High Radiation Dose to the Fornix Causes Symptomatic Radiation Necrosis in Patients with Anaplastic Oligodendroglioma

Purpose@#Surgery, radiotherapy (RT), and chemotherapy have prolonged the survival of patients with anaplastic oligodendroglioma. However, whether RT induces long-term toxicity remains unknown. We analyzed the relationship between the RT dose to the fornix and symptomatic radiation necrosis (SRN). @*Materials and Methods@#A total of 67 patients treated between 2009 and 2019 were analyzed. SRN was defined according to the following three criteria: 1) radiographic findings, 2) symptoms attributable to the lesion, and 3) treatment resulting in symptom improvement. Various contours, including the fornix, were delineated. Univariate and multivariate analyses of the relationship between RT dose and SRN, as well as receiver operating characteristic curve analysis for cut-off values, were performed. @*Results@#The most common location was the frontal lobe (n=40, 60%). Gross total resection was performed in 38 patients (57%), and 42 patients (63%) received procarbazine, lomustine, and vincristine chemotherapy. With a median follow-up of 42 months, the median overall and progression-free survival was 74 months. Sixteen patients (24%) developed SRN. In multivariate analysis, age and maximum dose to the fornix were associated with the development of SRN. The cut-off values for the maximum dose to the fornix and age were 59 Gy (equivalent dose delivered in 2 Gy fractions) and 46 years, respectively. The rate of SRN was higher in patients whose maximum dose to the fornix was >59 Gy (13% vs. 43%, p=0.005). @*Conclusion@#The maximum dose to the fornix was a significant factor for SRN development. While fornix sparing may help maintain neurocognitive function, additional studies are needed.

Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas

Purpose@#Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value. @*Materials and Methods@#In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012–February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models’ integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram. @*Results@#The study included 129 patients (median age, 44 years; interquartile range, 37–57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs.0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively. @*Conclusion@#Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lowergrade glioma.

A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases

Purpose@#Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set. @*Materials and Methods@#The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores. @*Results@#The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002). @*Conclusion@#Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.

Qualitative and Quantitative Magnetic Resonance Imaging Phenotypes May Predict CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytomas:A Multicenter Study

Objective@#Cyclin-dependent kinase inhibitor (CDKN)2A/B homozygous deletion is a key molecular marker of isocitrate dehydrogenase (IDH)-mutant astrocytomas in the 2021 World Health Organization. We aimed to investigate whether qualitative and quantitative MRI parameters can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytomas. @*Materials and Methods@#Preoperative MRI data of 88 patients (mean age ± standard deviation, 42.0 ± 11.9 years; 40 females and 48 males) with IDH-mutant astrocytomas (76 without and 12 with CDKN2A/B homozygous deletion) from two institutions were included. A qualitative imaging assessment was performed. Mean apparent diffusion coefficient (ADC), 5th percentile of ADC, mean normalized cerebral blood volume (nCBV), and 95th percentile of nCBV were assessed via automatic tumor segmentation.Logistic regression was performed to determine the factors associated with CDKN2A/B homozygous deletion in all 88 patients and a subgroup of 47 patients with histological grades 3 and 4. The discrimination performance of the logistic regression models was evaluated using the area under the receiver operating characteristic curve (AUC). @*Results@#In multivariable analysis of all patients, infiltrative pattern (odds ratio [OR] = 4.25, p = 0.034), maximal diameter (OR = 1.07, p = 0.013), and 95th percentile of nCBV (OR = 1.34, p = 0.049) were independent predictors of CDKN2A/B homozygous deletion. The AUC, accuracy, sensitivity, and specificity of the corresponding model were 0.83 (95% confidence interval [CI], 0.72–0.91), 90.4%, 83.3%, and 75.0%, respectively. On multivariable analysis of the subgroup with histological grades 3 and 4, infiltrative pattern (OR = 10.39, p = 0.012) and 95th percentile of nCBV (OR = 1.24, p = 0.047) were independent predictors of CDKN2A/B homozygous deletion, with an AUC accuracy, sensitivity, and specificity of the corresponding model of 0.76 (95% CI, 0.60–0.88), 87.8%, 80.0%, and 58.1%, respectively. @*Conclusion@#The presence of an infiltrative pattern, larger maximal diameter, and higher 95th percentile of the nCBV may be useful MRI biomarkers for CDKN2A/B homozygous deletion in IDH-mutant astrocytomas.

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Objective@#Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. @*Materials and Methods@#PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. @*Results@#External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. @*Conclusion@#The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.

Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications

Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases.Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well.

Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer’s Disease: A Roadmap for Moving Forward

Objective@#To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. @*Materials and Methods@#PubMed MEDLINE and EMBASE were searched using the terms ‘cognitive impairment’ or ‘Alzheimer’ or ‘dementia’ and ‘radiomic’ or ‘texture’ or ‘radiogenomic’ for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS.Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. @*Results@#The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer’s Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. @*Conclusion@#The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

Structural and Resting-State Brain Alterations in Trauma-Exposed Firefighters: Preliminary Results

Purpose@#To analyze the altered brain regions and intrinsic brain activity patterns in trauma-exposed firefighters without posttraumatic stress disorder (PTSD). @*Materials and Methods@#Resting-state functional MRI (rsfMRI) was performed for all subjects. Thirty-one firefighters over 40 years of age without PTSD (31 men; mean age, 49.8 ± 4.7 years) were included. Twenty-six non-traumatized healthy controls (HCs) (26 men; mean age, 65.3 ± 7.84 years) were also included. Voxel-based morphometry was performed to investigate focal differences in the brain anatomy. Seed-based functional connectivity analysis was performed to investigate differences in spontaneous brain characteristics. @*Results@#The mean z-scores of the Seoul Verbal Learning Test for immediate and delayed recall, Controlled Oral Word Association Test (COWAT) score for animals, and COWAT phonemic fluency were significantly lower in the firefighter group than in the HCs, indicating decreased neurocognitive function. Compared to HCs, firefighters showed reduced gray matter volume in the left superior parietal gyrus and left inferior temporal gyrus. Further, in contrast to HCs, firefighters showed alterations in rsfMRI values in multiple regions, including the fusiform gyrus and cerebellum. @*Conclusion@#Structural and resting-state functional abnormalities in the brain may be useful imaging biomarkers for identifying alterations in trauma-exposed firefighters without PTSD.

Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer’s Disease: A Roadmap for Moving Forward

Objective@#To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. @*Materials and Methods@#PubMed MEDLINE and EMBASE were searched using the terms ‘cognitive impairment’ or ‘Alzheimer’ or ‘dementia’ and ‘radiomic’ or ‘texture’ or ‘radiogenomic’ for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS.Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. @*Results@#The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer’s Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. @*Conclusion@#The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

Comparison of Diagnostic Performance of Two-Dimensionaland Three-Dimensional Fractal Dimension and LacunarityAnalyses for Predicting the Meningioma Grade

Background@#: To compare the diagnostic performance of two-dimensional (2D) and three-dimensional(3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade. @*Methods@#: This retrospective study included 123 meningioma patients [90 World Health Organization(WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weightedimaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumorwere calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariablelogistic regression analysis using 2D or 3D fractal features was performed to predict the meningiomagrade. The diagnostic ability of the 2D and 3D fractal models were compared. @*Results@#: The reproducibility between observers was excellent, with intraclass correlation coefficientsof 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively.WHO grade II/III meningiomas had a higher 2D and 3D FD (p=0.003 and p<0.001, respectively) andhigher 2D and 3D lacunarity (p=0.002 and p=0.006, respectively) than WHO grade I meningiomas.The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3Dfractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878),82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnosticperformance than the 2D fractal model (p<0.001). @*Conclusion@#: The 3D fractal analysis proved superiority in diagnostic performance to 2D fractalanalysis in grading meningioma.

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND METHODS: Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. RESULTS: The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). CONCLUSION: Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.

Imaging Findings of a Nonenhancing Intradural Paramedian Chordoma Mimicking an Epidermoid Cyst

Intracranial chordoma is a rare tumor, originating from embryonic remnants of the primitive notochord. It typically appears as an enhancing extradural midline tumor with bone involvement. We introduce a rare case of a 27-year-old male who had a nonenhancing intradural chordoma showing paramedian location, involving the left cavernous sinus, Meckel's cave, and prepontine cistern. The pathologic diagnosis was confirmed as an intradural chordoma. The imaging findings of this unusual case of a nonenhancing intradural paramedian chordoma will be presented with the differential diagnosis focused on the epidermoid cyst.

Complete Recovery of Perfusion Abnormalities in a Cardiac Arrest Patient Treated with Hypothermia: Results of Cerebral Perfusion MR Imaging

Therapeutic hypothermia in cardiac arrest patients is associated with favorable outcomes mediated via neuroprotective mechanisms. We report a rare case of a 32-year-old male who demonstrated complete recovery of signal changes on perfusion-weighted imaging after therapeutic hypothermia due to cardiac arrest. Brain MRI with perfusion-weighted imaging, performed three days after ending the hypothermia therapy, showed a marked decrease in relative cerebral blood flow (rCBF) and delay in mean transit time (MTT) in the bilateral basal ganglia, thalami, brain stem, cerebellum, occipitoparietal cortex, and frontotemporal cortex. However, no cerebral ischemia was not noted on diffusion-weighted imaging (DWI) or fluid-attenuated inversion recovery (FLAIR) sequences. A follow-up brain MRI after one week showed complete resolution of the perfusion deficit and the patient was discharged without any neurologic sequelae. The mechanism and interpretation of the perfusion changes in cardiac arrest patients treated with therapeutic hypothermia are discussed.

