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Background and purpose Increasing evidence suggests that genomic and molecular markers need to be integrated in grading of meningioma. Telomerase reverse transcriptase promoter (TERTp) mutation is receiving attention due to its clinical relevance in the treatment of meningiomas. The predictive ability of conventional and diffusion MRI parameters for determining the TERTp mutation status in grade II meningiomas has yet been identified. Material and methods In this study, 63 patients with surgically confirmed grade II meningiomas (56 TERTp wildtype, 7 TERTp mutant) were included. Conventional imaging features were qualitatively assessed. The maximum diameter, volume of the tumors and histogram parameters from the apparent diffusion coefficient (ADC) were assessed. Independent clinical and imaging risk factors for TERTp mutation were investigated using multivariable logistic regression. The discriminative value of the prediction models with and without imaging features was evaluated. Results In the univariable regression, older age (odds ratio [OR] = 1.13, P = 0.005), larger maximum diameter (OR = 1.09, P = 0.023), larger volume (OR = 1.04, P = 0.014), lower mean ADC (OR = 0.02, P = 0.025), and lower ADC 10th percentile (OR = 0.01, P = 0.014) were predictors of TERTp mutation. In multivariable regression, age (OR = 1.13, P = 0.009) and ADC 10th percentile (OR = 0.01, P = 0.038) were independent predictors of variables for predicting the TERTp mutation status. The performance of the prediction model increased upon inclusion of imaging parameters (area under the curves of 0.86 and 0.91, respectively, without and with imaging parameters). Conclusion Older age and lower ADC 10th percentile may be useful parameters to predict TERTp mutation in grade II meningiomas.

The incidence of pancreatic neuroendocrine tumor (PNET) has continued to rise. Due to their indolent feature, PNET patients often present with incurable, metastatic diseases. Novel therapies are urgently needed. We have previously shown that Receptor for Hyaluronic Acid-Mediated Motility isoform B (RHAMMB) and Bcl-xL are upregulated in PNETs and both of them promote PNET metastasis. Because RHAMM protein is undetectable in most adult tissues, we hypothesized that RHAMMB could be a gateway for nanomedicine delivery into PNETs. To test this, we developed RHAMMB-targeting nanoparticle. Inside this nanoparticle, we assembled siRNA against Bcl-xL (siBcl-xL) and mitochondria-fusing peptide KLA. We demonstratsed that RHAMMB-positive PNETs picked up the RHAMMB-targeting nanoparticles. siBcl-xL or KLA alone only killed 30% of PNET cells. In contrast, a synergistic killing effect was achieved with the co-delivery of siBcl-xL and KLA peptide in vitro. Unexpectedly, siBcl-xL induced cell death before reducing Bcl-xL protein levels. The systemically-injected RHAMMB-targeting nanoparticles carrying siBcl-xL and KLA peptide significantly reduced tumor burden in mice bearing RHAMMB-positive PNETs. Together, these findings indicate that the RHAMMB-targeting nanotherapy serves as a promising drug delivery system for PNET and possibly other malignancies upregulating RHAMMB. The combination of siBcl-xL and KLA peptide can be a therapy for PNET treatment.

Purpose In glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant. Methods From December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification). All selected patients had preoperative MRI and isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor amplification, and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss status. Patients were randomly split into training and test sets (7:3 ratio). Enhancing tumor and peritumoral T2-hyperintensity were auto-segmented, and 660 radiomics features were extracted. We built binary relevance (BR) and ensemble classifier chain (ECC) models for multi-label classification and compared their performance. In the classifier chain, we calculated the mean absolute Shapley value of input features. Results The micro-averaged area under the curves (AUCs) for the test set were 0.804 and 0.842 in BR and ECC models, respectively. IDH mutation status was predicted with the highest AUCs of 0.964 (BR) and 0.967 (ECC). The ECC model showed higher AUCs than the BR model for ATRX (0.822 vs. 0.775) and MGMT promoter methylation (0.761 vs. 0.653) predictions. The mean absolute Shapley values suggested that predicted outcomes from the prior classifiers were important for better subsequent predictions along the classifier chains. Conclusion We built a radiomics-based multiple gene prediction chained model that incorporates mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant and performs better than a simple bundle of binary classifiers using prior classifiers’ prediction probability.

