Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.

OBJECTIVES: To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. METHODS: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. RESULTS: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). CONCLUSION: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. CLINICAL RELEVANCE STATEMENT: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. KEY POINTS: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

Amphiregulin normalizes altered circuit connectivity for social dominance of the CRTC3 knockout mouse.

Social hierarchy has a profound impact on social behavior, reward processing, and mental health. Moreover, lower social rank can lead to chronic stress and often more serious problems such as bullying victims of abuse, suicide, or attack to society. However, its underlying mechanisms, particularly their association with glial factors, are largely unknown. In this study, we report that astrocyte-derived amphiregulin plays a critical role in the determination of hierarchical ranks. We found that astrocytes-secreted amphiregulin is directly regulated by cAMP response element-binding (CREB)-regulated transcription coactivator 3 (CRTC3) and CREB. Mice with systemic and astrocyte-specific CRTC3 deficiency exhibited a lower social rank with reduced functional connectivity between the prefrontal cortex, a major social hierarchy center, and the parietal cortex. However, this effect was reversed by astrocyte-specific induction of amphiregulin expression, and the epidermal growth factor domain was critical for this action of amphiregulin. These results provide evidence of the involvement of novel glial factors in the regulation of social dominance and may shed light on the clinical application of amphiregulin in the treatment of various psychiatric disorders.

Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based nomogram.

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.

Diagnostic yield of diffusion-weighted brain MR imaging in patients with cognitive impairment: Large cohort study with 3,298 patients.

OBJECTIVE: There is a paucity of large cohort-based evidence regarding the need and added value of diffusion-weighted imaging (DWI) in patients attending outpatient clinic for cognitive impairment. We aimed to evaluate the diagnostic yield of DWI in patients attending outpatient clinic for cognitive impairment. MATERIALS AND METHODS: This retrospective, observational, single-institution study included 3,298 consecutive patients (mean age ± SD, 71 years ± 10; 1,976 women) attending outpatient clinic for cognitive impairment with clinical dementia rating ≥ 0.5 who underwent brain MRI with DWI from January 2010 to February 2020. Diagnostic yield was defined as the proportion of patients in whom DWI supported the diagnosis that underlies cognitive impairment among all patients. Subgroup analyses were performed by age group and sex, and the Chi-square test was performed to compare the diagnostic yields between groups. RESULTS: The overall diagnostic yield of DWI in patients with cognitive impairment was 3.2% (106/3,298; 95% CI, 2.6-3.9%). The diagnostic yield was 2.5% (83/3,298) for acute or subacute infarct, which included recent small subcortical infarct for which the diagnostic yield was 1.6% (54/3,298). The diagnostic yield was 0.33% (11/3,298) for Creutzfeldt-Jakob disease (CJD), 0.15% (5/3,298) for transient global amnesia (TGA), 0.12% (4/3,298) for encephalitis and 0.09% (3/3,298) for lymphoma. There was a trend towards a higher diagnostic yield in the older age group with age ≥ 70 years old (3.6% vs 2.6%, P = .12). There was an incremental increase in the diagnostic yield from the age group 60-69 years (2.6%; 20/773) to 90-99 years (8.0%; 2/25). CONCLUSION: Despite its low overall diagnostic yield, DWI supported the diagnosis of acute or subacute infarct, CJD, TGA, encephalitis and lymphoma that underlie cognitive impairment, and there was a trend towards a higher diagnostic yield in the older age group.

Diagnostic yield of TOF-MRA for detecting incidental vascular lesions in patients with cognitive impairment: An observational cohort study.

