized numerical data will be shared upon request to the corresponding author. Research image data are not available at this time.



Body mass index


Mean heart dose


Mean lung dose


Deep inspiration breath-hold


Computed tomography


Absolute residuals






Breast-conserving surgery


Pulmonary function test


Lung volume


Vital capacity


Inspiratory reserve volume


Tidal volume


Expiratory reserve volume


Residual volume


Forced vital capacity


Forced expiratory volume in 1 s


Peak expiratory flow


Planning target volume


Clinical target volume


Model-based segmentation


Multileaf collimator


Digitally reconstructed radiograph


Variance inflation factor


  1. Early Breast Cancer Trialists' Collaborative Group (EBCTCG), Darby S, McGale P, et al. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet. 2011;378: 1707–1716.

  2. Correa CR, Litt HI, Hwang WT, et al. Coronary artery findings after left-sided compared with right-sided radiation treatment for early-stage breast cancer. J Clin Oncol. 2007;25:3031–7.

    Article  PubMed  Google Scholar 

  3. Bouchardy C, Rapiti E, Usel M, et al. Excess of cardiovascular mortality among node-negative breast cancer patients irradiated for inner-quadrant tumors. Ann Oncol. 2010;21:459–65.

    Article  CAS  PubMed  Google Scholar 

  4. Harris EE, Correa C, Hwang WT, et al. Late cardiac mortality and morbidity in early-stage breast cancer patients after breast-conservation treatment. J Clin Oncol. 2006;24:4100–6.

    Article  PubMed  Google Scholar 

  5. Cheng YJ, Nie XY, Ji CC, et al. Long-term cardiovascular risk after radiotherapy in women with breast cancer. J Am Heart Assoc. 2017;6: e005633.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Darby SC, Ewertz M, McGale P, et al. Risk of ischemic heart disease in women after radiotherapy for breast cancer. N Engl J Med. 2013;368:987–98.

    Article  CAS  PubMed  Google Scholar 

  7. Taylor C, Correa C, Duane FK, et al. Estimating the risks of breast cancer radiotherapy: evidence from modern radiation doses to the lungs and heart and from previous randomized trials. J Clin Oncol. 2017;35:1641–9.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lu HM, Cash E, Chen MH, et al. Reduction of cardiac volume in left-breast treatment fields by respiratory maneuvers: a CT study. Int J Radiat Oncol Biol Phys. 2000;47:895–904.

    Article  CAS  Google Scholar 

  9. Sixel KE, Aznar MC, Ung YC. Deep inspiration breath hold to reduce irradiated heart volume in breast cancer patients. Int J Radiat Oncol Biol Phys. 2001;49:199–204.

    Article  CAS  Google Scholar 

  10. Korreman SS, Pedersen AN, Nøttrup TJ, et al. Breathing adapted radiotherapy for breast cancer: comparison of free breathing gating with the breath-hold technique. Radiother Oncol. 2005;76:311–8.

    Article  PubMed  Google Scholar 

  11. Bartlett FR, Colgan RM, Carr K, et al. The UK HeartSpare Study: randomised evaluation of voluntary deep-inspiratory breath-hold in women undergoing breast radiotherapy. Radiother Oncol. 2013;108:242–7.

    Article  PubMed  Google Scholar 

  12. Lee HY, Chang JS, Lee IJ, et al. The deep inspiration breath hold technique using Abches reduces cardiac dose in patients undergoing left-sided breast irradiation. Radiat Oncol J. 2013;31:239–46.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Latty D, Stuart KE, Wang W, et al. Review of deep inspiration breath-hold techniques for the treatment of breast cancer. Med Radiat Sci. 2015;62:74–81.

    Article  Google Scholar 

  14. Cao N, Kalet AM, Young LA, et al. Predictors of cardiac and lung dose sparing in DIBH for left breast treatment. Phys Med. 2019;67:27–33.

    Article  PubMed  Google Scholar 

  15. Rochet N, Drake JI, Harrington K, et al. Deep inspiration breath-hold technique in left-sided breast cancer radiation therapy: Evaluating cardiac contact distance as a predictor of cardiac exposure for patient selection. Pract Radiat Oncol. 2015;5:e127–34.

    Article  PubMed  Google Scholar 

  16. Wang W, Purdie TG, Rahman M, et al. Rapid automated treatment planning process to select breast cancer patients for active breathing control to achieve cardiac dose reduction. Int J Radiat Oncol Biol Phys. 2012;82:386–93.

    Article  PubMed  Google Scholar 

  17. Dell’Oro M, Giles E, Sharkey A, et al. A retrospective dosimetric study of radiotherapy patients with left-sided breast cancer; patient selection criteria for deep inspiration breath hold technique. Cancers (Basel). 2019;11:E259.

    Article  Google Scholar 

  18. Tanna N, McLauchlan R, Karis S, et al. Assessment of upfront selection criteria to prioritise patients for breath-hold left-sided breast radiotherapy. Clin Oncol (R Coll Radiol). 2017;29:356–61.

    Article  CAS  Google Scholar 

  19. Kong FM, Klein EE, Bradley JD, et al. The impact of central lung distance, maximal heart distance, and radiation technique on the volumetric dose of the lung and heart for intact breast radiation. Int J Radiat Oncol Biol Phys. 2002;54:963–71.

    Article  PubMed  Google Scholar 

  20. Mohamad O, Shiao J, Zhao B, et al. Deep inspiration breathhold for left-sided breast cancer patients with unfavorable cardiac anatomy requiring internal mammary nodal irradiation. Pract Radiat Oncol. 2017;7:e361–7.

