Skip to main content
SpringerLink
Log in

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Data, including XLSX and CSV files containing the primary outcomes and measured variables are available upon reasonable request. The source code for the statistics section is included on our repository: https://github.com/qahathaway/CardioOncology.

Abbreviations

HF:

Heart failure

IVS:

Interventricular septum

LV:

Left ventricle or left ventricular

PW:

Posterior wall

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7–33

    Article  PubMed  Google Scholar 

  2. Akram M, Iqbal M, Daniyal M, Khan AU (2017) Awareness and current knowledge of breast cancer. Biol Res 50:33

    Article  PubMed  PubMed Central  Google Scholar 

  3. Monsuez JJ, Charniot JC, Vignat N, Artigou JY (2010) Cardiac side-effects of cancer chemotherapy. Int J Cardiol 144:3–15

    Article  PubMed  Google Scholar 

  4. Bikiewicz A, Banach M, von Haehling S, Maciejewski M, Bielecka-Dabrowa A (2021) Adjuvant breast cancer treatments cardiotoxicity and modern methods of detection and prevention of cardiac complications. ESC Heart Fail 8:2397–2418

    Article  PubMed  PubMed Central  Google Scholar 

  5. Abdel-Qadir H, Austin PC, Lee DS et al (2017) A population-based study of cardiovascular mortality following early-stage breast cancer. JAMA Cardiol 2:88–93

    Article  PubMed  Google Scholar 

  6. Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N (2020) Overview of radiomics in breast cancer diagnosis and prognostication. Breast 49:74–80

    Article  PubMed  Google Scholar 

  7. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 11:91

    Article  PubMed  PubMed Central  Google Scholar 

  8. Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M (2018) A new challenge for radiologists: radiomics in breast cancer. Biomed Res Int 2018:6120703

    Article  PubMed  PubMed Central  Google Scholar 

  9. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R (2019) A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292:60–66

    Article  PubMed  Google Scholar 

  10. Jiang B, Guo N, Ge Y, Zhang L, Oudkerk M, Xie X (2020) Development and application of artificial intelligence in cardiac imaging. Br J Radiol 93:20190812

    Article  PubMed  PubMed Central  Google Scholar 

  11. Knackstedt C, Bekkers SC, Schummers G et al (2015) Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs multicenter study. J Am Coll Cardiol 66:1456–1466

    Article  PubMed  Google Scholar 

  12. Demissei BG, Fan Y, Qian Y et al (2021) Left ventricular segmental strain and the prediction of cancer therapy-related cardiac dysfunction. Eur Heart J Cardiovasc Imaging 22:418–426

    Article  PubMed  Google Scholar 

  13. Kagiyama N, Shrestha S, Cho JS et al (2020) A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine 54:102726

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hathaway QA, Yanamala N, Siva NK, Adjeroh DA, Hollander JM, Sengupta pp. (2022) Ultrasonic texture features for assessing cardiac remodeling and dysfunction. J Am Coll Cardiol. 80:2187–2201

    Article  PubMed  Google Scholar 

  15. Bozkurt B, Coats AJ, Tsutsui H et al (2021) Universal definition and classification of heart failure: a report of the heart failure society of America, heart failure association of the European society of cardiology, Japanese heart failure society and writing committee of the universal definition of heart failure. J Card Fail. https://doi.org/10.1002/ejhf.2115

    Article  PubMed  Google Scholar 

  16. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26

    Article  Google Scholar 

  17. Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789

    Article  CAS  PubMed  Google Scholar 

  18. Nioche C. LIFEx. Online: The LIFEx team, 2024.

  19. Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2021.

  20. Therneau TM. A Package for Survival Analysis in R. 2022:R package version 3.4–0.

  21. Gerds TA, Kattan MW (2021) Medical risk prediction models: with ties to machine learning. Chapman and Hall/CRC, Boca Raton

    Book  Google Scholar 

  22. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  CAS  PubMed  Google Scholar 

  23. Mogensen UB, Ishwaran H, Gerds TA (2012) Evaluating random forests for survival analysis using prediction error curves. J Stat Softw 50:1–23

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kundu S, Aulchenko YS, van Duijn CM, Janssens AC (2011) PredictABEL: an R package for the assessment of risk prediction models. Eur J Epidemiol 26:261–264

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS (2014) Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology 25:114–121

    Article  PubMed  PubMed Central  Google Scholar 

  26. Frantz S, Hundertmark MJ, Schulz-Menger J, Bengel FM, Bauersachs J (2022) Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies. Eur Heart J 43:2549–2561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mehta LS, Watson KE, Barac A et al (2018) Cardiovascular disease and breast cancer: where these entities intersect: a scientific statement from the American Heart Association. Circulation 137:e30–e66

    Article  PubMed  PubMed Central  Google Scholar 

  28. Subramaniam S, Kong YC, Zaharah H et al (2021) Baseline cardiovascular comorbidities, and the influence on cancer treatment decision-making in women with breast cancer. Ecancermedicalscience 15:1293

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kabore EG, Macdonald C, Kabore A et al (2023) Risk prediction models for cardiotoxicity of chemotherapy among patients with breast cancer: a systematic review. JAMA Netw Open 6:e230569

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ezaz G, Long JB, Gross CP, Chen J (2014) Risk prediction model for heart failure and cardiomyopathy after adjuvant trastuzumab therapy for breast cancer. J Am Heart Assoc 3:e000472

