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Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer

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Abstract

Purpose

This study aims to develop and validate a deep learning radiomics nomogram (DLRN) for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients.

Materials and Methods

We retrospectively enrolled 196 patients with non-specific invasive breast cancer confirmed by pathology, radiomics and deep learning features were extracted from unenhanced and biphasic (arterial and venous phase) contrast-enhanced CT, and the non-linear support vector machine was used to construct the radiomics signature and the deep learning signature, respectively. Next, a DLRN was developed with independent predictors and evaluated the performance of models in terms of discrimination and clinical utility.

Results

Multivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and clinical n stage were independent predictors. The DLRN accurately predicted ALNM and yielded an area under the receiver operator characteristic curve of 0.893 (95% confidence interval, 0.814–0.972) in the validation set, with good calibration. Decision curve analysis confirmed that the DLRN had higher clinical utility than other predictors.

Conclusions

The DLRN had good predictive value for ALNM in breast cancer patients and provide valuable information for individual treatment.

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Data Availability

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

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Funding

This research was funded by Sichuan Science and Technology Program (Grant No. 2022YFS0616, 2021YFQ0002) and Applied Basic Research Program of Southwest Medical University (Grant No. 2021ZKMS048).

Author information

Author notes

  1. Jieqiu Zhang and Wei Yin contributed equally to this work.

Authors and Affiliations

  1. School of Public Health, Southwest Medical University, Luzhou, China

    Jieqiu Zhang

  2. Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China

    Wei Yin & Lu Yang

  3. School of Medical Information and Engineering, Southwest Medical University, Luzhou, China

    Xiaopeng Yao

  4. Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, China

    Xiaopeng Yao

Authors
  1. Jieqiu Zhang
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  2. Wei Yin
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  3. Lu Yang
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  4. Xiaopeng Yao
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Contributions

Study design: JQ, XP. Data analysis: JQ. Data collection: YW, JQ. Drafting the manuscript: JQ, XP. Supervision of the manuscript: all authors.

Corresponding author

Correspondence to Xiaopeng Yao.

Ethics declarations

Ethics Approval and Consent to Participate

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (protocol code KY2022216, 20 June 2022).

Conflict of Interest

The authors declare no competing interests.

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Zhang, J., Yin, W., Yang, L. et al. Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer. Mol Imaging Biol 26, 90–100 (2024). https://doi.org/10.1007/s11307-023-01839-0

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  • DOI: https://doi.org/10.1007/s11307-023-01839-0

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