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Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study

  • Breast
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Abstract

Objectives

To evaluate the prediction performance of deep convolutional neural network (DCNN) based on ultrasound (US) images for the assessment of breast cancer molecular subtypes.

Methods

A dataset of 4828 US images from 1275 patients with primary breast cancer were used as the training samples. DCNN models were constructed primarily to predict the four St. Gallen molecular subtypes and secondarily to identify luminal disease from non-luminal disease based on the ground truth from immunohistochemical of whole tumor surgical specimen. US images from two other institutions were retained as independent test sets to validate the system. The models’ performance was analyzed using per-class accuracy, positive predictive value (PPV), and Matthews correlation coefficient (MCC).

Results

The model achieved good performance in identifying the four breast cancer molecular subtypes in the two test sets, with accuracy ranging from 80.07% (95% CI, 76.49–83.23%) to 97.02% (95% CI, 95.22–98.16%) and 87.94% (95% CI, 85.08–90.31%) to 98.83% (95% CI, 97.60–99.43) for the two test cohorts for each sub-category, respectively. In terms of 4-class weighted average MCC, the model achieved 0.59 for test cohort A and 0.79 for test cohort B. Specifically, the DCNN also yielded good diagnostic performance in discriminating luminal disease from non-luminal disease, with a PPV of 93.29% (95% CI, 90.63–95.23%) and 88.21% (95% CI, 85.12–90.73%) for the two test cohorts, respectively.

Conclusion

Using pretreatment US images of the breast cancer, deep learning model enables the assessment of molecular subtypes with high diagnostic accuracy.

Trial registration

Clinical trial number: ChiCTR1900027676

Key Points

• Deep convolutional neural network (DCNN) helps clinicians assess tumor features with accuracy.

• Multicenter retrospective study shows that DCNN derived from pretreatment ultrasound imagine improves the prediction of breast cancer molecular subtypes.

• Management of patients becomes more precise based on the DCNN model.

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Abbreviations

AUC:

Area under the receiver operator characteristic curve

CI:

Confidence interval

DCNN:

Deep convolutional neural network

ER:

Estrogen receptor

HER2:

Human epidermal growth factor receptor 2

IHC:

Immunohistochemical

MCC:

Matthews correlation coefficient

PR:

Progesterone receptor

ROC:

Receiver operating characteristic curve

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Acknowledgements

The authors thank all radiologists of the three hospitals for assisting with collection of the imaging data used in this study.

Funding

This study has received funding by grant from the Project funded by China Postdoctoral Science Foundation (2020M682422), National Natural Science Foundation of China (No. 61976012), Wuhan Science and Technology Bureau (No. 2017060201010181), Health Commission of Hubei Province (WJ2019M077, WJ2019H227), Shihezi Science and Technology Bureau (2019ZH11), Key project supported by the Xinjiang Construction Corps (2019DB012), and Natural Science Foundation of Hubei Province (2019CFB286).

Author information

Authors and Affiliations

  1. Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan, 430030, Hubei Province, China

    Meng Jiang & Xin-Wu Cui

  2. Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, China

    Di Zhang & Xue-Jun Ni

  3. Department of Medical Ultrasound, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, China

    Shi-Chu Tang

  4. Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China

    Xiao-Mao Luo & Zhi-Rui Chuan

  5. Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China

    Wen-Zhi Lv

  6. Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, China

    Fan Jiang

  7. Department of Internal Medicine, Hirslanden Clinic, Schänzlihalde 11, 3013, Bern, Switzerland

    Christoph F. Dietrich

Authors
  1. Meng Jiang
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Corresponding author

Correspondence to Xin-Wu Cui.

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The scientific guarantor of this publication is Professor Xin-Wu Cui.

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

No complex statistical methods were necessary for this paper.

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Only if the study is on human subjects:

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Jiang, M., Zhang, D., Tang, SC. et al. 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 (2021). https://doi.org/10.1007/s00330-020-07544-8

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  • DOI: https://doi.org/10.1007/s00330-020-07544-8

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