Areas Under The ROC Curve Research Articles

Evaluation of Hypoxia Markers in Critically Ill Patients Categorized by Their Burden of Organ Dysfunction: A Novel Approach to Detect Pathophysiological and Clinical Relevance in a Secondary Analysis of a Prospective Observational Study

In critically ill patients, compromised microcirculation causes tissue hypoxia, organ failure, and death. These pathophysiological processes occur particularly in patients with high illness severity, so reliable hypoxia biomarkers should reflect this in their occurrence. This secondary analysis of a prospective study categorized patients by their burden of organ dysfunction (BOD) using the cohort’s median initial sequential organ failure assessment (SOFA) score of 8 as a cutoff. The kinetic parameters of the hypoxia markers lactate and S-adenosylhomocysteine (SAH) were analyzed for correlation with organ dysfunction severity and mortality prediction. In low BOD patients, neither marker correlated with SOFA. In high BOD patients, lactate showed a moderate correlation and SAH showed a strong correlation. Lactate correlated with organ dysfunction in survivors but not in non-survivors, while SAH correlated strongly in non-survivors but not in survivors. In univariate logistic regression, lactate predicted mortality moderately in low BOD (areas under the receiver operating characteristic curves (AUROCs) 0.7–0.8) but poorly in high BOD patients (AUROCs 0.5–0.7). SAH’s prediction improved from poor to excellent (AUROCs 0.8–0.9) with higher BOD. Thus, SAH appears superior to lactate in the detection of organ dysfunction severity and mortality prediction in high BOD patients.

Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer.

Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

C-reactive protein-to-albumin ratio and systemic immune-inflammatory index as potential markers in distinguishing acute cerebellar infarction from benign paroxysmal positional vertigo.

To investigate the potential utility of the C-reactive protein-to-albumin ratio (CAR) and the systemic immune-inflammatory index (SII) as a biomarker in distinguishing between BPPV and acute cerebellar infarction (ACI) due to posterior inferior cerebellar artery (PICA) involvement. The data of 2545 patients registered in our hospital database between 2017 and 2024 with a diagnosis of vertigo were retrospectively analyzed and 102 patients with benign paroxysmal positional vertigo (BPPV) and 100 patients with ACI were included in the study. Mann-Whitney U test, Chi-square test, or Fisher's exact test were used to compare variables between the two groups. Receiver operating characteristic (ROC) curve analysis was performed to investigate the predictive value of the data. The CAR and SII values were significantly higher in the ACI group compared to the BPPV group (p<0.001, p<0.001, respectively). The areas under the ROC curve (AUC) were as follows: CAR=0.768 (95% CI, 0.705-0.832), white blood cell count (WBC)=0.735 (95% CI, 0.667-0.802), monocytes=0.622 (95% CI, 0.544-0.699), red cell distribution width-standard deviation (RDW-SD)=0.600 (95% CI, 0.522-0.678), SII=0.674 (95% CI, 0.599-0.748), and neutrophil-to-lymphocyte ratio (NLR)=0.687 (95% CI, 0.613-0.761). CAR and SII could be useful biomarkers to differentiate between ACI and BPPV in vertigo patients, but further validation is needed in larger studies.

The Best ECG Lead for Predicting the Risk of Drug-Induced Torsade De Pointes Using Corrected QT Interval: A Comparative Prognostic Study.

Torsade de pointes (TdP) is a deadly complication from drug-induced QT prolongation. Each of the 12 lead of an electrocardiogram (ECG) has a different length of QT interval, and thus might have a different performance in TdP prediction. This study aimed to determine the best ECG lead or set of leads in this regard. This is a comparative prognostic accuracy study using a two-gate data gathering design. The population in this study was from two sources, a case group (Patients who had drug-induced TdP, which were identified through a systematic Medline search) and a control group (those who overdosed on QT-prolonging drugs, which included patients who were under the consultation of Medical Toxicology Services). The areas under the receiver operating characteristic curve (AUROC) of heart rate-corrected QT (QTc) in each single ECG lead and of a mean/median QTc from a set of ECG leads (17 index test) in predicting the risk of TdP were calculated and compared with each other, trying to find the best lead for this propose. QTc Interval measurements were done by four investigators (Interrater reliabilities 0.95). Finally, we included 136 and 148 ECGs from TdP cases and controls, respectively. V3 lead had the highest frequency of longest QTc interval, among the leads. The lead having the longest QTc yielded the greatest AUROC in predicting TdP regardless of QT correction formulas (QTcFRA=0.9915, QTcRTH=0.9893, QTcBZT=0.9904). The mean QTc of 3 leads (lead II, plus any two of leads V2-V4), and a median QTc of 6 leads (I, II, aVF, V2, V4, V6) provided similar overall performance for TdP prediction (regardless of the type of QTc formula). The longest QTc provided the greatest AUROC in predicting drug-induced TdP, however, the longest QTc is not located in a fixed individual lead in any patient. A less time-consuming method with comparable performance to that of the longest QTc was to use a mean QTc from 3 leads (lead II, plus any two of leads V2-V4). The potential clinical impact of this finding needs to be verified in a prospective cohort study.

Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach.

As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF. A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records. After a series of data pre-processing in the electronic medical record system, several machine learning models were used to evaluate predictions of HF mortality. The outcomes of those potential risk factors were visualized by different statistical analyses. In total, 3871hF patients were enrolled. Logistic regression showed that intensive care unit (ICU) history within 1week (OR: 9.765, 95% CI: 6.65, 14.34; p-value < 0.001) and prothrombin time (OR: 1.193, 95% CI: 1.098, 1.296; <0.001) were associated with mortality. Similar results were obtained when we analyzed the data using Cox regression instead of logistic regression. Random forest, support vector machine (SVM), Adaboost, and logistic regression had better overall performances with areas under the receiver operating characteristic curve (AUROCs) of >0.87. Naïve Bayes was the best in terms of both specificity and precision. With ensemble learning, age, ICU history within 1week, and respiratory rate (BF) were the top three compelling risk factors affecting mortality due to HF. To improve the explainability of the AI models, Shapley Additive Explanations methods were also conducted. Exploring HF mortality and its patterns related to clinical risk factors by machine learning models can help physicians make appropriate decisions when monitoring HF patients' health quality in the hospital.

Serologic predictors of thyroid immune-related adverse events during immune checkpoint inhibitors therapy: a retrospective cohort study

Rationale: Immune-related adverse events (irAEs) are a specific type of drug toxicity that can occur in cancer patients undergoing immunotherapy with immune checkpoint inhibitors (ICIs). Endocrine irAEs rank the 3rd after the skin and gastrointestinal ones. Clinical course of endocrine irAEs usually results in irreversible damage of the glands function. Prevailing thyroid disorders among endocrine irAEs, their reactivity compared to that of autoimmune thyroiditis, the risk of potential temporary or complete withdrawal of the immunotherapy would make it necessary to search for markers able to identify the most susceptible patient groups. Aim: To evaluate an association between baseline laboratory parameters (hormonal, biochemical, and serological) in patients with malignant solid neoplasms before the first course of anti-tumor immunotherapy with ICIs in monotherapy and the subsequent development of thyroid irAEs. Methods: In this retrospective cohort we analyzed medical files from 102 adult patients (50 (49%) men, median age 60 years) with confirmed solid malignant tumors who were treated in two specialized in-patient departments from January 2020 to February 2022. Their baseline blood samples for subsequent evaluation of thyroid function, carbohydrate and calcium metabolism, as well as to exclude adrenal insufficiency were taken before the initiation of the first course of specific immunotherapy with ICIs. Thereafter, the patients were monitored for any registered irAE for up to 34 months from the beginning of the antitumor immunotherapy with ICIs. Results: Thyroid irAEs were registered in 13/102 (12.7%) patients. Only two markers were significantly associated with the development of thyroid disorders under immunotherapy with ICIs: baseline levels of anti-thyroperoxidase antibodies (TPOAb) ≥ 7.54 IU/mL (reference range (RR) 0–5.6) and anti-thyroglobulin antibodies (TgAb) ≥ 16.45 IU/mL (RR 0–115) (p 0.001). For TPOAb ≥ 7.54 IU/mL and TgAb ≥ 16.45 IU/mL, the areas under the ROC curve (AUC) were 0.828 [95% confidence interval (CI) 0.678–0.979] and 0.875 [95% CI 0.742–1.000], diagnostic sensitivity was 75% [95% CI 48–92] and 92% [95% CI 64–100], diagnostic specificity 92% [95% CI 85–96] and 84% [95% CI 77–86], prognostic values of the positive result 69% [95% CI 44–85] and 58% [95% CI 40–63], and prognostic values of the negative results 94% [95% CI 87–98] and 98% [95% CI 90–100], respectively. Conclusion: Baseline levels of TPOAb and TgAb may serve as markers for the risk of thyroid irAEs in cancer patients with solid malignancies who are planned to receive anti-tumor immunotherapy with ICIs.

