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A ganglion cyst is a benign mass consisting of high-viscosity mucinous fluid. It can originate from the sheath of a tendon, peripheral nerve, or joint capsule. Compressive neuropathy caused by a ganglion cyst is rarely reported, with the majority of documented cases involving peroneal nerve palsy. To date, cases demonstrating both peroneal and tibial nerve palsies resulting from a ganglion cyst forming on a branch of the sciatic nerve have not been reported. In this paper, we present the case of a 74-year-old man visiting an outpatient clinic complaining of left-sided foot drop and sensory loss in the lower extremity, a lack of strength in his left leg, and a decrease in sensation in the leg for the past month without any history of trauma. Ankle dorsiflexion and great toe extension strength on the left side were Grade I. Ankle plantar flexion and great toe flexion were Grade II. We suspected peroneal and tibial nerve palsy and performed a screening ultrasound, which is inexpensive and rapid. In the operative field, several cysts were discovered, originating at the site where the sciatic nerve splits into peroneal and tibial nerves. After successful surgical decompression and a series of rehabilitation procedures, the patient's neurological symptoms improved. There was no recurrence. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the added value of MR dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI)-derived tumour microvascular and oxygenation information with cerebral blood volume (CBV) to distinguish pseudoprogression from true progression (TP) in post-treatment glioblastoma. Methods: This retrospective single-institution study included patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and a newly developed or enlarging measurable contrast-enhancing mass within 12 weeks after concurrent chemoradiotherapy. CBV, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2) were obtained from DSC-PWI. Predictors were selected using univariable logistic regression, and performance was measured with adjusted diagnostic odds with tumour volume and area under the curve (AUC) of receiver operating characteristics analysis. Results: A total of 103 patients were included (mean age, 59.6 years; 59 women), with 67 cases of TP and 36 cases of pseudoprogression. Pseudoprogression exhibited higher CTH (4.0 vs. 3.4, p =.019) and higher OEF (12.7 vs. 10.7, p =.014) than TP, but a similar CBV (1.48 vs. 1.53, p =.13) and CMRO2 (7.7 vs. 7.3s, p =.598). Independent of tumour volume, both high CTH (adjusted odds ratio [OR] 1.52; 95% confidence interval [CI]: 1.11–2.09, p =.009) and high OEF (adjusted OR 1.17; 95% CI:1.03–1.33, p =.016) were predictors of pseudoprogression. The combination of CTH, OEF, and CBV yielded higher diagnostic performance (AUC 0.71) than CBV alone (AUC 0.65). Conclusion: High intratumoural capillary transit heterogeneity and high oxygen extraction fraction derived from DSC-PWI have enhanced the diagnostic value of CBV in pseudoprogression of post-treatment IDH-wild type glioblastoma. Clinical relevance statement: In the early post-treatment stage of glioblastoma, pseudoprogression exhibited both high oxygen extraction fraction and high capillary transit heterogeneity and these dynamic susceptibility contrast-perfusion weighted imaging derived parameters have added value in cerebral blood volume-based noninvasive differentiation of pseudoprogression from true progression. Key Points: • Capillary transit time heterogeneity and oxygen extraction fraction can be measured noninvasively through processing of dynamic susceptibility contrast imaging. • Pseudoprogression exhibited higher capillary transit time heterogeneity and higher oxygen extraction fraction than true progression. • A combination of cerebral blood volume, capillary transit time heterogeneity, and oxygen extraction fraction yielded the highest diagnostic performance (area under the curve 0.71). [ABSTRACT FROM AUTHOR]

Objectives: To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL). Materials and methods: DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists. Results: Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90–96%) and dual-enhanced DL (92% [89–94%]). The PPV of the dual-enhanced DL was higher (89% [86–92%], p <.001) than that of GRE DL (76%, [72–80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00–0.58) compared with neuroradiologists (RD: 0.00, 95% CI: − 0.28, 0.15, p <.001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00–0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: − 0.20–0.10, p =.913). Conclusion: The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists. Clinical relevance statement: The use of deep learning–based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed. Key Points: •Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts. [ABSTRACT FROM AUTHOR]

Background and objectives: Diabetic foot stands out as one of the most consequential and devastating complications of diabetes. Many factors, including VIPS (Vascular management, Infection management, Pressure relief, and Source of healing), influence the prognosis and treatment of diabetic foot patients. There are many studies on VIPS, but relatively few studies on "sources of healing". Nutrients that affect wound healing are known, but objective data in diabetic foot patients are insufficient. We hypothesized that "sources of healing" would have many effects on wound healing. The purpose of this study is to know the affecting factors related to the source of healing for diabetic foot patients. Materials and Methods: A retrospective review identified 46 consecutive patients who were admitted for diabetic foot management from July 2019 to April 2021 at our department. Several laboratory tests were performed for influencing factor evaluation. We checked serum levels of total protein, albumin, vitamin B, iron, zinc, magnesium, copper, Hb, HbA1c, HDL cholesterol, and LDL cholesterol. These values of diabetic foot patients were compared with normal values. Patients were divided into two groups based on wound healing rate, age, length of hospital stay, and sex, and the test values between the groups were compared. Results: Levels of albumin (37%) and Hb (89%) were low in the diabetic foot patients. As for trace elements, levels of iron (97%) and zinc (95%) were low in the patients, but levels of magnesium and copper were usually normal or high. There were no differences in demographic characteristics based on wound healing rate. However, when compared to normal adult values, diabetic foot patients in our data exhibited significantly lower levels of hemoglobin, total protein, albumin, iron, zinc, copper, and HDL cholesterol. When compared based on age and length of hospital stay, hemoglobin levels were significantly lower in both the older age group and the group with longer hospital stays. Conclusions: Serum levels of albumin, Hb, iron, and zinc were very low in most diabetic foot patients. These low values may have a negative relationship with wound healing. Nutrient replacements are necessary for wound healing in diabetic foot patients. [ABSTRACT FROM AUTHOR]

Solid-state batteries (SSBs) using composite solid electrolytes (CSE) have attracted attention as next-generation battery materials with high stability and energy density. In this study, we manufactured CSE using a poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) polymer host and ceramic inorganic filler. Next, a 23.3 μm electrolyte was prepared via in situ polymerization using a poly(ethylene glycol) methyl ether acrylate (PEGMEA)-based liquid precursor. The introduction of ceramic fillers and in situ polymerization can improve the mechanical strength and ionic conductivity of the electrolyte and enable a high lithium transference number owing to the anion immobilization effect. LiFePO4/Li cells using the manufactured electrolyte exhibited a high capacity maintenance rate of 93.2% over 150 cycles. In addition, a pouch cell based on the prepared electrolyte exhibited excellent stability and flexibility. [ABSTRACT FROM AUTHOR]

Background: Wagstaffe fracture constitutes an indirect injury to the AITFL and can precipitate syndesmotic instability. The prevailing fixation methods often involve the use of mini-screws or K-wires, with absorbable suture repair reserved for cases with small or comminuted fragments exhibiting instability. In this study, we devised a mini-plate fixation method capable of securing the fracture fragment irrespective of its size or condition. Methods: A retrospective chart review was conducted on patients who underwent surgery for ankle fractures between May 2022 and October 2023. The surgical technique involved direct fixation of the Wagstaffe fracture using mini-plate fixation. Radiologic evaluation was performed using postoperative CT images, and clinical outcomes were assessed using the OMAS and VAS. Results: Fourteen patients with an average age of 62.5 years were included. Most fractures were associated with the supination-external rotation type. The average preoperative OMAS significantly improved from 5.95 to 83.57 postoperatively. The average VAS score decreased from 7.95 preoperatively to 0.19 postoperatively. Conclusions: The mini-plate technique for Wagstaffe fractures exhibited dependable fixation strength, effective fracture reduction, a minimal complication rate, and judicious surgical procedure duration. [ABSTRACT FROM AUTHOR]

Objectives: The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test–retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. Methods: To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). Results: Radiomics features from T2WI demonstrated higher repeatability (65–94%) than those from DWI (38–48%) or T1WI (2–92%). Across test–retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). Conclusion: Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. Clinical relevance statement: By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. Key Points: • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate whether deep learning–based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs. Methods: The deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated. Results: Among 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk. Conclusion: DLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs. Clinical relevance statement: For patients with newly diagnosed brain metastasis, deep learning–based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes. Key Points: • Deep learning–based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications. • By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%. • When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%. [ABSTRACT FROM AUTHOR]

The symbiotic community of microorganisms in the gut plays an important role in the health of the host. While many previous studies have been performed on the interactions between the gut microbiome and the host in mammals, studies in fish are still lacking. In this study, we investigated changes in the intestinal microbiome and pathogen susceptibility of zebrafish (Danio rerio) following chronic antibiotics exposure. The chronic antibiotics exposure assay was performed on zebrafish for 30 days using oxytetracycline (Otc), sulfamethoxazole/trimethoprim (Smx/Tmp), or erythromycin (Ery), which are antibiotics widely used in the aquaculture industry. The microbiome analysis indicated that Fusobacteria, Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla in the gut microbiome of the zebrafish used in this study. However, in Smx/Tmp-treated zebrafish, the compositions of Fusobacteria and Proteobacteria were changed significantly, and in Ery-treated zebrafish, the compositions of Proteobacteria and Firmicutes were altered significantly. Although alpha diversity analysis showed that there was no significant difference in the richness, beta diversity analysis revealed a community imbalance in the gut microbiome of all chronically antibiotics-exposed zebrafish. Intriguingly, in zebrafish with dysbiosis in the gut microbiome, the pathogen susceptibility to Edwardsiella piscicida, a representative Gram-negative fish pathogen, was reduced. Gut microbiome imbalance resulted in a higher count of goblet cells in intestinal tissue and an upregulation of genes related to the intestinal mucosal barrier. In addition, as innate immunity was enhanced by the increased mucosal barrier, immune and stress-related gene expression in the intestinal tissue was downregulated. In this study, we provide new insight into the effect of gut microbiome dysbiosis on pathogen susceptibility. [ABSTRACT FROM AUTHOR]

This study reveals the trajectories of Korean companies that employ technologies related to 4IR. The data were based on the 2017 and 2018 corporate surveys conducted by Korea Statistics' Microdata Integrated Service. The network analysis methodology was applied, with a focus on visualizing the relationships between nine technologies. The results are as follows; First, there were differences between the corporate technology absorption trajectories from the integrated perspective and from the perspective of the detailed application fields. Second, the Internet of Things occupied a greater proportion, compared to other technologies, in several technology absorption trajectories. This seems to reflect the hyper-connectivity of the Fourth Industrial Revolution. Third, hyper-intelligence appears to be at a nascent stage, in terms of utilization with other technologies. This paper primarily contributes empirical evidence to existing research on technological trajectories with unique data and results, which is generally depending on patents data, creating a foundation for further theoretical and practical applications by scholars and managers. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI). Methods and materials: This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning–based AD signature areas were investigated. Results: Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947–0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951–0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. Conclusions: TabNet shows high performance in AD classification and detailed interpretation of the selected regions. Clinical relevance statement: Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer's disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions. Key Points: • MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer's disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of natural language processing (NLP) models to predict isocitrate dehydrogenase (IDH) mutation status in diffuse glioma using routine MR radiology reports. Materials and methods: This retrospective, multi-center study included consecutive patients with diffuse glioma with known IDH mutation status from May 2009 to November 2021 whose initial MR radiology report was available prior to pathologic diagnosis. Five NLP models (long short-term memory [LSTM], bidirectional LSTM, bidirectional encoder representations from transformers [BERT], BERT graph convolutional network [GCN], BioBERT) were trained, and area under the receiver operating characteristic curve (AUC) was assessed to validate prediction of IDH mutation status in the internal and external validation sets. The performance of the best performing NLP model was compared with that of the human readers. Results: A total of 1427 patients (mean age ± standard deviation, 54 ± 15; 779 men, 54.6%) with 720 patients in the training set, 180 patients in the internal validation set, and 527 patients in the external validation set were included. In the external validation set, BERT GCN showed the highest performance (AUC 0.85, 95% CI 0.81−0.89) in predicting IDH mutation status, which was higher than LSTM (AUC 0.77, 95% CI 0.72−0.81; p =.003) and BioBERT (AUC 0.81, 95% CI 0.76−0.85; p =.03). This was higher than that of a neuroradiologist (AUC 0.80, 95% CI 0.76−0.84; p =.005) and a neurosurgeon (AUC 0.79, 95% CI 0.76−0.84; p =.04). Conclusion: BERT GCN was externally validated to predict IDH mutation status in patients with diffuse glioma using routine MR radiology reports with superior or at least comparable performance to human reader. Clinical relevance statement: Natural language processing may be used to extract relevant information from routine radiology reports to predict cancer genotype and provide prognostic information that may aid in guiding treatment strategy and enabling personalized medicine. Key Points: • A transformer-based natural language processing (NLP) model predicted isocitrate dehydrogenase mutation status in diffuse glioma with an AUC of 0.85 in the external validation set. • The best NLP models were superior or at least comparable to human readers in both internal and external validation sets. • Transformer-based models showed higher performance than conventional NLP model such as long short-term memory. [ABSTRACT FROM AUTHOR]

Introduction: Distal tibial fractures make up approximately 3% to 10% of all tibial fractures or about 1% of lower extremity fractures. MIPO is an appropriate procedure and method to achieve stable metal plate fixation and osseointegration by minimizing soft tissue damage and vascular integrity at the fracture site. MIPO to the medial tibia during distal tibial fractures induces skin irritation due to the thickness of the metal plate, which causes discomfort and pain on the medial side of the distal leg, and if severe, complications such as infection and skin defect may occur. The reverse sural flap is a well-researched approach for covering defects in the lower third of the leg, ankle, and foot. Materials and Methods: Among 151 patients with distal tibia fractures who underwent minimally invasive metal plate fixation, soft tissue was injured due to postoperative complications. We treated 13 cases with necrosis and exposed metal plates by retrograde nasogastric artery flap surgery. For these patients, we collected obligatory patient records, radiological data, and wound photographs of the treatment results and complications of reconstructive surgery. Results: In all the cases, flap survival was confirmed at the final outpatient follow-up. The exposed area of the metal plate was well coated, and there was no plate failure due to complete necrosis. Three out of four women complained of aesthetic dissatisfaction because the volume of the tunnel through which the skin mirror passed and the skin plate itself were thick. In two cases, defatting was performed to reduce the thickness of the plate while removing the metal plate. Conclusions: Metal plate exposure after distal tibial fractures have been treated with minimally invasive metal plate fusion and can be successfully treated with retrograde nasogastric artery flaps, and several surgical techniques are used during flap surgery. [ABSTRACT FROM AUTHOR]

Objectives: The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma. Methods: This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram. Results: SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94–0.99; p =.011) and definition 2 (HR: 0.96, 0.94–0.98, p <.001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94–0.99, p =.008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3 years (AUC: 0.735–0.738 vs. 0.687–0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%). Conclusion: SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility. Key Points: • Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. • Longer SVD was independently associated with longer overall survival. • Adding SVD improved time-dependent specificity for survival model in a prospective registry. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a nomogram based on MRI features for predicting iNPH. Methods: Patients aged ≥ 60 years (clinically diagnosed with iNPH, Parkinson's disease, or Alzheimer's disease or healthy controls) who underwent MRI including three-dimensional T1-weighted volumetric MRI were retrospectively identified from two tertiary referral hospitals (one hospital for derivation set and the other for validation set). Clinical and imaging features for iNPH were assessed. Deep learning–based brain segmentation software was used for 3D volumetry. A prediction model was developed using logistic regression and transformed into a nomogram. The performance of the nomogram was assessed with respect to discrimination and calibration abilities. The nomogram was internally and externally validated. Results: A total of 452 patients (mean age ± SD, 73.2 ± 6.5 years; 200 men) were evaluated as the derivation set. One hundred eleven and 341 patients were categorized into the iNPH and non-iNPH groups, respectively. In multivariable analysis, high-convexity tightness (odds ratio [OR], 35.1; 95% CI: 4.5, 275.5), callosal angle < 90° (OR, 12.5; 95% CI: 3.1, 50.0), and normalized lateral ventricle volume (OR, 4.2; 95% CI: 2.7, 6.7) were associated with iNPH. The nomogram combining these three variables showed an area under the curve of 0.995 (95% CI: 0.991, 0.999) in the study sample, 0.994 (95% CI: 0.990, 0.998) in the internal validation sample, and 0.969 (95% CI: 0.940, 0.997) in the external validation sample. Conclusion: A brain morphometry–based nomogram including high-convexity tightness, callosal angle < 90°, and normalized lateral ventricle volume can help accurately estimate the probability of iNPH. Key Points: • The nomogram with MRI findings (high-convexity tightness, callosal angle, and normalized lateral ventricle volume) helped in predicting the probability of idiopathic normal-pressure hydrocephalus. • The nomogram may facilitate the prediction of idiopathic normal-pressure hydrocephalus and consequently avoid unnecessary invasive procedures such as the cerebrospinal fluid tap test, drainage test, and cerebrospinal fluid shunt surgery. [ABSTRACT FROM AUTHOR]

Dopant-free lateral p–n junctions in the GaAs/AlGaAs material system have attracted interest due to their potential use in quantum optoelectronics (e.g., optical quantum computers or quantum repeaters) and ease of integration with other components, such as single electron pumps and spin qubits. A major obstacle to integration has been the unwanted charge accumulation at the p–n junction gap that suppresses light emission, either due to enhanced non-radiative recombination or due to inhibition of p–n current. Typically, samples must frequently be warmed to room temperature to dissipate this built-up charge and restore light emission in a subsequent cooldown. Here, we introduce a practical gate voltage protocol that clears this parasitic charge accumulation, in situ at low temperature, enabling the indefinite cryogenic operation of devices. This reset protocol enabled the optical characterization of stable, bright, dopant-free lateral p–n junctions with electroluminescence linewidths among the narrowest (<1 meV; <0.5 nm) reported in this type of device. It also enabled the unambiguous identification of the ground state of neutral free excitons (heavy and light holes) as well as charged excitons (trions). The free exciton emission energies for both photoluminescence and electroluminescence are found to be nearly identical (within 0.2 meV or 0.1 nm). The binding and dissociation energies for free and charged excitons are reported. A free exciton lifetime of 237 ps was measured by time-resolved electroluminescence, compared to 419 ps with time-resolved photoluminescence. [ABSTRACT FROM AUTHOR]

Introduction: The number of brain MRI with contrast media performed in patients with cognitive impairment has increased without universal agreement. We aimed to evaluate the detection rate of contrast-enhanced brain MRI in patients with cognitive impairment. Materials and methods: This single-institution, retrospective study included 4,838 patients who attended outpatient clinics for cognitive impairment evaluation and underwent brain MRI with or without contrast enhancement from December 2015 to February 2020. Patients who tested positive for cognitive impairment were followed-up to confirm whether the result was true-positive and provide follow-up management. Detection rate was defined as the proportion of patients with true-positive results and was compared between groups with and without contrast enhancement. Individual matching in a 1:2 ratio according to age, sex, and year of test was performed. Results: The overall detection rates of brain MRI with and without contrast media were 4.7% (57/1,203; 95% CI: 3.6%–6.1%) and 1.8% (65/3,635; 95% CI: 1.4%–2.3%), respectively (P<0.001); individual matching demonstrated similar results (4.7% and 1.9%). Among 1,203 patients with contrast media, 3.6% was only detectable with the aid of contrast media. The proportion of patients who underwent follow-up imaging or treatment for the detected lesions were significantly higher in the group with contrast media (2.0% and 0.6%, P <.001). Conclusions: Detection rate of brain MRI for lesions only detectable with contrast media in patients with cognitive impairment was not high enough and further study is needed to identify whom would truly benefit with contrast media. [ABSTRACT FROM AUTHOR]

