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Clinical Article
Construction of nomogram model for predicting prognosis of low-grade gliomas based on diffusion kurtosis imaging histogram
SHI Meng  MA Yuehu  REN Jun  WANG Tongxing  YIN Xindao  PENG Mingyang 

Cite this article as: Citation:Shi M, Ma YH, Ren J, et al. Construction of nomogram model for predicting prognosis of low-grade gliomas based on diffusion kurtosis imaging histogram[J]. Chin J Magn Reson Imaging, 2022, 13(8): 7-12, 18. DOI:10.12015/issn.1674-8034.2022.08.002.

[Abstract] Objective To establish a nomogram based on diffusion kurtosis imaging (DKI) histogram radiomics for predicting prognosis of low-grade gliomas (LGG) patients.Materials and Methods The DKI data of 88 patients with LGG treated in Nanjing First Hospital from January 2018 to June 2020 were analyzed retrospectively. The histogram parameters were obtained by using DKE software, and the DKI score was calculated after the least absolute shrinkage and selection operator screened the best image features. Univariate Cox regression and multivariate Cox regression were used to screen the independent risk factors closely related to the prognosis of LGG, and the nomogram model for predicting the prognosis of LGG was established in turn. Delong test was used to compare the difference between clinical variable model and nomogram model. Decision curve analysis (DCA) and calibration curve were used to evaluate the effectiveness of the models.Results Age, grade, lobar location, tumor location, postoperative radiotherapy and DKI score were the key risk factors for prognosis of LGG (all P<0.05). The nomogram model was constructed based on the above risk factors. The C-index was 0.838 (95% CI: 0.816-0.860), and the area under the curve for predicting the prognosis of LGG was 0.953, which was significantly greater than 0.745 of model based on clinical variables (Z=-3.42, P=0.005). DCA showed that the net benefit of nomogram model was better than that of clinical variable model. The calibration curve indicates that there was a good consistency between the observed value and the predicted value.Conclusions Nomogram based on DKI histogram can predict the prognosis of LGG patients intuitively and comprehensively. It can provide a relatively accurate prediction tool for neurosurgeons to individualized assessment of survival and prognosis for patients.
[Keywords] low-grade gliomas;magnetic resonance imaging;diffusion kurtosis imaging;histogram analysis;prognosis;nomogram

SHI Meng1   MA Yuehu2   REN Jun2   WANG Tongxing2   YIN Xindao2   PENG Mingyang2*  

1 Department of Radiology, Nanjing Integrated Traditional Chinese and Western Medicine Hospital, Nanjing 210000, China

2 Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China

Peng MY, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Jiangsu Provincial Special Program of Medical Science (No. BE2021604); Natural Science of Jiangsu Province (No. BK20201118).
Received  2022-04-18
Accepted  2022-07-27
DOI: 10.12015/issn.1674-8034.2022.08.002
Cite this article as: Citation:Shi M, Ma YH, Ren J, et al. Construction of nomogram model for predicting prognosis of low-grade gliomas based on diffusion kurtosis imaging histogram[J]. Chin J Magn Reson Imaging, 2022, 13(8): 7-12, 18. DOI:10.12015/issn.1674-8034.2022.08.002.

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