    Article  PubMed  Google Scholar 

  21. Register S, Takita C, Reis I, et al. Deep inspiration breath-hold technique for left-sided breast cancer: An analysis of predictors for organ-at-risk sparing. Med Dosim. 2015;40:89–95.

    Article  PubMed  Google Scholar 

  22. Taylor CW, McGale P, Povall JM, et al. Estimating cardiac exposure from breast cancer radiotherapy in clinical practice. Int J Radiat Oncol Biol Phys. 2009;73:1061–8.

    Article  CAS  PubMed  Google Scholar 

  23. Borger JH, Hooning MJ, Boersma LJ, et al. Cardiotoxic effects of tangential breast irradiation in early breast cancer patients: the role of irradiated heart volume. Int J Radiat Oncol Biol Phys. 2007;69:1131–8.

    Article  PubMed  Google Scholar 

  24. Lorenzen EL, Brink C, Taylor CW, et al. Uncertainties in estimating heart doses from 2D-tangential breast cancer radiotherapy. Radiother Oncol. 2016;119:71–6.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ueda Y, Gerber NK, Das IJ. Model-based cardiac dose estimation in radiation treatment of left breast cancer. Br J Radiol. 2018;91:20180287.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hiatt JR, Evans SB, Price LL, et al. Dose-modeling study to compare external beam techniques from protocol NSABP B-39/RTOG 0413 for patients with highly unfavorable cardiac anatomy. Int J Radiat Oncol Biol Phys. 2006;65:1368–74.

    Article  PubMed  Google Scholar 

  27. Hjelstuen MHB, Mjaaland I, Vikström J, et al. Pulmonary function tests - an easy selection method for respiratory-gated radiotherapy in patients with left-sided breast cancer. Acta Oncol. 2015;54:1025–31.

    Article  CAS  Google Scholar 

  28. Lee D, Dinniwell R, Lee G. A Retrospective Analysis of Lung Volume and Cardiac Dose in Left-Sided Whole Breast Radiotherapy. J Med Imaging Radiat Sci. 2016;47:S10–4.

    Article  Google Scholar 

  29. Yamauchi R, Mizuno N, Itazawa T, et al. Dosimetric evaluation of deep inspiration breath hold for left-sided breast cancer: analysis of patient-specific parameters related to heart dose reduction. J Radiat Res. 2020;61:447–56.

    Article  Google Scholar 

  30. Mkanna A, Mohamad O, Ramia P, et al. Predictors of cardiac sparing in deep inspiration breath-hold for patients with left sided breast cancer. Front Oncol. 2018;8:1–6.

    Article  Google Scholar 

  31. Czeremszyńska B, Drozda S, Górzyński M, et al. Selection of patients with left breast cancer for deep-inspiration breath-hold radiotherapy technique: Results of a prospective study. Rep Pract Oncol Radiother. 2017;22:341–8.

    Article  Google Scholar 

  32. Browne P, Beaton N-R, Sharma H, et al. Identifying breast cancer patients who gain the most dosimetric benefit from deep inspiration breath hold radiotherapy. J Med Radiat Sci. 2020;67:294–301.

    Article  Google Scholar 

  33. Pandeli C, Smyth L, David S, et al. Dose reduction to organs at risk with deep-inspiration breath-hold during right breast radiotherapy: a treatment planning study. Radiat Oncol. 2019;14:223.

    Article  Google Scholar 

  34. Koide Y, Shimizu H, Wakabayashi K, et al. Synthetic breath-hold CT generation from free-breathing CT: a novel deep learning approach to predict cardiac dose reduction in deep-inspiration breath-hold radiotherapy. J Radiat Res. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Marks LB. Dosimetric predictors of radiation-induced lung injury. Int J Radiat Oncol Biol Phys. 2002;54:313–6.

    Article  Google Scholar 

  36. Aznar MC, Duane FK, Darby SC, et al. Exposure of the lungs in breast cancer radiotherapy: a systematic review of lung doses published 2010–2015. Radiother Oncol. 2018;126:148–54.

    Article  Google Scholar 

  37. Offersen BV, Boersma LJ, Kirkove C, et al. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer. Radiother Oncol. 2015;114:3–10.

    Article  PubMed  Google Scholar 

  38. Feng M, Moran JM, Koelling T, et al. Development and validation of a heart atlas to study cardiac exposure to radiation following treatment for breast cancer. Int J Radiat Oncol Biol Phys. 2011;79:10–8.

    Article  PubMed  Google Scholar 

  39. Graham BL, Steenbruggen I, Miller MR, et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med. 2019;200:e70–88.

    Article  Google Scholar 

  40. Drost L, Yee C, Lam H, et al. A Systematic Review of Heart Dose in Breast Radiotherapy. Clin Breast Cancer. 2018;18:e819–24.

    Article  Google Scholar 

Download references


The authors thank all of the patients, investigators, and institutions involuved in this study.


This work was supported by JSPS KAKENHI Grant-in-Aid for Early-Career Scientists (Grant Number 20K16402), Hori Sciences and Arts Foundation, and Aichi Cancer Research Foundation.

Author information

Authors and Affiliations



Hidetoshi Shimizu, Takahiro Aoyama, Tomoki Kitagawa, Risei Miyauchi collected the data. Yui Watanabe, Hiroyuki Tachibana, and Takeshi Kodaira interpreted the data and supervised the study. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Yutaro Koide.

Ethics declarations

Ethics approval and consent to participate

The Institutional Review Board of Aichi Cancer Center approved this study.

Consent for publication

All participants provided written informed consent.

Competing interests

All authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koide, Y., Shimizu, H., Aoyama, T. et al. Preoperative spirometry and BMI in deep inspiration breath-hold radiotherapy: the early detection of cardiac and lung dose predictors without radiation exposure. Radiat Oncol 17, 35 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: