CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

BACKGROUND: Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment. METHODS: Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis. RESULTS: The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells. CONCLUSION: The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.

Circulating Tumor Cells as Diagnostic Markers of Early Gastric Cancer and Gastric Precancerous Lesions.

INTRODUCTION: Circulating tumor cells (CTCs) may be potential diagnostic biomarkers of various malignancies including gastric cancer. This study aimed to evaluate whether CTCs could be used to facilitate the diagnosis of early gastric cancer (EGC) or precancerous gastric lesions. METHODS: The diagnostic study included consecutive patients with EGC, gastric precancerous lesions, or fundic gland polyps admitted to the Gastroenterology Department, Beijing Friendship Hospital Affiliated to Capital Medical University (National Center for Digestive Diseases) between October 2016 and January 2018. RESULTS: A total of 92 patients were enrolled, including 57 patients with EGC, 14 patients with gastric precancerous lesions, and 21 patients with fundic gland polyps (control group). CTCs were detected in 47.89% (34/71) of patients with EGC/gastric precancerous lesions and 4.76% (1/21) of patients with fundic gland polyps (p < 0.001). CTC detection distinguished EGC/precancerous lesions from fundic gland polyps with an area under the receiver operating characteristic curve of 0.740 (95% confidence interval, 0.640-0.840; p = 0.001), a sensitivity of 49.10%, a specificity of 95.00%, a positive predictive value of 97.00%, and a negative predictive value of 64.90%. CONCLUSIONS: Peripheral blood CTCs are more common in patients with EGC or gastric precancerous lesions than in patients with fundic gland polyps. Measurement of CTCs may be a useful tool to aid in the diagnosis of EGC and precancerous lesions.

Abnormal network properties and fiber connections of DMN across major mental disorders: a probability tracing and graph theory study.

The default mode network (DMN) is related to brain functions and its abnormalities were associated with mental disorders' pathophysiology. To further understand the common and distinct DMN alterations across disorders, we capitalized on the probability tracing method and graph theory to analyze the role of DMN across three major mental disorders. A total of 399 participants (156 schizophrenia [SCZ], 90 bipolar disorder [BP], 58 major depression disorder [MDD], and 95 healthy controls [HC]) completed magnetic resonance imaging (MRI)-scanning, clinical, and cognitive assessment. The MRI preprocessing of diffusion-tensor-imaging was conducted in FMRIB Software Library and probabilistic fiber tracking was applied by PANDA. This study had three main findings. First, patient groups showed significantly lower cluster coefficient in whole-brain compared with HC. SCZ showed significantly longer characteristic path compared with HC. Second, patient groups showed inter-group specificity in abnormalities of DMN connections. Third, SCZ was sensitive to left_medial_superior_frontal_gyrus (L_SFGmed)-right_anterior_cingulate_gyrus (R_ACG) connection relating to positive symptoms; left_ACG-right_ACG connection was the mania's antagonistic factor in BP. This trans-diagnostic study found disorder-specific structural abnormalities in the fiber connection of R_SFGmed-L_SFGmed-R_ACG_L_ACG within DMN, where SCZ showed more disconnections compared with other disorders. And these connections are diagnosis-specifically correlated to phenotypes. The current study may provide further evidence of shared and distinct endo-phenotypes across psychopathology.

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.

BACKGROUND: Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. OBJECTIVE: This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. METHODS: For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. RESULTS: The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. CONCLUSIONS: Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort.

INTRODUCTION: We explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals. METHODS: The cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured. RESULTS: Distinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver-operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877). DISCUSSION: Detection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.

[Trial of the GeriNOT screening tool : Identification of geriatric risk potentials on admission to hospital care from the age of 70 years]. / Prüfung des Screening-Tools GeriNOT : Identifikation geriatrischer Risikopotenziale bei Aufnahme ab 70-Jähriger in die Krankenhausversorgung.

