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Prognosis prediction of high grade serous adenocarcinoma based on multi-modal convolution neural network

  • S.I.: Neural Networks and Machine Learning Empowered Methods and Applications in Healthcare
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Abstract

The prognostic analysis for high grade serous adenocarcinoma (HGSC) holds significant clinical importance. However, current prognostic analysis primarily relies on statistical techniques like logistic regression and chi-square analysis alongside traditional machine learning methods based on pattern recognition. These approaches face challenges in addressing the limited reliability and validity of evaluation results, as well as the absence of reliable prognostic indicators. To identify a reliable prognostic evaluation method for high grade serous adenocarcinoma, a novel prognostic evaluation method was constructed using multi-modal deep learning techniques and compared with existing methods using data from 210 patients with high grade serous adenocarcinoma (stage III). The experimental results showed that the accuracy of this method for prognostic analysis was 80.0%, and the detection rate for poor prognosis cases was 82.87%, which was superior to current methods. Our proposed method could also automatically extract key features from different datasets and efficiently predict patient outcomes. Overall, this study laid the groundwork to overcome the difficulties in the prognostic evaluation of HGSC, help clinicians better understand the pathogenesis, and improve the long-term survival rates of this patient population.

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Data availability statement

All data supporting this study is available upon request by contact with the first author and corresponding authors.

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Funding

This research was supported by National Key Research and Development Program of China (2020YFB1711500), the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYYC21004), the DICOM Standard National and Local Collaborated Engineering Laboratory Open Foundation (No. KFKT20220001), Provincial College Student Innovation and Entrepreneurship Training Program Project (S202310644063), and the Multi-dimensional Data Sensing and Intelligent Information Processing Key Laboratory Open Foundation (No. DWSJ2204).

Author information

Authors and Affiliations

  1. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Xin Liao & Kang Li

  2. Pathology Department, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Xin Liao

  3. School of Intelligent Manufacturing, Sichuan University of Arts and Science, Dazhou, 635000, Sichuan, China

    Zongyuan Gan & Xin Zheng

  4. Institute of Clinical Pathology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China

    Li Li

  5. Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Kang Li

  6. Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, People’s Republic of China

    Kang Li

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  1. Xin Liao
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  2. Li Li
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  3. Zongyuan Gan
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  4. Kang Li
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Contributions

All authors contributed to the study conception and design. Methodology, material preparation, data collection and analysis were performed by XL & KL & XZ. The first draft of the manuscript was written by XL, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kang Li or Xin Zheng.

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Liao, X., Li, L., Gan, Z. et al. Prognosis prediction of high grade serous adenocarcinoma based on multi-modal convolution neural network. Neural Comput & Applic 36, 9805–9817 (2024). https://doi.org/10.1007/s00521-023-09231-3

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