Unified measures of target detection system performance evaluation

Restriction spectrum imaging (RSI), as an advanced quantitative diffusion-weighted magnetic resonance imaging technique, has the potential to distinguish primary benign and malignant lung lesions. To explore how well the tri-compartmental RSIperforms in distinguishing primary benign from malignant lung lesions compared with diffusion-weighted imaging (DWI), and to further explore whether positron emission tomography/magnetic resonance imaging (PET/MRI) can improve diagnostic efficacy. Prospective. 137 patients, including 108 malignant and 29 benign lesions (85 males, 52 females; average age = 60.0 ± 10.0 years). T2WI, T1WI, multi-b value DWI, MR-based attenuation correction, and PET imaging on a 3.0 T whole-body PET/MR system. The apparent diffusion coefficient (ADC), RSI-derived parameters (restricted diffusion , hindered diffusion , and free diffusion ) and the maximum standardized uptake value (SUVmax) were calculated and analyzed for diagnostic efficacy individually or in combination. Student's t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curves, Delong test, Spearman's correlation analysis. P < 0.05 was considered statistically significant. The , SUVmax were significantly higher, and , ADC were significantly lower in the malignant group [0.717 ± 0.131, 9.125 (5.753, 13.058), 0.194 ± 0.099, 1.240 (0.972, 1.407)] compared to the benign group [0.504 ± 0.236, 3.390 (1.673, 6.030), 0.398 ± 0.195, 1.485 ± 0.382]. The area under the ROC curve (AUC) values ranked from highest to lowest as follows: AUC (SUVmax) > AUC ( ) > AUC ( ) > AUC (ADC) > AUC ( ) (AUC = 0.819, 0.811, 0.770, 0.745, 0549). The AUC (AUC = 0.900) of the combined model of RSI with PET was significantly higher than that of either single-modality imaging. RSI-derived parameters ( , ) might help to distinguish primary benign and malignant lung lesions and the discriminatory utility of was not observed. The RSI exhibits comparable or potentially enhanced performance compared with DWI, and the combined RSI and PET model might improve diagnostic efficacy. Stage 2.

Primary application of mean apparent propagator-MRI diffusion model in the grading of diffuse glioma

Ratio of visceral fat area to body fat mass (VBR) is a superior predictor of coronary heart disease.

Outcome prediction by serum calprotectin in patients with COVID-19 in the emergency department

Cognitive reserve in patients with mood disorders: Validation study of the Chinese version of the cognitive reserve assessment scale in health

AIMS Around half of patients with brain metastases respond to immune checkpoint inhibition and these treatments have significant side effects, highlighting the critical need for treatment response biomarkers. Diffusion Tensor Imaging (DTI) represents a rapidly obtained MRI sequence that has been shown to discriminate high from low peritumoural immune infiltration and could therefore help personalise treatment approaches. METHODS This prospective single-centre biomarker study (CRUK funded) recruited adult patients with intracranial melanoma metastases eligible for immunotherapy. All patients received standard ipilimumab/nivolumab com- bination immunotherapy. We assessed pre-treatment DTI and 6-week follow-up scans using RANO and RE- CIST criteria. Peritumoral fractional anisotropy (FA) values from the DTI scans were measured blind to clin- ical outcomes using both manual (two raters; one being a neuroradiologist with 10+ years experience) and semi-automated techniques. We performed receiver operating characteristic (ROC) curve analysis to assess the discriminative performance of FA measurements in predicting both intracranial and extracranial response, calculating area under the ROC curve (AUC) values for both measurement techniques. RESULTS Eight patients with 18 brain metastases were included. Median overall survival was 13.1 months (95% CI: 1.6-24.6), with median intracranial progression-free survival of 12.9 months (95% CI: 7.2-18.6) and median ex- tracranial progression-free survival of 14.3 months (95% CI: 11.7-16.9). Both manual and semi-automated FA measurements demonstrated excellent predictive value for brain metastasis response (AUC=0.85 and 0.825, re- spectively), while showing poor discrimination for extracranial response (AUC=0.333 and 0.556, respectively). There was discordance between automated and manual FA readings but excellent inter-rater agreement for manual recording of FA. CONCLUSION This study demonstrates that DTI-derived FA measurements show promise as a predictive biomarker for im- munotherapy response in brain metastases, providing a potentially useful tool for optimizing patient selection. Further refinement of the semi-automated technique for measurement (e.g. via a plugin for PACS) and valida- tion in a larger cohort with diverse primary tumour types is needed

