Combined texture analysis of diffusion-weighted imaging with conventional MRI for non-invasive assessment of IDH1 mutation in anaplastic gliomas

Structured reporting of gliomas based on VASARI criteria to improve report content and consistency

PURPOSE

Gliomas are the commonest malignant brain tumours. Baseline characteristics on structural MRI, such as size, enhancement proportion and eloquent brain involvement inform grading and treatment planning. Currently, free-text imaging reports depend on the individual style and experience of the radiologist. Standardisation may increase consistency of feature reporting.

METHODS

We compared 100 baseline free-text reports for glioma MRI scans with a structured feature list based on VASARI criteria and performed a full second read to document which VASARI features were in the baseline report.

RESULTS

We found that quantitative features including tumour size and proportion of necrosis and oedema/infiltration were commonly not included in free-text reports. Thirty-three percent of reports gave a description of size only, and 38% of reports did not refer to tumour size at all. Detailed information about tumour location including involvement of eloquent areas and infiltration of deep white matter was also missing from the majority of free-text reports. Overall, we graded 6% of reports as having omitted some key VASARI features that would alter patient management.

CONCLUSIONS

Tumour size and anatomical information is often omitted by neuroradiologists. Comparison with a structured report identified key features that would benefit from standardisation and/or quantification. Structured reporting may improve glioma reporting consistency, clinical communication, and treatment decisions.

Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance

BACKGROUND

Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking.

PURPOSE

Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment.

DATA SOURCES

MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023.

STUDY SELECTION

Original articles predicting diagnosis, progression, and survival in patients with glioma were included.

DATA ANALYSIS

The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided.

DATA SYNTHESIS

Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium (n = 33) and low (n = 2). Recurring objectives were overall survival (n = 18) and isocitrate dehydrogenase mutation (IDH; n = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma n = 2, grade 2/3 glioma n = 1, grade 3 glioma n = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I2 = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I2 = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I2 = 52%). IDH mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I2 = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies (n = 17).

LIMITATIONS

Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for IDH was limited due to a high study heterogeneity.

CONCLUSIONS

Some VASARI features perform well in predicting overall survival and IDH mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.

Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas

BACKGROUND

Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging.

PURPOSE

To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas.

MATERIAL AND METHODS

This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas.

RESULTS

Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05).

CONCLUSION

Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

Correlating MRI features with additional genetic markers and patient survival in histological grade 2-3 IDH-mutant astrocytomas

PURPOSE

The increasing importance of molecular markers for classification and prognostication of diffuse gliomas has prompted the use of imaging features to predict genotype ("radiogenomics"). CDKN2A/B homozygous deletion has only recently been added to the diagnostic paradigm for IDH[isocitrate dehydrogenase]-mutant astrocytomas; thus, associated radiogenomic literature is sparse. There is also little data on whether different IDH mutations are associated with different imaging appearances. Furthermore, given that molecular status is now generally obtained routinely, the additional prognostic value of radiogenomic features is less clear. This study correlated MRI features with CDKN2A/B status, IDH mutation type and survival in histological grade 2-3 IDH-mutant brain astrocytomas.

METHODS

Fifty-eight grade 2-3 IDH-mutant astrocytomas were identified, 50 with CDKN2A/B results. IDH mutations were stratified into IDH1-R132H and non-canonical mutations. Background and survival data were obtained. Two neuroradiologists independently assessed the following MRI features: T2-FLAIR mismatch (<25%, 25-50%, >50%), well-defined tumour margins, contrast-enhancement (absent, wispy, solid) and central necrosis.

RESULTS

8/50 tumours with CDKN2A/B results demonstrated homozygous deletion; slightly shorter survival was not significant (p=0.571). IDH1-R132H mutations were present in 50/58 (86%). No MRI features correlated with CDKN2A/B status or IDH mutation type. T2-FLAIR mismatch did not predict survival (p=0.977), but well-defined margins predicted longer survival (HR 0.36, p=0.008), while solid enhancement predicted shorter survival (HR 3.86, p=0.004). Both correlations remained significant on multivariate analysis.

CONCLUSION

MRI features did not predict CDKN2A/B homozygous deletion, but provided additional positive and negative prognostic information which correlated more strongly with prognosis than CDKN2A/B status in our cohort.

Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart

OBJECTIVE

To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas.

METHODS

Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma.

RESULTS

There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618).

CONCLUSIONS

Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.

Vasari Scoring System in Discerning between Different Degrees of Glioma and IDH Status Prediction: A Possible Machine Learning Application?

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software.