Objectives: An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical referral suggestion for IMLLs, and to validate it in the setting of nontraumatic emergency neuroradiology. Methods: A DL system was developed in 747 patients with IMLLs ranging 30 diseases who underwent pre- and post-contrast T1-weighted (T1CE), FLAIR, and diffusion-weighted imaging (DWI). A DL system that segments IMLLs, classifies tumourous conditions, and suggests clinical referral among surgery, systematic work-up, medical treatment, and conservative treatment, was developed. The system was validated in an independent cohort of 130 emergency patients, and performance in referral suggestion and tumour discrimination was compared with that of radiologists using receiver operating characteristics curve, precision-recall curve analysis, and confusion matrices. Multiparametric interpretable visualisation of high-relevance regions from layer-wise relevance propagation overlaid on contrast-enhanced T1WI and DWI was analysed. Results: The DL system provided correct referral suggestions in 94 of 130 patients (72.3%) and performed comparably to radiologists (accuracy 72.6%, McNemar test; p =.942). For distinguishing tumours from non-tumourous conditions, the DL system (AUC, 0.90 and AUPRC, 0.94) performed similarly to human readers (AUC, 0.81~0.92, and AUPRC, 0.88~0.95). Solid portions of tumours showed a high overlap of relevance, but non-tumours did not (Dice coefficient 0.77 vs. 0.33, p <.001), demonstrating the DL's decision. Conclusions: Our DL system could appropriately triage patients using multiparametric MRI and provide interpretability through multiparametric heatmaps, and may thereby aid neuroradiologic diagnoses in emergency settings. Clinical relevance statement: Our AI triages patients with raw MRI images to clinical referral pathways in brain intra-axial mass-like lesions. We demonstrate that the decision is based on the relative relevance between contrast-enhanced T1-weighted and diffusion-weighted images, providing explainability across multiparametric MRI data. Key Points: • A deep learning (DL) system using multiparametric MRI suggested clinical referral to patients with intra-axial mass-like lesions (IMLLs) similar to radiologists (accuracy 72.3% vs. 72.6%). • In the differentiation of tumourous and non-tumourous conditions, the DL system (AUC, 0.90) performed similar with radiologists (AUC, 0.81–0.92). • The DL's decision basis for differentiating tumours from non-tumours can be quantified using multiparametric heatmaps obtained via the layer-wise relevance propagation method. [ABSTRACT FROM AUTHOR]

Purpose: Immune checkpoint inhibitors (ICIs) markedly improve the clinical outcomes of advanced non-small-cell lung cancer (NSCLC). However, the intracranial efficacy of ICI is not well elucidated, and previous studies showed discordant outcomes of ICI between intracranial and extracranial diseases. We aimed to evaluate the clinical outcomes and the intracranial and extracranial response of patients with NSCLC and brain metastasis who were treated with ICI in the real-world setting. Methods: A total of 55 patients (median age, 63 years [range 42–80]; male, 78%) who had NSCLC with brain metastasis and treated with ICI monotherapy were retrospectively analyzed. We separately assessed the response rates of brain lesions and systemic lesions, and estimated the overall survival (OS) and progression-free survival (PFS). Results: The median OS and overall PFS were 17.0 months (95% CI 10.3–25.6) and 3.19 months (95% CI 2.24–5.03), respectively. The intracranial objective response rate and disease control rate of ICI were 36 and 54%, respectively. Among the 44 patients who showed disease progression, only 32% (n = 14) showed concordant outcomes and 9 patients (20%) showed opposing discordant outcomes. Eight patients continued ICI with local brain therapy after intracranial progression, and their median extracranial PFS and OS were 15 months (95% CI 5.0—not assessed [NA]) and 23.8 months (95% CI 14.7—NA), respectively. Conclusions: ICI monotherapy had a clinically meaningful intracranial efficacy in NSCLC patients with brain metastasis. Watchful waiting and close monitoring without local radiotherapy might be feasible in NSCLC patients with asymptomatic active brain metastasis. [ABSTRACT FROM AUTHOR]

Objectives: Anti-angiogenic therapy may not benefit all patients with recurrent glioblastomas, and imaging biomarker predicting treatment response to anti-angiogenic therapy is currently limited. We aimed to develop and validate vascular habitats based on perfusion and vessel size to predict time to progression (TTP) in patients with recurrent glioblastomas treated with bevacizumab. Methods: Sixty-nine patients with recurrent glioblastomas treated with bevacizumab who underwent pretreatment MRI with dynamic susceptibility contrast imaging and vessel architectural imaging were enrolled. Vascular habitats were constructed using vessel size index (VSI) and relative cerebral blood volume (rCBV). Associations with vascular habitats and TTP were analyzed using Cox proportional hazard regression analysis. In a prospectively enrolled validation cohort consisting of 15 patients (ClinicalTrials.gov identifier; NCT04143425), stratification of TTP was demonstrated by the Kaplan-Meier method (log-rank test) using vascular habitats. Results: Three vascular habitats consisting of high, intermediate, and low angiogenic habitats were identified with rCBV and VSI. Both high angiogenic and intermediate angiogenic habitats were significantly associated with a shorter TTP (hazard ratio [HR], 2.78 and 1.82, respectively; largest p =.003) and so was rCBV (HR, 2.15; p =.02). Concordance probability index of vascular habitat combining high and intermediate angiogenic habitats was 0.74. Vascular habitats stratified patients as good or poor responder in a prospective cohort (p =.059). Conclusions: Perfusion- and vessel size-derived vascular habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy and aided patient stratification in a prospective validation cohort. Trial registration: ClinicalTrials.gov identifier: NCT04143425 Key Points: • High and intermediate angiogenic habitats predicted TTP in recurrent glioblastomas treated with anti-angiogenic therapy. • Vascular habitats combining high and intermediate angiogenic habitats aided patient stratification for anti-angiogenic therapy in recurrent glioblastomas. [ABSTRACT FROM AUTHOR]