Objectives: The role of three-dimensional (3D) TOF-MRA in patients with cognitive impairment is not well established. We evaluated the diagnostic yield of 3D TOF-MRA for detecting incidental extra- or intracranial artery stenosis and intracranial aneurysm in this patient group. Methods: This retrospective study included patients with cognitive impairment undergoing our brain MRI protocol from January 2013 to February 2020. The diagnostic yield of TOF-MRA for detecting incidental vascular lesions was calculated. Patients with positive TOF-MRA results were reviewed to find whether additional treatment was performed. Logistic regression analysis was conducted to identify the clinical risk factors for positive TOF-MRA findings. Results: In total, 1,753 patients (mean age, 70.2 ± 10.6 years; 1,044 women) were included; 199 intracranial aneurysms were detected among 162 patients (9.2%, 162/1,753). A 3D TOF-MRA revealed significant artery stenoses (>50% stenosis) in 162 patients (9.2%, 162/1,753). The overall diagnostic yield of TOF-MRA was 16.8% (294/1,753). Among them, 92 patients (31.3%, 92/294) underwent either medical therapy, endovascular intervention, or surgery. In total, eighty-one patients with stenosis were prescribed with either antiplatelet medications or lipid-lowering agent. In total, fifteen patients (aneurysm: 11 patients, stenosis: 4 patients) were further treated with endovascular intervention or surgery. Thus, the "number needed to scan" was 19 for identifying one patient requiring treatment. Multivariate logistic regression analysis showed that being female (odds ratio [OR] 2.05) and old age (OR 1.04) were the independent risk factors for intracranial aneurysm; being male (OR 1.52), old age (OR 1.06), hypertension (OR 1.78), and ischemic heart disease history (OR 2.65) were the independent risk factors for significant artery stenosis. Conclusions: Our study demonstrated the potential benefit of 3D TOF-MRA, given that it showed high diagnostic yield for detecting vascular lesions in patients with cognitive impairment and the considerable number of these lesions required further treatment. A 3D TOF-MRA may be included in the routine MR protocol for the work-up of this patient population, especially in older patients and patients with vascular risk factors.

Incidence of Amyloid-Related Imaging Abnormalities in Patients With Alzheimer Disease Treated With Anti-ß-Amyloid Immunotherapy: A Meta-analysis.

BACKGROUND AND OBJECTIVES: To assess the incidence of amyloid-related imaging abnormalities (ARIA) in clinical trials of anti-ß-amyloid (Aß) immunotherapy and compare the incidence among different agents and clinical characteristics to identify possible predisposing factors for ARIA. METHODS: The PubMed and Embase databases were searched for clinical trials of anti-Aß immunotherapy published on or before January 12, 2022. Phase 2 or 3 randomized controlled trials reporting detailed data sufficient to assess the incidence of ARIA were selected. The pooled incidences of ARIA and subgroup analyses according to agent and ApoE-4 carrier status were calculated using the DerSimonian-Liard random-effects model. The proportion of symptomatic ARIA cases was also calculated. RESULTS: In total, 19 eligible studies, including 24 cohorts, were identified and 9,429 patients were analyzed. The overall pooled incidence of ARIA-effusion (E) and ARIA-hemorrhage (H) was 6.5% and 7.8%, respectively. In the subgroup analysis, the incidence of ARIA was different according to the anti-Aß immunotherapy agent. The cohorts treated with aducanumab had a significantly higher incidence of ARIA-E and ARIA-H (30.7% and 30.0%, respectively; both p < 0.001) compared with cohorts from other drugs. In subgroup analysis according to ApoE-4 carrier status, the incidences of ARIA-E and ARIA-H were higher in the ApoE-4 carrier group than those in the ApoE-4 noncarrier group, but there was no statistical significance (ApoE-4 carrier vs noncarrier, ARIA-E: 8.6% vs 6.9%, p = 0.663, and ARIA-H: 10.5% vs 6.6%, p = 0.398). The pooled proportion of asymptomatic ARIA, detected by routine scheduled MRI surveillances, was 80.4%. DISCUSSION: The overall incidences of ARIA-E and ARIA-H were 6.5% and 7.8%, respectively, and the pooled proportion of asymptomatic ARIA was 80.4%. The cohorts treated with aducanumab showed a significantly higher incidence of ARIA-E and ARIA-H (30.7% and 30.0%) compared with other drugs.

The impact of subthreshold levels of amyloid deposition on conversion to dementia in patients with amyloid-negative amnestic mild cognitive impairment.