Актуальность. Новые возможности улучшения функции внешнего дыхания (ФВД) связаны с применением высокочастотной осцилляции грудной клетки (ВЧОГК), которая позволяет восстановить дренаж бронхиального дерева и оптимизировать легочную вентиляцию. Цель исследования — изучение и оценка ФВД у детей с внебольничной пневмонией (ВП). Материалы и методы. Проведено обследование 107 детей (основная группа (ОГ) — 55 человек и контрольная группа (КГ) — 52 человека) в возрасте 6–17 лет с ВП с острым и неосложненным течением. Дети ОГ получали базисную терапию (БТ) с проведением процедур ВЧОГК, тогда как дети КГ получали исключительно БТ. Исследовали ФВД в динамике лечения, применяя спирометрию. Результаты. В начале терапии ВП показатели ФВД в исследуемых группах имели несущественные различия. При анализе параметров ФВД у детей ОГ на 10-й день БТ отмечено повышение объема форсированного выдоха за 1-ю секунду (ОФВ1) (88,36 ± 1,55 %, p = 0,02), жизненной емкости легких (ЖЕЛ) (88,18 ± 1,53 %, p = 0,02), форсированной жизненной емкости легких (ФЖЕЛ) (86,77 ± 1,37 %, p = 0,03), максимальной объемной скорости воздуха на уровне выдоха 25 % (МОШ25) (90,10 ± 2,99 %, p = 0,02) и максимальной вентиляции легких (МВЛ) (88,31 ± 1,70 %, p = 0,04). Сравнивая показатели ФВД детей КГ, выявили менее выраженную динамику, в частности, ОФВ1 (81,65 ± 2,44 %), ЖЕЛ (82,95 ± 2,56), ФЖЕЛ (80,85 ± 2,09), МОШ25 (82,63 ± 3,08 %) и МВЛ (85,65 ± 1,99 %). При проведении ROC-анализа динамика восстановления ФВД у детей ОГ более заметна за счет улучшения МВЛ, о чем свидетельствует самая большая площадь под кривой ROC (ППК) — 0,99, ОФВ1 — ППК 0,94 и ОФВ1/ФЖЕЛ — ППК 0,94. У детей КГ была менее выражена динамика исследуемых параметров ФВД, что подтверждается МВЛ — ППК 0,63, ОФВ1 — ППК 0,79 и ОФВ1/ФЖЕЛ — ППК 0,89. Выводы. Для улучшения вентиляционной функции легких у детей с ВП необходимо проводить процедуры бронходренажа, используя ВЧОГК в составе БТ, о чем свидетельствуют полученные положительные результаты спирометрии.

Objective To explore the value of quantitative T2 weighted magnetic resonance imaging (T2WI) and diffusion tensor imaging (DTI) parameters for predicting functional outcomes after surgery for cervical spondylotic myelopathy (CSM). Methods One hundred and forty CSM patients received T2WI and DTI before surgery at Tianjin Hospital between April 2014 and April 2016. They were then given systematic rehabilitation treatment after the surgery. The Japanese Orthopaedics Association (JOA) scoring system was applied to evaluate their neurological function before and after the surgery. According to the JOA recovery rate at 1-year follow-up, the patients were divided into a good recovery group (recovery rate≥50%) and a poor recovery group (recovery rate <50%). Maximum spinal cord compression (MSCC), signal change ratio (SCR), transverse area (TA), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were compared between the two groups. Receiver operating characteristics (ROC) curves were used to measure the prognostic ability and determine the best cut-off value for each variable. The independent predictors of a poor recovery were estimated using univariate and multivariate analysis. Results ROC analysis showed that the area under the ROC curve (AUC) of MSCC was 0.593. For SCR it was 0.682. For the TA it was 0.706. For the ADC it was 0.719 and for FA it was 0.749. The respective cut-off values were 44% compression, 1.561, 0.46 cm2, 1.339×10-3 mm2/s and 0.386. FA had the largest AUC, followed by ADC, TA, SCR and MSCC. MSCC and SCR had low discrimination power (AUC<0.7) in predicting a poor recovery, whereas TA, ADC and FA had moderate discrimination power (AUC 0.7-0.9). Logistic multivariate regression showed that a low JOA score, TA≤0.46 cm2 or FA≤0.386 were independent risk factors for a poor recovery. A predicting model built according to the results of the logistic regression analysis gave an AUC of 0.87, significantly better than that of the JOA score. With a cut-off value of 0.36, the sensitivity and specificity were 80% and 77% respectively. Conclusions Combining T2WI and DTI parameters with the JOA score may better predict the recovery of patients with CSM. The values can also provide references for making up rehabilitation plans. Key words: T2 weighted magnetic resonance imaging; Diffusion tensor imaging; Cervical vertebrae; Spinal cord compression; Prognosis

