Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study.

OBJECTIVES: To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas. METHODS: Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000 s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades. RESULTS: Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r = - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006). CONCLUSIONS: IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas. KEY POINTS: • Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Contrast-enhanced 3D-T2-weighted SPACE sequence for MRI detection and localization of adrenocorticotropin (ACTH)-secreting pituitary microadenomas.

OBJECTIVE: Cushing disease is a potentially fatal endocrine disorder caused by adrenocorticotropin (ACTH)-secreting microadenomas in the pituitary gland. Accurate detection and localization of the adenomas is the key to clinical treatment. This study analysed the value of contrast-enhanced Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) sequence in magnetic resonance imaging (MRI) assessment of ACTH-secreting pituitary microadenomas. DESIGN AND PATIENTS: We carried out a retrospective study in which 45 patients with ACTH-secreting pituitary microadenomas were enrolled. Dynamic contrast-enhanced (DCE) coronal T1-SE sequence was performed. A contrast-enhanced coronal SPACE sequence was added immediately after DCE MRI finished. Two independent observers assessed the tumour existence and location, then the results were compared with surgical findings. RESULTS: Twenty-four lesions (53.3%) were detected by the DCE T1-SE sequence alone, while 35 lesions (80.0%) were detected with the addition of contrast-enhanced SPACE sequence. The sensitivity (58.5% vs. 85.3%; p < .05) and best diagnostic accuracy (62.0% vs. 84.4%; p < .05) were significantly better for addition with SPACE sequence than DCE-SE images alone in detection of ACTH-secreting pituitary microadenomas. For lesions <5 mm, the detected numbers were 4 (16.6%) versus 10 (27.8%) by DCE T1-SE sequence and combined DCE T1-SE with SPACE sequence. CONCLUSIONS: A combination of contrast-enhanced SPACE with DCE T1-SE sequence could improve the detection of ACTH-secreting pituitary microadenomas. Contrast-enhanced SPACE sequence could be a supplementary sequence for imaging of ACTH-secreting pituitary adenomas when T1-SE sequence provides negative or equivocal findings.

A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.

OBJECTIVES: To construct a radiomics nomogram based on multiparametric MRI data for predicting isocitrate dehydrogenase 1 mutation (IDH +) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression (ATRX -) in patients with lower-grade gliomas (LrGG; World Health Organization [WHO] 2016 grades II and III). METHODS: A total of 111 LrGG patients (76 mutated IDH and 35 wild-type IDH) were enrolled, divided into a training set (n = 78) and a validation set (n = 33) for predicting IDH mutation. IDH + LrGG patients were further stratified into the ATRX - (n = 38) and ATRX + (n = 38) subtypes. A total of 250 radiomics features were extracted from the region of interest of each tumor, including that from T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1 WI, ASL-derived cerebral blood flow (CBF), DWI-derived ADC, and exponential ADC (eADC). A radiomics signature was selected using the Elastic Net regression model, and a radiomics nomogram was finally constructed using the age, gender information, and above features. RESULTS: The radiomics nomogram identified LrGG patients for IDH mutation (C-index: training sets = 0.881, validation sets = 0.900) and ATRX loss (C-index: training sets = 0.863, validation sets = 0.840) with good calibration. Decision curve analysis further confirmed the clinical usefulness of the two nomograms for predicting IDH and ATRX status. CONCLUSIONS: The nomogram incorporating age, gender, and the radiomics signature provided a clinically useful approach in noninvasively predicting IDH and ATRX mutation status for LrGG patients. The proposed method could facilitate MRI-based clinical decision-making for the LrGG patients. KEY POINTS: • Non-invasive determination of IDH and ATRX gene status of LrGG patients can be obtained with a radiomics nomogram. • The proposed nomogram is constructed by radiomics signature selected from 250 radiomics features, combined with age and gender. • The proposed radiomics nomogram exhibited good calibration and discrimination for IDH and ATRX gene mutation stratification of LrGG patients in both training and validation sets.

Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas.

OBJECTIVE: To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics. METHODS: A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson's correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC. RESULTS: Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction. CONCLUSION: Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies. KEY POINTS: • Radiomics might improve the preoperative diagnosis of SCAs by MR images. • T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs. • The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.

Evaluation of Inferior Intercavernous Sinus in Patients With Pituitary Microadenoma by Contrast-Enhanced SPACE Imaging.

