Radiomics: The New Promise for Differentiating Progression, Recurrence, Pseudoprogression, and Radionecrosis in Glioma and Glioblastoma Multiforme

被引:9
|
作者
Alizadeh, Mohammadreza [1 ]
Lomer, Nima Broomand [2 ]
Azami, Mobin [3 ]
Khalafi, Mohammad [4 ]
Shobeiri, Parnian [5 ]
Arab Bafrani, Melika [5 ]
Sotoudeh, Houman [6 ]
机构
[1] Iran Univ Med Sci, Physiol Res Ctr, Tehran 1449614535, Iran
[2] Guilan Univ Med Sci, Fac Med, Rasht 4193713111, Iran
[3] Kurdistan Univ Med Sci, Student Res Comm, Sanandaj 6618634683, Iran
[4] Tabriz Univ Med Sci, Radiol Dept, Tabriz 5165665931, Iran
[5] Univ Tehran Med Sci, Sch Med, Tehran 1416753955, Iran
[6] Univ Alabama Birmingham UAB, Heersink Sch Med, Dept Radiol & Neurol, Birmingham, AL 35294 USA
关键词
glioma; glioblastoma multiform (GBM); radiomics; MRI; PET; tumor progression; tumor recurrence; pseudoprogression; radionecrosis; HIGH-GRADE GLIOMAS; INCREASED SIGNAL INTENSITY; AMINO-ACID PET; RADIATION NECROSIS; TUMOR RECURRENCE; DIAGNOSTIC-ACCURACY; CONCOMITANT RADIOCHEMOTHERAPY; TREATMENT RESPONSE; RESECTION CAVITY; CONVENTIONAL MRI;
D O I
10.3390/cancers15184429
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary Progression/recurrence, pseudoprogression, and radionecrosis are all scenarios that can be expected during the treatment course of glioma and GBM. Although MRI, PET, CT, and MRS have shown some capabilities in differentiating these conditions, there is still a considerable need for the emergence of state-of-the-art techniques to assist field professionals. Here, we introduce radiomics, a process that extracts many features from medical images using data characterization algorithms and a promising tool to differentiate these scenarios. The results could significantly impact patients' care by enhancing the understanding and accuracy of post-treatment follow-ups in brain cancer patients.Abstract Glioma and glioblastoma multiform (GBM) remain among the most debilitating and life-threatening brain tumors. Despite advances in diagnosing approaches, patient follow-up after treatment (surgery and chemoradiation) is still challenging for differentiation between tumor progression/recurrence, pseudoprogression, and radionecrosis. Radiomics emerges as a promising tool in initial diagnosis, grading, and survival prediction in patients with glioma and can help differentiate these post-treatment scenarios. Preliminary published studies are promising about the role of radiomics in post-treatment glioma/GBM. However, this field faces significant challenges, including a lack of evidence-based solid data, scattering publication, heterogeneity of studies, and small sample sizes. The present review explores radiomics's capabilities in following patients with glioma/GBM status post-treatment and to differentiate tumor progression, recurrence, pseudoprogression, and radionecrosis.
引用
收藏
页数:19
相关论文
共 42 条
  • [31] Diffusion kurtosis imaging combined with dynamic susceptibility contrast-enhanced MRI in differentiating high-grade glioma recurrence from pseudoprogression
    Shi, Wenwei
    Qu, Chongxiao
    Wang, Xiaochun
    Liang, Xiao
    Tan, Yan
    Zhang, Hui
    EUROPEAN JOURNAL OF RADIOLOGY, 2021, 144
  • [32] Meta-analysis of the diagnostic performance of diffusion magnetic resonance imaging with apparent diffusion coefficient measurements for differentiating glioma recurrence from pseudoprogression
    Yu, Yang
    Ma, Yue
    Sun, Mengyao
    Jiang, Wenyan
    Yuan, Tingting
    Tong, Dan
    MEDICINE, 2020, 99 (23)
  • [33] Reversing glioma malignancy: a new look at the role of antidepressant drugs as adjuvant therapy for glioblastoma multiforme
    Anna M. Bielecka-Wajdman
    Marta Lesiak
    Tomasz Ludyga
    Aleksander Sieroń
    Ewa Obuchowicz
    Cancer Chemotherapy and Pharmacology, 2017, 79 : 1249 - 1256
  • [34] Genetic analysis of a multifocal glioblastoma multiforme: A suitable tool to gain new aspects in glioma development
    Krex, D
    Mohr, B
    Appelt, H
    Schackert, HK
    Schackert, G
    NEUROSURGERY, 2003, 53 (06) : 1377 - 1384
  • [35] Reversing glioma malignancy: a new look at the role of antidepressant drugs as adjuvant therapy for glioblastoma multiforme
    Bielecka-Wajdman, Anna M.
    Lesiak, Marta
    Ludyga, Tomasz
    Sieron, Aleksander
    Obuchowicz, Ewa
    CANCER CHEMOTHERAPY AND PHARMACOLOGY, 2017, 79 (06) : 1249 - 1256
  • [36] USING MACHINE LEARNING TO BUILD RADIOMICS MODELS THAT DISTINGUISH REGIONS OF GLIOBLASTOMA RECURRENCE VS TUMOR PROGRESSION ON MRI
    Yoon, Hyunsoo
    Hawkins-Daarud, Andrea
    Save, Akshay
    Singleton, Kyle
    Clark-Swanson, Kamala
    Wang, Lujia
    Bendok, Bernard
    Mrugala, Maciej
    Wu, Teresa
    Bruce, Jeffrey
    Hu, Leland
    Li, Jing
    Canoll, Peter D.
    Swanson, Kristin
    NEURO-ONCOLOGY, 2019, 21 : 175 - 175
  • [37] Exosomes Secreted by Inflammatory Cytokine Stimulated Glioma Cells Carry a Repertoire of Proteins Which Influence Progression of Glioblastoma Multiforme
    Kore, Rajshekhar A.
    Abraham, Edathara C.
    Griffin, Robert J.
    FASEB JOURNAL, 2016, 30
  • [38] Genetic analysis of a multifocal glioblastoma multiforme: A suitable tool to gain new aspects in glioma development - Comment
    Waziri, AE
    Bruce, JN
    NEUROSURGERY, 2003, 53 (06) : 1384 - 1384
  • [39] The efficacy of using a multiparametric magnetic resonance imaging-based radiomics model to distinguish glioma recurrence from pseudoprogression (vol 111, pg 168, 2024)
    Fu, Fang-Xiong
    Cai, Qin-Lei
    Li, Guo
    Wu, Xiao-Jing
    Hong, Lan
    Chen, Wang-Sheng
    MAGNETIC RESONANCE IMAGING, 2024, 111 : 265 - 265
  • [40] Umbrella review and network meta-analysis of diagnostic imaging test accuracy studies in differentiating between brain tumor progression versus pseudoprogression and radionecrosis (vol 166, pg 1, 2024)
    Dagher, Richard
    Gad, Mona
    de Santana, Paloma da Silva
    Sadeghi, Mohammad Amin
    Yewedalsew, Selome F.
    Gujar, Sachin K.
    Yedavalli, Vivek
    Kohler, Cristiano Andre
    Khan, Majid
    Tavora, Daniel Gurgel Fernandes
    Kamson, David Olayinka
    Sair, Haris I.
    Luna, Licia P.
    JOURNAL OF NEURO-ONCOLOGY, 2024, 169 (01) : 219 - 219