Share：
Share this content in WeChat
X
[Chinese][PDF]59515
• 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: pengyang.0000@163.com

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.

[1]
Hadidchi S, Surento W, Lerner A, et al. Headache and brain tumor[J]. Neuroimaging Clin N Am, 2019, 29(2): 291-300. DOI: 10.1016/j.nic.2019.01.008.
[2]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary[J]. Acta Neuropathol, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1.
[3]
Wang TJC, Mehta MP. Low-grade glioma radiotherapy treatment and trials[J]. Neurosurg Clin N Am, 2019, 30(1): 111-118. DOI: 10.1016/j.nec.2018.08.008.
[4]
Tom MC, Cahill DP, Buckner JC, et al. Management for different glioma subtypes: are all low-grade gliomas created equal?[J]. Am Soc Clin Oncol Educ Book, 2019, 39: 133-145. DOI: 10.1200/EDBK_238353.
[5]
Hempel JM, Brendle C, Adib SD, et al. Glioma-specific diffusion signature in diffusion kurtosis imaging[J/OL]. J Clin Med, 2021, 10(11) [2022-04-18]. https://doi.org/10.3390/jcm10112325. DOI: 10.3390/jcm10112325.
[6]
Liang X, Shi WW, Tan Y. Diffusion kurtosis imaging: research advances in brain tumors[J]. Chin J Magn Reson Imaging, 2020, 11(3): 221-223. DOI: 10.12015/issn.1674-8034.2020.03.013.
[7]
Liang TT, Zhang H, Wang XC, et al. Prognostic value of preoperative dynamic susceptibility contrast-enhanced MR imaging in patients with gliomas[J]. Chin J Magn Reson Imaging, 2018, 9(6): 406-410. DOI: 10.12015/issn.1674-8034.2018.06.002.
[8]
Zhang ZW, Chen J, Huo XH, et al. Identification of a mesenchymal-related signature associated with clinical prognosis in glioma[J]. Aging (Albany NY), 2021, 13(9): 12431-12455. DOI: 10.18632/aging.202886.
[9]
Lin MJ, Wang WB, Xia XW, et al. Nomogram model construction for predicting survival rate of patients with low-grade glioma[J]. Chongqing Med, 2021, 50(13): 2283-2288. DOI: 10.3969/j.issn.1671-8348.2021.13.025.
[10]
Gittleman H, Sloan AE, Barnholtz-Sloan JS. An independently validated survival nomogram for lower-grade glioma[J]. Neuro-oncology, 2020, 22(5): 665-674. DOI: 10.1093/neuonc/noz191.
[11]
Zheng DC, Xu SG, Lai GJ, et al. The performance of pretreatment MRI based nomogram in neoadjuvant chemotherapy response prediction in nasopharyngeal carcinoma: a primary study[J]. Chin J Magn Reson Imaging, 2021, 12(4): 23-29. DOI: 10.12015/issn.1674-8034.2021.04.005.
[12]
Morshed RA, Young JS, Hervey-Jumper SL, et al. The management of low-grade gliomas in adults[J]. J Neurosurg Sci, 2019, 63(4): 450-457. DOI: 10.23736/S0390-5616.19.04701-5.
[13]
Zhang L, He ZL, Li DJ, et al. Clinical observation of radiotherapy combined with temozolomide and radiotherapy alone for high-risk low-grade glioma[J]. Chin J Gen Pract, 2020, 18(12): 1994-1997. DOI: 10.16766/j.cnki.issn.1674-4152.001671.
[14]
McDuff SGR, Dietrich J, Atkins KM, et al. Radiation and chemotherapy for high-risk lower grade gliomas: choosing between temozolomide and PCV[J]. Cancer Med, 2020, 9(1): 3-11. DOI: 10.1002/cam4.2686.
[15]
Li DP, Chen ZP. Current statue and advances of treatment for gliomas[J]. J Pract Med, 2021, 37(18): 2312-2316. DOI: 10.3969/j.issn.1006-5725.2021.18.002.
[16]
Zhang LP, Liu XJ, Lin H, et al. Factors affecting survival prognosis of advanced gastric cancer and establishment of a nomogram predictive model[J]. J South Med Univ, 2021, 41(4): 621-627. DOI: 10.12122/j.issn.1673-4254.2021.04.21.
[17]
Wang TW, Yang YP, Xu XK, et al. An integrative survival analysis for multicentric low-grade glioma[J/OL]. World Neurosurg, 2020, 134 [2022-04-18]. https://doi.org/10.1016/j.wneu.2019.10.001. DOI: 10.1016/j.wneu.2019.10.001.
[18]