Prognostic Value of Systemic Inflammatory Markers in Elderly Patients with Tumor-associated Venous Thromboembolism.

Elderly tumor patients are more prone to venous thrombotic events than nontumor patients. To investigate the relationship between systemic inflammatory markers and overall survival (OS) in elderly patients with tumorassociated venous thromboembolism (TAVTE). And to evaluate the prognostic value of combined lymphocyte platelettolymphocyte ratio (PLR) with neoplasm metastasis in elderly patients. A prospective study was conducted. A total of 172 elderly patients with TAVTE admitted to the hospital from January 2017 to December 2019 were included in the study, which were followed up for 2 years. Clinical and laboratory data were collected. All-cause mortality within after discharge were followed up. The optimal cutoff values of neutrophiltolymphocyte ratio (NLR), PLR, systemic immuneinflammation index (SII), and monocyte to highdensity lipoprotein ratio (MHR) for predicting efficacy and prognosis were determined according to receiver operating characteristic (ROC) curve and the areas under the ROC curve (AUC). Kaplan-meier curves were used to analyze the survival time. Univariate and multivariate COX logistic regression analyses were used to analyze the independent predictors of OS in elderly patients with TAVTE. The cut-off values for NLR, PLR, SII, and MHR were 3.375, 274.63, 399.73 and 0.58, respectively. And the area under the curve (AUC) was 0.639(95%CI: 0.556-0.721), 0.628(95%CI:0.544-0.712), 0.595(95%CI:0.510-0.680) and 0.596(95%CI: 0.510-0.683). Survival analysis showed that OS was longer in the NLR≤3.375 group (181.07 weeks, 95% CI: 150.11 ~ 212.03) than in the NLR >3.375 group (108.95 weeks, 95%CI: 90.38 ~ 127.51) (P = 0.005). The OS of PLR≤274.63 group (160.40 weeks, 95%CI: 138.41 ~ 182.38) was longer than that of PLR >274.63 group (43.85 weeks, 95%CI: 34.08 ~ 53.63) (P < 0.001). The OS of SII≤399.73 group (176.62 weeks, 95%CI:147.26 ~ 205.97) was longer than that of SII>399.73 group (126.55 weeks,95%CI: 105.04 ~ 148.05) (P = 0.012). The OS was longer in the MHR≤0.58 group (156.24 weeks, 95% CI: 127.05-185.43) than in the MHR>0.58 group (108.11 weeks, 95%CI:86.85-129.38) (P = 0.011). Univariate and multivariate Cox analysis showed that tumor metastasis and PLR>274.63 were independent predictors of the lower OS in elderly patients with TAVTE (P < 0.001). According to the tumor metastasis and the cut-off value of PLR, a combined scoring system MPS (Metastasis and PLR System) was designed. The OS of the 0, 1 and 2 score groups was 184.08 weeks (95%CI:158.11-210.05), 82.60 weeks (95%CI:61.57-103.64), and 23.83 weeks (95%CI: 9.575-38.09) (P < 0.001), respectively. Our findings suggest that the systemic inflammatory markers (NLR, PLR, SII, MHR) may have predictive value for all-cause mortality in elderly patients with TAVTE. PLR combined with tumor metastasis may be an effective index to predict the prognosis of elderly patients with TAVTE.

Comparison of apparent diffusion coefficients of resectable mid‑high rectal adenocarcinoma and distal paracancerous tissue.