OBJECTIVE: Inferior intercavernous sinus (iICS) is a venous channel below the pituitary gland. Inferior intercavernous sinus injury is predisposed to cause venous bleeding during dura incision in transsphenoidal surgery for pituitary adenomas. Therefore, this study aimed to perform a radiological assessment of iICS before transsphenoidal surgery for pituitary microadenoma. METHODS: A retrospective evaluation was performed on 156 patients who underwent magnetic resonance imaging examinations in our hospital before endoscopic transsphenoidal surgery for pituitary microadenoma. Both sagittal reformatted and coronal contrast-enhanced (CE) sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) images were interpreted for the presence, shape, and size of the iICS. RESULTS: In CE SPACE, the iICS was identified in 72 patients (46.15%) with pituitary microadenoma. The iICS was appeared as a filiform-shaped hyperintense structure below the pituitary gland on coronal CE SPACE planes and a crescent-shaped hyperintense structure on sagittal CE SPACE planes. The mean ± SD width, depth, and height of iICS were 11.15 ± 3.47 mm, 5.29 ± 1.24 mm, and 1.41 ± 0.19 mm, respectively. CONCLUSIONS: Contrast-enhanced SPACE may serve as a promising technique in evaluating iICS and individualized preoperative planning before transsphenoidal surgery for pituitary microadenoma.

Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy.

OBJECTIVE: To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy. METHODS: A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test. RESULTS: Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation. CONCLUSIONS: Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.

Optimization of Contrast Agent Dosage on Contrast-Enhanced T2 Fluid-Attenuated Inversion Recovery: An In Vitro and In Vivo Study.

OBJECTIVE: This study aimed to ascertain the minimum gadolinium dosage on contrast-enhanced (CE) T2 fluid-attenuated inversion recovery (FLAIR) at appropriate imaging time. METHODS: Different dosages of gadodiamide were imaged with a 3.0-T magnetic resonance scanner for T2-FLAIR and T1WI. Twenty glioma-induced rat models were randomly assigned into 4 groups (1/2, 1/4, 1/6, 1/8 of routine dosage) and imaged for T2-FLAIR and T1WI preinjection and postinjection of gadodiamide. Contrast-enhanced T2-FLAIR was acquired for 8 repetitions postinjection. Enhancement effects were assessed by calculating contrast-noise ratio and contrast ratio using Kruskal-Wallis and Mann-Whitney rank sum test. RESULTS: The in vitro experiment showed that gadodiamide at 1/4 of the T1WI dosage presented the best contrast on CE-T2-FLAIR. For in vivo study, the best enhancement effect on CE-T2-FLAIR was achieved at 1/2 of the routine dosage at 8 to 12 minutes of delayed scanning. Compared with CE-T1WI at routine dosage, CE-T2-FLAIR at 1/2 gadodiamide dosage presented similar enhancement effects with no statistical difference (P = 0.244 and 0.090 for contrast-noise ratio and contrast ratio, respectively). CONCLUSIONS: Contrast-enhanced T2-FLAIR imaging with half of T1WI routine gadodiamide dosage can produce similar enhancement effects to CE-T1WI when characterizing brain gliomas. The cut-down of contrast agent dosage may help reduce gadolinium accumulation in certain tissues.

Iron Sucrose as MRI Contrast Agent in Ischemic Stroke Model.

BACKGROUND: Despite the growing concern about the safety of gadolinium-based contrast agents (GBCAs), they are still the most commonly used. Ferumoxytol, as an off-label alternative MRI contrast agent, cannot be administered by a rapid bolus for dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI). PURPOSE: To assess the feasibility of iron sucrose (IS) as a contrast agent for MR angiography (MRA) and DSC-PWI. STUDY TYPE: Prospective animal model. ANIMAL MODEL: Thirty-six normal rats (16 for MRA, 20 for biocompability tests) and 36 occlusion of the middle cerebral artery (MCAO) model rats. FIELD STRENGTH/SEQUENCE: 3.0T; head and neck angiography, using a fast spoiled gradient-recalled-echo (FSPGR) sequence and DSC-MRI using echo planar imaging(EPI) sequence. ASSESSMENT: MRA was performed on normal rats to examine the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of different doses of IS. DSC-PWI was performed on MCAO rats at 0, 24, 48, and 72 hours postreperfusion to investigate the lesion detectability of IS. Arterial spin labeling (ASL) and DSC-PWI enhanced by GBCAs were conducted on MCAO rats as controls. STATISTICAL TESTS: Kruskal-Wallis test was used to compare qualitative assessment. One-way analysis of variance (ANOVA) was used to compare the parametric data. Pearson's r values were evaluated between relative cerebral blood flow(rCBF)-ASL, rCBF-DSCIS , and rCBF obtained from DSC-PWI enhanced by GBCA. RESULTS: The mean SNR and CNR of the common carotid artery at doses of 10 mg Fe/kg of IS were comparable with the standard dose of GBCAs (SNR: 68.04 ± 12.55 vs. 67.72 ± 14.66; CNR: 23.78 ± 7.21vs. 21.63 ± 6.83). In MCAO rat models, rCBF and relative cerebral blood volume (rCBV) of ipsilateral striatum declined (0.72 ± 0.14, 0.86 ± 0.11) with prolonged relative mean transit time (rMTT) and relative time-to-peak (rTTP) (1.27 ± 0.24, 1.07 ± 0.03) following occlusion. Hyperperfusion was observed in all rats at 48 and 72 hours postreperfusion, in 4/6 rats at 24 hours postreperfusion for IS-mediated DSC-PWI. DATA CONCLUSION: IS may be an effective contrast agent for both MRA and DSC-PWI in ischemic stroke models. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:836-849.

Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low-Grade Gliomas Using Multiparametric MR Radiomic Features.

BACKGROUND: Noninvasive detection of isocitrate dehydrogenase 1 mutation (IDH1(+)) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression ((ATRX(-)) are clinically meaningful for molecular stratification of low-grade gliomas (LGGs). PURPOSE: To study a radiomic approach based on multiparametric MR for noninvasively determining molecular status of IDH1(+) and ATRX(-) in patients with LGG. STUDY TYPE: Retrospective, radiomics. POPULATION: Fifty-seven LGG patients with IDH1(+) (n = 36 with 19 ATRX(-) and 17 ATRX(+) patients) and IDH1(-) (n = 21). FIELD STRENGTH/SEQUENCE: 3.0T MRI / 3D arterial spin labeling (3D-ASL), T2 /fluid-attenuated inversion recovery (T2 FLAIR), and diffusion-weighted imaging (DWI). ASSESSMENT: In all, 265 high-throughput radiomic features were extracted on each tumor volume of interest from T2 FLAIR and the other three parametric maps of ASL-derived cerebral blood flow (CBF), DWI-derived apparent diffusion coefficient (ADC), and exponential ADC (eADC). Optimal feature subsets were selected as using the support vector machine with a recursive feature elimination algorithm (SVM-RFE). Receiver operating characteristic curve (ROC) analysis was employed to assess the efficiency for identifying the IDH1(+) and ATRX(-) status. STATISTICAL TESTS: Student's t-test, chi-square test, and Fisher's exact test were applied to confirm whether intergroup significant differences exist between molecular subtypes decided by IDH1 and ATRX. RESULTS: Optimal SVM predictive models of IDH1(+) and ATRX(-) were established using 28 features from T2 Flair, ADC, eADC, and CBF and six features from T2 Flair, ADC, and CBF. The accuracies/AUCs/sensitivity/specifity/PPV/NPV of predicting IDH1(+) in LGG were 94.74%/0.931/100%/85.71%/92.31%/100%, and those of predicting ATRX(-) in LGG with IDH1(+) were 91.67%/0.926/94.74%/88.24%/90.00%/93.75%, respectively. DATA CONCLUSION: Using the optimal texture features extracted from multiple MR sequences or parametric maps, a promising stratifying strategy was acquired for predicting molecular subtypes of IDH1 and ATRX in LGGs. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;49:808-817.

MR textural analysis on T2 FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification.