BACKGROUND: About 40-50% of patients with amnestic mild cognitive impairment (MCI) are found to have no significant Alzheimer's pathology based on amyloid PET positivity. Notably, conversion to dementia in this population is known to occur much less often than in amyloid-positive MCI. However, the relationship between MCI and brain amyloid deposition remains largely unknown. Therefore, we investigated the influence of subthreshold levels of amyloid deposition on conversion to dementia in amnestic MCI patients with negative amyloid PET scans. METHODS: This study was a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain magnetic resonance imaging, and [18F]-florbetaben (FBB) positron emission tomography scan (PET). Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Regional cortical amyloid levels were calculated, and a receiver operating characteristic (ROC) curve for conversion to dementia was obtained. To increase the reliability of the results of the study, we analyzed the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset together. RESULTS: During the follow-up period, 36% (39/107) of patients converted to dementia from amnestic MCI. The dementia converter group displayed increased standardized uptake value ratio (SUVR) values of FBB on PET in the bilateral temporal, parietal, posterior cingulate, occipital, and left precuneus cortices as well as increased global SUVR. Among volume of interests, the left parietal SUVR predicted conversion to dementia with the highest accuracy in the ROC analysis (area under the curve [AUC] = 0.762, P < 0.001). The combination of precuneus, parietal cortex, and FBB composite SUVRs also showed a higher accuracy in predicting conversion to dementia than other models (AUC = 0.763). Of the results of ADNI data, the SUVR of the left precuneus SUVR showed the highest AUC (AUC = 0.596, P = 0.006). CONCLUSION: Our findings suggest that subthreshold amyloid levels may contribute to conversion to dementia in patients with amyloid-negative amnestic MCI.

Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics.

BACKGROUND: To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software. METHODS: By retrospectively reviewing our institutional database (January 2017-September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0-100; > 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers. RESULTS: Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as "AI-detected cancers." As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients (p < 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p < 0.001), and accompany axillary lymph node metastasis (p = 0.003). CONCLUSIONS: AI conferred an added value in the detection of mammographically occult breast cancers.

Impact of Brain MRI on the Diagnosis of Infective Endocarditis and Treatment Decisions: Systematic Review and Meta-Analysis.

BACKGROUND. Despite increasing use of brain MRI to evaluate patients with suspected infective endocarditis, the clinical impact of brain MRI in this setting has not yet been systematically reviewed. OBJECTIVE. The purpose of this study was to evaluate the frequency of brain MRI findings in patients with suspected or confirmed infective endocarditis and to determine the impact of such findings on clinical decisions. EVIDENCE ACQUISITION. A systematic search of the PubMed, Embase, and Cochrane databases was performed from January 1, 1990, to December 31, 2020, to identify original research investigations of brain MRI in patients with suspected or confirmed infective endocarditis. Study quality was assessed with QUADAS-2. Study endpoints included the frequency of brain MRI findings and the frequency of diagnostic modifications, modification of therapeutic plan, and modification of valve surgery plan resulting from MRI findings. Frequencies were pooled by means of the inverse variance method. Subgroup analysis was performed. EVIDENCE SYNTHESIS. A total of 21 studies with 2133 patients were included. Overall study quality was considered moderate. In terms of brain MRI findings, the pooled frequency of acute ischemic lesions was 61.9% (95% CI, 50.7-71.9%); of cerebral microbleeds, 52.9% (95% CI, 41.6-63.9%); hemorrhagic lesions, 24.7% (95% CI, 15.1-37.9%); abscess or meningitis, 9.5% (95% CI, 5.6-15.6%); and intracranial mycotic aneurysm, 6.2% (95% CI, 4.0-9.4%). Subgroup analysis after exclusion of three studies in which neurologic findings were the indication for all brain MRI examinations yielded similar frequencies of these findings. Six studies included results on the impact of brain MRI findings on clinical decisions. The frequencies of diagnostic modifications in two studies were 5.4% and 32.1%. The pooled frequency of therapeutic plan modification in six studies was 12.8% (95% CI, 6.5-23.7%) and of surgical plan modification in five studies was 14.2% (95% CI, 8.2-23.4%). CONCLUSION. In patients with suspected or confirmed infective endocarditis, brain MRI examinations commonly show relevant abnormalities that affect diagnostic and therapeutic clinical decisions. CLINICAL IMPACT. The findings support a potential role for screening brain MRI in the evaluation of patients with suspected or confirmed infective endocarditis, regardless of the presence or absence of neurologic symptoms.

Clinical Features and Brain MRI Findings in Korean Patients with AGel Amyloidosis.

