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Texture analysis using machine learning–based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy

  • Breast
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European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC).

Methods

This retrospective study included 130 women who received NAC and underwent subsequent surgery for breast cancer between January 2012 and August 2017. We assessed common features, including standard morphologic MRI features and clinicopathologic features. We used a  commercial software and analyzed texture features from pretreatment and midtreatment MRI. A random forest (RF) method was performed to build a model for predicting recurrence. The diagnostic performance of this model for predicting recurrence was assessed and compared with those of five other machine learning classifiers using the Wald test.

Results

Of the 130 women, 21 (16.2%) developed recurrence at a median follow-up of 35.4 months. The RF classifier with common features including clinicopathologic and morphologic MRI features showed the lowest diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.83). The texture analysis with the RF method showed the highest diagnostic performances for pretreatment T2-weighted images and midtreatment DWI and ADC maps showed better diagnostic performance than that of an analysis of common features (AUC, 0.94 vs. 0.83, p < 0.05). The RF model based on all sequences showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.

Conclusions

Texture analysis using an RF model for pretreatment and midtreatment MRI may provide valuable prognostic information for predicting recurrence in patients with breast cancer treated with NAC and surgery.

Key Points

• RF model-based texture analysis showed a superior diagnostic performance than traditional MRI and clinicopathologic features (AUC, 0.94 vs.0.83, p < 0.05) for predicting recurrence in breast cancer after NAC.

• Texture analysis using RF classifier showed the highest diagnostic performances (AUC, 0.94) for pretreatment T2-weighted images and midtreatment DWI and ADC maps.

• RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the receiver operating characteristic curve

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

HER2:

Human epidermal growth factor receptor 2

HR:

Hazard ratio

MRI:

Magnetic resonance imaging

NAC:

Neoadjuvant chemotherapy

RF:

Random forest

ROI:

Region of interest

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Acknowledgements

This study was supported by a faculty research grant from the Yonsei University College of Medicine (grant no. 6-2020-0103).

Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. Na Lae Eun and Daesung Kang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, 06273, Seoul, Republic of Korea

    Na Lae Eun, Eun Ju Son, Ji Hyun Youk, Jeong-Ah Kim & Hye Mi Gweon

  2. Department of Healthcare Information Technology, Inje University, Gimhae, Republic of Korea

    Daesung Kang

Authors
  1. Na Lae Eun
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  2. Daesung Kang
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Corresponding author

Correspondence to Hye Mi Gweon.

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Guarantor

The scientific guarantor of this publication is Hye Mi Gweon.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

One hundred and thirty study subjects overlap with a study published in Radiology in January 2020. Unlike a previous study which showed the association between texture features of breast MRI and treatment response in breast cancer, this study was to investigate the value of texture features of breast MRI for predicting recurrence, compared to standard clinicopathologic and morphologic MRI analysis.

- Eun NL, Kang D, Son EJ, et al (2020) Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer. Radiology 294:31-41

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Eun, N.L., Kang, D., Son, E.J. et al. Texture analysis using machine learning–based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy. Eur Radiol 31, 6916–6928 (2021). https://doi.org/10.1007/s00330-021-07816-x

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  • DOI: https://doi.org/10.1007/s00330-021-07816-x

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