BACKGROUND: The genetic status of 1p/19q is important for differentiating oligodendroglioma, isocitrate-dehydrogenase (IDH)-mutant, and 1p/19q-codeleted from diffuse astrocytoma, IDH-mutant according to the 2016 World Health Organization (WHO) criteria. PURPOSE: To assess the value of magnetic resonance textural analysis (MRTA) on T2 fluid-attenuated inversion recovery (FLAIR) images for making a genetically integrated diagnosis of true oligodendroglioma by WHO guidelines. STUDY TYPE: Retrospective case control. SUBJECTS: In all, there were 54 patients with a histopathological diagnosis of diffuse glioma (grade II). All were tested for IDH and 1p/19q. FIELD STRENGTH/SEQUENCE: 3.0T, including T2 FLAIR sequence, axial T1 -weighted, and T2 -weighted sequence. ASSESSMENT: MRTA on a representative tumor region of interest (ROI) was made on preoperative T2 FLAIR images around the area that had the largest diameter of solid tumor using Omni Kinetics software. STATISTICAL TESTS: Differences between IDH-mutant and 1p/19q-codeleted and IDH-mutant and 1p/19q-intact gliomas were analyzed by the Mann-Whitney rank sum test. Receiver operating characteristic curves (ROC) were created to assess MRTA diagnostic performance. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with a cutoff value according to the Youden Index. RESULTS: Comparisons demonstrated significant differences in kurtosis (P = 0.007), energy (0.008), entropy (0.008), mean deviation (MD) (<0.001), and high gray-level run emphasis (HGLRE) (0.002), cluster shade (0.025), and sum average (0.002). First-order features comprising entropy (area under the curve [AUC] = 0.718, sensitivity = 97.1%) and energy (0.719, 94.1%) had the highest sensitivity but lower specificity (both 45%). Second-order features such as HGLRE (AUC = 0.750, sensitivity = 73.5%, specificity = 80.0%) and sum average (0.751, 70.6%, 80.0%) had relatively higher specificity, and all had AUC >0.7. MD had the highest diagnostic performance, with AUC = 0.878, sensitivity = 94.1%, specificity = 75.0%, PPV = 86.5%, and NPV = 88.2%. DATA CONCLUSION: MRTA on T2 FLAIR images may be helpful in identifying oligodendroglioma, IDH-mutant, and 1p/19q-codeleted. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

[The Application of Compound Tabletop in Medical Imaging Equipment].

This paper mainly introduces the material composition and structure of two kinds of compound tabletop which currently used in large medical imaging equipment, also introduces the proper material choosing and production processing, together with the advantage and shortage of this two kinds of tabletop and different application occasion.

Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study.

Background: To understand the pathological correlations of multi-b-value diffusion-weighted imaging (MDWI) stretched-exponential model (SEM) parameters of α and diffusion distribution index (DDC) in patients with glioma. SEM parameters, as promising biomarkers, played an important role in histologically grading gliomas. Methods: Biopsy specimens were grouped as high-grade glioma (HGG) or low-grade glioma (LGG). MDWI-SEM parametric mapping of DDC1500, α1500 fitted by 15 b-values (0-1,500 sec/mm2)and DDC5000 and α5000 fitted by 22 b-values (0-5,000 sec/mm2) were matched with pathological samples (stained by MIB-1 and CD34) by coregistered localized biopsies, and all SEM parameters were correlated with these pathological indices pMIB-1(percentage of MIB-1 expression positive rate) and CD34-MVD (CD34 expression positive microvascular density for each specimen). The two-tailed Spearman's correlation was calculated for pathological indexes and SEM parameters, as well as WHO grades and SEM parameters. Results: MDWI-derived α1500 negatively correlated with CD34-MVD in both LGG (6 specimens) and HGG (26 specimens) (r=-0.437, P =0.012). MDWI-derived DDC1500 and DDC5000 negatively correlated with MIB-1 expression in all glioma patients (P<0.05). WHO grades negatively correlated with α1500(r=-0.485; P=0.005) and α5000(r=-0.395; P=0.025). Conclusions: SEM-derived DDC and α are significant in histologically grading gliomas, DDC may indicate the proliferative ability, and CD34 stained microvascular perfusion may be an important determinant of water diffusion inhomogeneity α in glioma.

Deep Learning-Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas.

This study aimed to explore the value of deep learning (DL)-assisted quantitative susceptibility mapping (QSM) in glioma grading and molecular subtyping. Forty-two patients with gliomas, who underwent preoperative T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1-weighted imaging (T1WI + C), and QSM scanning at 3.0T magnetic resonance imaging (MRI) were included in this study. Histopathology and immunohistochemistry staining were used to determine glioma grades, and isocitrate dehydrogenase (IDH) 1 and alpha thalassemia/mental retardation syndrome X-linked gene (ATRX) subtypes. Tumor segmentation was performed manually using Insight Toolkit-SNAP program (www.itksnap.org). An inception convolutional neural network (CNN) with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices. Fivefold cross-validation was utilized as the training strategy (seven samples for each fold), and the ratio of sample size of the training, validation, and test dataset was 4:1:1. The performance was evaluated by the accuracy and area under the curve (AUC). With the inception CNN, single modal of QSM showed better performance in differentiating glioblastomas (GBM) and other grade gliomas (OGG, grade II-III), and predicting IDH1 mutation and ATRX loss (accuracy: 0.80, 0.77, 0.60) than either T2 FLAIR (0.69, 0.57, 0.54) or T1WI + C (0.74, 0.57, 0.46). When combining three modalities, compared with any single modality, the best AUC/accuracy/F1-scores were reached in grading gliomas (OGG and GBM: 0.91/0.89/0.87, low-grade and high-grade gliomas: 0.83/0.86/0.81), predicting IDH1 mutation (0.88/0.89/0.85), and predicting ATRX loss (0.78/0.71/0.67). As a supplement to conventional MRI, DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades, IDH1 mutation, and ATRX loss. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