Background We aimed to investigate the detection rate of body CT or PET/CT for sites of extracranial disease in patients with a new pathological diagnosis of CNS DLBCL and to identify factors associated with sites of extracranial disease. Methods An international multicenter cohort study of consecutive immunocompetent patients with a new diagnosis of CNS DLBCL confirmed by brain biopsy who underwent CT and/or PET/CT to evaluate for sites of extracranial disease between 1998 and 2019. The primary outcome was the detection rate of extracranial lymphoma by CT or PET/CT. Subgroup analyses according to age and EBV status were also performed. Logistic regression analyses were performed to determine factors related to sites of extracranial disease. Detection rates of CT and PET/CT were compared. Results One thousand and forty-three patients were included. The overall detection rate of CT or PET/CT was 2.6% (27/1043). The treatment approach was adjusted in 74% of these patients. Multivariable analysis demonstrated that age >61 years (OR, 3.10; P = .016) and EBV positivity (OR, 3.78; P = .045) were associated with greater odds of extracranial lymphoma. There was no statistically significant difference in detection rate between CT and PET/CT (P = .802). In patients ≤61 years old, the false-referral rates were significantly higher than the detection rates (P < .001). Conclusion Our results showed increased odds of extracranial lymphoma in patients with older age or EBV-positive lymphoma. Treatment was adjusted in a majority of patients diagnosed with extracranial lymphoma, thereby supporting the current guidelines for the use of contrast-enhanced body CT or PET/CT in patients with newly diagnosed CNS DLBCL.

Purpose This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. Materials and methods In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. Results The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall false-positive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. Conclusion The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.

Objective: To evaluate the quality of radiomics studies on pituitary adenoma according to the radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). Methods: PubMed MEDLINE and EMBASE were searched to identify radiomics studies on pituitary adenomas. From 138 articles, 20 relevant original research articles were included. Studies were scored based on RQS and TRIPOD guidelines. Results: Most included studies did not perform pre-processing; isovoxel resampling, signal intensity normalization, and N4 bias field correction were performed in only five (25%), eight (40%), and four (20%) studies, respectively. Only two (10%) studies performed external validation. The mean RQS and basic adherence rate were 2.8 (7.6%) and 26.6%, respectively. There was a low adherence rate for conducting comparison to “gold-standard” (20%), multiple segmentation (25%), and stating potential clinical utility (25%). No study stated the biological correlation, conducted a test–retest or phantom study, was a prospective study, conducted cost-effectiveness analysis, or provided open-source code and data, which resulted in low-level evidence. The overall adherence rate for TRIPOD was 54.6%, and it was low for reporting the title (5%), abstract (0%), explaining the sample size (10%), and suggesting a full prediction model (5%). Conclusion: The radiomics reporting quality for pituitary adenoma is insufficient. Pre-processing is required for feature reproducibility and external validation is necessary. Feature reproducibility, clinical utility demonstration, higher evidence levels, and open science are required. Titles, abstracts, and full prediction model suggestions should be improved for transparent reporting. Advances in knowledge: Despite the rapidly increasing number of radiomics researches on pituitary adenoma, the quality of science in these researches is unknown. Our study indicates that the overall quality needs to be significantly improved in radiomics studies on pituitary adenoma, and since the concept of RQS and IBSI is still unfamiliar to clinicians and radiologist researchers, our study may help to reach higher technical and clinical impact in the future study.

Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinical settings. Purpose To investigate whether cortical thickness from MRI can help predict conversion from mild cognitive impairment (MCI) to dementia in PD at an individual level using a machine learning-based model. Materials and Methods In this retrospective study, patients with PD and MCI who underwent MRI from September 2008 to November 2016 were included. Features were selected from clinical and cortical thickness variables in 10 000 randomly generated training sets. Features selected 5000 times or more were used to train random forest and support vector machine models. Each model was trained and tested in 10 000 randomly resampled data sets, and a median of 10 000 areas under the receiver operating characteristic curve (AUCs) was calculated for each. Model performances were validated in an external test set. Results Forty-two patients progressed to PDD (converters) (mean age, 71 years ± 6 [standard deviation]; 22 women), and 75 patients did not progress to PDD (nonconverters) (mean age, 68 years ± 6; 40 women). Four PDD converters (mean age, 74 years ± 10; four men) and 20 nonconverters (mean age, 67 years ± 7; 11 women) were included in the external test set. Models trained with cortical thickness variables (AUC range, 0.75-0.83) showed fair to good performances similar to those trained with clinical variables (AUC range, 0.70-0.81). Model performances improved when models were trained with both variables (AUC range, 0.80-0.88). In pair-wise comparisons, models trained with both variables more frequently showed better performance than others in all model types. The models trained with both variables were successfully validated in the external test set (AUC range, 0.69-0.84). Conclusion Cortical thickness from MRI helped predict conversion from mild cognitive impairment to dementia in Parkinson disease at an individual level, with improved performance when integrated with clinical variables. © RSNA, 2021

Brain atrophy is related to vascular risk factors and can increase cognitive dysfunction risk. This community-based, cross-sectional study investigated whether glucose metabolic disorders due to body fatness are linked to regional changes in brain structure and a decline in neuropsychological function in cognitively healthy older adults. From 2016 to 2019, 429 participants underwent measurements for cortical thickness and subcortical volume using 3 T magnetic resonance imaging and for cognitive function using the neuropsychological screening battery. The effects of body fatness mediated by impaired glucose metabolism on neuroimaging markers and cognitive function was investigated using partial least square structural equation modeling. Total grey matter volume (β = −0.020; bias-corrected (BC) 95% confidence interval (CI) = -0.047 to −0.006), frontal (β = −0.029; BC 95% CI = −0.063 to −0.005) and temporal (β = −0.022; BC 95% CI = −0.051 to −0.004) lobe cortical thickness, and hippocampal volume (β = −0.029; BC 95% CI = −0.058 to −0.008) were indirectly related to body fatness. Further, frontal/temporal lobe thinning was associated with recognition memory (β = −0.005; BC 95% CI = −0.012 to −0.001/β = −0.005; BC 95% CI = −0.013 to −0.001) and delayed recall for visual information (β = −0.005; BC 95% CI = −0.013 to −0.001/β = −0.005; BC 95% CI = −0.013 to −0.001). Additionally, the smaller the hippocampal volume, the lower the score in recognition memory (β = −0.005; BC 95% CI = −0.012 to −0.001), delayed recall for visual information (β = −0.005; BC 95% CI = −0.012 to −0.001), and verbal learning (β = −0.008; BC 95% CI = −0.017 to −0.002). Our findings indicate that impaired glucose metabolism caused by excess body fatness affects memory decline as well as regional grey matter atrophy in elderly individuals with no neurological disease.

Little is known about the effect of air pollution on Alzheimer's disease (AD)-specific brain structural pathologies. There is also a lack of evidence on whether this effect leads to poorer cognitive function. We investigated whether, and the extent to which, AD-like cortical atrophy mediated the association between air pollution exposures and cognitive function in dementia-free adults. We used cross-sectional data from 640 participants who underwent brain magnetic resonance imaging and the Montreal Cognitive Assessment (MoCA). Mean cortical thickness (as the measure of global cortical atrophy) and machine learning-based AD-like cortical atrophy score were estimated from brain images. Concentrations of particulate matter with diameters ≤ 10 μm (PM

To investigate whether type-specific sex differences in survival exist independently of clinical and molecular factors in adult-type diffuse gliomas according to the 2021 World Health Organization (WHO) classification.A retrospective chart and imaging review of 1325 patients (mean age, 54 ± 15 years; 569 females) with adult-type diffuse gliomas (oligodendroglioma, IDH-mutant, and 1p/19q-codeleted, n = 183; astrocytoma, IDH-mutant, n = 211; glioblastoma, IDH-wildtype, n = 800; IDH-wildtype diffuse glioma, NOS, n = 131) was performed. The demographic information, extent of resection, imaging data, and molecular data including OIn patients with glioblastoma, IDH-wildtype, female sex remained as an independent predictor of better overall survival (hazard ratio = 0.91, P = 0.031), along with age, histological grade 4, MGMT promoter methylation status, and gross total resection. Female sex showed a higher prevalence of MGMT promoter methylation (40.2% vs 32.0%, P = 0.017) but there was no interaction effect between female sex and MGMT promoter methylation status (P-interaction = 0.194), indicating independent role of female sex. The median OS for females were 19.2 months (12.3-35.0) and 16.2 months (10.5-30.6) for males. No sex difference in survival was seen in other types of adult-type diffuse gliomas.There was a female survival advantage in glioblastoma, IDH-wildtype, independently of clinical data or MGMT promoter methylation status. There was no sex difference in survival in other types of adult-type diffuse gliomas, suggesting type-specific sex effects solely in glioblastoma, IDH-wildtype.