Maximizing area under ROC curve for biometric scores fusion

Receiver Operating Characteristic (ROC) curves are used to describe and compare the accuracy of diagnostic test or the ability of a continuous biomarker in discriminating between the subjects into healthy or diseased cases in medical field. The most familiar form of ROC curve is Bi-normal (Gaussian) ROC curve model, which assumes that the test scores or a monotone transformation of the test scores are from two normal populations (i.e. healthy and diseased). It may not be true all the time, it may violate the assumptions of normal distribution in some situations and also we cannot adopt the model as it is when the sample size is small. In this paper, we have proposed ROC curve model for Rayleigh distribution which can be used even when sample size is small. The properties of Bi-Rayleigh ROC model are studied and Area Under the ROC Curve (AUC) are derived. The proposed model is supported by real life example as well as simulation studies. The confidence interval for the population parameter is studied with simulation studies of varying sample sizes. It is found that Bi-Rayleigh ROC model provides better accuracy of classification than the conventional bi-normal ROC model. In the ROC model section, Bi-Rayleigh ROC model and its AUC are derived and its properties are discussed. In application section, two real life example viz. Head trauma data and Multiple Sclerosis data were used for the proposed model and then these are compared with the conventional bi- normal model. In the simulation section, simulation studies have been carried out for different values of parameters to support the Bi-Rayleigh ROC model. In the last section, the confidence intervals for the parameter σi, i=0,1 are discussed for real life example and simulated data sets. II. ROC MODEL AND AREA UNDER THE CURVE Let the scores (S) be a random variable in healthy (H) and diseased (D) populations respectively. The ROC curve is plotted over values of threshold value (t) and is given by y(t) = h (x(t)) where x(t) is the False positive rate which is given by x(t) = P(S>t|H) and y(t) is True positive rate which is given by y(t) = P (S>t|D) The general functional form of ROC can be represented as

Objectives: Herpes virus entry mediator (HVEM) is a costimulatory molecule, and has been proved to play an important role in airway inflammatory and remodeling processes of asthma. We aimed to investigate the expression of HVEM gene in patients with asthma as a means of assessing disease severity.Methods: This study was carried out on 59 subjects, 16 patients with mild persistent asthma, 11 patients with moderate persistent asthma, 13 patients with severe persistent asthma, and 19 age and gender matched healthy controls. The HVEM mRNA expressions of all subjects were determined by real time PCR. Correlations between HVEM mRNA expression and fractional exhaled nitric oxide (FeNO), pulmonary function test values, total blood white cell count and differential, total immunoglobulin E (IgE) level, and Asthma Control Test (ACT) score were analyzed, respectively. The discrimination abilities of HVEM mRNA between different groups were tested using receiver operating characteristics (ROC) curve analyses.Results: This study showed the expressions of HVEM mRNA were significantly higher in the patients with severe and moderate persistent asthma than in patients with mild persistent asthma and healthy subjects (2.97 ± 1.23 vs. 1.17 ± 0.42 vs. 0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p < 0.001), but there was no significant difference between patients with mild persistent asthma and health controls (0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p = 0.557). HVEM mRNA expression at cut off point [1.01/NAPDH, area under the ROC curve (AUC) = 0.99] is sufficient to discriminate severe patients from mild-to-moderate patients, and at cut off point (0.93/NAPDH, AUC = 0.91) for discrimination of moderate-to-severe patients from mild ones, while at cut off point (0.76/NAPDH, AUC = 0.75) for discrimination of asthmatic patients from controls. Furthermore, HVEM mRNA expression was positively correlated with FeNO level (r = 0.524, p = 0.015), and total lymphocyte count (r = 0.426, p = 0.017) in patients with persistent asthma.Conclusions: HVEM gene expressions can be used as a potential biomarker for evaluating the severity of patients with persistent asthma.

Little is known about the prognostic ability of nCD64 in critically ill patients. This study aimed to assess the prognostic values of nCD64 in adult ICU patients with sepsis. A prospective cohort study was conducted at the ICU of Cho Ray Hospital in Vietnam between January 2019 to September 2020. All newly admitted 86 septic patients diagnosed based on sepsis-3 criteria were included. An evaluation of nCD64 was performed at admission (T0) and 48 h thereafter (T48). Delta nCD64 (nCD64 T48 - nCD64 T0), %delta nCD64 [(nCD64 T48 - nCD64 T0)/nCD64 T0 x 100%], APACHE II and SOFA scores were calculated and examined. Serum procalcitonin levels and white blood cell counts were documented. Spearman's rank correlation coefficient was used to test the correlation between nCD64 and severity scores. Receiver-operating characteristic (ROC) curve was performed to evaluate the predictive efficacy of the sepsis parameters. Patients with septic shock had significantly higher nCD64 levels than septic patients [3,568 (2,589; 5,999) vs. 1,514 (1,416;2,542) molecules/cell, p < 0.001]. nCD64 T0 and SOFA scores had a moderately positive linear correlation (R = 0.31, p = 0.004). In the survivor group, nCD64 levels significantly decreased within the first 48 h of admission (p < 0.001), while this trend was not statistically significant in the non-survivor group (p = 0.866). The area under the ROC curve (AUC) value of %delta nCD64 combined with APACHE II score (0.81) was higher than that of any other parameter alone or in combination with each other. The nCD64 index may serve as a valuable biomarker for predicting the course of sepsis. Monitoring changes in nCD64 during the initial 48 h of admission can aid in predicting the prognosis of septic patients. The use of a combination of the trends of nCD64 index in the first 48 h with APACHE II score would further enhance the predictive accuracy. More studies with longer follow-ups are needed to fully understand the implications of serial trend and kinetics of nCD64 in septic patients.