Paracancerous tissues actively communicate with the tumor and undergo molecular alterations associated with tumorigenesis. Apparent diffusion coefficient (ADC) can help distinguish between rectal adenocarcinoma (RA), tumor-adjacent and tumor-distant tissues. Preoperative determining optimal distal resection margin (DRM) is crucial for formulating surgical options. The present study aimed to assess ADC differences between RA and 1 cm-layer distal paracancerous tissues, providing a potential reference basis for preoperatively determining optimal DRM. A total of 110 consecutive patients with mid-high RA undergoing preoperative diffusion-weighted imaging were included. ADCs of RA and distal paracancerous tissues located ~1, 2 and 3 cm from the tumor margin (defined as D1, D2 and D3, respectively) were measured using five b-value pairs (0 and 50; 0 and 100; 0 and 800; 0 and 1,000; and 0 and 1,500 sec/mm2). Differences in ADCs between RA, D1, D2 and D3 were compared using the Friedman test with a post hoc Bonferroni correction. Variables that demonstrated statistical differences in multiple pairwise comparisons underwent receiver operating characteristic (ROC) analysis to assess diagnostic performance of ADCs in distinguishing between tissues. ADC at all b-value pairs demonstrated satisfactory performance in distinguishing RA from D1, D2 and D3 [areas under the ROC curves (AUCs), 0.838 to 0.996)]. When the maximum b-value was ≥800 sec/mm2, the ADC of D1 was significantly lower compared with those of D2 and D3 (P<0.001). ADC exhibited an optimal performance in differentiating D1 from D2 at b-values of 0 and 800 sec/mm2, and D1 from D3 at b-values of 0 and 1,000 sec/mm2 (AUCs: 0.652 and 0.692, respectively). However, ADCs of D2 and D3 demonstrated no differences at all b-value pairs (all P>0.05). In conclusion, ADC may distinguish RA from D1, D2 and D3, and D1 from D2/D3, but cannot distinguish between D2 and D3.

Predictive validity of interleukin 6 (IL-6) for the mortality in critically ill COVID-19 patients with the B.1.617.2 (Delta) variant in Vietnam: a single-centre, cross-sectional study

ObjectivesTo investigate the serum IL-6 levels and their rate of change in predicting the mortality of critically ill patients with COVID-19 in Vietnam.DesignA single-centre, cross-sectional study.SettingAn Intensive Care Centre for...

Application value of strain elastography in testicular injury assessment after torsion.

To evaluate the capability of strain elastography (SE) in assessing the degree of testicular injury after torsion. In total, 50 rabbits were divided into four groups according to different degrees of testicular torsion (TT) at 0°, 180°, 360°, and 720°. For each animal, according to the tissue stiffness distribution, an SE score and strain ratio (SR) were obtained. With the histopathological results as the reference, the correlation coefficients of the apoptotic index and SE score or SR were calculated, and the receiver operating characteristic (ROC) curves were created to assess the capability of SE in assessing the degree of testicular injury. A significant positive correlation was found between the apoptotic index and SE score, as well as the SR, with corresponding correlation coefficients of 0.70 (<0.001) and 0.68 (P = 0.001), respectively. The areas under the ROC curves (AUCs) of the SE score and SR for identifying ischemia/hypoxia injury were found to be 0.81 (95% CI, 0.71-0.93) and 0.73 (95% CI, 0.60-0.86), respectively. For identifying irreversible damage, the AUCs were 0.69 (95% CI, 0.56-0.83) and 0.71 (95% CI, 0.59-0.84) for the SE score and SR, respectively. SE scores exhibited good diagnostic capability for detecting ischemia/hypoxia injury after TT. In early identification of severe injury/necrosis following TT, SE demonstrated some value but was not ideal.

Measurement Properties of the Patient Health Questionnaire–15 and Somatic Symptom Scale–8

The subjective experience of somatic symptoms is a key concern throughout the health care system. Valid and clinically useful instruments are needed. To evaluate the measurement properties of 2 widespread patient-reported outcomes: the Patient Health Questionnaire-15 (PHQ-15) and Somatic Symptom Scale-8 (SSS-8). Medline, PsycINFO, and Web of Science were last searched February 1, 2024. English-language studies reporting estimates pertaining to factor analysis, taxometric analysis, internal consistency, construct validity, mean scores in relevant groups, cutoffs, areas under the receiver operating characteristic curves (AUROCs), minimal clinically important difference, test-retest reliability, or sensitivity to change. Search hits were reviewed by independent raters. Cronbach α, Pearson r, means, and between-group effect sizes indicative of sensitivity to change were pooled in random-effects meta-analysis. Study quality was assessed using 3 instruments. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 reporting guideline. Comprehensive overview of evidence pertaining to the measurement properties of the PHQ-15 and SSS-8. A total of 305 studies with 361 243 participants were included. Most concerned routine care (178 studies) and the general population (27 studies). In factor analyses, both scales reflected a combination of domain-specific factors (cardiopulmonary, fatigue, gastrointestinal, pain) and a general symptom burden factor. The pooled PHQ-15 α was 0.81 (95% CI, 0.80-0.82), but with low item-total correlations for items concerning menstrual problems, fainting spells, and sexual problems (item-total correlations <0.40), and the SSS-8 α was 0.80 (0.77-0.83). Pooled correlations with other measures of somatic symptom burden were 0.71 (95% CI, 0.64-0.78) for the PHQ-15 and 0.82 (95% CI, 0.72-0.92) for the SSS-8. Reported AUROCs for identification of somatoform disorders ranged from 0.63 (95% CI, 0.50-0.76) to 0.79 (95% CI, 0.73-0.85) for the PHQ-15 and from 0.71 (95% CI, 0.66-0.77) to 0.73 (95% CI, 0.69-0.76) for the SSS-8. The minimal clinically important difference on both scales was 3 points. Test-retest reliability could not be pooled and was inconsistent for the PHQ-15 (PHQ-15: r = 0.65-0.93; ICC, 0.87; SSS-8: r = 0.996, ICC = 0.89). The PHQ-15 showed tentative sensitivity to change (g = 0.32; 95% CI, 0.08-0.56), but data for the SSS-8 were lacking. In this systematic review and meta-analysis, findings supported use of the PHQ-15 and SSS-8 for the assessment of symptom burden, but users should be aware of the complex, multifactorial structures of these scales. More evidence is needed concerning longitudinal measurement properties.