BACKGROUND: Geriatric-specific characteristics influence patient-relevant outcomes of inpatient hospital care in patients aged 70 years and older: prolonged length of stay, complications, increase in utilization of required services as well as mortality rates. OBJECTIVE: The screening tool GeriNOT, identification of geriatric risk potential with 7 items, of which mobility and cognition are double-weighted, score 9 points, was tested for its predictive content and diagnostic quality. MATERIAL AND METHODS: Diagnostic study from a retrospective, bicentric complete survey in all types of admission from 70 years with 2541 patient cases. Regression analyses in linked samples of the 7 items in GeriNOT and as noncombined end points: prolonged length of stay, complications, increase in need-based service at discharge and death. RESULTS: Mean age ± SD: 77.0 ± 6.4 years. ROC analyses report at a cut-off value calculated using the Youden index of ≥ 4 points in 2541 cases: increase in need-based service at discharge (AUC = 0.693, 95% CI = 0.663-0.723, sensitivity 75.2%, specificity 59.7%), complications (AUC = 0.662, 95% CI = 0.636-0.688, sensitivity 64.2%, specificity 61.6%) and death (AUC = 0.734, 95% CI = 0.682-0.786, sensitivity 76.4%, specificity 57.5%). Possibly suitable for use as screening to identify geriatric risk potentials at a cut-off of ≥ 4 points. DISCUSSION: Provide an initial filter screening with regard to mobility. Such identification could provide the involved persons with the opportunity for an improved treatment outcome by adapting the inpatient process. Prospective validation of GeriNOT needed.

How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data.

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.

Blood biomarkers in epilepsy.

Robust and accessible biomarkers are greatly needed in epilepsy. Diagnostic and prognostic precision in the clinic needs to improve, and there is a need for objective quantification of seizure burden. In recent years, there have been advances in the development of accessible and cost-effective blood-based biomarkers in neurology, and these are increasingly studied in epilepsy. However, the field is in its infancy and specificity and sensitivity for most biomarkers in most clinical situations are not known. This review describes advancements regarding human blood biomarkers in epilepsy. Examples of biochemical markers that have been shown to have higher blood concentrations in study subjects with epilepsy include brain proteins like S100B or neuronal specific enolase, and neuroinflammatory proteins like interleukins, and tumor necrosis factor-alpha. Some of the blood biomarkers also seem to reflect seizure duration or frequency, and levels decrease in response to treatment with antiseizure medication. For most biomarkers, the literature contains seemingly conflicting results. This is to be expected in an emerging field and could reflect different study populations, sampling or analysis techniques, and epilepsy classification. More studies are needed with emphasis put on the classification of epilepsy and seizure types. More standardized reporting could perhaps decrease result heterogeneity and increase the potential for data sharing and subgroup analyses.

Evaluation of the clinical effect of an artificial intelligence-assisted diagnosis and treatment system for neonatal seizures in the real world: a multicenter clinical study protocol. / æ–°ç”Ÿå„¿æƒŠåŽ¥æ™ºèƒ½è¯Šç–—ç³»ç»ŸçœŸå®žåœºæ™¯ä¸´åºŠå®žæ–½æ•ˆæžœç»¼åˆè¯„ä»·çš„å¤šä¸­å¿ƒä¸´åºŠç ”ç©¶æ–¹æ¡ˆ.

Neonatal seizures are the most common clinical manifestations of critically ill neonates and often suggest serious diseases and complicated etiologies. The precise diagnosis of this disease can optimize the use of anti-seizure medication, reduce hospital costs, and improve the long-term neurodevelopmental outcomes. Currently, a few artificial intelligence-assisted diagnosis and treatment systems have been developed for neonatal seizures, but there is still a lack of high-level evidence for the diagnosis and treatment value in the real world. Based on an artificial intelligence-assisted diagnosis and treatment systems that has been developed for neonatal seizures, this study plans to recruit 370 neonates at a high risk of seizures from 6 neonatal intensive care units (NICUs) in China, in order to evaluate the effect of the system on the diagnosis, treatment, and prognosis of neonatal seizures in neonates with different gestational ages in the NICU. In this study, a diagnostic study protocol is used to evaluate the diagnostic value of the system, and a randomized parallel-controlled trial is designed to evaluate the effect of the system on the treatment and prognosis of neonates at a high risk of seizures. This multicenter prospective study will provide high-level evidence for the clinical application of artificial intelligence-assisted diagnosis and treatment systems for neonatal seizures in the real world.

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU). METHODS: The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018-03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days. RESULTS: Sensitivity and specificity was 91.7% (95% CI 85.5-95.4%) and 54.1% (95% CI 45.4-62.5%) on patient level, and 97.5% (95% CI 95.1-98.7%) and 91.5% (95% CI 89.3-93.3%) on the level of patient-days. Physicians' SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8-66.9%)/specificity of 83.3% (95% CI 80.4-85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine. CONCLUSIONS: We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study. TRIAL REGISTRATION: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

External Validation of a Periodontal Prediction Model for Identification of Diabetes among Saudi Adults.

AIM AND OBJECTIVE: To externally validate the performance of a novel periodontal prediction model (PPM) for identification of diabetes among Saudi adults. MATERIALS AND METHODS: The study was carried out among 150 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a temporal external validation approach, where the performance of the PPM was evaluated in the same location as the development study, but at a later time to allow for some variation between samples. A case-control approach was adopted, where diabetes status was first ascertained, followed by the completion of the Finnish Diabetes Risk Score (FINDRISC), Canadian Diabetes Risk (CANRISK) tools, and periodontal examinations. RESULTS: The area under the curve (AUC) of the PPM (based on the number of missing teeth, the proportion of sites with pocket probing depth ≥6 mm, and mean pocket probing depth) was 0.514 (95% CI: 0.385, 0.642). The FINDRISC and CANRISK tools had AUC values of 0.871 (95% CI: 0.811-0.931) and 0.927 (95% CI: 0.884-0.971), respectively. The addition of the PPM did not improve the AUC of FINDRISC (p = 0.479) or CANRISK (p = 0.920). The decision curve analysis showed that there was no clinical benefit in adding the PPM to either tool. The PPM was updated with an overall adjustment factor for all existing predictors and three more periodontal measures. CONCLUSION: In an external sample, the PPM had poor performance for identification of diabetes and no added value when combined with FINDRISC and CANRISK. The performance of the PPM improved after recalibration and extension. CLINICAL SIGNIFICANCE: The results underscore the value of externally validating prediction models before applying them in clinical dental practice.

Diagnostic accuracy of capnovolumetry for the identification of airway obstruction - results of a diagnostic study in ambulatory care.

BACKGROUND: One of the known weaknesses of spirometry is its dependence on patients' cooperation, which can only partially be alleviated by educational efforts. Therefore, procedures less dependent on cooperation might be of value in clinical practice. We investigated the diagnostic accuracy of ultrasound-based capnovolumetry for the identification of airway obstruction. METHODS: Consecutive patients from a pulmonary outpatient clinic were included in the diagnostic study. As reference standard, the presence of airway obstruction was evaluated via spirometry and bodyplethysmography. Capnovolumetry was performed as index test with an ultrasound spirometer providing a surrogate measure of exhaled carbon dioxide. Receiver operating characteristic (ROC) analysis was performed using the ratio of slopes of expiratory phases 3 and 2 (s3/s2) ≥ 0.10 as primary capnovolumetric parameter for the recognition of airway obstruction. Logistic regression was performed as secondary analysis to identify further useful capnovolumetric parameters. The diagnostic potential of capnovolumetry to identify more severe degrees of airway obstruction was evaluated additionally. RESULTS: Of 1400 patients recruited, 1287 patients were included into the analysis. Airway obstruction was present in 29% of patients. The area under the ROC-curve (AUC) of s3/s2 was 0.678 (95% CI 0.645, 0.710); sensitivity of s3/s2 ≥ 0.10 was 47.7 (95% CI 42.7, 52.8)%, specificity 79.0 (95% CI 76.3, 81.6)%. When combining this parameter with three other parameters derived from regression analysis (ratio area/volume phase 3, slope phase 3, volume phase 2), an AUC of 0.772 (95% CI 0.743, 0.801) was obtained. For severe airway obstruction (FEV1 ≤ 50% predicted) sensitivity of s3/s2 ≥ 0.10 was 75.9 (95% CI 67.1, 83.0)%, specificity 75.8 (95% CI 73.3, 78.1)%; for very severe airway obstruction (FEV1 ≤ 30% predicted) sensitivity was 86.7 (95% CI 70.3, 94.7)%, specificity 72.8 (95% CI 70.3, 75.2)%. Sensitivities increased and specificities decreased considerably when the combined capnovolumetric score was used as index test. CONCLUSIONS: Capnovolumetry by way of an ultrasound spirometer had a statistically significant albeit moderate potential for the recognition of airway obstruction in a heterogeneous population of patients typically found in clinical practice. Diagnostic accuracy of the capnovolumetric device increased with the severity of airway obstruction. TRIAL REGISTRATION: The study is registered under DRKS00013935 at German Clinical Trials Register (DRKS).

Value of Cardiogoniometry in Diagnosis of Coronary Artery Disease in Patients with Suspected Stable Ischemic Heart Disease: A Systematic Review and Meta-Analysis.

Cardiogoniometry (CGM) has been proposed as a new diagnostic tool for coronary artery disease (CAD) in recent years. Although different studies have evaluated the diagnostic value of CGM in CAD diagnosis, no pooled analysis of its diagnostic accuracy has been performed so far. This study aimed to assess the value of CGM in diagnosing CAD in patients with suspected stable ischemic heart disease (SIHD).This was a systematic review and meta-analysis conducted on available literature until May 2018. Studies considered coronary angiography as the reference standard for CAD diagnosis and reported CGM diagnostic value parameters were included. No language and time restrictions for enrolling the studies were considered. Statistical analysis was performed using Meta-DiSc software.The findings of the 10 studies published in 9 articles were enrolled in the meta-analysis. Overall pooled sensitivity was 71.7% (69.1 to 74.1; Cochrane Q = 39.5; P < 0.00001; I2 = 77.3%), and pooled specificity was 78.8% (76.3 to 81.1; Cochrane Q = 37.39; P < 0.00001; I2 = 75.9%). Regarding Egger's regression test (P = 0.32), there was no published bias in the studies.It seems that CGM, as an easy-to-use and non-invasive modality, should be considered as a part of risk stratifying strategies for CAD in patients with SIHD, mainly in patients with contraindications for stress tests. However, further studies with a high quality of methodology are still needed to assess the diagnostic value of CGM for CAD in patients with suspected SIHD.

Coronary CT angiography characteristics of OCT-defined thin-cap fibroatheroma: a section-to-section comparison study.

OBJECTIVES: To evaluate whether plaque characteristics as assessed by coronary computed tomography angiography (CCTA) were associated with the presence of a thin-cap fibroatheroma (TCFA)-a precursor of plaque rupture-defined by optical coherence tomography (OCT) in a section-to-section-level comparison. METHODS: From 28 symptomatic patients, 31 coronary lesions were evaluated on 727 cross-sections co-registered by both CCTA and OCT. CCTA plaque characteristics included low attenuation plaque (LAP, <30 HU), napkin ring sign (NRS), positive remodelling (PR, remodelling index ≥1.10), and spotty calcification and plaque area and plaque burden. By OCT, presence of TCFA, lumen area and arc of lipid were determined. RESULTS: OCT revealed a TCFA in 69 (9.4%) sections from 19 (61.2 %) lesions. In per-section analysis, OCT-TCFA showed higher frequency of CCTA-detected LAP (58.0% vs. 18.5%), NRS (31.9% vs. 8.8%) and PR (68.1% vs. 48.0%) and greater plaque burden (70.6% vs. 61.9%) as compared to sections without OCT-TCFA (all p < 0.05). In multivariable analysis, LAP (odds ratio [OR] 4.05, p < 0.001) and NRS (OR 2.47, p = 0.005) were associated with OCT-TCFA. CCTA-measured lumen area correlated well with OCT-measured lumen area (R = 0.859, limits of agreement -0.5 ± 3.7 mm2). CONCLUSIONS: LAP and NRS in CCTA were associated with the presence of OCT-defined TCFA in a section-to-section comparison. KEY POINTS: • CT-defined LAP and NRS were associated with OCT-defined TCFA • OCT-TCFA showed higher frequency of LAP, NRS, PR and greater plaque burden • Non-calcified plaque area was correlated with OCT-measured lipid arc.

Diagnostic Accuracy of Sex-Specific Chest CT Measurements Compared With Cardiac MRI Findings in the Assessment of Cardiac Chamber Enlargement.

