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Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning

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Abstract

Early and accurate diagnosis of osteosarcomas (OS) is of great clinical significance, and machine learning (ML) based methods are increasingly adopted. However, current ML-based methods for osteosarcoma diagnosis consider only X-ray images, usually fail to generalize to new cases, and lack explainability. In this paper, we seek to explore the capability of deep learning models in diagnosing primary OS, with higher accuracy, explainability, and generality. Concretely, we analyze the added value of integrating the biochemical data, i.e., alkaline phosphatase (ALP) and lactate dehydrogenase (LDH), and design a model that incorporates the numerical features of ALP and LDH and the visual features of X-ray imaging through a late fusion approach in the feature space. We evaluate this model on real-world clinic data with 848 patients aged from 4 to 81. The experimental results reveal the effectiveness of incorporating ALP and LDH simultaneously in a late fusion approach, with the accuracy of the considered 2608 cases increased to 97.17%, compared to 94.35% in the baseline. Grad-CAM visualizations consistent with orthopedic specialists further justified the model’s explainability.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy restrictions and ethical considerations related to patient data.

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Acknowledgements

This work is supported by the Natural Science Foundation of Jiangsu Province under grant BK20230083, Xicheng district financial science and technology special project XCSTS-SD2022-15, Peking University People’s Hospital research and development funds RDX2023-01.

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Author notes

  1. Shidong Wang and Yangyang Shen have contributed equally to this work.

Authors and Affiliations

  1. Musculoskeletal Tumor Center, Peking University People’s Hospital, Beijing, China

    Shidong Wang, Fanwei Zeng & Xiaodong Tang

  2. School of Computer Science and Technology, Southeast University, Nanjing, China

    Yangyang Shen, Dian Shen & Beilun Wang

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  4. College of Design and Innovation, Tongji University, Shanghai, China

    Meng Wang

  5. Ministry of Industry and Information Technology, Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

    Bohan Li

  6. National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology, Xi’an, China

    Bohan Li

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  1. Shidong Wang
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  3. Fanwei Zeng
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Correspondence to Xiaodong Tang or Beilun Wang.

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This research was approved by the Ethics Review Committee, Peking University People’s Hospital (Approval No. 2023PHB271-001).

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Wang, S., Shen, Y., Zeng, F. et al. Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning. Health Inf Sci Syst 12, 31 (2024). https://doi.org/10.1007/s13755-024-00288-5

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