The application of nanoscale energetic materials (nEMs) composed of metal and oxidizer nanoparticles (NPs) in thermal engineering systems is limited by their relatively high sensitivity and complex three-dimensional (3D) formability. Polymers can be added to nEMs to lower the sensitivity and improve the formability of 3D structures. In this study, the effect of the addition of polyethylene oxide (PEO; polymer) on the combustion characteristics of aluminum (Al; fuel)/copper oxide (CuO; oxidizer)-based nEMs is investigated. With an increase in the PEO content, the resulting PEO/nEM composites are desensitized to relatively high electrical spark discharges. However, the maximum explosion-induced pressure decreases significantly, and the combustion flame fails to propagate when the PEO content exceeds 15 wt.%. Therefore, the optimal PEO content in a nEM matrix must be accurately determined to achieve a compromise between sensitivity and reactivity. To demonstrate their potential application as composite solid propellants (CSPs), 3D-printed disks composed of PEO/nEM composites were assembled using additive manufacturing. They were cross-stacked with conventional potassium nitrate (KNO3)/sucrose (C12H22O11)-based disk-shaped CSPs in a combustion chamber of small rocket motors. Propulsion tests indicated that the specific impulse of KNSU/PEO/nEM (nEMs: 3.4 wt.%)-based CSPs was at a maximum value, which is approximately three times higher than that of KNSU CSPs without nEMs. This suggests that the addition of an optimized amount of polymer to nEMs is beneficial for various CSPs with compromised sensitivity and reactivity and excellent 3D formability, which can significantly enhance the propulsion of small projectiles. [ABSTRACT FROM AUTHOR]

Recently, all-solid-state lithium ion batteries (ASLBs) have emerged as the next generation lithium ion batteries due to safety issue for electric vehicles and energy storage system. This application can be achieved through layered structure cathode material of Li[Ni0.8Co0.1Mn0.1]O2, (NCM811) with high specific capacity and composite solid electrolyte. The composite solid electrolyte is composed by poly ethylene oxide (PEO) based polymer solid electrolyte and garnet type oxide based Li6.75La3Zr2Al0.25O12, (LLZO-Al) solid electrolyte. Here, in this article, we describe the effect of improved electrochemical performance on the controlling particle size of Ni-rich cathode material for ASLBs with oxide-based solid electrolyte. Therefore, the structure and electrochemical performance of Ni-rich cathode material in ASLBs were investigated by using powder X-ray diffraction analysis, Brunauer–Emmett–Teller adsorption experiments, electrochemical impedance spectroscopy, and galvanostatic measurements. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether imaging-based risk stratification enables prognostication in diffuse glioma, NOS (not otherwise specified). Methods: Data from 220 patients classified as diffuse glioma, NOS, between January 2011 and December 2020 were retrospectively included. Two neuroradiologists analyzed pre-surgical CT and MRI to assign gliomas to the three imaging-based risk types considering well-known imaging phenotypes (e.g., T2/FLAIR mismatch). According to the 2021 World Health Organization classification, the three risk types included (1) low-risk, expecting oligodendroglioma, isocitrate dehydrogenase (IDH)-mutant, and 1p/19q-codeleted; (2) intermediate-risk, expecting astrocytoma, IDH-mutant; and (3) high-risk, expecting glioblastoma, IDH-wildtype. Progression-free survival (PFS) and overall survival (OS) were estimated for each risk type. Time-dependent receiver operating characteristic analysis using 10-fold cross-validation with 100-fold bootstrapping was used to compare the performance of an imaging-based survival model with that of a historical molecular-based survival model published in 2015, created using The Cancer Genome Archive data. Results: Prognostication according to the three imaging-based risk types was achieved for both PFS and OS (log-rank test, p < 0.001). The imaging-based survival model showed high prognostic value, with areas under the curves (AUCs) of 0.772 and 0.650 for 1-year PFS and OS, respectively, similar to the historical molecular-based survival model (AUC = 0.74 for PFS and 0.87 for OS). The imaging-based survival model achieved high long-term performance in both 3-year PFS (AUC = 0.806) and 5-year OS (AUC = 0.812). Conclusion: Imaging-based risk stratification achieved histomolecular-level prognostication in diffuse glioma, NOS, and could aid in guiding patient referral for insufficient or unsuccessful molecular diagnosis. Key Points: • Three imaging-based risk types enable distinct prognostication in diffuse glioma, NOS (not otherwise specified). • The imaging-based survival model achieved similar prognostic performance as a historical molecular-based survival model. • For long-term prognostication of 3 and 5 years, the imaging-based survival model showed high performance. [ABSTRACT FROM AUTHOR]

This paper presents a 13.2 kV class 3-phase solid-state transformer (SST) based on EtherCAT communication. In general, when the structure of the unit module is determined, the number of high-frequency isolated transformers (HFIT) is also proportional to the number of modules. The structure most considered in SST is a 1:1 combination of AC/DC converter and DC/DC converter. To optimally implement a 3-phase SST, a topology for reducing passive elements such as switching elements and HFIT is proposed. It also describes the design of HFIT used in DC/DC converter. EtherCAT communication with high transmission speed and expandability is applied to control the SST composed of unit modules stably, and a multi-core microcontroller unit (MCU) is applied to achieve both a high-speed communication cycle and complicated control algorithm execution. The discussions are validated using a 300 kW 13.2 kV class 3-phase SST prototype in various conditions. [ABSTRACT FROM AUTHOR]

Despite the proliferation of innovative technologies during the Fourth Industrial Revolution (4IR), there is a severe lack of quantitative and empirical studies that deal with the effectiveness of recently emerging technologies. This study examines the impact of employing core technologies of the 4IR on small and medium enterprises (SMEs). We used the firm-level cross-sectional data on Korean manufacturing SMEs, including the information on technology utilization. The stochastic production frontier estimation with selectivity correction is employed, besides matching technique to obtain unbiased estimates on technology efficiency. The empirical analysis finds that adopting emerging technologies enhances the productivity of SMEs. After observed and unobserved factors are controlled, the technical efficiency of adopters is higher by more than 26% on average, compared to non-adopters. Moreover, if the gap among production frontiers is considered, the difference in productivity would rise further. Additionally, a strategic alliance is a crucial route for SMEs to accept new technologies. Plain English Summary: Adopting emerging technologies in the Fourth Industrial Revolution makes production process more efficient by more than 26% on average. This research using the data on Korean manufacturing small and medium enterprises (SMEs) revealed that new digital technologies have a significantly positive impact on the productivity improvement. More specifically, SMEs adopting emerging technologies like "AI," "Big Data," and "Robotics" in the production process have remarkably higher productivity by more than 26%, compared to non-adopters. This result suggests that digital transformation is an unparalleled opportunity for SMEs suffering from lower productivity attributed to some reasons like the outbreak of COVID-19 pandemic. Additionally, the government needs to establish a pan-government control tower and consider a variety of policy measures to promote SMEs to utilize advanced technologies in their production. [ABSTRACT FROM AUTHOR]

