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• Clinical Article •
Prediction based on CE-T1WI omics and pathological parameter models research on postoperative recurrence of glioma
JIN Yixuan  LÜ Ruirui  YANG Zhihua  SUN Meng  NIU Fang  LÜ Hongjie  MA Rong  WANG Xiaodong 

Cite this article as: JIN Y X, LÜ R R, YANG Z H, et al. Prediction based on CE-T1WI omics and pathological parameter models research on postoperative recurrence of glioma[J]. Chin J Magn Reson Imaging, 2024, 15(10): 103-108. DOI:10.12015/issn.1674-8034.2024.10.018.


[Abstract] Objective To explore the application value of using a column chart based on preoperative contrast enhancement T1WI (CE-T1WI) omics combined with pathological parameters to predict postoperative recurrence in patients with gliomas.Materials and Methods A retrospective analysis was conducted on 115 patients diagnosed with glioma after surgery at the General Hospital of Ningxia Medical University from April 2020 to April 2023. They were randomly divided into a training set (n=81) and a validation set (n=34) at a ratio of 7∶3. Draw the volume of interest (VOI) on preoperative enhanced T1WI (CE-T1WI) and extract imaging omics features. U test and least absolute shrinkage and selection operator (LASSO) algorithm were used to screen imaging omics features. The final selected features were included in imaging omics labels and an imaging omics model was established. Calculate the radiomics score (Radscore) based on the corresponding coefficients of the selected omics features. Screening pathological predictive factors that are correlated with recurrence through logistic regression and establishing a pathological parameter model. The combination of the two forms a joint model, and a column chart is drawn to visualize the joint model. Evaluate the predictive performance of each model using the area under curve (AUC) of the subject's working characteristic curve. Using the DeLong test to compare the differences in AUC values between different models, and observe the clinical value of each model using decision curve analysis (DCA).Results Based on preoperative CE-T1WI image delineation, a total of 200 imaging omics features were extracted from VOI, and 6 omics features related to recurrence were selected. Logistic regression analysis was used to include isocitrate dehydrogenase 1 (IDH-1) genotype (OR=2.070, P=0.041) and Ki-67 expression level (OR=1.065, P<0.001) as pathological parameters associated with glioma recurrence. Compared to individual pathological parameter models and radiomics models, the combined model showed the best predictive performance (AUC: 0.875 vs. 0.835, 0.769 in the training group, Z=-1.585, -2.458, P=0.013, 0.014). DCA analysis showed that when the probability of risk threshold was greater than 0.32, the clinical benefit level of using the combined model was higher than the other two models.Conclusions The combined model based on preoperative CE-T1WI imaging omics and pathological parameters has good clinical application value in predicting glioma recurrence, providing important predictive information for treatment decision-making and prognosis of glioma patients.
[Keywords] glioma；recurrence；imaging omics；magnetic resonance imaging；column chart；prediction model

JIN Yixuan1   LÜ Ruirui2   YANG Zhihua3   SUN Meng1   NIU Fang1   LÜ Hongjie1   MA Rong2   WANG Xiaodong1*  

1 Ningxia Medical University School of Clinical Medicine, Yinchuan 750004, China

2 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750004, China

3 Department of Radiotherapy, Cancer Hospital of General Hospital of Ningxia Medical University, Yinchuan 750004, China

Corresponding author: WANG X D, E-mail: xdw80@yeah.net

Conflicts of interest   None.

Received  2024-06-11
Accepted  2024-10-10
DOI: 10.12015/issn.1674-8034.2024.10.018
Cite this article as: JIN Y X, LÜ R R, YANG Z H, et al. Prediction based on CE-T1WI omics and pathological parameter models research on postoperative recurrence of glioma[J]. Chin J Magn Reson Imaging, 2024, 15(10): 103-108. DOI:10.12015/issn.1674-8034.2024.10.018.