PURPOSE: AGel amyloidosis is systemic amyloidosis caused by pathogenic variants in the GSN gene. In this study, we sought to characterize the clinical and brain magnetic resonance image (MRI) features of Korean patients with AGel amyloidosis. MATERIALS AND METHODS: We examined 13 patients with AGel amyloidosis from three unrelated families. Brain MRIs were performed in eight patients and eight age- and sex-matched healthy controls. Therein, we analyzed gray and white matter content using voxel-based morphometry (VBM), tract-based spatial statistics (TBSS), and FreeSurfer. RESULTS: The median age at examination was 73 (interquartile range: 64-76) years. The median age at onset of cutis laxa was 20 (interquartile range: 15-30) years. All patients over that age of 60 years had dysarthria, cutis laxa, dysphagia, and facial palsy. Two patients in their 30s had only mild cutis laxa. The median age at dysarthria onset was 66 (interquartile range: 63.5-70) years. Ophthalmoparesis was observed in three patients. No patient presented with muscle weakness of the limbs. Axial fluid-attenuated inversion recovery images of the brain showed no significant differences between the patient and control groups. Also, analysis of VBM, TBSS, and FreeSurfer revealed no significant differences in cortical thickness between patients and healthy controls at the corrected significance level. CONCLUSION: Our study outlines the clinical manifestations of prominent bulbar palsy and early-onset cutis laxa in 13 Korean patients with AGel amyloidosis and confirms that AGel amyloidosis mainly affects the peripheral nervous system rather than the central nervous system.

Utility of 7 Tesla Magnetic Resonance Imaging in Patients With Epilepsy: A Systematic Review and Meta-Analysis.

Objective: 7 Tesla magnetic resonance imaging (MRI) enables high resolution imaging and potentially improves the detection of morphologic abnormalities in patients with epilepsy. However, its added value compared with conventional 1.5T and 3.0T MRI is unclear. We reviewed the evidence for the use of 7 Tesla MRI in patients with epilepsy and compared the detection rate of focal lesions with clinical MRI. Methods: Clinical retrospective case studies were identified using the indexed text terms "epilepsy" AND "magnetic resonance imaging" OR "MR imaging" AND "7T" OR "7 Tesla" OR "7T" in Medline (2002-September 1, 2020) and Embase (1999-September 1, 2020). The study setting, MRI protocols, qualitative, and quantitative assessment were systematically reviewed. The detection rate of morphologic abnormalities on MRI was reported in each study in which surgery was used as the reference standard. Meta-analyses were performed using a univariate random-effects model in diagnostic performance studies with patients that underwent both 7T MRI and conventional MRI. Results: Twenty-five articles were included (467 patients and 167 healthy controls) consisting of 10 case studies, 10 case-control studies, 4 case series, and 1 cohort study. All studies included focal epilepsy; 12 studies (12/25, 48%) specified the disease etiology and 4 studies reported focal but non-lesional (MRI-negative on 1.5/3.0T) epilepsy. 7T MRI showed superior detection and delineation of morphologic abnormalities in all studies. In nine comparative studies, 7T MRI had a superior detection rate of 65% compared with the 22% detection rate of 1.5T or 3.0T. Significance: 7T MRI is useful for delineating morphologic abnormalities with a higher detection rate compared with conventional clinical MRI. Most studies were conducted using a case series or case study; therefore, a cohort study design with clinical outcomes is necessary. Classification of Evidence: Class IV Criteria for Rating Diagnostic Accuracy Studies.

The cerebellum could serve as a potential imaging biomarker of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.

BACKGROUND AND PURPOSE: As part of network-specific neurodegeneration, changes in cerebellar gray matter (GM) volume and impaired cerebello-cerebral functional networks have been reported in Alzheimer disease (AD). Compared with healthy controls, a volume loss in the cerebellum has been observed in patients with continuum of AD. However, little is known about the anatomical or functional changes in patients with clinical AD but no brain amyloidosis. We aimed to identify the relationship between cerebellar volume and dementia conversion of amyloid-negative mild cognitive impairment (MCI). METHODS: This study was a retrospective cohort study of patients over the age 50 years with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center with no less than a 36-month follow-up period. All subjects underwent detailed neuropsychological tests, 3 T brain magnetic resonance imaging scans including three-dimensional T1 imaging, and fluorine-18[F18 ]-florbetaben amyloid positron emission tomography scans. A spatially unbiased atlas template of the cerebellum and brainstem was used for analyzing cerebellar GM volume. RESULTS: During the 36 months of follow-up, 39 of 107 (36.4%) patients converted to dementia from amnestic MCI. The converter group had more severe impairments in all visual memory tasks. In terms of volumetric analysis, reduced crus I/II volume adjusted with total intracranial volume, and age was observed in the converter group. CONCLUSIONS: Significant cerebellar GM atrophy involving the bilateral crus I/II may be a novel imaging biomarker for predicting dementia progression in amyloid-negative amnestic MCI patients.

Association between ARID2 and RAS-MAPK pathway in intellectual disability and short stature.

BACKGROUND: ARID2 belongs to the Switch/sucrose non-fermenting complex, in which the genetic defects have been found in patients with dysmorphism, short stature and intellectual disability (ID). As the phenotypes of patients with ARID2 mutations partially overlap with those of RASopathy, this study evaluated the biochemical association between ARID2 and RAS-MAPK pathway. METHODS: The phenotypes of 22 patients with either an ARID2 heterozygous mutation or haploinsufficiency were reviewed. Comprehensive molecular analyses were performed using somatic and induced pluripotent stem cells (iPSCs) of a patient with ARID2 haploinsufficiency as well as using the mouse model of Arid2 haploinsufficiency by CRISPR/Cas9 gene editing. RESULTS: The phenotypic characteristics of ARID2 deficiency include RASopathy, Coffin-Lowy syndrome or Coffin-Siris syndrome or undefined syndromic ID. Transient ARID2 knockout HeLa cells using an shRNA increased ERK1 and ERK2 phosphorylation. Impaired neuronal differentiation with enhanced RAS-MAPK activity was observed in patient-iPSCs. In addition, Arid2 haploinsufficient mice exhibited reduced body size and learning/memory deficit. ARID2 haploinsufficiency was associated with reduced IFITM1 expression, which interacts with caveolin-1 (CAV-1) and inhibits ERK activation. DISCUSSION: ARID2 haploinsufficiency is associated with enhanced RAS-MAPK activity, leading to reduced IFITM1 and CAV-1 expression, thereby increasing ERK activity. This altered interaction might lead to abnormal neuronal development and a short stature.

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation.

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability.

Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural network was applied to estimate SV from arterial blood pressure waveform data recorded from liver transplantation (LT) surgeries. The model was trained using a gold standard referential SV measured via pulmonary artery thermodilution method. Merging a gold standard SV and corresponding 10.24 seconds of arterial blood pressure waveform as an input/output data set with 2-senconds of sliding overlap, 484,384 data sets from 34 LT surgeries were used for training and validation of DL model. The performance of DL model was evaluated by correlation and concordance analyses in another 491,353 data sets from 31 LT surgeries. We also evaluated the performance of pre-existing commercialized model (EV1000), and the performance results of DL model and EV1000 were compared. The DL model provided an acceptable performance throughout the surgery (r = 0.813, concordance rate = 74.15%). During the reperfusion phase, where the most severe hemodynamic instability occurred, DL model showed superior correlation (0.861; 95% Confidence Interval, (CI), 0.855-0.866 vs. 0.570; 95% CI, 0.556-0.584, P < 0.001) and higher concordance rate (90.6% vs. 75.8%) over EV1000. In conclusion, the DL-based model was superior for estimating intraoperative SV and thus might guide physicians to precise intraoperative hemodynamic management. Moreover, the DL model seems to be particularly promising because it outperformed EV1000 in circumstance of rapid hemodynamic changes where physicians need most help.

Effect of Prone Positional Apparatus on the Occurrence of Acute Kidney Injury After Spine Surgery.

BACKGROUND AND OBJECTIVE: Increased intra-abdominal pressure with prone positioning for spinal surgery is associated with intraoperative hemodynamic alterations and the potential for postoperative complications. This study investigated the incidence of postoperative acute kidney injury (AKI) in patients undergoing spine surgery on a Jackson spinal table or a Wilson frame. METHODS: A total of 1374 patients who underwent spine surgery were divided into 2 groups: Jackson spinal table (n = 598) and Wilson frame group (n = 776). After 1:1 propensity score matching, a final analysis was performed on 970 patients. The primary endpoint was a comparison of the incidence of AKI in the 2 groups. RESULTS: After propensity score matching analysis, the mean ± standard deviations of spine surgery invasiveness index were 4.7 ± 3.5 and 2.1 ± 1.4 in patients with the Jackson spinal table and the Wilson frame, respectively (P < 0.001). Considering the differences in surgical invasiveness, operative time, estimated blood loss, and administration of packed red blood cells were higher in the Jackson spinal table group than in the Wilson frame group (P < 0.001). However, the incidence of AKI was less with the Jackson spinal table than with the Wilson frame (1.7% vs. 3.7%, 2.25 [0.978-5.175], P = 0.056), not reaching statistical significance. CONCLUSION: This analysis showed that postoperative AKI in patients undergoing spine surgery in the prone position was not different with the Wilson frame than in the Jackson spinal table despite higher surgical severity, longer operative times, and more blood loss in the latter group. In spine surgery, the appropriate selection of prone positioning apparatus can potentially be an important consideration in reducing the risk of AKI.