OBJECTIVE Leptomeningeal metastases (LMs) in glioma have been underestimated given their low incidence and the lack of reliable imaging. Authors of this study aimed to investigate the real-world incidence of LMs using cerebrospinal fluid (CSF)–sensitive imaging, namely postcontrast fluid-attenuated inversion recovery (FLAIR) imaging, and to analyze molecular predictors for LMs in the molecular era. METHODS A total of 1405 adult glioma (World Health Organization [WHO] grade 2–4) patients underwent postcontrast FLAIR imaging at initial diagnosis and during treatment monitoring between 2001 and 2021. Collected molecular data included isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion, H3 K27 alteration, and O6-methylguanine–DNA methyltransferase (MGMT) promoter methylation status. LM diagnosis was performed with MRI including postcontrast FLAIR sequences. Logistic regression analysis for LM development was performed with molecular, clinical, and imaging data. Overall survival (OS) was compared between patients with and those without LM. RESULTS LM was identified in 228 patients (16.2%), 110 (7.8%) at the initial diagnosis and 118 (8.4%) at recurrence. Among the molecular diagnostics, IDH-wildtype (OR 3.14, p = 0.001) and MGMT promoter unmethylation (OR 1.43, p = 0.034) were independent predictors of LM. WHO grade 4 (OR 10.52, p < 0.001) and nonlobar location (OR 1.56, p = 0.048) were associated with LM at initial diagnosis, whereas IDH-wildtype (OR 5.04, p < 0.001) and H3 K27 alteration (OR 3.39, p = 0.003) were associated with LM at recurrence. Patients with LM had a worse median OS than those without LM (16.7 vs 32.0 months, p < 0.001, log-rank test), which was confirmed as an independent factor on multivariable Cox analysis (p = 0.004). CONCLUSIONS CSF-sensitive imaging aids the diagnosis of LM, demonstrating a high incidence of LM in adult gliomas. Furthermore, molecular markers are associated with LM development in glioma, and patients with aggressive molecular markers warrant imaging surveillance for LM.

Background: Leptomeningeal metastases (LM) in glioma have been underestimated due to low incidence and lack of reliable imaging. This study aimed to investigate a real-world incidence of LM using a CSF-sensitive imaging, namely post-contrast FLAIR, and analyze molecular predictors for LM in the molecular era. Patients and Methods: Total 1,405 adult glioma patients underwent post-contrast FLAIR imaging at initial diagnosis and during treatment monitoring between 2001 and 2021. Molecular data included isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion, H3 K27M alteration, and O6-methylguanine-methyltransferase (MGMT) promoter methylation status. LM diagnosis was performed with MRI including post-contrast FLAIR. Logistic regression analysis for LM development was performed with molecular, clinical, and imaging data. Overall survival (OS) was compared between patients with and without LM. Results: LM was identified in 228 patients (16.2%), with 110 patients (7.8%) at initial diagnosis, and 118 patients (8.4%) at recurrence. Among molecular diagnostics, IDH-wildtype (odds ratio [OR] 3.14, P = .001) and MGMT promoter unmethylation (OR=1.43, P=.034) were independent predictors of LM. WHO grade 4 (OR=10.52, P

