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Advances in the application of peritumoral radiomics in gliomas
OUYANG Qunhui  HUA Kelei  FANG Laiping  ZHAN Wenfeng  WANG Wei  ZHOU Tianxing  JIANG Guihua  LIU Ping 

Cite this article as: OUYANG Q H, HUA K L, FANG L P, et al. Advances in the application of peritumoral radiomics in gliomas[J]. Chin J Magn Reson Imaging, 2025, 16(2): 149-153, 164. DOI:10.12015/issn.1674-8034.2025.02.024.


[Abstract] Glioma represents the most prevalent primary intracranial neoplasm, and surgical resection constitutes the preferred primary therapeutic regimen. Approximately 90% of postoperative recurrences in glioma patients occur within the peritumoral brain zone (PBZ), which serves as the boundary between tumor tissue and adjacent normal brain tissue. The PBZ is a pivotal component of the tumor microenvironment and provides insights into the invasive behavior of glioma towards surrounding tissues. A comprehensive understanding and exploration of the biological information contained within the PBZ are crucial for improving the prognosis of glioma patients. In recent years, radiomics has made significant progress in applications such as glioma grading, genotyping, and prognostic evaluation, but primarily focusing on the solid component. As research advances, the importance of the PBZ in assessing the biological behavior of gliomas has gradually come to light, rendering radiomic studies of the PBZ a key area of investigation. This article reviews the concept and significance of the PBZ, along with the advancements in the utilization of peritumoral radiomics in gliomas, including differential diagnosis, molecular typing, prediction of postoperative recurrence and prognosis prediction. The objective is to enhance the understanding of PBZ in gliomas, provide insights and guidelines for conducting pertinent research, and ultimately establish a foundation for precise management strategies for patients.
[Keywords] glioma；peritumoral brain zone；radiomics；magnetic resonance imaging；artificial intelligence

OUYANG Qunhui   HUA Kelei   FANG Laiping   ZHAN Wenfeng   WANG Wei   ZHOU Tianxing   JIANG Guihua   LIU Ping*  

Imaging Center, the Affiliated Guangdong Second Provincial People's Hospital of Jinan University, Guangzhou 510317, China

Corresponding author: LIU P, E-mail: ping0625liu0318@163.com

Conflicts of interest   None.

Received  2024-10-10
Accepted  2025-01-10
DOI: 10.12015/issn.1674-8034.2025.02.024
Cite this article as: OUYANG Q H, HUA K L, FANG L P, et al. Advances in the application of peritumoral radiomics in gliomas[J]. Chin J Magn Reson Imaging, 2025, 16(2): 149-153, 164. DOI:10.12015/issn.1674-8034.2025.02.024.