Early treadmill exercise increases macrophage migration inhibitory factor expression after cerebral ischemia/reperfusion.

The neuroprotective function of macrophage migration inhibitory factor (MIF) in ischemic stroke was rarely evaluated. This study aimed to investigate the effects of early treadmill exercise on recovery from ischemic stroke and to determine whether these effects are associated with the expression levels of MIF and brain-derived neurotrophic factor (BDNF) in the ischemic area. A total of 40 male Sprague-Dawley rats were randomly assigned to the ischemia and exercise group [middle cerebral artery occlusion (MCAO)-Ex, n = 10), ischemia and sedentary group (MCAO-St, n = 10), sham-surgery and exercise group (Sham-Ex, n = 10), or sham-surgery and sedentary group (Sham-St, n = 10). The MCAO-Ex and MCAO-St groups were subjected to MCAO for 60 minutes, whereas the Sham-Ex and Sham-St groups were subjected to an identical operation without MCAO. Rats in the MCAO-Ex and Sham-Ex groups then ran on a treadmill for 30 minutes once a day for 5 consecutive days. After reperfusion, the hanging time tested by the wire hang test was longer and the relative fractional anisotropy determined by MRI was higher in the peri-infarct region of the MCAO-Ex group compared with the MCAO-St group. The expression levels of MIF and BDNF in the peri-infarct region were upregulated in the MCAO-Ex group. Increased MIF and BDNF levels were positively correlated with relative fractional anisotropy changes in the peri-infarct region. There was no significant difference in the levels of MIF and BDNF in the peri-infarct region between the Sham-Ex and Sham-St groups. Our study demonstrated that early exercise (initiated 48 hours after the MCAO) could improve motor and neuronal recovery after ischemic stroke. Furthermore, the increased levels of MIF and BDNF in the peri-infarct region (penumbra) may be one of the mechanisms of enhanced neurological function recovery. All experiments were approved by the Institutional Animal Care and Use Committee in Asan Medical Center in South Korea (2016-12-126).

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.

BACKGROUND: Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma. METHODS: The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23). RESULTS: Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness. CONCLUSIONS: Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study.

BACKGROUND: The presence of metastatic lymph nodes is a prognostic indicator for patients with thyroid carcinomas and is an important determinant of clinical decision making. However, evaluating neck lymph nodes requires experience and is labor- and time-intensive. Therefore, the development of a computer-aided diagnosis (CAD) system to identify and differentiate metastatic lymph nodes may be useful. METHODS: From January 2008 to December 2016, we retrieved clinical records for 804 consecutive patients with 812 lymph nodes. The status of all lymph nodes was confirmed by fine-needle aspiration. The datasets were split into training (263 benign and 286 metastatic lymph nodes), validation (30 benign and 33 metastatic lymph nodes), and test (100 benign and 100 metastatic lymph nodes). Using the VGG-Class Activation Map model, we developed a CAD system to localize and differentiate the metastatic lymph nodes. We then evaluated the diagnostic performance of this CAD system in our test set. RESULTS: In the test set, the accuracy, sensitivity, and specificity of our model for predicting lymph node malignancy were 83.0%, 79.5%, and 87.5%, respectively. The CAD system clearly detected the locations of the lymph nodes, which not only provided identifying data, but also demonstrated the basis of decisions. CONCLUSION: We developed a deep learning-based CAD system for the localization and differentiation of metastatic lymph nodes from thyroid cancer on ultrasound. This CAD system is highly sensitive and may be used as a screening tool; however, as it is relatively less specific, the screening results should be validated by experienced physicians.