Yan P, Li JW, Mo LG, et al. A nomogram combining inflammatory markers and clinical factors predicts survival in patients with diffuse glioma[J/OL]. Medicine, 2021, 100(47) [2022-04-18]. http://dx.doi.org/10.1097/MD.0000000000027972. DOI: 10.1097/MD.0000000000027972.
[19]
Pan XY, Su ZZ, Zhang J, et al. Construction of an individualized prognostic evaluation model for lower-grade glioma[J]. Chin J Exp Surg, 2021, 38(10): 2016-2018. DOI: 10.3760/cma.j.cn421213-20210107-01006.
[20]
Witteler J, Kjaer TW, Tvilsted S, et al. Seizures prior to radiotherapy of gliomas: prevalence, risk factors and survival prognosis[J]. Anticancer Res, 2020, 40(7): 3961-3965. DOI: 10.21873/anticanres.14388.
[21]
Alattar AA, Carroll KT, Bryant AK, et al. Prognostic importance of age, tumor location, and tumor grade in grade II astrocytomas: an integrated analysis of the cancer genome atlas and the surveillance, epidemiology, and end results database[J/OL]. World Neurosurg, 2019, 121 [2022-04-18]. https://doi.org/10.1016/j.wneu.2018.09.124. DOI: 10.1016/j.wneu.2018.09.124.
[22]
Li WF, Bao XR, Liang SS, et al. Construction of nomogram model for predicting prognosis of low-grade gliomas(LGG) based on multimodal MRI parameters[J]. Chin J Metastatic Cancer, 2021, 4(2): 139-143. DOI: 10.3760/cma.j.cn101548-20200216-00016.
[23]
Goghari VM, Kusi M, Shakeel MK, et al. Diffusion kurtosis imaging of white matter in bipolar disorder[J/OL]. Psychiatry Res Neuroimaging, 2021, 317 [2022-04-18]. https://doi.org/10.1016/j.pscychresns.2021.111341. DOI: 10.1016/j.pscychresns.2021.111341.
[24]
Hasan KM, Yamada K. Overview of diffusion tensor, diffusion kurtosis, and Q-space imaging and software tools[J]. Magn Reson Imaging Clin N Am, 2021, 29(2): 263-268. DOI: 10.1016/j.mric.2021.02.003.
[25]
Li DD, Cui YF, Hou LN, et al. Diffusion kurtosis imaging-derived histogram metrics for prediction of resistance to neoadjuvant chemoradiotherapy in rectal adenocarcinoma: preliminary findings[J/OL]. Eur J Radiol, 2021, 144 [2022-04-18]. https://linkinghub.elsevier.com/retrieve/pii/S0720048X21004447. DOI: 10.1016/j.ejrad.2021.109963.
[26]
Lin T, Zhao Y, Tian SF, et al. Preoperative prediction of pathological grading of hepatocellular carcinoma based on whole-tumor histogram analysis derived from diffusion kurtosis imaging[J]. Chin J Med Imaging, 2021, 29(7): 691-696. DOI: 10.3969/j.issn.1005-5185.2021.07.010.
[27]
Hempel JM, Brendle C, Bender B, et al. Diffusion kurtosis imaging histogram parameter metrics predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study[J]. Eur J Radiol, 2019, 112: 144-152. DOI: 10.1016/j.ejrad.2019.01.014.
[28]
Huang ZX, Lu CY, Li G, et al. Prediction of lower grade insular glioma molecular pathology using diffusion tensor imaging metric-based histogram parameters[J/OL]. Front Oncol, 2021, 11 [2022-04-18]. https://www.frontiersin.org/articles/10.3389/fonc.2021.627202/full. DOI: 10.3389/fonc.2021.627202.
[29]
Pan T, Zhang X, Su CQ, et al. Application of histogram analysis of diffusion kurtosis imaging in grading glioma[J]. Chin J Med Imaging, 2020, 30(4): 541-546.
[30]
Gao EY, Gao AK, Kit Kung W, et al. Histogram analysis based on diffusion kurtosis imaging: Differentiating glioblastoma multiforme from single brain metastasis and comparing the diagnostic performance of two region of interest placements[J/OL]. Eur J Radiol, 2022, 147 [2022-04-18]. https://doi.org/10.1016/j.ejrad.2021.110104. DOI: 10.1016/j.ejrad.2021.110104.

PREV Dynamic functional connectivity analysis of stable and progressive mild cognitive impairment
NEXT The value of apparent diffusion coefficient value in evaluating the IDH-1 mutation status and tumor cell proliferation activity of lower-grade gliomas
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn