Share:
Share this content in WeChat
X
Review
Research progress in radiomics on prognosis prediction of lower-grade gliomas
LI Yangyang  TAN Yan 

Cite this article as: Li YY, Tan Y. Research progress in radiomics on prognosis prediction of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2022, 13(11): 129-132, 148. DOI:10.12015/issn.1674-8034.2022.11.025.


[Abstract] Lower-grade gliomas (LGGs) are World Health Organization (WHO) grade 2 and 3 gliomas. Compared with glioblastoma, LGGs have lower pathological grade and better prognosis. However, due to its aggressive growth mode, some patients still have recurrence or malignant transformation after treatment. Therefore, early prognosis prediction is expected to provide individualized and accurate treatment for LGGs patients and improve their quality of life. Radiomics, extracting and analyzing high-throughput imaging features from images, and converting the image information into intuitive data to reflect the internal heterogeneity of tumors, is helpful for clinicians to select the appropriate treatment plan for patients. The radiomics based on magnetic resonance imaging can directly predict the prognosis of LGGs, and can also combine the radiomics features with gene phenotype or immune features to predict the prognosis. However, many studies still have limitations. It is the direction of future research to develop radiomics based on MRI functional imaging and combine radiomics with newly discovered prognostic related genes or immunological features for prognosis prediction. This article reviews the prognostic factors of LGGs and the role of radiomics in predicting the prognosis of LGGs, in order to expand the method of predicting the prognosis based on radiomics and provide a new idea for accurate clinical diagnosis and treatment.
[Keywords] lower-grade gliomas;glioma;prognosis;radiomics;radiogenomics;magnetic resonance imaging

LI Yangyang1   TAN Yan2*  

1 College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China

2 Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan 030001, China

Tan Y, E-mail: tanyan123456@sina.com

Conflicts of interest   None.

Received  2022-07-09
Accepted  2022-11-07
DOI: 10.12015/issn.1674-8034.2022.11.025
Cite this article as: Li YY, Tan Y. Research progress in radiomics on prognosis prediction of lower-grade gliomas[J]. Chin J Magn Reson Imaging, 2022, 13(11): 129-132, 148.DOI:10.12015/issn.1674-8034.2022.11.025

[1]
Brat DJ, Verhaak RG, Aldape KD, et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas[J]. N Engl J Med, 2015, 372(26): 2481-2498. DOI: 10.1056/NEJMoa1402121.
[2]
Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[3]
Jakola AS, Skjulsvik AJ, Myrmel KS, et al. Surgical resection versus watchful waiting in low-grade gliomas[J]. Ann Oncol, 2017, 28(8): 1942-1948. DOI: 10.1093/annonc/mdx230.
[4]
Youssef G, Miller JJ. Lower Grade Gliomas[J/OL]. Curr Neurol Neurosci Rep, 2020, 20(7): 21 [2022-07-08]. https://doi.org/10.1007/s11910-020-01040-8. DOI: 10.1007/s11910-020-01040-8.
[5]
Murphy ES, Leyrer CM, Parsons M, et al. Risk Factors for Malignant Transformation of Low-Grade Glioma[J]. Int J Radiat Oncol Biol Phys, 2018, 100(4): 965-971. DOI: 10.1016/j.ijrobp.2017.12.258.
[6]
Weller M, van den Bent M, Tonn JC, et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas[J/OL]. Lancet Oncol, 2017, 18(6): e315-e329 [2022-07-08]. https://doi.org/10.1016/s1470-2045(17)30194-8. DOI: 10.1016/s1470-2045(17)30194-8.
[7]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[8]
Li F, Zhang Y, Wang N, et al. Evaluation of the Prognosis of Neuroglioma Based on Dynamic Magnetic Resonance Enhancement[J]. World Neurosurg, 2020, 138: 663-671. DOI: 10.1016/j.wneu.2020.01.087.
[9]
Zhao YY, Chen SH, Hao Z, et al. A Nomogram for Predicting Individual Prognosis of Patients with Low-Grade Glioma[J/OL]. World Neurosurg, 2019, 130: e605-e612 [2022-07-08]. https://doi.org/10.1016/j.wneu.2019.06.169. DOI: 10.1016/j.wneu.2019.06.169.
[10]
Yahanda AT, Patel B, Shah AS, et al. Impact of Intraoperative Magnetic Resonance Imaging and Other Factors on Surgical Outcomes for Newly Diagnosed Grade Ⅱ Astrocytomas and Oligodendrogliomas: A Multicenter Study[J]. Neurosurgery, 2020, 88(1): 63-73. DOI: 10.1093/neuros/nyaa320.
[11]
Liu H, Shen L, Huang X, et al. Maximal tumor diameter in the preoperative tumor magnetic resonance imaging (MRI) T2 image is associated with prognosis of Grade Ⅱ Glioma[J/OL]. Medicine (Baltimore), 2021, 100(10): e24850 [2022-07-08]. https://doi.org/10.1097/md.0000000000024850. DOI: 10.1097/md.0000000000024850.
[12]
Xia L, Fang C, Chen G, et al. Relationship between the extent of resection and the survival of patients with low-grade gliomas: a systematic review and meta-analysis[J/OL]. BMC Cancer, 2018, 18(1): 48 [2022-07-08]. https://doi.org/10.1186/s12885-017-3909-x. DOI: 10.1186/s12885-017-3909-x.
[13]
Kavouridis VK, Boaro A, Dorr J, et al. Contemporary assessment of extent of resection in molecularly defined categories of diffuse low-grade glioma: a volumetric analysis[J]. J Neurosurg, 2019, 133(5): 1291-1301. DOI: 10.3171/2019.6.Jns19972.
[14]
Wang P, Luo C, Hong PJ, et al. The Role of Surgery in IDH-Wild-Type Lower-Grade Gliomas: Threshold at a High Extent of Resection Should be Pursued[J]. Neurosurgery, 2021, 88(6): 1136-1144. DOI: 10.1093/neuros/nyab052.
[15]
Liu Y, Liu S, Li G, et al. Association of high-dose radiotherapy with improved survival in patients with newly diagnosed low-grade gliomas[J]. Cancer, 2022, 128(5): 1085-1092. DOI: 10.1002/cncr.34028.
[16]
Wei S, Li J. Efficacy and Safety of Temozolomide Combined with Radiotherapy in the Treatment of Malignant Glioma[J/OL]. J Healthc Eng, 2022, 2022: 3477918 [2022-07-08]. https://doi.org/10.1155/2022/3477918. DOI: 10.1155/2022/3477918.
[17]
Kizilbash SH, Giannini C, Voss JS, et al. The impact of concurrent temozolomide with adjuvant radiation and IDH mutation status among patients with anaplastic astrocytoma[J]. J Neurooncol, 2014, 120(1): 85-93. DOI: 10.1007/s11060-014-1520-4.
[18]
Franceschi E, Mura A, De Biase D, et al. The role of clinical and molecular factors in low-grade gliomas: what is their impact on survival?[J]. Future Oncol, 2018, 14(16): 1559-1567. DOI: 10.2217/fon-2017-0634.
[19]
Noor H, Briggs NE, McDonald KL, et al. TP53 Mutation Is a Prognostic Factor in Lower Grade Glioma and May Influence Chemotherapy Efficacy[J/OL]. Cancers (Basel), 2021, 13(21): 5362 [2022-07-08]. https://doi.org/10.3390/cancers13215362. DOI: 10.3390/cancers13215362.
[20]
Wei J, Yang G, Hao X, et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication[J]. Eur Radiol, 2019, 29(2): 877-888. DOI: 10.1007/s00330-018-5575-z.
[21]
Lu VM, O'Connor KP, Shah AH, et al. The prognostic significance of CDKN2A homozygous deletion in IDH-mutant lower-grade glioma and glioblastoma: a systematic review of the contemporary literature[J]. J Neurooncol, 2020, 148(2): 221-229. DOI: 10.1007/s11060-020-03528-2.
[22]
Appay R, Dehais C, Maurage CA, et al. CDKN2A homozygous deletion is a strong adverse prognosis factor in diffuse malignant IDH-mutant gliomas[J]. Neuro Oncol, 2019, 21(12): 1519-1528. DOI: 10.1093/neuonc/noz124.
[23]
Shirahata M, Ono T, Stichel D, et al. Novel, improved grading system(s) for IDH-mutant astrocytic gliomas[J]. Acta Neuropathol, 2018, 136(1): 153-166. DOI: 10.1007/s00401-018-1849-4.
[24]
Riche M, Amelot A, Peyre M, et al. Complications after frame-based stereotactic brain biopsy: a systematic review[J]. Neurosurg Rev, 2021, 44(1): 301-307. DOI: 10.1007/s10143-019-01234-w.
[25]
Liu X, Li Y, Qian Z, et al. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas[J]. Neuroimage Clin, 2018, 20: 1070-1077. DOI: 10.1016/j.nicl.2018.10.014.
[26]
Qian Z, Li Y, Sun Z, et al. Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction[J]. Aging (Albany NY), 2018, 10(10): 2884-2899. DOI: 10.18632/aging.101594.
[27]
Wu W, Wang Y, Xiang J, et al. A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients[J/OL]. Front Oncol, 2022, 12: 729002 [2022-07-08]. https://doi.org/10.3389/fonc.2022.729002. DOI: 10.3389/fonc.2022.729002.
[28]
Lu J, Li X, Li H. A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas[J/OL]. Clin Radiol, 2022, 77(8): e560-e567 [2022-07-08]. https://doi.org/10.1016/j.crad.2022.04.005. DOI: 10.1016/j.crad.2022.04.005.
[29]
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.
[30]
Wang J, Zheng X, Zhang J, et al. An MRI-based radiomics signature as a pretreatment noninvasive predictor of overall survival and chemotherapeutic benefits in lower-grade gliomas[J]. Eur Radiol, 2021, 31(4): 1785-1794. DOI: 10.1007/s00330-020-07581-3.
[31]
Choi YS, Ahn SS, Chang JH, et al. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction[J]. Eur Radiol, 2020, 30(7): 3834-3842. DOI: 10.1007/s00330-020-06737-5.
[32]
Park CJ, Han K, Kim H, et al. Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas[J]. Eur Radiol, 2020, 30(12): 6464-6474. DOI: 10.1007/s00330-020-07089-w.
[33]
Park YW, Kim S, Park CJ, et al. Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status[J/OL]. Eur Radiol, 2022 [2022-07-08]. https://doi.org/10.1007/s00330-022-08941-x. DOI: 10.1007/s00330-022-08941-x.
[34]
Xu C, Peng Y, Zhu W, et al. An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics[J/OL]. Front Oncol, 2022, 12: 969907 [2022-10-09]. https://doi.org/10.3389/fonc.2022.969907. DOI: 10.3389/fonc.2022.969907.
[35]
Ding J, Zhao R, Qiu Q, et al. Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study[J]. Quant Imaging Med Surg, 2022, 12(2): 1517-1528. DOI: 10.21037/qims-21-722.
[36]
Ning Z, Luo J, Xiao Q, et al. Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features[J/OL]. Ann Transl Med, 2021, 9(4): 298 [2022-07-08]. https://doi.org/10.21037/atm-20-4076. DOI: 10.21037/atm-20-4076.
[37]
Zhang Z, He K, Wang Z, et al. Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study[J/OL]. Front Oncol, 2021, 11: 779202 [2022-07-08]. https://doi.org/10.3389/fonc.2021.779202. DOI: 10.3389/fonc.2021.779202.
[38]
Wang ZH, Xiao XL, Zhang ZT, et al. A Radiomics Model for Predicting Early Recurrence in Grade Ⅱ Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging[J/OL]. Front Oncol, 2021, 11: 684996 [2022-07-08]. https://doi.org/10.3389/fonc.2021.684996. DOI: 10.3389/fonc.2021.684996.
[39]
Liu C, Li Y, Xia X, et al. Application of radiomics feature captured from MRI for prediction of recurrence for glioma patients[J]. J Cancer, 2022, 13(3): 965-974. DOI: 10.7150/jca.65366.
[40]
Ma C, Yao Z, Zhang Q, et al. Quantitative integration of radiomic and genomic data improves survival prediction of low-grade glioma patients[J]. Math Biosci Eng, 2020, 18(1): 727-744. DOI: 10.3934/mbe.2021039.
[41]
Wu S, Zhang X, Rui W, et al. A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas[J]. Eur Radiol, 2022, 32(5): 3187-3198. DOI: 10.1007/s00330-021-08444-1.
[42]
Cao M, Suo S, Zhang X, et al. Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach[J/OL]. Biomed Res Int, 2021, 2021: 1235314 [2022-07-08]. https://doi.org/10.1155/2021/1235314. DOI: 10.1155/2021/1235314.
[43]
Kha QH, Le VH, Hung TNK, et al. Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas[J/OL]. Cancers (Basel), 2021, 13(21): 5398 [2022-07-08]. https://doi.org/10.3390/cancers13215398. DOI: 10.3390/cancers13215398.
[44]
Jiang C, Kong Z, Zhang Y, et al. Conventional magnetic resonance imaging-based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in gradeⅡandⅢgliomas[J]. Neuroradiology, 2020, 62(7): 803-813. DOI: 10.1007/s00234-020-02392-1.
[45]
Yogananda CGB, Shah BR, Nalawade SS, et al. MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status[J]. AJNR Am J Neuroradiol, 2021, 42(5): 845-852. DOI: 10.3174/ajnr.A7029.
[46]
Sha YJ, Wang XC, Tan Y, et al. The value of predicting the subtype of IDH mutation combining with MGMT promoter methylation in lower grade gliomas by radiomics based on preoperative MRI[J]. Chin J Magn Reson Imaging, 2022, 13(7): 6-11. DOI: 10.12015/issn.1674-8034.2022.07.002.
[47]
Takano S, Ishikawa E, Sakamoto N, et al. Immunohistochemistry on IDH 1/2, ATRX, p53 and Ki-67 substitute molecular genetic testing and predict patient prognosis in grade Ⅲ adult diffuse gliomas[J]. Brain Tumor Pathol, 2016, 33(2): 107-116. DOI: 10.1007/s10014-016-0260-x.
[48]
Li Y, Qian Z, Xu K, et al. Radiomic features predict Ki-67 expression level and survival in lower grade gliomas[J]. J Neurooncol, 2017, 135(2): 317-324. DOI: 10.1007/s11060-017-2576-8.
[49]
Yang L, Shi P, Zhao G, et al. Targeting cancer stem cell pathways for cancer therapy[J/OL]. Signal Transduct Target Ther, 2020, 5(1): 8 [2022-07-08]. https://doi.org/10.1038/s41392-020-0110-5. DOI: 10.1038/s41392-020-0110-5.
[50]
Wu G, Song X, Liu J, et al. Expression of CD44 and the survival in glioma: a meta-analysis[J/OL]. Biosci Rep, 2020, 40(4): BSR20200520 [2022-07-08]. https://doi.org/10.1042/BSR20200520. DOI: 10.1042/bsr20200520.
[51]
Li B, McCrudden CM, Yuen HF, et al. CD133 in brain tumor: the prognostic factor[J]. Oncotarget, 2017, 8(7): 11144-11159. DOI: 10.18632/oncotarget.14406.
[52]
Wang H, Wang X, Xu L, et al. A pan-cancer perspective analysis reveals the opposite prognostic significance of CD133 in lower grade glioma and papillary renal cell carcinoma[J/OL]. Sci Prog, 2021, 104(2): 368504211010938 [2022-07-08]. https://doi.org/10.1177/00368504211010938. DOI: 10.1177/00368504211010938.
[53]
Wang Z, Tang X, Wu J, et al. Radiomics features based on T2-weighted fluid-attenuated inversion recovery MRI predict the expression levels of CD44 and CD133 in lower grade gliomas[J]. Future Oncol, 2022, 18(7): 807-819. DOI: 10.2217/fon-2021-1173.
[54]
Zhang L, Giuste F, Vizcarra JC, et al. Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma[J/OL]. Front Oncol, 2020, 10: 937 [2022-10-09]. https://doi.org/10.3389/fonc.2020.00937. DOI: 10.3389/fonc.2020.00937.
[55]
Li ZZ, Liu PF, An TT, et al. Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients[J/OL]. Transl Oncol, 2021, 14(6): 101065 [2022-07-08]. https://doi.org/10.1016/j.tranon.2021.101065. DOI: 10.1016/j.tranon.2021.101065.

PREV Research progress of deep learning in stroke diagnosis and prevention
NEXT Research progress of diffusion magnetic resonance imaging in autoimmune encephalitis
  



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