OBJECTIVE: The purpose of this study was to establish sex-specific chest CT measurement thresholds for detection of cardiac chamber enlargement with cardiac MRI as the reference standard. MATERIALS AND METHODS: Consecutive patients who underwent contrast-enhanced chest CT (64- or 320-MDCT) and cardiac MRI within a 7-day interval between August 2006 and August 2016 were included in this retrospective study (n = 217; 115 men, 102 women; mean age, 52.8 ± 15.8 years). Measurements were performed on axial CT images to evaluate right atrial (RA), right ventricular (RV), left atrial (LA), and left ventricular (LV) chamber size. The presence of chamber enlargement (RAE, RVE, LAE, and LVE) was established with cardiac MRI as the reference standard. ROC analysis was performed. Optimal sex-specific CT measurement thresholds were identified that ensured specificity of 90% or greater and maximized sensitivity. RESULTS: The prevalence of chamber enlargement in men was 26% for RAE, 11% for RVE, 40% for LAE, and 24% for LVE. In women the prevalence was 16% for RAE, 15% for RVE, 27% for LAE, and 12% for LVE. The following CT measurement thresholds were optimal: for RAE, RA transverse diameter ≥ 67 mm for men (AUC, 0.825) and ≥ 64 mm for women (AUC, 0.926); for RVE, RV transverse diameter ≥ 60 mm for men (AUC, 0.846) and ≥ 57 mm for women (AUC, 0.858); for LAE, LA anteroposterior diameter ≥ 50 mm for men (AUC, 0.795) and ≥ 45 mm for women (AUC, 0.841); for LVE, LV transverse diameter ≥ 58 mm for men (AUC, 0.917) and ≥ 53 mm for women (AUC, 0.840). CONCLUSION: Cardiac chamber enlargement can be identified with high specificity and reasonable sensitivity on axial chest CT images by use of sex-specific measurement thresholds.

Ultrasonographic diagnosis of severe placental invasion.

AIM: Placental invasion is a life-threatening obstetric complication. The aim of this study was to identify the optimal ultrasonographic (US) criteria for placenta increta/percreta in order to improve diagnostic accuracy. METHODS: In a retrospective diagnostic study, all 116 patients at Peking University Third Hospital who had been diagnosed with placental invasion from October 2006 to October 2013 were included. Depending on their clinical and/or histopathological diagnosis, the study was divided into two groups: the Placenta Accreta Group (63 cases) and the Placenta Increta/Percreta Group (53 cases). The US images were analyzed for differences between placenta accreta and placenta increta/percreta. RESULTS: The sonographic criteria found to have predictive value for placenta increta/percreta using a regression model were: deficiency of retroplacental sonolucent zone and/or segmental retroplacental myometrial thinning less than 1 mm, multiple vascular lacunae presenting a 'moth hole' appearance, and placenta previa. Using a cut-off point of 0.589, the sensitivity and specificity were 81.1% and 77.8%, respectively. The area under the receiver-operator curve was 0.848 (P < 0.001). CONCLUSION: US diagnosis not only allows the detection of placental invasion, but also facilitates preliminary classification. The three aforementioned criteria facilitate the identification of placenta increta/percreta for precise and comprehensive clinical decision-making.

Comparative Analysis of the Reconstruction of Individual Hip Anatomy Using 3 Different Cementless Stem Designs in Patients With Primary Hip Osteoarthritis.

BACKGROUND: We questioned whether there was a radiographic difference in hip geometry reconstruction and implant fixation between 3 different cementless stem design concepts in patients with primary end-stage hip osteoarthritis. METHODS: We retrospectively evaluated the preoperative and postoperative radiographs by 2 independent and blinded reviewers in a series of 264 consecutive patients who had received either a straight double-tapered stem with 3 offset options (group A), a straight double-tapered stem with 2 shape options and modular necks (group B), and a bone-preserving curved tapered stem with 4 offset options (group C). The following parameters were assessed: acetabular, femoral and hip offset (HO), center of rotation height, leg length difference (LLD), and the endosteal fit of stem in the proximal femur (canal fill index). Group comparisons were performed using a one-way analysis of variance and subsequent pairwise comparisons (t-test). RESULTS: Postoperatively, HO could be equally restored with all 3 stem designs (P = .079). The postoperative LLD was smaller in group C compared to group A (0.8 mm [standard deviation, 3.2] vs 2.6 mm [standard deviation, 4.5], P = .002). Best combined reconstruction of HO and LLD could be achieved with the short curved stem by junior and senior surgeons (HO: -2.0 and -2.1 mm; LLD: 1.9 and 0.7 mm, respectively). The proximal and mid-height canal fill indexes were higher in groups B and C compared to group A, indicating a better metaphyseal and diaphyseal fit in the proximal femur (both P < .001). CONCLUSION: All 3 cementless stem designs allowed for good hip geometry reconstruction. Multiple shape and offset options allowed for a better metaphyseal stem fit and offered minor clinical advantages for leg length reconstruction. Modular necks did not provide reconstructive advantages in patients with primary hip osteoarthritis.

