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Quantitative assessment of breast volume changes after whole-breast irradiation for breast cancer using breast auto-segmentation

  • Clinical trial
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Abstract

Purpose

This study aimed to quantitatively estimate the changes in breast volume associated with radiotherapy in patients undergoing breast-conserving surgery and whole-breast irradiation (WBI).

Methods

Pre-WBI simulation computed tomography (CT) scans and post-WBI follow-up chest CT scans from a total of 1,151 breast cancer patients were analyzed using a deep-learning-driven auto-segmentation approach. The CT-based asymmetry index (CTAI) was calculated by dividing the volume of the irradiated breast by the volume of the contralateral breast. Significant breast shrinkage was defined as a CTAI < 0.85. To quantify changes in CTAI over the follow-up period, the CTAI ratio was determined as the post-WBI CTAI divided by the pre-WBI CTAI. A multivariate logistic regression analysis was conducted to identify potential variables associated with post-WBI significant breast shrinkage.

Results

The median CTAI values for pre- and post-WBI CT scans were 0.973 (interquartile range: 0.887–1.069) and 0.866 (interquartile range: 0.773–0.967), respectively. The difference between them was statistically significant (p < 0.001). Following WBI, there was an increase in the rate of significant breast shrinkage from 16.3 to 44.8%. The CTAI ratio showed a negative association with the time interval (p < 0.001, Pearson r = − 0.310). In the multivariate logistic regression analysis, lower pre-WBI CTAI, younger age, and longer interval between CT scans were found to be significantly associated with a higher occurrence of post-WBI significant breast shrinkage.

Conclusion

Breast volume decreases following WBI, and this decrease is correlated with an increased duration after WBI. These findings highlight the long-term consequences of WBI on breast asymmetry.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

BCS:

Breast-conserving surgery

CNN:

Convolutional deep neural network

CI:

Confidence interval

CT:

Computed tomography

CTAI:

CT-based asymmetry index

DVH:

Dose-volume histogram

HI:

Homogeneity index

IQR:

Interquartile range

OR:

Odds ratio

WBI:

Whole-breast irradiation

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  2. Kang MJ, Won Y-J, Lee JJ et al (2022) Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2019. Cancer Res Treat 54:330–344. https://doi.org/10.4143/crt.2022.128

    Article  PubMed  PubMed Central  Google Scholar 

  3. Giaquinto AN, Sung H, Miller KD et al (2022) Breast cancer statistics, 2022. CA Cancer J Clin 72:524–541. https://doi.org/10.3322/caac.21754

    Article  PubMed  Google Scholar 

  4. Kang SY, Lee SB, Kim YS et al (2021) Breast cancer statistics in Korea, 2018. J Breast Cancer 24:123. https://doi.org/10.4048/jbc.2021.24.e22

    Article  PubMed  PubMed Central  Google Scholar 

  5. EBCTCG (Early Breast Cancer Trialists’ Collaborative Group) (2011) 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 378:1707–1716. https://doi.org/10.1016/S0140-6736(11)61629-2

    Article  Google Scholar 

  6. Wojtyla C, Bertuccio P, Wojtyla A, La Vecchia C (2021) European trends in breast cancer mortality, 1980–2017 and predictions to 2025. Eur J Cancer 152:4–17. https://doi.org/10.1016/j.ejca.2021.04.026

    Article  PubMed  Google Scholar 

  7. Straub JM, New J, Hamilton CD et al (2015) Radiation-induced fibrosis: mechanisms and implications for therapy. J Cancer Res Clin Oncol 141:1985–1994. https://doi.org/10.1007/s00432-015-1974-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Waljee JF, Hu ES, Newman LA, Alderman AK (2008) Predictors of breast asymmetry after breast-conserving operation for breast cancer. J Am Coll Surg 206:274–280. https://doi.org/10.1016/j.jamcollsurg.2007.07.029

    Article  PubMed  Google Scholar 

  9. Dahlbäck C, Ullmark JH, Rehn M et al (2017) Aesthetic result after breast-conserving therapy is associated with quality of life several years after treatment. Swedish women evaluated with BCCT.core and BREAST-Q™. Breast Cancer Res Treat 164:679–687. https://doi.org/10.1007/s10549-017-4306-5

    Article  PubMed  PubMed Central  Google Scholar 

  10. Olivotto IA, Link E, Phillips C et al (2020) International comparison of cosmetic outcomes of breast conserving surgery and radiation therapy for women with ductal carcinoma in situ of the breast. Radiother Oncol 142:180–185. https://doi.org/10.1016/j.radonc.2019.07.024

    Article  PubMed  Google Scholar 

  11. Batenburg MCT, Gregorowitsch ML, Maarse W et al (2020) Patient-reported cosmetic satisfaction and the long-term association with quality of life in irradiated breast cancer patients. Breast Cancer Res Treat 179:479–489. https://doi.org/10.1007/s10549-019-05470-y

    Article  CAS  PubMed  Google Scholar 

  12. Brunt AM, Haviland JS, Wheatley DA et al (2020) Hypofractionated breast radiotherapy for 1 week versus 3 weeks (FAST-Forward): 5-year efficacy and late normal tissue effects results from a multicentre, non-inferiority, randomised, phase 3 trial. Lancet 395:1613–1626. https://doi.org/10.1016/S0140-6736(20)30932-6

    Article  Google Scholar 

  13. Stolpner I, Heil J, Riedel F et al (2021) Long-term patient satisfaction and quality of life after breast-conserving therapy: a prospective study using the BREAST-Q. Ann Surg Oncol 28:8742–8751. https://doi.org/10.1245/s10434-021-10377-4

