Diagnostic Accuracy of Magnetic Resonance Spectroscopy in Predicting the Grade of Glioma Keeping Histopathology as the Gold Standard

Rafique, Zunaira; Awan, Muhammad Wasim; Iqbal, Shaghaf; Usmani, N.; Kamal, Mahjabeen Mahmood; Arshad, Wajiha; Ahmad, Mashkoor; Mumtaz, Hassan; Ahmad, Shahzaib; Hasan, Mohammad

doi:10.7759/cureus.22056

Cited by 11 publications

(19 citation statements)

References 17 publications

(22 reference statements)

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“…In their investigation, Mahmud et al discovered MRI specificity and sensitivity of 84 and 75%, respectively, which is close to our findings. It has been demonstrated that including an MR Spectroscopy sequence can increase the diagnosis accuracy of brain malignancies (Rafique et al, 2022). In their investigation, Jesrani et al discovered that sensitivity was 87.5%, specificity was 93.3%, positive predictive value was 95.5%, negative predictive value was 89.7%, and accuracy was 92.1% using MRS, which corresponds to the results observed in our study (Musawar et al, 2022).…”

Section: Discussionsupporting

confidence: 89%

Comparison of diagnostic accuracy of magnetic resonance spectroscopy with conventional magnetic resonance imaging for brain tumors and correlation with histopathology as gold standard

Farzana

Naseer²,

Ahmad

et al. 2023

ijhs

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Background; Despite a large number of revolutionized advancements in Imaging technology clinicians are unable to get hundred percent accurate results. Multiple limitations have been observed in Imaging modalities. Multiple published trials are available online on the evaluations of these imaging modalities individually and in combinations to find out the level of accuracy of these technologies. Objective; This study was design to compare the diagnostic accuracy of MR Spectroscopy with MRI which is a conventional diagnostic tool for brain tumor diagnosis while keeping the Histopathology evaluation as Gold Standard. Methodology; The patients who fulfilled the inclusion criteria were included in study. All scans were carried out on GE 1.5 Tesla MR Scanner using head coil. Axial T1W, T2W, Fluid Attenuated Inversion Recovery (FLAIR), Sagittal T2W, Coronal Fluid Attenuation. Inversion Recovery (FLAIR) and T1W post- contrast Axial, Coronal and Sagittal images were acquired. MR spectroscopy was performed through single voxel technique. Final diagnosis was made on histopathology results. The reports of biopsy were co related with radiological diagnosis of MRI and MRS. Results; The study was conducted with 150 suspected cases. There were 85(56.7%) males and 65(43.3%) females. Histopathology 100(66.7%) cases were confirmed positive and 50(33.3%) negative.

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Section: Discussionsupporting

confidence: 89%

Comparison of diagnostic accuracy of magnetic resonance spectroscopy with conventional magnetic resonance imaging for brain tumors and correlation with histopathology as gold standard

Farzana

Naseer²,

Ahmad

et al. 2023

ijhs

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“…5 The diagnosis of localised brain lesions with MRI or CT scan is typically challenging since they have similar appearances most of the time, and it is hard to differentiate neoplastic lesions from non-neoplastic. 6,7 MRS is a non-invasive diagnostic procedure that analyses chemical composition of human tissues. MRS was initially used in therapeutic settings in the 1980s.…”

Section: Introductionmentioning

confidence: 99%

“…Magnetic Resonance Spectroscopy (MRS) 2 provide crucial knowledge that can aid in the identification of abnormal conditions. 7 Aim of present research is to determine diagnostic accuracy of MRS for differentiating malignant brain lesions in patients presenting with focal brain lesions. Because existing literature has reported variable diagnostics accuracy of MRS and most of these studies were conducted using small sample size.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic accuracy of magnetic resonance spectroscopy (MRS) for diagnosis of focal brain lesions taking histopathology as gold standard.

Musawar¹,

Shaukat²,

Manzoor

et al. 2022

TPMJ

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Objective: To determine diagnostic accuracy of MRS for diagnosis of malignant brain lesions taking histopathology as gold standard. Study Design: Cross Sectional Validation study. Setting: Department of Radiology, Lahore General Hospital, Lahore. Material & Methods: A total number of 127 patients who were referred for the diagnosis of focal brain lesions were included. In all patients, MRS was done by consultant radiologist to diagnose malignancy. All patients underwent surgery in the neurosurgery department and after surgery biopsy specimen were taken for diagnosis of malignancy using histopathology findings. Results: Mean age of patients was 40.88±11.37 years. There were 88 (69.29%) males and 39 (30.71%) female patients. On MRS, malignancy was diagnosed in 99 (77.95%) patients and on histopathology malignancy was diagnosed in 102 (80.31%) patients. Regarding diagnostic accuracy, MRS was 97.00% sensitive, 94.10% specific, with PPV of 78.60% and NPV of 88.00%. Conclusion: Magnetic resonance spectroscopy (MRS) has good sensitivity and specificity in differentiating neoplasm from non-neoplastic focal brain lesions.

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“…Glioma, one of the most common central nervous system tumors (CNS), has a five-year survival rate of less than 5% and is widely recognized as a highly malignant tumor ( 1 , 2 ). According to the WHO classification of brain tumors, pathology and clinical practices typically divide gliomas into low and high grades ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

The combination of radiomics features and VASARI standard to predict glioma grade

et al. 2023

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Background and PurposeRadiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.Materials and MethodsPatients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.ResultsOur study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.ConclusionThe combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

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Diagnostic Accuracy of Magnetic Resonance Spectroscopy in Predicting the Grade of Glioma Keeping Histopathology as the Gold Standard

Cited by 11 publications

References 17 publications

Comparison of diagnostic accuracy of magnetic resonance spectroscopy with conventional magnetic resonance imaging for brain tumors and correlation with histopathology as gold standard

Comparison of diagnostic accuracy of magnetic resonance spectroscopy with conventional magnetic resonance imaging for brain tumors and correlation with histopathology as gold standard

Diagnostic accuracy of magnetic resonance spectroscopy (MRS) for diagnosis of focal brain lesions taking histopathology as gold standard.

The combination of radiomics features and VASARI standard to predict glioma grade

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