A Combination Analysis of IVIM-DWI Biomarkers and T2WI-Based Texture Features for Tumor Differentiation Grade of Cervical Squamous Cell Carcinoma

Purpose

To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and texture analysis on T2-weighted imaging (T2WI) for evaluating pathological differentiation of cervical squamous cell carcinoma.

Method

This retrospective study included a total of 138 patients with pathologically confirmed poor/moderate/well-differentiated (71/49/18) who underwent conventional MRI and IVIM-DWI scans. The values of ADC, D, D ∗ , and f and 58 T2WI-based texture features (18 histogram features, 24 gray-level co-occurrence matrix features, and 16 gray-level run length matrix features) were obtained. Multiple comparison, correlation, and regression analyses were used.

Results

For IVIM-DWI, the ADC, D, D ∗ , and f were significantly different among the three groups (p < 0.05). ADC, D, and D ∗ were positively correlated with pathological differentiation (r = 0.262, 0.401, 0.401; p < 0.05), while the correlation was negative for f (r = -0.221; p < 0.05). The comparison of 52 parameters of texture analysis on T2WI reached statistically significant levels (p < 0.05). Multivariate logistic regression analysis incorporated significant IVIM-DWI, and texture features on T2WI showed good diagnostic performance both in the four differentiation groups (poorly vs. moderately, area under the curve(AUC) = 0.797; moderately vs. well, AUC = 0.954; poorly vs. moderately and well, AUC = 0.795; and well vs. moderately and poorly, AUC = 0.952). The AUCs of each parameters alone were smaller than that of each regression model (0.503∼0.684, 0.547∼0.805, 0.511∼0.712, and 0.636∼0.792, respectively; pairwise comparison of ROC curves between regression model and individual variables, p < 0.05).

Conclusions

IVIM-DWI biomarkers and T2WI-based texture features had potential to evaluate the pathological differentiation of cervical squamous cell carcinoma. The combination of IVIM-DWI with texture analysis improved the predictive performance.

Investigated diagnostic value of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling and diffusion-weighted imaging in the grading of glioma

BACKGROUND

To investigate the performance of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling (pCASL) and diffusion-weighted imaging (DWI) in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs) and to compare with the conventional MRI.

METHODS

Seventy-two patients with gliomas (including 27 LGGs and 45 HGGs) were studied using synthetic magnetic resonance imaging (sy-MRI), pCASL, and DWI with a 3.0 T MR scanner. T1 relaxometry (T1), T2 relaxometry (T2), as well as proton density (PD) from sy-MRI, cerebral blood flow (CBF) from pCASL, apparent diffusion coefficient (ADC) from DWI and enhancement quality (EQ), proportion enhancing (PE) from conventional contrast enhanced image based Visually-Accessible-Rembrandt-Images (VASARI) scoring system, were all analyzed by two radiologists. The Student's t-test, Mann-Whitney U test or Fisher's exact test was used to compare the parameters between LGGs and HGGs. The diagnostic performance of each parameter and their combination for glioma grading were analyzed.

RESULTS

Significant statistical differences in T1, PD, CBF, ADC, EQ and PE are observed between LGGs and HGGs (all P < 0.001). The ADC values have higher discrimination abilities compared with other univariable parameters, with the AUC of 0.905. AUC values for conventional contrast-enhanced method, EQ and PE from VASARI, and conventional contrast-free method, CBF + ADC, are 0.873 and 0.912 respectively. The combined T1, PD, CBF and ADC model had the best performance for differentiating LGGs and HGGs with AUC, sensitivity and specificity of 0.993, 95.5%, 100%, respectively.

CONCLUSIONS

Relaxometry parameters derived from synthetic MRI contributed to the discrimination of low-grade gliomas from high-grade gliomas. Proposed contrast-free approach combining T1, PD, CBF and ADC showed a strong discriminative power, and outperformed conventional approaches.

Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system

OBJECTIVES

To determine reproducible MRI parameters predictive of molecular subtype and risk stratification in glioma and develop a structured reporting system.

METHODS

All study patients were initially diagnosed with glioma, 141 from the Cancer Genome Atlas and 131 from our tertiary institution, as training and validation sets, respectively. Images were analyzed by three neuroradiologists with 1-7 years of experience. MRI features including contrast enhancement pattern, necrosis, margin, edema, T2/FLAIR mismatch, internal cyst, and cerebral blood volume higher than normal cortex were reported using a structured reporting system. The pathology was stratified into five risk types: (1) oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, 1p19q co-deleted; (2) diffuse astrocytoma, IDH-mutant, grade II-III; (3) glioblastoma, IDH-mutant, grade IV; (4) diffuse astrocytoma, IDH-wild, grade II-III; and (5) glioblastoma, IDH-wild, grade IV. Significant predictors were selected using multivariate logistic regression, and diagnostic performance was tested using a validation set.