The expanded perlite-based building material for drywall application consisting of sodium silicate solution as a binder was manufactured by varying the degree of compaction and sodium silicate content to investigate moisture diffusion behavior and the effect of moisture treatment on flexural properties of the composites. Moisture treatment was conducted on specimens in a climatic chamber at a temperature of 37°C and a relative humidity of 90% until saturation. Results show that moisture absorption decreased with increasing compaction ratio for a constant sodium silicate content in binder and increased with increasing sodium silicate content in binder for a constant compaction ratio. A range of volume fractions of solid sodium silicate in the foam is identified, in which the fully Fickian diffusion gradually transformed to non-Fickian diffusion as sodium silicate content in foam increased. The concentration-dependent diffusion method was found to be suitable to explain this behavior. The moisture diffusion below this transition range showed an entirely Fickian diffusion and changed to concentration-dependent diffusion above the range. As a result of moisture treatment, the flexural strength of medium density foams was decreased but the lowest- and highest-density foams were not affected while the flexural modulus was increased only for the highest density foam and no significant effects were seen in other cases. The bending failure mechanism of the composite was not affected by the moisture treatment. [ABSTRACT FROM AUTHOR]

A practical procedure for predicting the remaining fatigue life at an arbitrary stress ratio is developed and verified. The procedure was based on the validated damage function, in conjunction with the Kim and Zhang S-N curve model. The damage function was used for finding various iso-damage points dependent on three independent variables (i.e., stress level, number of fatigue cycles, and stress ratio). The verification was conducted using Alclad 24S-T aluminium alloy, available in the literature for fatigue loading varied under three different loading schemes. The first scheme was for two different stress ratios, the second was for three different stress ratios, and the last was for a single stress ratio as a special case. The prediction accuracies were found to be in an error range of −0.1 to 5.6%, −0.5 to −0.6, and 1.5 to 1.7% for the 1st, 2nd, and 3rd schemes, respectively. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the value of the contrast enhancing pattern on pre-treatment MRI for predicting the response to anti-angiogenic treatment in patients with IDH-wild type recurrent glioblastoma. Methods: This retrospective study enrolled 65 patients with IDH wild-type recurrent glioblastoma who received standard therapy and then received either bevacizumab (46 patients) or temozolomide (19 patients) as a secondary treatment. The contrast enhancing pattern on pre-treatment MRI was visually analyzed and dichotomized into contrast enhancing lesion (CEL) dominant and non-enhancing lesion (NEL) dominant types. Quantitative volumetric analysis was used to support the dichotomization. The Kaplan–Meier method and Cox proportional hazards regression analysis were used to stratify progression free survival (PFS) according to the treatment in the entire patients, CEL dominant group, and NEL dominant group. Results: In all patients, the PFS of those treated with bevacizumab was not significantly different from those treated with temozolomide (log-rank test, P = 0.96). When the contrast enhancing pattern was considered, bevacizumab was associated with longer PFS in the CEL dominant group (P = 0.031), whereas temozolomide showed longer PFS in the NEL dominant group (P = 0.022). Quantitative analysis revealed mean values for the proportion of solid-enhancing tumor of 13.7% for the CEL dominant group and 4.3% for the NEL dominant group. Conclusion: Patients with the CEL dominant type showed a better treatment response to bevacizumab, whereas NEL dominant types showed a better response to temozolomide. The contrast enhancing pattern on pre-treatment MRI can be used to stratify patients with IDH wild-type recurrent glioblastoma according to the effect of anti-angiogenic treatment. [ABSTRACT FROM AUTHOR]

Objectives: The identification of viable tumor after stereotactic radiosurgery (SRS) is important for future targeted therapy. This study aimed to determine whether tumor habitat on structural and physiologic MRI can distinguish viable tumor from radiation necrosis of brain metastases after SRS. Method: Multiparametric contrast-enhanced T1- and T2-weighted imaging, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) were obtained from 52 patients with 69 metastases, showing enlarging enhancing masses after SRS. Voxel-wise clustering identified three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable). Habitat-based predictors for viable tumor or radiation necrosis were identified by logistic regression. Performance was validated using the area under the curve (AUC) of the receiver operating characteristics curve in an independent dataset with 24 patients. Results: None of the physiologic MRI habitats was indicative of viable tumor. Viable tumor was predicted by a high-volume fraction of solid low-enhancing habitat (low T2-weighted and low CE-T1-weighted values; odds ratio [OR] 1.74, p <.001) and a low-volume fraction of nonviable tissue habitat (high T2-weighted and low CE-T1-weighted values; OR 0.55, p <.001). Combined structural MRI habitats yielded good discriminatory ability in both development (AUC 0.85, 95% confidence interval [CI]: 0.77–0.94) and validation sets (AUC 0.86, 95% CI:0.70–0.99), outperforming single ADC (AUC 0.64) and CBV (AUC 0.58) values. The site of progression matched with the solid low-enhancing habitat (72%, 8/11). Conclusion: Solid low-enhancing and nonviable tissue habitats on structural MRI can help to localize viable tumor in patients with brain metastases after SRS. Key Points: • Structural MRI habitats helped to differentiate viable tumor from radiation necrosis. • Solid low-enhancing habitat was most helpful to find viable tumor. • Providing spatial information, the site of progression matched with solid low-enhancing habitat. [ABSTRACT FROM AUTHOR]

The physiological conditions of small animals are an essential component to be considered when acquiring images for pre-clinical studies, and they play a vital role in the overall results of a study. However, several previous studies did not consider these conditions. In this study, a new animal cradle that can be modified and adjusted to suit multiple imaging modalities such as positron emission tomography (PET)/computed tomography (CT) and magnetic resonance imaging (MRI) was developed. Unlike previous cradles where only one mouse can be imaged at a time, a total of four mice can be imaged simultaneously using this new cradle. Additionally, fusion images with high-throughput multiple-mouse imaging (MMI) of PET/MRI and PET/CT images can be acquired using this newly developed cradle. The dynamic brain images were also acquired simultaneously by applying PET dynamic imaging technology to high-throughput MMI methods. The results of this study suggest that the newly developed small animal cradle can be widely used in pre-clinical studies. [ABSTRACT FROM AUTHOR]