This study investigated the empathic response of postpartum women to babies in pain and the underlying neural mechanism. Postpartum women responded with more empathy and speed to babies over other stimuli compared to controls. Brain scans taken 3 months after birth showed more elevated activation in the Middle cingulate cortex/middle frontal gyrus (MCC/MFG) than the controls regardless of the task condition. When compared to the adult and neutral conditions, the posterior cingulate cortex (PCC) region was consistently more activated when postpartum women saw babies than controls. In addition, higher activation levels in the PCC region for the baby condition significantly correlated with faster and more empathic responses to babies. Considering that PCC is a core region for the theory of mind or mentalizing which requires cognitive reasoning to understand others, these results suggest that PCC might be a pivotal neural locus facilitating cognitive efforts to empathize with babies during the postpartum period. In a follow‐up experiment at 12 months after birth, we were still able to observe higher activity in the MCC/MFG of postpartum women. However, previously observed PCC activation patterns disappeared 12 months after birth, despite the women's response patterns to babies still being maintained. These results suggest that the mentalizing process activated to empathize with babies in the early postpartum period becomes less cognitively demanding over time., This study investigated the empathic response of postpartum women to babies in pain and the underlying neural mechanism. Our results suggest that posterior cingulate cortex (PCC) might be a pivotal neural locus facilitating cognitive efforts to empathize with babies during the postpartum period.

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..

Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC). Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models. Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.

PURPOSE Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are generalizable in the updated multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium and predictive of neoplastic infiltration and future recurrence. METHODS We used the most recent update of the ReSPOND consortium to evaluate and further refine generalizability of our methods with different scanners and acquisition settings. 179 de novo glioblastoma patients with available T1, T1Gd, T2, T2-FLAIR, and ADC sequences at pre-resection baseline and after complete resection with subsequent pathology-confirmed recurrence were included. To establish generalizability of the predictive models, training and testing of the refined AI model was performed through Leave-One-Institution-Out-Cross-Validation schema. The multi-institutional cohort consisted of the Hospital of the University of Pennsylvania (UPenn, 124), Case Western Reserve University/University Hospitals (CWRU/UH, 27), New York University (NYU, 13), Ohio State University (OSU, 13), and University Hospital Río Hortega (RH, 2). Features extracted from pre-resection MRI were used to build the model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence. RESULTS Our model predicted the locations that later harbored tumor recurrence with overall odds ratio (99% CI)/AUC (99% CI), 12.0(11.8-12.2)/0.80(0.76-0.85), and per institute, CWRU/UH, 11.0(10.7-11.3)/0.80 (0.64-0.97); NYU, 7.0(6.7-7.3)/0.78(0.56-1.00); OSU, 18.3(17.5-19.1)/0.83(0.54-1.00); RH, 40.0(35.3-45.5)/0.93(0.00-1.00); UPenn, 8.00(7.7-8.3)/0.80(0.75-0.84). CONCLUSION This study provides extensive multi-institutional validated evidence that machine learning tools can identify peritumoral neoplastic infiltration and predict location of future recurrence, by decrypting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and validate AI-based biomarkers for individualized prediction and prognostication and establish generalizability in a multi-institutional setting.

Purpose Lower-grade gliomas of histologic grade 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. 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, were performed. The models’ integrated area under the receiver operating characteristic curves (iAUC) 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; 66 women): 90 patients for training, 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 = .002) and test sets (0.88 vs. 0.76, P = .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 set, respectively. Conclusion Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.

To assess whether radiomic features could improve the accuracy of survival predictions of IDH-wildtype (IDHwt) histological lower-grade gliomas (LGGs) over clinicopathological features.Preoperative MRI data of 61 patients with IDHwt histological LGGs were included as the institutional training set. The test set consisted of 32 patients from The Cancer Genome Atlas. Radiomic features (n = 186) were extracted using conventional MRIs. The radiomics risk score (RRS) for overall survival (OS) was derived from the elastic net. Multivariable Cox regression analyses with clinicopathological features (including epidermal growth factor receptor [EGFR] amplification and telomerase reverse transcriptase promoter [TERTp] mutation status) and the RRS were performed. The integrated area under the receiver operating curves (iAUCs) from the models with and without the RRS were compared. The net reclassification index (NRI) for 1-year OS was also calculated. The prognostic value of the RRS was evaluated using the external validation set.The RRS independently predicted OS (hazard ratio = 48.08; p = 0.001). Compared with the clinicopathological model alone, adding the RRS had a better OS prediction performance (iAUCs 0.775 vs. 0.910), which was internally validated (iAUCs 0.726 vs. 0.884, 1-year OS NRI = 0.497), and a similar trend was found on external validation (iAUCs 0.683 vs. 0.705, 1-year OS NRI = 0.733). The prognostic significance of the RRS was confirmed in the external validation set (p = 0.001).Integrating radiomics with clinicopathological features (including EGFR amplification and TERTp mutation status) can improve survival prediction in patients with IDHwt LGGs.• Radiomics risk score has the potential to improve survival prediction when added to clinicopathological features (iAUCs increased from 0.775 to 0.910). • NRIs for 1-year OS showed that the radiomics risk score had incremental value over the clinicopathological model. • The prognostic significance of the radiomics risk score was confirmed in the external validation set (p = 0.001).