Evaluation of predictive capacities of biomarkers based on research synthesis.

The objective of diagnostic studies or prognostic studies is to evaluate and compare predictive capacities of biomarkers. Suppose we are interested in evaluation and comparison of predictive capacities of continuous biomarkers for a binary outcome based on research synthesis. In analysis of each study, subjects are often classified into two groups of the high-expression and low-expression groups according to a cut-off value, and statistical analysis is based on a 2 × 2 table defined by the response and the high expression or low expression of the biomarker. Because the cut-off is study specific, it is difficult to interpret a combined summary measure such as an odds ratio based on the standard meta-analysis techniques. The summary receiver operating characteristic curve is a useful method for meta-analysis of diagnostic studies in the presence of heterogeneity of cut-off values to examine discriminative capacities of biomarkers. We develop a method to estimate positive or negative predictive curves, which are alternative to the receiver operating characteristic curve based on information reported in published papers of each study. These predictive curves provide a useful graphical presentation of pairs of positive and negative predictive values and allow us to compare predictive capacities of biomarkers of different scales in the presence of heterogeneity in cut-off values among studies. Copyright © 2016 John Wiley & Sons, Ltd.

Diagnostic accuracy of audio-based seizure detection in patients with severe epilepsy and an intellectual disability.

UNLABELLED: We evaluated the performance of audio-based detection of major seizures (tonic-clonic and long generalized tonic) in adult patients with intellectual disability living in an institute for residential care. METHODS: First, we checked in a random sample (n=17, 102 major seizures) how many patients have recognizable sounds during these seizures. In the second part of this trial, we followed 10 patients (who had major seizures with recognizable sounds) during four weeks with an acoustic monitoring system developed by CLB ('CLB-monitor') and video camera. In week 1, we adapted the sound detection threshold until, per night, a maximum of 20 sounds was found. During weeks 2-4, we selected the epilepsy-related sounds and performed independent video verification and labeling ('snoring', 'laryngeal contraction') of the seizures. The video images were also fully screened for false negatives. In the third part, algorithms in the CLB-monitor detected one specific sound (sleep-related snoring) to illustrate the value of automatic sound recognition. RESULTS: Part 1: recognizable sounds (louder than whispering) occurred in 23 (51%) of the 45 major seizures, 20 seizures (45%) were below this threshold, and 2 (4%) were without any sound. Part 2: in the follow-up group (n=10, 112 major seizures; mean: 11.2, range: 1-30), we found a mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00). All false positive alarms (mean value: 1.29 per night) were due to minor seizures. We missed 4 seizures (3%) because of lack of sound and 10 (9%) because of sounds below the system threshold. Part 3: the machine-learning algorithms in the CLB-monitor resulted in an overall accuracy for 'snoring' of 98.3%. CONCLUSIONS: Audio detection of major seizures is possible in half of the patients. Lower sound detection thresholds may increase the proportion of suitable candidates. Human selection of seizure-related sounds has a high sensitivity and moderate positive predictive value because of minor seizures which do not need intervention. Algorithms in the CLB-monitor detect seizure-related sounds and may be used alone or in multimodal systems.

Ellipsoid zone on optical coherence tomography: a review.

Emergence of the high-resolution optical coherence tomography has allowed better delineation of retinal layers, and many of the anatomical correlations of these layers have now been agreed upon. However, some anatomical correlates still remain contentious, such as the second hyper-reflective band, which is now termed ellipsoid zone. Despite the lack of consensus of the actual origin of the ellipsoid zone, there has been much interest in evaluating its integrity and intensity in different disease processes. This review paper aims to provide an overview of the ellipsoid zone and its clinical and research applications.