What is the optimal outcome for evaluating the triage Systems? Insights from a prospective observational study

BackgroundCurrently, there is no universally accepted gold standard outcome for assessing the effectiveness of the Triage Systems. This study aimed to comprehensively evaluate and compare various outcomes utilized in triage studies. MethodsA prospective observational study was conducted at the Emergency Department (ED) of Merano Hospital from June 1 to December 31, 2023. We assessed the predictive capability of the Manchester Triage System (MTS) across multiple outcomes using areas under the receiver operating characteristic curve (AUROC), along with their corresponding 95% confidence intervals (95% CI), and frequency distributions. ResultsThe MTS demonstrated strong performance concerning the most objective outcomes, such as mortality (at 72 h: AUROC 0.914; 95 %CI: 0.815–1; at 7 days: 0.845; 95 %CI: 0.729–0.965; at 30 days: 0.794; 95 %CI: 0.706–0.881), admission to the intensive care unit (0.831; 95 %CI: 0.763–0.899), and need for life-saving interventions (0.870; 95 %CI: 0.806–0.934). Additionally, outcomes such as urgency status and clinical priority, as judged by physicians, exhibited excellent performance and optimal frequency distribution. ConclusionsThe performance of the MTS varied significantly depending on the specific outcome under evaluation. Currently, no single outcome appears superior to others, nor does any seem poised to serve as a potential gold standard for the assessment of triage systems. It is advisable for dedicated working groups to convene and reach a consensus on the most effective outcomes for evaluating the performance of MTS and other triage systems. This should be accomplished through a systematic, standardized, and transparent approach, grounded in the best available evidence.

Improving Deep Learning Models for Pediatric Low-Grade Glioma Tumours Molecular Subtype Identification Using MRI-based 3D Probability Distributions of Tumour Location.

Purpose: Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and augment the models using tumour location probability maps. Materials and Methods: MRI FLAIR sequences of 214 patients (110 male, mean age of 8.54 years, 143 BRAF fused and 71 BRAF V600E mutated pLGG tumours) from January 2000 to December 2018 were included in this retrospective REB-approved study. Tumour segmentations (volumes of interest-VOIs) were provided by a pediatric neuroradiology fellow and verified by a pediatric neuroradiologist. Patients were randomly split into development and test sets with an 80/20 ratio. The 3D binary VOI masks for each class in the development set were combined to derive the probability density functions of tumour location. Three pipelines for molecular diagnosis of pLGG were developed: location-based, CNN-based, and hybrid. The experiment was repeated 100 times each with different model initializations and data splits, and the Areas Under the Receiver Operating Characteristic Curve (AUROC) was calculated, and Student's t-test was conducted. Results: The location-based classifier achieved an AUROC of 77.9, 95% confidence interval (CI) (76.8, 79.0). CNN-based classifiers achieved an AUROC of 86.1, 95% CI (85.0, 87.3), while the tumour-location-guided CNNs outperformed the other classifiers with an average AUROC of 88.64, 95% CI (87.6, 89.7), which was statistically significant (P-value .0018). Conclusion: Incorporating tumour location probability maps into CNN models led to significant improvements for molecular subtype identification of pLGG.

Accuracy of second and third molar maturity indices, Olze, Haavikko, and Demirjian methods for 14- and 16-year-old age thresholds assessment in Croatian children and adolescents.