[1]
OSTROM Q T, CIOFFI G, WAITE K, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014–2018[J]. Neuro Oncol, 2021, 23: iii1-iii105. DOI: 10.1093/neuonc/noab200.
[2]
WELLER M, WEN P Y, CHANG S M, et al. Glioma[J/OL]. Nat Rev Dis Primers, 2024, 10(1): 33 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38724526/. DOI: 10.1038/s41572-024-00516-y.
[3]
PÖHLMANN J, WELLER M, MARCELLUSI A, et al. High costs, low quality of life, reduced survival, and room for improving treatment: an analysis of burden and unmet needs in glioma[J/OL]. Front Oncol, 2024, 14: 1368606 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38571509/. DOI: 10.3389/fonc.2024.1368606.
[4]
RODGERS L T, VILLANO J L, HARTZ A M S. Glioblastoma Standard of Care: Effects on Tumor Evolution and Reverse Translation in Preclinical Models[J/OL]. Cancers (Basel), 2024, 16(15): 2638 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/39123366/. DOI: 10.3390/cancers16152638.
[5]
LEMÉE J M, CLAVREUL A, MENEI P. Intratumoral heterogeneity in glioblastoma: don't forget the peritumoral brain zone[J]. Neuro Oncol. 2015, 17(10): 1322-1332. DOI: 10.1093/neuonc/nov119.
[6]
SILVA M, VIVANCOS C, DUFFAU H. The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude[J/OL]. Brain Sci. 2022;12(4):504 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/35448035/. DOI: 10.3390/brainsci12040504.
[7]
TREVISI G, MANGIOLA A. Current Knowledge about the Peritumoral Microenvironment in Glioblastoma[J/OL]. Cancers (Basel), 2023, 15(22): 5460 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/35448035/. DOI: 10.3390/cancers15225460.
[8]
ELLINGSON B M, SANVITO F, CLOUGHESY T F, et al. A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults[J]. AJNR Am J Neuroradiol, 2024, 45(12): 1846-1856. DOI: 10.3174/ajnr.A8396.
[9]
HEIDARI M, SHOKRANI P. Imaging Role in Diagnosis, Prognosis, and Treatment Response Prediction Associated with High-grade Glioma[J/OL]. J Med Signals Sens, 2024, 14: 7 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38993200/. DOI: 10.4103/jmss.jmss_30_22.
[10]
AERTS H J, VELAZQUEZ E R, LEIJENAAR R T, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J/OL]. Nat Commun, 2014, 5: 4644 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/24892406/. DOI: 10.1038/ncomms5006.
[11]
GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
[12]
SHAHEEN A, BUKHARI S T, NADEEM M. Overall Survival Prediction of Glioma Patients With Multiregional Radiomics[J/OL]. Front Neurosci, 2022, 16: 911065 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/35873825/. DOI: 10.3389/fnins.2022.911065.
[13]
INDORIA A, KULANTHAIVELU K, PRASAD C, et al. Radiomics features for the discrimination of tuberculomas from high grade gliomas and metastasis: a multimodal study[J]. Neuroradiology, 2024, 66(11): 1979-1992. DOI: 10.1007/s00234-024-03435-7.
[14]
LIU X, JIANG Z, ROTH H R, et al. Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study[J/OL]. Neurooncol Adv, 2024, 6(1): vdae108 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/39027132/. DOI: 10.1093/noajnl/vdae108.
[15]
HAN Q, LU Y, WANG D, et al. Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images[J]. Eur Radiol, 2023, 33(12): 9139-9151. DOI: 10.1007/s00330-023-09924-2.
[16]
BALLESTÍN A, ARMOCIDA D, RIBECCO V, Seano G. Peritumoral brain zone in glioblastoma: biological, clinical and mechanical features[J/OL]. Front Immunol, 2024, 15: 1347877 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38487525/. DOI: 10.3389/fimmu.2024.1347877.
[17]
ZHAO Y, ZHANG J, WANG N, et al. Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma[J/OL]. BMC Cancer. 2023, 23(1): 1026 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/37875815/. DOI: 10.1186/s12885-023-11491-0.
[18]
MAO N, SHI Y, LIAN C, et al. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography[J]. Eur Radiol, 2022, 32(5): 3207-3219. DOI: 10.1007/s00330-021-08414-7.
[19]
JIANG W, MENG R, CHENG Y, et al. Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer[J]. J Magn Reson Imaging, 2024, 59(2): 613-625. DOI: 10.1002/jmri.28776.
[20]
FAN H, LUO Y, GU F, et al. Artificial intelligence-based MRI radiomics and radiogenomics in glioma[J/OL]. Cancer Imaging, 2024, 24(1): 36 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38486342/. DOI: 10.1186/s40644-024-00682-y.
[21]
GIAMBRA M, DI CRISTOFORI A, VALTORTA S, et al. The peritumoral brain zone in glioblastoma: where we are and where we are going[J]. J Neurosci Res, 2023, 101(2): 199-216. DOI: 10.1002/jnr.25134.
[22]
PRASANNA P, PATEL J, PARTOVI S, et al. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings[J]. Eur Radiol, 2017, 27(10): 4188-4197. DOI: 10.1007/s00330-016-4637-3.
[23]
YU H, CHEN M, HUANG Y, et al. Differential Diagnosis of Intracranial Malignant Tumors Using MRI Based on Morphological Features and Signal Intensity Ratio of Lesions[J]. Altern Ther Health Med, 2023, 29(8): 816-821.
[24]
CSUTAK C, ȘTEFAN P A, LENGHEL L M, et al. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone[J/OL]. Brain Sci, 2020, 10(9): 638 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/32947822/. DOI: 10.3390/brainsci10090638.
[25]
XIAO D, WANG J, WANG X, et al. Distinguishing brain abscess from necrotic glioblastoma using MRI-based intranodular radiomic features and peritumoral edema/tumor volume ratio[J]. J Integr Neurosci, 2021, 20(3): 623-634. DOI: 10.31083/j.jin2003066.
[26]