    Article  PubMed  PubMed Central  Google Scholar 

  14. Immink JM, Putter H, Bartelink H et al (2012) Long-term cosmetic changes after breast-conserving treatment of patients with stage I–II breast cancer and included in the EORTC ‘boost versus no boost’ trial. Ann Oncol 23:2591–2598. https://doi.org/10.1093/annonc/mds066

    Article  CAS  PubMed  Google Scholar 

  15. Chapman BV, Lei X, Patil P et al (2020) Quantitative 3-dimensional photographic assessment of breast cosmesis after whole breast irradiation for early stage breast cancer: a secondary analysis of a randomized clinical trial. Adv Radiat Oncol 5:824–833. https://doi.org/10.1016/j.adro.2020.04.035

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cardoso MJ, Cardoso J, Amaral N et al (2007) Turning subjective into objective: the BCCT.core software for evaluation of cosmetic results in breast cancer conservative treatment. Breast 16:456–461. https://doi.org/10.1016/j.breast.2007.05.002

    Article  PubMed  Google Scholar 

  17. Fitzal F, Krois W, Trischler H et al (2007) The use of a breast symmetry index for objective evaluation of breast cosmesis. Breast 16:429–435. https://doi.org/10.1016/j.breast.2007.01.013

    Article  CAS  PubMed  Google Scholar 

  18. Batenburg MCT, Bartels M, Maarse W et al (2022) Factors associated with late local radiation toxicity after post-operative breast irradiation. Breast J 2022:1–13. https://doi.org/10.1155/2022/6745954

    Article  Google Scholar 

  19. Chung SY, Chang JS, Choi MS et al (2021) Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery. Radiat Oncol 16:44. https://doi.org/10.1186/s13014-021-01771-z

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jégou S, Drozdzal M, Vazquez D et al (2017) The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, Honolulu, pp 1175–1183

    Google Scholar 

  21. Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. ArXiv. https://doi.org/10.48550/arXiv.1412.6980.

  22. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  23. Yarnold J, Ashton A, Bliss J et al (2005) Fractionation sensitivity and dose response of late adverse effects in the breast after radiotherapy for early breast cancer: long-term results of a randomised trial. Radiother Oncol 75:9–17. https://doi.org/10.1016/j.radonc.2005.01.005

    Article  PubMed  Google Scholar 

  24. Barnett GC, Wilkinson JS, Moody AM et al (2012) Randomized controlled trial of forward-planned intensity modulated radiotherapy for early breast cancer: interim results at 2 years. Int J Radiat Oncol Biol Phys 82:715–723. https://doi.org/10.1016/j.ijrobp.2010.10.068

    Article  PubMed  Google Scholar 

  25. Fujii T, Yamaguchi S, Yajima R et al (2012) Accurate assessment of breast volume by computed tomography using three-dimensional imaging device. Am Surg 78:933–935. https://doi.org/10.1177/000313481207800930

    Article  PubMed  Google Scholar 

  26. Osman NM, Botros SM, Ghany AFA, Farid AM (2015) Contralateral breast volume measurement during chest CT for postmastectomy breast reconstruction. Int J Comput Assist Radiol Surg 10:141–147. https://doi.org/10.1007/s11548-014-1061-5

    Article  PubMed  Google Scholar 

  27. Collette S, Collette L, Budiharto T et al (2008) Predictors of the risk of fibrosis at 10 years after breast conserving therapy for early breast cancer—a study based on the EORTC trial 22881–10882 ‘boost versus no boost.’ Eur J Cancer 44:2587–2599. https://doi.org/10.1016/j.ejca.2008.07.032

    Article  PubMed  Google Scholar 

  28. Donovan E, Bleakley N, Denholm E et al (2007) Randomised trial of standard 2D radiotherapy (RT) versus intensity modulated radiotherapy (IMRT) in patients prescribed breast radiotherapy. Radiother Oncol 82:254–264. https://doi.org/10.1016/j.radonc.2006.12.008

    Article  PubMed  Google Scholar 

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Funding

The authors have not disclosed any funding.

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Author notes

  1. Tae Hoon Lee and Sang Hee Ahn have equal contribution to the work as co-first authors.

Authors and Affiliations

  1. Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea

    Tae Hoon Lee, Sang Hee Ahn, Kwangzoo Chung, Won Park, Won Kyung Cho, Nalee Kim & Haeyoung Kim

  2. Department of Radiation Oncology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea

    Tae Gyu Kim

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Contributions

THL was responsible for data analysis and manuscript writing. SHA was responsible for data acquisition and analysis. KC was responsible for data acquisition and project administration. WP, WKC, NK, and TGK were responsible for data acquisition. HK was responsible for study designing, manuscript writing, and project administration. All authors contributed to the revision of the manuscript and approval of the final manuscript.

Corresponding author

Correspondence to Haeyoung Kim.

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Competing interests

All authors declare no financial or non-financial competing interests.

Ethical approval

This study was approved by the Institutional Review Board of Samsung Medical Center (approval no. 2021–04-175) before gathering patient information.

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The requirement for informed consent was waived because of the retrospective nature of the study.

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Supplementary Information

Below is the link to the electronic supplementary material.

10549_2023_7146_MOESM1_ESM.pdf

The receiver operating curve of the total points from the nomogram and significant breast shrinkage after whole-breast irradiation. Supplementary file1 (PDF 73 KB)

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Lee, T.H., Ahn, S.H., Chung, K. et al. Quantitative assessment of breast volume changes after whole-breast irradiation for breast cancer using breast auto-segmentation. Breast Cancer Res Treat 203, 205–214 (2024). https://doi.org/10.1007/s10549-023-07146-0

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