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Circulating miRNAs previously associated with androgen excess in women might be used as diagnostic biomarkers for polycystic ovary syndrome (PCOS). Models based on circulating miR-142-3p and miR-598-3p expression show good discrimination among women with and without PCOS, particularly when coupled with easily available measurements such as waist-to-hip ratio (WHR) and circulating LH-to-FSH (LH/FSH) ratios. The lack of standardization of the signs, methods, and threshold values used to establish the presence of the diagnostic criteria (hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology) complicates the diagnosis of PCOS. Certain biomarkers may help with such a diagnosis. We conducted a validation study to check the diagnostic accuracy for PCOS of several miRNAs that were associated with the syndrome in a small pilot study that had been previously carried out by our research group. This was a diagnostic test study involving 140 premenopausal women. We included 71 women with PCOS and 69 healthy control women in the study. Both groups were selected as to be similar in terms of body mass index. We used miRCURY LNA™ Universal RT microRNA PCR to analyse the five miRNAs that had shown the strongest associations with PCOS in a much smaller pilot study previously conducted by our group. We studied diagnostic accuracy using receiver operating characteristics (ROC) curve analysis. Only the expression of two miRNAs, miR-142-3p and miR-598-3p, of the five studied, was different between the women with PCOS and the non-hyperandrogenic controls. The diagnostic accuracy of the combination of these circulating miRNAs was good (area under the ROC curve (AUC) 0.801; 95% CI: 0.72-0.88) and was further improved when adding WHR (AUC 0.834, 95% CI: 0.756-0.912), LH/FSH ratio (AUC = 0.869, 95% CI: 0.804-0.934) or both (AUC = 0.895, 95% CI: 0.835-0.954). We developed several models by selecting different threshold values for these variables favouring either sensitivity or specificity, with positive and negative predictive values as high as 88% or 85%, respectively. Patients included here had the classic PCOS phenotype, consisting of hyperandrogenism and ovulatory dysfunction; hence, the present results might not apply to milder phenotypes lacking androgen excess. If confirmed in larger studies addressing different populations and PCOS phenotypes, these biomarkers may be useful to simplify the clinical diagnosis of this prevalent syndrome. This research was funded by Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (grants PI15/01686, PIE16/00050, PI18/01122 & PI21/00116) and co-funded by European Regional Development Fund 'A way to make Europe'. Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) and Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) are also initiatives of the Instituto de Salud Carlos III. The authors have no competing interests to disclose. N/A.

With the aging of the world population, the number of elderly patients with acute pancreatitis (AP) is increasing. The morbidity and mortality are significantly increased in elderly AP patients. This study aims to establish a model to predict the mortality of elderly patients with AP. The retrospective cohort study included 329 patients aged ≥65 with AP in the Medical Information Mart for Intensive Care-III (MIMIC-III) public database. The nomogram was used to visualize the prognostic model and the calibration curve to evaluate the stability and accuracy of the prediction model. Receiver operating characteristic (ROC) curves were drawn to show the area under the ROC curve (AUC) value which determine the predictive value. The Z test was utilized to compare the difference of AUCs. The independent risk factors for mortality in elderly patients with AP included age (P = .019), pulse oxygen saturation (SpO2) (P = .048), Sequential Organ Failure Assessment (SOFA) (P = .007), simplified acute physiological score II (SAPS II) (P = .013) and red cell distribution width (RDW) (P = .018). The AUC of the nomogram prognostic model based on the above 5 indicators predicting mortality in elderly patients with AP was 0.805, with a sensitivity of 65.2% and a specificity of 85.9%, and the calibration curve showed good performance. In addition, the AUCs of RDW, SOFA, and SAPS II alone were 0.618, 0.759, and 0.729, respectively. A nomogram prognostic model included age, SpO2, SOFA, SAPS II and RDW showed good performance in predicting the mortality of elderly patients with AP.