KIM Y, CHO H H, KIM S T, et al. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI[J]. Neuroradiology, 2018, 60(12): 1297-1305. DOI: 10.1007/s00234-018-2091-4.
[27]
DONG F, LI Q, JIANG B, et al. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers[J]. Eur Radiol, 2020, 30(5): 3015-3022. DOI: 10.1007/s00330-019-06460-w.
[28]
SAMANI Z R, PARKER D, WOLF R, et al. Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases[J/OL]. Sci Rep, 2021, 11(1): 14469 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/34262079/. DOI: 10.1038/s41598-021-93804-6.
[29]
PARVAZE P S, BHATTACHARJEE R, VERMA Y K, et al. Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis[J/OL]. NMR Biomed, 2023, 36(5): e4884 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/36453877/. DOI: 10.1002/nbm.4884.
[30]
BATHLA G, DHRUBA D D, SONI N, et al. AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods[J]. J Neuroradiol. 2024, 51(3): 258-264. DOI: 10.1016/j.neurad.2023.08.007.
[31]
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.
[32]
MORSHED R A, YOUNG J S, HERVEY-JUMPER S L, 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.
[33]
MACDONALD T J, AGUILERA D, KRAMM C M. Treatment of high-grade glioma in children and adolescents[J]. Neuro Oncol, 2011, 13(10): 1049-1058. DOI: 10.1093/neuonc/nor092
[34]
GAO M, HUANG S, PAN X, et al. Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas[J/OL]. Front Oncol, 2020, 10: 1676 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/33014836/. DOI: 10.3389/fonc.2020.01676.
[35]
CHENG J, LIU J, YUE H, et al. Prediction of Glioma Grade Using Intratumoral and Peritumoral Radiomic Features From Multiparametric MRI Images[J]. IEEE/ACM Trans Comput Biol Bioinform, 2022, 19(2): 1084-1095. DOI: 10.1109/TCBB.2020.3033538.
[36]
MALIK N, GERAGHTY B, DASGUPTA A, et al. MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region[J]. J Neurooncol, 2021, 155(2): 181-191. DOI: 10.1007/s11060-021-03866-9.
[37]
TAN R, SUI C, WANG C, et al. MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study[J/OL]. Front Oncol, 2024, 14: 1401977 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38803534/. DOI: 10.3389/fonc.2024.1401977.
[38]
LOUIS D N, 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.
[39]
SINGH G, SINGH A, BAE J, et al. -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates[J/OL]. Cancer Imaging, 2024, 24(1): 133 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/39375809/. DOI: 10.1186/s40644-024-00769-6.
[40]
NIMBALKAR V P, KRUTHIKA B S, SRAVYA P, et al. Differential gene expression in peritumoral brain zone of glioblastoma: role of SERPINA3 in promoting invasion, stemness and radioresistance of glioma cells and association with poor patient prognosis and recurrence[J]. J Neurooncol. 2021, 152(1): 55-65. DOI: 10.1007/s11060-020-03685-4.
[41]
LIN D, LIU J, KE C, et al. Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas[J]. Clin Neuroradiol, 2024, 34(4): 817-826. DOI: 10.1007/s00062-024-01421-3.
[42]
SUN X, PANG P, LOU L, et al. Radiomic prediction models for the level of Ki-67 and p53 in glioma[J/OL]. J Int Med Res. 2020, 48(5): 300060520914466 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/32431205/. DOI: 10.1177/0300060520914466.
[43]
VILS A, BOGOWICZ M, TANADINI-LANG S, et al. Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial[J/OL]. Front Oncol, 2021, 11: 636672 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/33937035/. DOI: 10.3389/fonc.2021.636672.
[44]
YABO Y A, HEILAND D H. Understanding glioblastoma at the single-cell level: Recent advances and future challenges[J/OL]. PLoS Biol, 2024, 22(5): e3002640 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/38814900/. DOI: 10.1371/journal.pbio.3002640.
[45]
CUDDAPAH V A, ROBEL S, WATKINS S, et al. A neurocentric perspective on glioma invasion[J]. Nat Rev Neurosci, 2014, 15(7): 455-465. DOI: 10.1038/nrn3765.
[46]
GAO M, CHENG J, QIU A, et al. Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients[J/OL]. Clin Radiol, 2024, 79(11): e1383-e1393 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/39218720/. DOI: 10.1016/j.crad.2024.08.005.
[47]
CHOUGULE T, GUPTA R K, SAINI J, et al. Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging[J/OL]. NMR Biomed, 2022, 35(3): e4647 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/34766380/. DOI: 10.1002/nbm.4647.
[48]
RATHORE S, AKBARI H, DOSHI J, et al. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning[J/OL]. J Med Imaging (Bellingham), 2018, 5(2): 021219 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/29531967/. DOI: 10.1117/1.JMI.5.2.021219.
[49]
REN J, ZHAI X, YIN H, et al. Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma[J]. Neurol Ther, 2023, 12(5): 1729-1743. DOI: 10.1007/s40120-023-00524-2.
[50]
WANG B, ZHANG S, WU X, et al. Multiple Survival Outcome Prediction of Glioblastoma Patients Based on Multiparametric MRI[J/OL]. Front Oncol, 2021, 11: 778627 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/34900728/. DOI: 10.3389/fonc.2021.778627.
[51]
LATYSHEVA A, GEIER O M, HOPE T R, et al. Diagnostic utility of Restriction Spectrum Imaging in the characterization of the peritumoral brain zone in glioblastoma: Analysis of overall and progression-free survival[J/OL]. Eur J Radiol, 2020, 132: 109289 [2024-10-10]. https://pubmed.ncbi.nlm.nih.gov/33002815/. DOI: 10.1016/j.ejrad.2020.109289.

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