Objectives: To determine reproducible MRI parameters predictive of molecular subtype and risk stratification in glioma and develop a structured reporting system.Methods: All study patients were initially diagnosed with glioma, 141 from the Cancer Genome Atlas and 131 from our tertiary institution, as training and validation sets, respectively. Images were analyzed by three neuroradiologists with 1-7 years of experience. MRI features including contrast enhancement pattern, necrosis, margin, edema, T2/FLAIR mismatch, internal cyst, and cerebral blood volume higher than normal cortex were reported using a structured reporting system. The pathology was stratified into five risk types: (1) oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, 1p19q co-deleted; (2) diffuse astrocytoma, IDH-mutant, grade II-III; (3) glioblastoma, IDH-mutant, grade IV; (4) diffuse astrocytoma, IDH-wild, grade II-III; and (5) glioblastoma, IDH-wild, grade IV. Significant predictors were selected using multivariate logistic regression, and diagnostic performance was tested using a validation set.Results: Reproducible imaging parameters exhibiting > 50% agreement across readers included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. In the validation set, prediction of risk type 5 exhibited the highest diagnostic performance with AUCs of 0.92 (reader 1) and 0.93 (reader 2) with predominant enhancement, followed by risk type 2 with AUCs of 0.95 and 0.95 with T2/FLAIR mismatch sign and no necrosis, and risk type 1 with AUCs of 0.84 and 0.83 with internal cyst or necrosis. Risk types 3 and 4 were difficult to visually predict.Conclusions: Imaging parameters with high reproducibility enabling prediction of IDH-wild-type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant diffuse astrocytoma were identified.Key Points: • Reproducible MRI parameters for determining molecular subtypes of glioma included the presence of necrosis, T2/FLAIR mismatch, internal cyst, and predominant contrast enhancement. • IDH-wild type glioblastoma, IDH-mutant/1p19q co-deletion oligodendroglioma, and IDH-mutant low-grade astrocytoma were identified using MRI parameters with high inter-reader reproducibility. • Identification of IDH-wild type low-grade glioma and IDH-mutant glioblastoma was difficult by visual analysis. [ABSTRACT FROM AUTHOR]

Objectives: Tissue conductivity measurements made with electrical properties tomography (EPT) can be used to define temporal changes in tissue habitats on longitudinal multiparametric MRI. We aimed to demonstrate the added insights for identifying tumor habitats obtained by including EPT with diffusion- and perfusion-weighted MRI, and to evaluate the use of these tumor habitats for determining tumor treatment response in post-treatment glioblastoma. Methods: Tumor habitats were developed from EPT, diffusion-weighted, and perfusion-weighted MRI in 60 patients with glioblastoma who underwent concurrent chemoradiotherapy. Voxels from EPT, apparent diffusion coefficient (ADC), and cerebral blood volume (CBV) maps were clustered into habitats, and each habitat was serially examined to assess its temporal change. The usefulness of temporal changes in tumor habitats for diagnosing tumor progression and treatment-related change was investigated using logistic regression. The performance of significant predictors was measured using the area under the curve (AUC) from receiver-operating-characteristics analysis with 1000-fold bootstrapping. Results: Five tumor habitats were identified, and of these, the hypervascular cellular habitat (odds ratio [OR] 5.45; 95% CI, 1.75–31.42; p =.02), hypovascular low conductivity habitat (OR 2.00; 95% CI, 1.45–3.05; p <.001), and hypovascular intermediate habitat (OR 1.57; 95% CI, 1.18–2.30; p =.006) were predictive of tumor progression. Low EPT and low CBV reflected a unique hypovascular low conductivity habitat that showed the highest diagnostic performance (AUC 0.86; 95% CI, 0.76–0.96). The combined habitats showed high performance (AUC 0.90; 95% CI, 0.82–0.98) in the differentiation of tumor progression from treatment-related change. Conclusion: EPT reveals low conductivity habitats that can improve the diagnosis of tumor progression in post-treatment glioblastoma. Key Points: • Electrical properties tomography (EPT) demonstrated lower conductivity in tumor progression than in treatment-related change. • EPT allowed identification of a unique hypovascular low conductivity habitat when combined with cerebral blood volume mapping. • Tumor habitats with a hypovascular low conductivity habitat, hypervascular cellular habitat, and hypovascular intermediate habitat yielded high diagnostic performance for diagnosing tumor progression. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to develop multiparametric physiologic MRI-based spatial habitats and to evaluate whether temporal changes in these habitats help to distinguish tumor progression from treatment-related change in post-treatment glioblastoma. Methods: This retrospective, single-institution study included patients with glioblastoma treated by concurrent chemoradiotherapy who had newly developed or enlarging, measurable contrast-enhancing mass. Contrast-enhancing mass was divided into three spatial habitats by K-means clustering of voxel-wise ADC and CBV values. Temporal changes of these habitats between two consecutive examinations prior to the diagnosis of tumor progression or treatment-related change were assessed. Predictors were selected using logistic regression and the performance was measured with an area under the receiver operating characteristics curve (AUC). Spatiotemporal habitats were further analyzed for correlation with the site of tumor progression. Results: There were 75 patients (mean, 58 years; range, 26−81 years; 43 men) with 48 cases of tumor progression and 39 cases of treatment-related change including 12 patient overlaps at different time points. Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. Increase in the hypervascular cellular (OR 4.55, p =.002) and hypovascular cellular habitat (OR 1.22, p <.001) was predictive of tumor progression. Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87–0.92). An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% [21/25] of cases with tumor progression. Conclusions: Temporal changes in spatial habitats derived from multiparametric physiologic MRI provided diagnostic value in distinguishing tumor progression from treatment-related change and predicted site of tumor progression in post-treatment glioblastoma. Key Points: • In post-treatment glioblastoma, three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified, and an increase in the hypervascular cellular (OR 4.55, p =.002) and hypovascular cellular habitat (OR 1.22, p <.001) was predictive of tumor progression. • Combination of spatiotemporal habitats yielded a high diagnostic performance with an AUC of 0.89 (95% CI, 0.87–0.92). • An increase in hypovascular cellular habitat predicted the site of tumor progression in 84% (21/25) of cases with tumor progression. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic performance of the T2-FLAIR mismatch sign for prediction of isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted lower-grade gliomas (LGGs) and review studies with false positive results. Methods: The MEDLINE and EMBASE databases were searched up to March 13, 2020, to identify articles reporting the diagnostic performance of the T2-FLAIR mismatch sign for prediction of IDH-mutant, 1p/19q-noncodeleted LGGs (IDHmut-Noncodel) using the search terms (T2 FLAIR mismatch). Pooled sensitivity, specificity, and correlation coefficient for interobserver agreement were calculated. Results: Twelve studies including a total of 1053 patients were included. The median age was 43 (median; range, 14–56). The pooled sensitivity and specificity were 42% (95% CI, 28–58%) and 100% (95% CI, 88–100%), respectively. According to the HSROC curve, the area under the curve was 0.77 (95% CI, 0.73–0.80). Considerable heterogeneity was possible among the studies in terms of both sensitivity and specificity. A threshold effect was suggested and was considered to explain most of the heterogeneity. Four studies reported false positive results for the T2-FLAIR mismatch sign, including dysembryoplastic neuroepithelial tumor, pediatric-type gliomas, and non-neoplastic lesions. The 2 original articles with false positive results showed the highest sensitivities among the 10 studies included in the quantitative analysis, supporting the probability of the threshold effect. The pooled correlation coefficient was 0.87 (95% CI, 0.73–0.94). Conclusions: The T2-FLAIR mismatch sign had a high specificity and interobserver agreement for the prediction of IDHmut-Noncodel. However, the sign demonstrated low sensitivity, and a few studies with false positive cases were also reported. Key Points: • The pooled sensitivity and specificity of the T2-FLAIR mismatch sign for prediction of IDH-mutant, 1p/19q-noncodeleted lower-grade gliomas were 42% and 100%, respectively. • Four studies reported false positive results. • The pooled correlation coefficient was 0.87, suggesting almost perfect interobserver agreement. [ABSTRACT FROM AUTHOR]