[1]
ZHENG L L, LUO C, LANG J Y, et al. Current status and new progress of targeted therapy and immunotherapy for high-grade glioma[J]. J Canc Control Treat, 2023, 36(9): 806-814. DOI: 10.3969/j.issn.1674-0904.2023.09.014.
[2]
LOUIS D N, 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]
ORONSKY B, REID T R, ORONSKY A, et al. A review of newly diagnosed glioblastoma[J/OL]. Front Oncol, 2021, 10: 574012 [2024-06-11]. https://doi.org/10.3389/fonc.2020.574012. DOI: 10.3389/fonc.2020.574012.
[4]
YASINJAN F, XING Y, GENG H, et al. Immunotherapy: a promising approach for glioma treatment[J/OL]. Front Immunol, 2023, 14: 1255611 [2024-06-11]. https://doi.org/10.3389/fimmu.2023.1255611. DOI: 10.3389/fimmu.2023.1255611.
[5]
WEN P Y, VAN DEN BENT M, YOUSSEF G, et al. RANO 2.0: update to the response assessment in neuro-oncology criteria for high-and low-grade gliomas in adults[J]. J Clin Oncol, 2023, 41(33): 5187-5199. DOI: 10.1200/JCO.23.01059.
[6]
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, 43(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[7]
ZHANG Y P, ZHANG X Y, CHENG Y T, et al. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling[J/OL]. Mil Med Res, 2023, 10(1): 22 [2024-06-11]. https://doi.org/10.1186/s40779-023-00458-8. DOI: 10.1186/s40779-023-00458-8.
[8]
ZHANG X, ZHANG Y, ZHANG G, et al. Deep learning with radiomics for disease diagnosis and treatment: Challenges and potential[J/OL]. Front Oncol, 2022, 12:773840 [2024-06-11]. https://doi.org/10.3389/fonc.2022.773840. DOI: 10.3389/fonc.2022.773840.
[9]
REUSS D E. Updates on the WHO diagnosis of IDH-mutant glioma[J]. J Neurooncol, 2023, 162(3): 461-469. DOI: 10.1007/s11060-023-04250-5.
[10]
YOSHIMURA H, KAWAHARA D, SAITO A, et al. Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN[J]. Phys Eng Sci Med, 2024, 47(3): 1227-1243. DOI: 10.1007/s13246-024-01443-8.
[11]
LIANG J, LV X, LU C, et al. Prognostic factors of patients with gliomas-an analysis on 335 patients with glioblastoma and other forms of gliomas[J/OL]. BMC cancer, 2020, 20: 1-7 [2024-06-11]. https://doi.org/10.1186/s12885-019-6511-6. DOI: 10.1186/s12885-019-6511-6.
[12]
TUSTISON N J, AVANTS B B, COOK P A, et al. N4ITK: improved N3 bias correction[J]. IEEE Trans Med Imaging, 2010, 29(6): 1310-1320. DOI: 10.1109/TMI.2010.2046908.
[13]
KING J L, BENHABBOUR S R. Glioblastoma multiforme—A look at the past and a glance at the future[J/OL]. Pharmaceutics, 2021, 13(7): 1053 [2024-06-11]. https://doi.org/10.3390/pharmaceutics13071053. DOI: 10.3390/pharmaceutics13071053.
[14]
FURTAK J, KWIATKOWSKI A, ŚLEDZIŃSKA P, et al. Survival after reoperation for recurrent glioblastoma multiforme: A prospective study[J/OL]. Surg Oncol, 2022, 42: 101771 [2024-06-11]. https://doi.org/10.1016/j.suronc.2022.101771. DOI: 10.1016/j.suronc.2022.101771.
[15]
SHOFTY B, HAIM O, COSTA M, et al. Impact of repeated operations for progressive low-grade gliomas[J]. Eur J Surg Oncol, 2020, 46(12): 2331-2337. DOI: 10.1016/j.ejso.2020.07.013.
[16]
ALVES T R, LIMA F R S, KAHN S A, et al. Glioblastoma cells: A heterogeneous and fatal tumor interacting with the parenchyma[J]. Life Sci, 2011, 89(15-16): 532-539. DOI: 10.1016/j.lfs.2011.04.022.
[17]
GREAVES M, MALEY C C. Clonal evolution in cancer[J]. Nature, 2012, 481(7381): 306-313. DOI: 10.1038/nature10762.
[18]
CONTRERAS-TRUJILLO H, EERDENG J, AKRE S, et al. Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses[J/OL]. Nat Commun, 2021, 12(1): 6522 [2024-06-11]. https://doi.org/10.1038/s41467-021-26771-1. DOI: 10.1038/s41467-021-26771-1.
[19]