Background and purpose Hippocampal atrophy (HA) resulting from a central nervous system (CNS) infection might be a relevant lesion responsible for the clinical characteristics of medial temporal lobe epilepsy. Methods The clinical characteristics of 54 patients with CNS infection-related medial temporal lobe epilepsy (MTLE) with isolated HA (CNS infection group) and 155 patients with conventional MTLE with HA (conventional group) were compared retrospectively. CNS infection alone and bilateral involvement of the HA were analyzed as prognostic factors, in addition to the detailed clinical characteristics, such as limbic aura and the presence and proportion of each type of automatism, between the two groups, and both medical and surgical prognoses were separately considered. A logistic regression analysis was performed. Results A statistical analysis including all clinical factors, including CNS infection with bilateral HA, did not reveal significant differences between the two groups. An analysis comparing the prognosis of the two groups based on good or poor prognosis among patients who received medical treatment and good or poor outcomes among patients who received surgical treatment did not produce significant differences. Conclusions In addition to bilateral HA, CNS infection alone was not a poor prognostic factor for the CNS infection-related epilepsy with HA group compared with the conventional MTLE with HA group. Based on these negative results, HA is a plausible and relevant lesion with similar clinical characteristics to HA in patients with conventional MTLE. Therefore, CNS infection-related MTLE with isolated HA might represent another subtype of MTLE with HA with a different etiology.

To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients. Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC). Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738–0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447–0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523–0.759], P = 0.037). Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.

To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC). A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC). EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively. Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC.

We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.

We report a rare case of oligodendroglioma with gangliocytic differentiation. A 31-year-old male without a past medical history was admitted with a sudden seizure. On magnetic resonance imaging, an approximately 7-cm mass with necrosis was noted in the right frontal lobe. The patient underwent surgical resection. On microscopy, two morphologically distinct areas with oligodendroglioma- and ganglioglioma-like features were found. Immunohistochemistry showed an absence of CD34 expression, whereas isocitrate dehydrogenase 1 (IDH1) was positive in the glial component. Moreover, IDH1 was positive in the ganglion-like cells as well as in the glial component. Subsequent 1p/19q co-deletion was confirmed by fluorescence in situ hybridization. Finally, a diagnosis of oligodendroglioma with gangliocytic differentiation was made. IDH1/2 molecular test would be basic and essential diagnostic tool in central nervous system tumor of young patients.

To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance. In a retrospective and multicenter study, MR images with aneurysms based on radiological reports were extracted. The examinations were randomly divided into two data sets: training set of 468 examinations and internal test set of 120 examinations. Additionally, 50 examinations without aneurysms were randomly selected and added to the internal test set. External test data set consisted of 56 examinations with intracranial aneurysms and 50 examinations without aneurysms, which were extracted based on radiological reports from a different institution. After manual ground truth segmentation of aneurysms, a deep learning algorithm based on 3D ResNet architecture was established with the training set. Its sensitivity, positive predictive value, and specificity were evaluated in the internal and external test sets. MR images included 551 aneurysms (mean diameter, 4.17 ± 2.49 mm) in the training, 147 aneurysms (mean diameter, 3.98 ± 2.11 mm) in the internal test, 63 aneurysms (mean diameter, 3.23 ± 1.69 mm) in the external test sets. The sensitivity, the positive predictive value, and the specificity were 87.1%, 92.8%, and 92.0% for the internal test set and 85.7%, 91.5%, and 98.0% for the external test set, respectively. A deep learning algorithm detected intracranial aneurysms with a high diagnostic performance which was validated using external data set. • A deep learning-based algorithm for the automated diagnosis of intracranial aneurysms demonstrated a high sensitivity, positive predictive value, and specificity. • The high diagnostic performance of the algorithm was validated using external test data set from a different institution with a different scanner. • The algorithm might be robust and effective for general use in real clinical settings.