The Role of Surgery in IDH-Wild-Type Lower-Grade Gliomas: Threshold at a High Extent of Resection Should be Pursued.

BACKGROUND: While maximizing extent of resection (EOR) is associated with longer survival in lower-grade glioma (LGG) patients, the number of cases remains insufficient in determining a EOR threshold to elucidate the clinical benefits, especially in IDH-wild-type LGG patients. OBJECTIVE: To identify the effects of EOR on the survival outcomes of IDH-wild-type LGG patients. METHODS: IDH-wild-type LGG patients were retrospectively reviewed. The effect of EOR and other predictor variables on overall survival (OS) and progression-free survival (PFS) was analyzed using Cox regression models and the Kaplan-Meier method. RESULTS: A total of 94 patients (median OS: 48.9 mo; median follow-up: 30.6 mo) were included in this study. In the multivariable Cox regression analysis, postoperative residual volume was associated with prolonged OS (HR = 2.238; 95% confidence interval [CI], 1.130-4.435; P = .021) and PFS (HR = 2.075; 95% CI, 1.113-3.869; P = .022). Thresholds at a minimum EOR of 97.0% or a maximum residue of 3.0 cm3 were necessary to impact OS positively. For the telomerase reverse transcriptase (TERT)p-wild-type group, such an association was absent. Significant differences in survival existed between the TERTp-wild-type and mutant patients who underwent relatively incomplete resections (residual ≥2.0 cm3 + TERTp wild type: median OS of 62.6 mo [95% CI: 39.7-85.5 mo]; residual ≥2.0 cm3 + TERTp mutant: median OS of 20.0 mo [95% CI:14.6-25.4 mo]). CONCLUSION: Our results support the core role of maximal safe resection in the treatment of IDH-wild-type LGGs, especially for IDH-wild-type + TERTp-mutant LGGs. Importantly, the survival benefits of surgery could only be elucidated at a high EOR cut-off point.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.

PURPOSE: We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence. MATERIALS AND METHODS: Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370-1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962-1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778-1.0000)/100%/90%/83.33%/100%, respectively. CONCLUSION: Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

MR textural analysis on contrast enhanced 3D-SPACE images in assessment of consistency of pituitary macroadenoma.

OBJECTIVES: To explore the value of magnetic resonance textural analysis (MRTA) in assessing consistency of pituitary macroadenoma (PMA) based on contrast enhanced (CE) three-dimensional sampling perfection with application-optimized contrasts by using different flip angle evolution (3D-SPACE) images. MATERIALS AND METHODS: Fifty-three patients with PMAs that underwent CE 3D-SPACE scanning by 3.0 T MRI and endoscopic trans-sphenoidal surgery were included in the present study. Consistency levels of PMAs were evaluated intraoperatively by two neurosurgeons. Each resection specimen was stained with H&E and anti-collagen IV. MRTA was conducted and texture features were calculated. An unpaired t-test was used to analyze the differences of texture features between soft and hard PMAs. ROC curves by individual and combined features were used to calculate the diagnostic accuracy of MRTA in predicting PMA consistency. RESULTS: First-order energy and second-order correlation negatively correlated with hard PMAs, while first-order entropy and second-order variance, sum variance, and sum entropy positively correlated with stiffness. All showed significant differences between soft and hard PMAs (P < 0.05). Diagnostic accuracy of combined negative features could achieve an AUC of 0.819, sensitivity of 88.9%, specificity of 61.5%, PPV of 70.6%, NPV of 84.2% and positive features could achieve an AUC of 0.836, sensitivity of 85.2%, specificity of 69.2%, PPV of 74.2%, NPV of 81.8% (P < 0.001). CONCLUSION: MRTA using CE 3D-SPACE images is helpful for assessing PMA consistency preoperatively and noninvasively.