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MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas

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Abstract

The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.

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Acknowledgements

We thank Dr. Di Zhou for providing financial support.

Funding

This study was supported by the National Natural Science Foundation of China (No.82002538 and No. 82372502). Science and Technology Commission of Shanghai Municipality-Medical Innovation Research Special Project (23Y11902300); Zhongshan Hospital Clinical Research Special Funding (ZSLCYJ202346); Natural Science Foundation of Shanghai Municipal Commission of Science and Technology (No. 23ZR1411400).

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Author notes

  1. Yuling Lai, Yiyang Wu and Xiangyuan Chen these authors have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Anesthesiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China

    Yuling Lai, Yiyang Wu, Xiangyuan Chen & Meilin Weng

  2. Shanghai Key Laboratory of Perioperative Stress and Protection, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Yuling Lai, Yiyang Wu, Xiangyuan Chen & Meilin Weng

  3. Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan

    Wenchao Gu

  4. Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan

    Wenchao Gu

  5. Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China

    Guoxia Zhou

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  1. Yuling Lai
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Contributions

M.L.W. designed the study; M.L.W., Y.L.L, Y.Y.W. and X.Y.C. performed the research; M.L.W., Y.L.L. and Y.Y.W analyzed the data; M.L.W., Y.L.L. wrote the paper. M.L.W., G.X.Z. and W.C.G revised the paper.

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Correspondence to Wenchao Gu, Guoxia Zhou or Meilin Weng.

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Lai, Y., Wu, Y., Chen, X. et al. MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas. J Digit Imaging. Inform. med. 37, 209–229 (2024). https://doi.org/10.1007/s10278-023-00905-x

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