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-weighted imaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumor were calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariable logistic regression analysis using 2D or 3D fractal features was performed to predict the meningioma grade. The diagnostic ability of the 2D and 3D fractal models were compared. RESULTS The reproducibility between observers was excellent, with intraclass correlation coefficients of 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

BACKGROUND Clinicopathological studies have demonstrated that the neuropsychological profiles and outcomes are different between two dementia subtypes, namely Alzheimer's disease (AD) and Lewy bodies-related disease. OBJECTIVE We investigated the neural correlates of cognitive dysfunction in patients with AD-related cognitive impairment (ADCI) and those with Lewy bodies-related cognitive impairment (LBCI). METHODS We enrolled 216 ADCI patients, 183 LBCI patients, and 30 controls. Cortical thickness and diffusion tensor imaging analyses were performed to correlate gray matter and white matter (WM) abnormalities to cognitive composite scores for memory, visuospatial, and attention/executive domains in the ADCI spectrum (ADCI patients and controls) and the LBCI spectrum (LBCI patients and controls) separately. RESULTS Memory dysfunction correlated with cortical thinning and increased mean diffusivity in the AD-prone regions, particularly the medial temporal region, in ADCI. Meanwhile, it only correlated with increased mean diffusivity in the WM adjacent to the anteromedial temporal, insula, and basal frontal cortices in LBCI. Visuospatial dysfunction correlated with cortical thinning in posterior brain regions in ADCI, while it correlated with decreased fractional anisotropy in the corpus callosum and widespread WM regions in LBCI. Attention/executive dysfunction correlated with cortical thinning and WM abnormalities in widespread brain regions in both disease spectra; however, ADCI had more prominent correlation with cortical thickness and LBCI did with fractional anisotropy values. CONCLUSIONS Our study demonstrated that ADCI and LBCI have different neural correlates with respect to cognitive dysfunction. Cortical thinning had greater effects on cognitive dysfunction in the ADCI, while WM disruption did in the LBCI.

The interplay between schizophrenia liability and environmental influences has been considered to be responsible for the development of schizophrenia. Recent neuroimaging studies have linked aberrant functional connectivity (FC) between the default-mode network (DMN) and the frontoparietal network (FPN) in the resting-state to the underlying neural mechanism of schizophrenia. By using schizotypy as the proxy for genetic-based liability to schizophrenia and methylation of brain-derived neurotrophic factor (BDNF) to represent environmental exposure, this study investigated the impact of the interaction between vulnerability and the environment on the neurobiological substrates of schizophrenia. Participants in this study included 101 healthy adults (HC) and 46 individuals with ultra-high risk for psychosis (UHR). All participants were tested at resting-state by functional magnetic resonance imaging, and group-independent component analysis was used to identify the DMN and the FPN. The Perceptual Aberration Scale (PAS) was used to evaluate the schizotypy level. The methylation status of BDNF was measured by pyrosequencing. For moderation analysis, the final sample consisted of 83 HC and 32 UHR individuals. UHR individuals showed reduced DMN-FPN network FC compared to healthy controls. PAS scores significantly moderated the relationship between the percentage of BDNF methylation and DMN-FPN network FC. The strength of the positive relationship between BDNF methylation and the network FC was reduced when the schizotypy level increased. These findings support the moderating role of schizotypy on the neurobiological mechanism of schizophrenia in conjunction with epigenetic changes.

