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Radiomic features predict Ki-67 expression level and survival in lower grade gliomas

  • Clinical Study
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Abstract

To investigate the radiomic features associated with Ki-67 expression in lower grade gliomas and assess the prognostic values of these features. Patients with lower grade gliomas (n = 117) were randomly assigned into the training (n = 78) and validation (n = 39) sets. A total of 431 radiological features were extracted from each patient. Differential radiological features between the low and high Ki-67 expression groups were screened by significance analysis of microarrays. Then, generalized linear analysis was performed to select features that could predict the Ki-67 expression level. Predictive efficiencies were further evaluated in the validation set. Cox regression analysis was performed to investigate the prognostic values of Ki-67 expression level and Ki-67-related radiological features. A group of nine radiological features were screened for prediction of Ki-67 expression status; these achieved accuracies of 83.3% and 88.6% (areas under the curves, 0.91 and 0.93) in the training and validation sets, respectively. Of these features, only spherical disproportion (SD) was found to be a prognostic factor. Patients in the high SD group exhibited worse outcomes in the whole cohort (overall survival, p < 0.0001; progression-free survival, p < 0.0001). Ki-67 expression level and SD were independent prognostic factors in the multivariate Cox regression analysis. This study identified a radiomic signature for prediction of Ki-67 expression level as well as a prognostic radiological feature in patients with lower grade gliomas.

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Funding

This study was supported by the Beijing Natural Science Foundation (No. 7174295) and the National Natural Science Foundation of China (No. 81601452).

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    Authors and Affiliations

    1. Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China

      Yiming Li, Zenghui Qian, Xing Fan, Shaowu Li, Xing Liu & Tao Jiang

    2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

      Kaibin Xu

    3. Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

      Kai Wang

    4. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China

      Yinyan Wang

    5. Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China

      Tao Jiang

    6. China National Clinical Research Center for Neurological Diseases, Beijing, China

      Tao Jiang

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    1. Yiming Li
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    Corresponding authors

    Correspondence to Xing Liu, Yinyan Wang or Tao Jiang.

    Additional information

    Yiming Li and Zenghui Qian contributed equally to this work and they are both the first authors of this article.

    Electronic supplementary material

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    Supplementary Fig. 1 Flow diagram of the inclusion and exclusion criteria. (TIF 3471 KB)

    11060_2017_2576_MOESM2_ESM.tif

    Supplementary Fig. 2 Magnetic resonance images of two patients with lower glioma with different Ki-67 expression levels. Case 1 was that of a 25-year-old woman with low Ki-67 expression level, who was correctly classified into the low Ki-67 group on basis of the radiomic signature. Case 2 was that of a 39-year-old man with high Ki-67 expression level, who was correctly classified into the high Ki-67 group. (TIF 3856 KB)

    Supplementary material 3 (DOCX 15 KB)

    Supplementary material 4 (DOCX 15 KB)

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    Li, Y., Qian, Z., Xu, K. et al. Radiomic features predict Ki-67 expression level and survival in lower grade gliomas. J Neurooncol 135, 317–324 (2017). https://doi.org/10.1007/s11060-017-2576-8

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    • DOI: https://doi.org/10.1007/s11060-017-2576-8

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