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Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes

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Abstract

Invasive gene expression profiling studies have exposed prognostically significant breast cancer subtypes: normal-like, luminal, HER-2 enriched, and basal-like, which is defined in large part by human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER). However, while dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been generally employed in the screening and therapy of breast cancer, there is a challenging problem to noninvasively predict breast cancer molecular subtypes, which have extremely low-data regimes. In this paper, a novel few-shot learning scheme, which combines lightweight contrastive convolutional neural network (LC-CNN) and multi-contrast learning strategy (MCLS), is worthwhile to be developed for predicting molecular subtype of breast cancer in DCE-MRI. Moreover, MCLS is designed to construct One-vs-Rest and One-vs-One classification tasks, which addresses inter-class similarity among normal-like, luminal, HER-2 enriched, and basal-like. Extensive experiments demonstrate the superiority of our proposed scheme over state-of-the-art methods. Furthermore, our scheme is able to achieve competitive results on few samples due to joint LC-CNN and MCLS for excavating contrastive correlations of a pair of DCE-MRI.

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Code availability

Our source code will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/LCCNN-Classification.

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Funding

This work is supported in part by the National Key R&D Program of China under Grants 2021YFE0203700 and 2018YFA0701700; the Postgraduate Research & Practice Innovation Program of Jiangsu Province SJCX22_1106; the National Natural Science Foundation of China Grants 61602007, U21A20521, and 61731008; the Zhejiang Provincial Natural Science Foundation of China (LZ15F010001), the Jiangsu Provincial Maternal and Child Health Research Project (F202034), the Wuxi Health Commission Precision Medicine Project (J202106); the Jiangsu Provincial Six Talent Peaks Project (YY-124), and the Science and Technology Development Fund, Macau SAR (File no. 0004/2019/AFJ and 0011/2019/AKP).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

    Xiang Pan, Pei Wang, Shunyuan Jia, Yihang Wang & Yuan Liu

  2. Cancer Center, Faculty of Health Sciences, University of Macau, Macau, SAR, China

    Xiang Pan

  3. Department of Oncology, Wuxi Maternal and Child Health Care Hospital, Jiangnan University, Wuxi, China

    Yan Zhang

  4. Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China

    Chunjuan Jiang

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  1. Xiang Pan
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  5. Yuan Liu
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  6. Yan Zhang
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Contributions

Chunjuan Jiang and Yan Zhang are the guarantors of this study. Xiang Pan, Yihang Wang, Yuan Liu, and Yan Zhang collected the data and performed the preprocessing. Xiang Pan, Yihang Wang, and Pei Wang designed and conducted the experiments. Xiang Pan, Pei Wang, Yihang Wang, and Chunjuan Jiang performed statistical analyses of the results. Xiang Pan, Pei Wang, Shunyuan Jia, and Yihang Wang drafted the manuscript. Pei Wang, Yan Zhang, and Shunyuan Jia revised the paper. All authors approved the manuscript.

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Correspondence to Yan Zhang or Chunjuan Jiang.

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Pan, X., Wang, P., Jia, S. et al. Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes. Med Biol Eng Comput 62, 1601–1613 (2024). https://doi.org/10.1007/s11517-024-03031-0

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