Objectives: To compare the incidence of treatment-related necrosis between combination SRS+ICI therapy and SRS therapy alone in patients with brain metastases from melanoma and non-small cell lung cancer (NSCLC). Methods: A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to August 10, 2020. The difference in the pooled incidence of treatment-related necrosis after SRS+ICI or SRS alone was evaluated. The cumulative incidence of treatment-related necrosis at the specific time point after the treatment was calculated and plotted. Subgroup and meta-regression analyses were additionally performed. Results: Sixteen studies (14 on melanoma, 2 on NSCLC) were included. In NSCLC brain metastasis, the reported incidences of treatment-related necrosis in SRS+ICI and SRS alone ranged 2.9–3.4% and 0–2.9%, respectively. Meta-analysis was conducted including 14 studies on melanoma brain metastasis. The incidence of treatment-related necrosis was higher in SRS+ICI than SRS alone (16.0% vs. 6.5%; p = 0.065; OR, 2.35). The incidence showed rapid increase until 12 months after the SRS when combined with ICI therapy (14%; 95% CI, 8–22%) and its pace of increase slowed thereafter. Histopathologic diagnosis as the reference standard for treatment-related necrosis and inclusion of only symptomatic cases were the source of heterogeneity in SRS+ICI. Conclusions: Treatment-related necrosis tended to occur 2.4 times more frequently in the setting of combination SRS+ICI therapy compared with SRS alone in melanoma brain metastasis showing high cumulative incidence within the first year. Treatment-related necrosis should be considered when SRS+ICI combination therapy is used for melanoma brain metastasis, especially in the first year. Key Points: • Treatment-related necrosis occurred 2.4 times more frequently in the setting of combination SRS+ICI therapy compared with SRS alone in melanoma brain metastasis. • Treatment-related necrosis more frequently occurred in brain metastases from melanoma than NSCLC. • Reference standard for treatment-related necrosis and inclusion of only symptomatic treatment-related necrosis were a significant source of heterogeneity, indicating varying definitions of treatment-related necrosis in the literature need to be unified. [ABSTRACT FROM AUTHOR]

Background: Updated response assessment in neuro-oncology (RANO) does not consider peritumoral non-enhancing lesion (NEL) and baseline (residual) contrast enhancement (CE) volume. The objective of this study is to explore helpful imaging characteristics to refine RANO for assessing early treatment response (pseudoprogression and time-to-progression [TTP]) in patients with IDH wild-type glioblastoma.Methods: This retrospective study enrolled 86 patients with IDH wild-type glioblastoma who underwent consecutive MRI examinations before and after concurrent chemoradiotherapy (CCRT). NEL was classified as edema- or tumor-dominant type on pre-CCRT MRI. CE evolution was categorized into 4 patterns based on post-operative residual CE (measurable vs. non-measurable) and CE volume change (same criteria with RANO) during CCRT. Multivariable logistic regression, including clinical parameters, NEL type, and CE evolution pattern, was used to analyze pseudoprogression rate. TTP and OS according to NEL type and CE evolution pattern was analyzed by the Kaplan-Meier method.Results: Pseudoprogression rate was significantly lower (chi-square test, P = .047) and TTP was significantly shorter (hazard ratio [HR] = 2.03, P = .005) for tumor-dominant type than edema-dominant type of NEL. NEL type was the only predictive marker of pseudoprogression on multivariate analysis (odds ratio = 0.26, P = .046). Among CE evolution patterns, TTP and OS was shortest in patients with residual CE compared with those exhibiting new CE (HR = 4.33, P < 0.001 and HR = 3.71, P = .009, respectively). In edema-dominant NEL type, both TTP and OS was stratified by CE evolution pattern (log-rank, P = .001), whereas it was not in tumor-dominant NEL.Conclusions: NEL type improves prediction of pseudoprogression and, together with CE evolution pattern, further stratifies TTP and OS in patients with IDH wild-type glioblastoma and may become a helpful biomarker for refining RANO. [ABSTRACT FROM AUTHOR]

Purpose: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T1, T2, and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI. Methods: The JPI and JDL methods are extended and combined to improve reconstruction for better‐quality, synthesized images. JPI is performed as a first step to estimate the missing k‐space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS‐Net) is modified and extended to form a joint variable splitting network (JVS‐Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under‐sampling using acceleration factors between 4 and 8. Results: It is demonstrated that the normalized root‐mean‐square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast‐weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts. Conclusion: Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs. [ABSTRACT FROM AUTHOR]

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P < 0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model. [ABSTRACT FROM AUTHOR]

Objectives: Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI features, thereby assisting in the accurate assessment of treatment response in glioblastoma patients.Methods: A DLAS model was trained on 238 glioblastomas and validated on an independent set of 98 pre- and 86 post-treatment glioblastomas from two tertiary hospitals. A total of 1618 radiomics features from contrast-enhanced T1-weighted images (CE-T1w) and histogram features from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) mapping were extracted. The diagnostic performance of radiomics features and ADC and CBV parameters for identifying treatment response was tested using area under the curve (AUC) from receiver operating characteristics analysis. Feature reproducibility was tested using a 0.80 cutoff for concordance correlation coefficients.Results: Reproducibility was excellent for ADC and CBV features (ICC, 0.82-0.99) and first-order features (pre- and post-treatment, 100% and 94.1% remained), but lower for texture (79.0% and 69.1% remained) and wavelet-transformed (81.8% and 74.9% remained) features of CE-T1w. DLAS-based radiomics showed similar performance to human-performed segmentations in internal validation (AUC, 0.81 [95% CI, 0.64-0.99] vs. AUC, 0.81 [0.60-1.00], p = 0.80), but slightly lower performance in external validation (AUC, 0.78 [0.61-0.95] vs. AUC, 0.65 [0.46-0.84], p = 0.23).Conclusion: DLAS-based feature extraction showed high reproducibility for first-order features from anatomical and physiological MRI, and comparable diagnostic performance to human manual segmentations in the identification of pseudoprogression, supporting the utility of DLAS in quantitative MRI analysis.Key Points: • Deep learning-based automatic segmentation (DLAS) enables fast and robust feature extraction from diffusion- and perfusion-weighted MRI. • DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers. • DLAS-based radiomics features showed comparable diagnostic accuracy to manual segmentations in post-treatment glioblastoma. [ABSTRACT FROM AUTHOR]

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.. [ABSTRACT FROM AUTHOR]