RESULTS

Reproducible imaging parameters exhibiting > 50% agreement across readers included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. In the validation set, prediction of risk type 5 exhibited the highest diagnostic performance with AUCs of 0.92 (reader 1) and 0.93 (reader 2) with predominant enhancement, followed by risk type 2 with AUCs of 0.95 and 0.95 with T2/FLAIR mismatch sign and no necrosis, and risk type 1 with AUCs of 0.84 and 0.83 with internal cyst or necrosis. Risk types 3 and 4 were difficult to visually predict.

CONCLUSIONS

Imaging parameters with high reproducibility enabling prediction of IDH-wild-type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant diffuse astrocytoma were identified.

KEY POINTS

• Reproducible MRI parameters for determining molecular subtypes of glioma included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. • IDH-wild type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant low-grade astrocytoma were identified using MRI parameters with high inter-reader reproducibility. • Identification of IDH-wild type low-grade glioma and IDH-mutant glioblastoma was difficult by visual analysis.

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach

Purpose

Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs.

Materials and Methods

A total of 102 LGG patients were allocated to training (n = 67) and validation (n = 35) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves.

Results

After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879.

Conclusion

The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

Diagnostic Performance of MR Imaging-based Features and Texture Analysis in the Differential Diagnosis of Ovarian Thecomas/Fibrothecomas and Uterine Fibroids in the Adnexal Area

RATIONALE AND OBJECTIVES

To investigate the value of MRI-based features and texture analysis (TA) in the differential diagnosis between ovarian thecomas/fibrothecomas (OTCA/f-TCAs) and uterine fibroids in the adnexal area (UF-iaas).

MATERIALS AND METHODS

This retrospective study included 16 OTCA/f-TCA and 37 UF-iaa patients who underwent conventional MRI and DWI between August 2014 and September 2018. Three-dimensional TA was performed with T2-weighted MRI. The clinical, MRI-based and texture features were compared between OTCA/f-TCAs and UF-iaas. Multivariate logistic regression analysis was used for filtering the independent discriminative features and constructing the discriminating model. ROCs were generated to analyse MRI-based features, texture features and their combination for discriminating between the two diseases.

RESULTS

Six imaging-based features (ipsilateral ovary detection, arterial period enhancement, lesion components, peripheral cysts, "whorl signs", mean ADCs) and six texture features (Histogram-energy, Histogram-entropy, Histogram-kurtosis, GLCM-energy, GLCM-entropy, and Haralick correlation) were significantly different between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the MRI-based features revealed that arterial period enhancement (OR = 0.104), peripheral cysts (OR = 16.513), and whorl signs (OR = 0.029) were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the texture features showed that Histogram-energy and GLCM-energy were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). The area under the curve of imaging-based diagnosis was 0.85, and the combination of imaging-based diagnosis and TA improved the area under the curve to 0.87, with higher accuracy, specificity and sensitivity of 86%, 92%, and 84%, respectively (p < 0.05).

CONCLUSIONS

MRI-based features can be useful in differentiating OTCA/f-TCAs from UF-iaas. Furthermore, combining imaging-based diagnosis and TA can improve diagnostic performance.

Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review

PURPOSE

Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status.

METHODS

A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours.

RESULTS

Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values.

CONCLUSION

Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.

Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas

PURPOSE

To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG).

MATERIALS AND METHODS

A total of 142 patients with HGG were included in the study. IDH1 mutation was present in 48 of 142 HGG (33.8%). Patients were randomly divided into the training cohort (n = 96) and the validation cohort (n = 46). Texture features were extracted via regions of interest on axial T2WI FLAIR, post-contrast T1WI, and ADC maps covering the whole volume of the tumors. The training cohort was used to train the random forest classifier, and the diagnostic performance of the pre-trained model was tested on the validation cohort.

RESULTS

The random forest model of conventional MRI sequences and ADC images achieved diagnostic accuracy of 82.2% and 80.4% in predicting IDH1 status in the validation cohorts, respectively. The combined model of T2WI FLAIR, post-contrast T1WI, and ADC images exhibited the highest diagnostic accuracy equating 86.94% in the validation cohort.

CONCLUSION

Texture analysis of conventional MRI sequences enhanced by ML analysis can accurately predict the IDH1 status of HGG. Adding textural analysis of ADC maps to conventional MRI results in incremental diagnostic performance.