    Article  PubMed  PubMed Central  Google Scholar 

  31. Fogarassy G, Vathy-Fogarassy A, Kenessey I, Kasler M, Forster T (2019) Risk prediction model for long-term heart failure incidence after epirubicin chemotherapy for breast cancer - A real-world data-based, nationwide classification analysis. Int J Cardiol 285:47–52

    Article  PubMed  Google Scholar 

  32. Kim DY, Park MS, Youn JC et al (2021) Development and validation of a risk score model for predicting the cardiovascular outcomes after breast cancer therapy: the CHEMO-RADIAT score. J Am Heart Assoc 10:e021931

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Goel S, Liu J, Guo H et al (2019) Decline in left ventricular ejection fraction following anthracyclines predicts trastuzumab cardiotoxicity. JACC Heart Fail 7:795–804

    Article  PubMed  Google Scholar 

  34. Romond EH, Jeong JH, Rastogi P et al (2012) Seven-year follow-up assessment of cardiac function in NSABP B-31, a randomized trial comparing doxorubicin and cyclophosphamide followed by paclitaxel (ACP) with ACP plus trastuzumab as adjuvant therapy for patients with node-positive, human epidermal growth factor receptor 2-positive breast cancer. J Clin Oncol 30:3792–3799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Upshaw JN, Ruthazer R, Miller KD et al (2019) Personalized decision making in early stage breast cancer: applying clinical prediction models for anthracycline cardiotoxicity and breast cancer mortality demonstrates substantial heterogeneity of benefit-harm trade-off. Clin Breast Cancer 19(259–267):e1

    Google Scholar 

  36. Chang WT, Liu CF, Feng YH et al (2022) An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline. Arch Toxicol 96:2731–2737

    Article  CAS  PubMed  Google Scholar 

  37. Mango VL, Sun M, Wynn RT, Ha R (2020) Should we ignore, follow, or biopsy? Impact of artificial intelligence decision support on breast ultrasound lesion assessment. AJR Am J Roentgenol 214:1445–1452

    Article  PubMed  PubMed Central  Google Scholar 

  38. Qian X, Pei J, Zheng H et al (2021) Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat Biomed Eng 5:522–532

    Article  PubMed  Google Scholar 

  39. Shen Y, Shamout FE, Oliver JR et al (2021) Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 12:5645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jiang M, Li CL, Luo XM et al (2022) Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer. Eur Radiol 32:2313–2325

    Article  PubMed  Google Scholar 

  41. Jiang M, Zhang D, Tang SC et al (2021) Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study. Eur Radiol 31:3673–3682

    Article  PubMed  Google Scholar 

  42. Wu L, Zhao Y, Lin P et al (2021) Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ. BMC Med Imaging 21:84

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zheng X, Yao Z, Huang Y et al (2021) Author correction: deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 12:4370

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gu J, Tong T, He C et al (2022) Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. Eur Radiol 32:2099–2109

    Article  PubMed  Google Scholar 

  45. Jiang M, Li CL, Luo XM et al (2021) Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. Eur J Cancer 147:95–105

    Article  CAS  PubMed  Google Scholar 

  46. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  PubMed  Google Scholar 

  47. Smith GC, Seaman SR, Wood AM, Royston P, White IR (2014) Correcting for optimistic prediction in small data sets. Am J Epidemiol 180:318–324

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bertrand PB, Levine RA, Isselbacher EM, Vandervoort PM (2016) Fact or artifact in two-dimensional echocardiography: avoiding misdiagnosis and missed diagnosis. J Am Soc Echocardiogr 29:381–391

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank the West Virginia University Cancer Institute Mary Babb Randolph Cancer Center and the individuals involved in the acquisition of the echocardiographic imaging. We would thank the West Virginia Clinical and Translational Science Institute.

Funding

None.

Author information

Author notes

  1. Quincy A. Hathaway and Yahya Abdeen have contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Education, West Virginia University, Morgantown, WV, USA

    Quincy A. Hathaway

  2. Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA

    Quincy A. Hathaway, Yahya Abdeen, Justin Conte, Rotem Hass, Matthew J. Santer, Bandar Alyami, Juan Carlo Avalon & Brijesh Patel

  3. Department of Cardiovascular and Thoracic Surgery, West Virginia University School of Medicine, 1 Medical Center Drive, Morgantown, WV, 26505, USA

    Brijesh Patel

Authors
  1. Quincy A. Hathaway
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yahya Abdeen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Justin Conte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rotem Hass
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew J. Santer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bandar Alyami
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Juan Carlo Avalon
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Brijesh Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QAH, YA, and BP conceived and planned the study. YA and BP analyzed the echocardiographic imaging. QAH completed the statistical analyses. QAH, YA, JC, RH, and MJS processed participant outcomes. QAH, YA, JC, MJS, BA, JCA, and BP contributed to interpreting the results. All authors had full access to all the data in the study and take responsibility for the integrity and accuracy of the data analysis. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Brijesh Patel.

Ethics declarations

Conflict interest

The authors have not disclosed any competing interests.

Ethical approval

All studies were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration. All participants provided written consent. Participants were included regardless of gender, race, ethnicity, or other demographic factors. The study was approved under IRB protocol #: 2101211681.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 657 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hathaway, Q.A., Abdeen, Y., Conte, J. et al. Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer. Int J Cardiovasc Imaging (2024). https://doi.org/10.1007/s10554-024-03101-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10554-024-03101-2

Keywords

Search

Navigation