To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) usingThis retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwentIn the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48,Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from

Multiple burr hole surgery is considered to be an option for achieving indirect revascularization in patients with ischemic Moyamoya disease (MMD). We aimed to investigate the efficacy of stand-alone multiple burr hole surgery for salvage revascularization in patients with MMD by assessing the hemodynamic changes via normalized time-to-peak (nTTP) analysis and independent component analysis (ICA) of preoperative and postoperative dynamic susceptibility contrast (DSC) perfusion MRI data.The DSC perfusion MRI data of 25 hemispheres from 21 patients with MMD, who underwent multiple burr hole surgery for salvage revascularization due to persistent or recurrent symptoms after primary revascularization with modified encephaloduroarteriosynangiosis (mEDAS), were analyzed. The nTTP, which was measured using the region of interests covering the entire surgical hemisphere, was compared between the preoperative and postoperative images. ICA was used to compare the relative arterial and venous components of the surgical hemispheres between the respective preoperative and postoperative images.The median postoperative nTTP (1.80 s) was significantly shorter than the median preoperative nTTP (4.10 s) (P 0.001). The postoperative relative arterial component of the surgical hemisphere (median: 0.04) was significantly higher than the preoperative relative arterial component (median: - 0.02, P 0.001). In contrast, the postoperative relative venous component of the surgical hemisphere (median: - 0.05) was significantly lower than the preoperative value (median: 0.05, P 0.001).The improvement in cerebral perfusion parameters observed on postoperative DSC perfusion MRI demonstrated that stand-alone multiple burr hole surgery could be a favorable salvage revascularization technique after mEDAS failure in patients with ischemic MMD.

Pilocytic astrocytoma (PA) is rare in adults, and only limited knowledge on the clinical course and prognosis has been available. The combination of clinical information and comprehensive imaging parameters could be used for accurate prognostic stratification in adult PA patients. This study was conducted to predict the prognostic factors from clinical information and conventional magnetic resonance imaging (MRI) features in adult PAs.A total of 56 adult PA patients were enrolled in the institutional cohort. Clinical characteristics including age, sex, anaplastic PA, presence of neurofibromatosis type 1, Karnofsky performance status, extent of resection, and postoperative treatment were collected. MRI characteristics including major axis length, tumor location, presence of the typical 'cystic mass with enhancing mural nodule appearance', proportion of enhancing tumor, the proportion of edema, conspicuity of the nonenhancing margin, and presence of a cyst were evaluated. Univariable and multivariable Cox proportional hazard modeling were performed.The 5-year progression-free survival (PFS) and overall survival (OS) rates were 83.9% and 91.l%, respectively. On univariable analysis, older age, larger proportion of edema, and poor definition of nonenhancing margin were predictors of shorter PFS and OS, respectively (all Ps .05). On multivariable analysis, older age (hazard ratio [HR] = 1.04, P = .014; HR = 1.14, P = .030) and poor definition of nonenhancing margin (HR = 3.66, P = .027; HR = 24.30, P = .024) were independent variables for shorter PFS and OS, respectively.Age and the margin of the nonenhancing part of the tumor may be useful biomarkers for predicting the outcome in adult PAs.

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81–0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.

Early and accurate diagnosis of panic disorder with or without agoraphobia (PDA) is crucial to reducing disease burden and individual suffering. However, its diagnosis is challenging for lack of validated biomarkers. This study aimed to investigate whether radiomic features extracted from T1-weighted images (T1) of major fear-circuit structures (amygdala, insula, and anterior cingulate cortex [ACC]) could differentiate patients with PDA from healthy controls (HCs).The 213 participants (93 PDA, 120 HCs) were allocated to training (n = 149) and test (n = 64) sets after undergoing magnetic resonance imaging. Radiomic features (n = 1498) were extracted from T1 of the studied structures. Machine learning models were trained after feature selection and then validated in the test set. SHapley Additive exPlanations (SHAP) explored the model interpretability.We identified 29 radiomic features to differentiate participants with PDA from HCs. The area under the curve, accuracy, sensitivity, and specificity of the best performing radiomics model in the test set were 0.84 (95% confidence interval: 0.74-0.95), 81.3%, 75.0%, and 86.1%, respectively. The SHAP model explanation suggested that the energy features extracted from the bilateral long insula gyrus and central sulcus of the insula and right ACC were highly associated with the risk of PDA.This was a cross-sectional study with a relatively small sample size, and the causality of changes in radiomic features and their biological and clinical meanings remained to be elucidated.Our findings suggest that radiomic features from the fear-circuit structures could unveil hidden microstructural aberrations underlying the pathogenesis of PDA that could help identify PDA.