Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach

Suh, Hie Bum; Choi, Yoon Seong; Bae, Soong June; Ahn, Sung Soo; Chang, Jong Hee; Kang, Seok‐Gu; Kim, Eui Hyun; Kim, Se Hoon; Lee, Seung Koo

doi:10.1007/s00330-018-5368-4

Cited by 110 publications

(99 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Glioblastoma imaging features may overlap primary central nervous system lymphomas (PCNSLs) and metastases, rendering a noninvasive distinction truly challenging. 59 Recent MRTA studies, however, have shown promising results in differentiating glioblastomas from PCNSLs and metastases (On-line Table 4). [59][60][61][62][63]66 Kunimatsu et al 60,61 differentiated glioblastomas from PCNSLs with 75% accuracy by selecting the top 4 best-performing texture features from CE-MR images.…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

“…Xiao et al 62 found skewness and kurtosis to be the best first-order features on CE-MR imaging in a similar population. Suh et al 59 reported 90% accuracy of radiomics-based machine learning algorithms compared with visual analysis by 3 readers in differentiating PCNSLs from glioblastomas. Similarly, Alcaide-Leon et al 63 showed superiority of the SVM classifier over human evaluation.…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

Texture analysis is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, systemic oncologic applications of texture analysis have been increasingly explored. Here we discuss the basic concepts and methodologies of texture analysis, along with a review of various MR imaging texture analysis applications in glioma imaging. We also discuss MR imaging texture analysis limitations and the technical challenges that impede its widespread clinical implementation. With continued advancement in computational processing, MR imaging texture analysis could potentially develop into a valuable clinical tool in routine oncologic imaging. ABBREVIATIONS:AUC ϭ area under the curve; CE ϭ contrast-enhanced; GLCM ϭ gray-level co-occurrence matrix; GLRLM ϭ gray-level run-length matrix; HGG ϭ high-grade glioma; IDH ϭ isocitrate dehydrogenase; IDM ϭ inverse difference moment; LGG ϭ low-grade glioma; MRTA ϭ MR imaging texture analysis; PCNSLϭ primary central nervous system lymphoma; PCA ϭ principal component analysis; SVM ϭ support vector machine; TA ϭ texture analysis Indicates open access to non-subscribers at www.ajnr.org Indicates article with supplemental on-line tables.

show abstract

Section: Miscellaneous Applicationsmentioning

confidence: 99%

Section: Miscellaneous Applicationsmentioning

confidence: 99%

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni

Priya

Bathla

2019

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

“…37 A total of 4 studies prospectively assessed the ability of radiologists to distinguish between GBM and PCNSL, providing a comparison group for ML. 1,12,33,37 In all 4 of these studies, ML algorithms performed as well as or better than radiologists ( Table 3).…”

Section: Systematic Reviewmentioning

confidence: 88%

“…1). 1,5,12,19,33,[36][37][38] Overall, 6 (75%) of the studies exclusively compared GBM to PCNSL, and 2 of the studies included other tumors as well ( Table 2). All studies only investigated de novo tumors that were imaged using T1-/T2-weighted MRI, FLAIR, and/or diffusion-weighted MRI prior to steroid administration, biopsy, or any other treatment.…”

Section: Systematic Reviewmentioning

confidence: 99%

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis

Nguyen¹,

Blears²,

Ross³

et al. 2018

Neurosurgical Focus

View full text Add to dashboard Cite

OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.METHODSThe authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).RESULTSEight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.CONCLUSIONSFew studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.

show abstract

“…The neuroradiologists were blinded to the clinical information, but were aware that the tumors were either ODG2 or ODG3, without knowing the exact number of patients with each entity. The three readers assessed only conventional MR images (T1WI, T2WI, FLAIR and T1 CE), and recorded the final diagnosis using a 4-point scale (1 = definite ODG2; 2 = likely ODG2; 3 = likely ODG3; and 4 = definite ODG3) [24].…”

Section: Radiologist's Assessmentmentioning

confidence: 99%

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

Zhao

Feng

et al. 2020

BMC Neurol

View full text Add to dashboard Cite

Background: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy.Methods: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. Results: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. Conclusions: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

show abstract

Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach

Cited by 110 publications

References 28 publications

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Texture Analysis in Cerebral Gliomas: A Review of the Literature

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

Contact Info

Product

Resources

About