KOBRAS C M, FENTON A K, SHEPPARD S K. Next-generation microbiology: from comparative genomics to gene function[J/OL]. Genome Biol, 2021, 22(1): 123 [2024-06-11]. https://doi.org/10.1186/s13059-021-02344-9. DOI: 10.1186/s13059-021-02344-9.
[20]
YI Z, LONG L, ZENG Y, et al. Current advances and challenges in radiomics of brain tumors[J/OL]. Front Oncol, 2021, 11: 732196 [2024-06-11]. https://doi.org/10.3389/fonc.2021.732196. DOI: 10.3389/fonc.2021.732196.
[21]
YU Y, HE Z, OUYANG J, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study[J/OL]. EBioMedicine, 2021, 69: 103460 [2024-06-11]. https://doi.org/10.1016/j.ebiom.2021.103460. DOI: 10.1016/j.ebiom.2021.103460.
[22]
LIU Z, HONG X, WANG L, et al. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas[J/OL]. BMC cancer, 2023, 23(1): 848 [2024-06-11]. https://doi.org/10.1186/s12885-023-11338-8. DOI: 10.1186/s12885-023-11338-8.
[23]
PARK C J, KIM S, HAN K, et al. Diffusion-and perfusion-weighted MRI radiomics for survival prediction in patients with lower-grade gliomas[J/OL]. Yonsei Med J, 2024, 65(5): 283 [2024-06-11]. https://doi.org/10.3349/ymj.2023.0323. DOI: 10.3349/ymj.2023.0323.
[24]
BICCI E, CALAMANDREI L, DI FINIZIO A, et al. Predicting response to exclusive combined radio-chemotherapy in naso-oropharyngeal cancer: The role of texture analysis[J/OL]. Diagnostics, 2024, 14(10): 1036 [2024-06-11]. https://doi.org/10.3390/diagnostics14101036. DOI: 10.3390/diagnostics14101036.
[25]
WANG J, YI X, FU Y, et al. Preoperative magnetic resonance imaging radiomics for predicting early recurrence of glioblastoma[J/OL]. Front Oncol, 2021, 11: 769188 [2024-06-11]. https://doi.org/10.3389/fonc.2021.769188. DOI: 10.3389/fonc.2021.769188.
[26]
KAZEROONI A F, AKBARI H, SHUKLA G, et al. Cancer imaging phenomics via CaPTk: Multi-institutional prediction of progression-free survival and pattern of recurrence in glioblastoma[J]. JCO Clin Cancer Inform, 2020, 4(4): 234-244. DOI: 10.1200/CCI.19.00121.
[27]
HU G, HU X, YANG K, et al. Radiomics-based machine learning to predict recurrence in glioma patients using magnetic resonance imaging[J]. J Comput Assist Tomogr, 2023, 47(1): 129-135. DOI: 10.1097/RCT.0000000000001386.
[28]
LONG Z H, LIU R Z. Research progress on the mechanism of action and imaging omics of Ki-67 in gliomas[J]. Chin J Clin Neurosurg, 2022, 27(5): 412-415. DOI: 10.13798/j.issn.1009-153X.2022.05.026.
[29]
CHEN A J, GOU R B. Expression and pathological significance of Ki-67 and glial fibrillary acidic protein in glioma patients[J]. J Clin Pathol Res, 2020, 40(7): 1688-1691. DOI: 10.3978/j.issn.2095-6959.2020.07.009.
[30]
GATES E D H, LIN J S, WEINBERG J S, et al. Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging[J]. Neuro Oncol, 2019, 21(4): 527-536. DOI: 10.1093/neuonc/noz004.
[31]
XU T T, WANG B R, ZHAO H S, et al. DWI explore the prognosis of the glioma and correlation between its parameter and the expression of Ki-67 and IDH1 gene mutation status in tumor cells[J]. Chin J CT & MRI, 2023, 21(10): 5-7. DOI: 10.3969/j.issn.1672-5131.2023.10.002.
[32]
HAN S, LIU Y, CAI S J, et al. IDH mutation in glioma: molecular mechanisms and potential therapeutic targets[J]. Br J Cancer, 2020, 122(11): 1580-1589. DOI: 10.1038/s41416-020-0814-x.
[33]
BHAVYA B, ANAND C, MADHUSOODANAN U, et al. To be wild or mutant: Role of isocitrate dehydrogenase 1 (IDH1) and 2-hydroxy glutarate (2-HG) in gliomagenesis and treatment outcome in glioma[J]. Cell Mol Neurobiol, 2020, 40(1): 53-63. DOI: 10.1007/s10571-019-00730-3.

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