Comparison of 3D Volumetric Subtraction Technique and 2D Dynamic Contrast Enhancement Technique in the Evaluation of Contrast Enhancement for Diagnosing Cushing's Disease

PURPOSE: The purpose of this study is to compare the performance of the T1 3D subtraction technique and the conventional 2D dynamic contrast enhancement (DCE) technique in diagnosing Cushing's disease. MATERIALS AND METHODS: Twelve patients with clinically and biochemically proven Cushing's disease were included in the study. In addition, 23 patients with a Rathke's cleft cyst (RCC) diagnosed on an MRI with normal pituitary hormone levels were included as a control, to prevent non-blinded positive results. Postcontrast T1 3D fast spin echo (FSE) images were acquired after DCE images in 3T MRI and image subtraction of pre- and postcontrast T1 3D FSE images were performed. Inter-observer agreement, interpretation time, multiobserver receiver operating characteristic (ROC), and net benefit analyses were performed to compare 2D DCE and T1 3D subtraction techniques. RESULTS: Inter-observer agreement for a visual scale of contrast enhancement was poor in DCE (κ = 0.57) and good in T1 3D subtraction images (κ = 0.75). The time taken for determining contrast-enhancement in pituitary lesions was significantly shorter in the T1 3D subtraction images compared to the DCE sequence (P < 0.05). ROC values demonstrated increased reader confidence range with T1 3D subtraction images (95% confidence interval [CI]: 0.94–1.00) compared with DCE (95% CI: 0.70–0.92) (P < 0.01). The net benefit effect of T1 3D subtraction images over DCE was 0.34 (95% CI: 0.12–0.56). For Cushing's disease, both reviewers misclassified one case as a nonenhancing lesion on the DCE images, while no cases were misclassified on T1 3D subtraction images. CONCLUSION: The T1 3D subtraction technique shows superior performance for determining the presence of enhancement on pituitary lesions compared with conventional DCE techniques, which may aid in diagnosing Cushing's disease.

Comparison of Contrast-Enhanced T2 FLAIR and 3D T1 Black-Blood Fast Spin-Echo for Detection of Leptomeningeal Metastases

PURPOSE: Imaging plays a significant role in diagnosing leptomeningeal metastases. However, the most appropriate sequence for the detection of leptomeningeal metastases has yet to be determined. This study compares the efficacies of contrast-enhanced T2 fluid attenuated inversion recovery (FLAIR) and contrast-enhanced 3D T1 black-blood fast spin echo (FSE) imaging for the detection of leptomeningeal metastases. MATERIALS AND METHODS: Tube phantoms containing varying concentrations of gadobutrol solution were scanned using T2 FLAIR and 3D T1 black-blood FSE. Additionally, 30 patients with leptomeningeal metastases were retrospectively evaluated to compare conspicuous lesions and the extent of leptomeningeal metastases detected by T2 FLAIR and 3D T1 black-blood FSE. RESULTS: The signal intensities of low-concentration gadobutrol solutions (< 0.5 mmol/L) on T2 FLAIR images were higher than in 3D T1 black-blood FSE. The T2 FLAIR sequences exhibited significantly greater visual conspicuity scores than the 3D T1 black-blood sequence in leptomeningeal metastases of the pial membrane of cistern (P = 0.014). T2 FLAIR images exhibited a greater or equal extent (96.7%) of leptomeningeal metastases than 3D T1 black-blood FSE images. CONCLUSION: Because of its high sensitivity even at low gadolinium concentrations, contrast-enhanced T2 FLAIR images delineated leptomeningeal metastases in a wider territory than 3D T1 black-blood FSE.

Ischemic Stroke after Use of Cannabis: a Case Report and Review of Literature

Cannabis or marijuana is the most commonly used recreational drug after alcohol in the world, and usage is generally recognized as having few serious adverse effects. However, usage is restricted in South Korea. The report of ischemic stroke associated with cannabis is rare in literature. We present a case of a 47-year-old female patient with no underlying disease presenting with acute ischemic stroke after smoking cannabis in South Korea. The result for synthetic cannabinoid metabolites (delta-9 tetrahydrocannabinol) screening was positive. Absence of other vascular risk factors and drug screening results suggest a causal role of cannabis in this ischemic stroke case. The patient eventually progressed to brain death. The underlying mechanism, clinical manifestation, and imaging findings of cannabis-related stroke will be reviewed.