This study explores the reliability of four established legal age threshold estimation approaches in a Croatian sample. We applied Haavikko stages, Demirjian stages, Olze's third molar eruption stages, and second and third molar maturity indices measurement in 593 orthopantomograms of Croatian children and adolescents aged 11.00-20.99years old. The left mandibular second and third molar were assessed. Logistic regression analysis was conducted to test the significance of predictive variables. Logistic Receiver operating characteristic (ROC) curves were performed to evaluate the classification ability of variables for estimating 14- and 16-year-old thresholds. The areas under the ROC curve (AUC), accuracy (Acc), sensitivity (Se), specificity (Sp), Positive Likelihood Ratio (LR +), Negative Likelihood Ratio (LR-), and Bayes post-test probability (Bayes PTP) were calculated to evaluate classification performance. Results suggest that the combination of I2M&I3M is the best classifier for the 14-year-old threshold (AUC = 0.879); for males alone, I2M is an even better classifier (AUC = 0.881). The highest Acc 80.1% (95%CI, 75.9%-83.9%), Bayes PTP 86.5% (95%CI, 82.8%-89.7%) and Sp 88.9% (95%CI, 83.0%-93.3%) were by I3M < 0.81 & I2M < 0.03 in total samples; the highest Acc 86.1% (80.6%- 90.6%), Bayes PTP 87.2% (95%CI, 81.7%- 91.4%) and Sp 87.8% (95%CI, 78.2%- 94.3%) were by I2M < 0.01 in males, Acc of Haavikko Ac and Demirjian H stage in second molar is very close with slightly lower Bayes PTP and Sp. I3M is a good classifier for 16-year-old threshold (AUC = 0.889). The cut-off value I3M < 0.34 can be used to classify the 16-year-old threshold with Acc of 80.6% (95%CI, 77.2%-83.7%), Sp of 83.4% (95%CI, 79.0%-87.3%), and 81.7% (95%CI, 78.4%-84.8%) Bayes PTP. In conclusion, to classify the 14-year-old threshold, a pair of cut-off values I3M < 0.81 & I2M < 0.03 can be used in Croatian females; I2M < 0.01, Demirjian H stage, Haavikko Ac stage in second molar, and the pair I3M < 0.81 & I2M < 0.03 can all be used in Croatian males. I3M < 0.34 can classify the 16-year-old threshold in Croatian populations.

. Predictive Value of Pre-delivery Serum β-human Chorionic Gonadotropin, Fibrinogen, and Homocysteine for Hypertensive Disorders of Pregnancy

Introduction: it was to investigate the relationship between pre-delivery serumβ-human chorionic gonadotropin (β-HCG), fibrinogen (FIB), and homocysteine (HCY) with hypertensive disorder complicating pregnancy (HDCP). Materials and Methods: 200 HDCP patients and 150 normal pregnant women were selected as study subjects. Fasting cubital venous blood samples were collected to measure serum triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL), as well as β-HCG, FIB, and HCY levels. Pearson correlation analysis examined relationship between β-HCG, FIB, HCY, and HDCP. Receiver operating characteristic (ROC) curve analysis evaluated the predictive value of these indicators for HDCP. Multiple logistic regression analysis identified risk factors (RFs) for HDCP.Results: serum TG, TC, HDL, LDL, FIB,β-HCG, and HCY were greatly elevated in the HDCP group versus control group (CG) (P&lt;0.05). Serumβ-HCG, FIB, and HCY showed notable positive correlations with HDCP (r = 0.935, 0.547, 0.811; P&lt;0.05), and the areas under the ROC curve (AUC) for predicting HDCP based on serum β-HCG, FIB, HCY, and their combination were 0.743, 0.659, 0.801, and 0.886, respectively (P&lt;0.05). Lipid indicators and serum levels of FIB, β-HCG, and HCY were RFs for HDCP.Conclusion:pregnant women with HDCP exhibited markedly elevated serum lipid levels and FIB, β-HCG, and HCY levels before delivery, which can serve as predictive indicators for HDCP.

Predicting pathological complete response following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer using merged model integrating MRI-based radiomics and deep learning data

BackgroundTo construct and compare merged models integrating clinical factors, MRI-based radiomics features and deep learning (DL) models for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).MethodsTotally 197 patients with LARC administered surgical resection after nCRT were assigned to cohort 1 (training and test sets); meanwhile, 52 cases were assigned to cohort 2 as a validation set. Radscore and DL models were established for predicting pCR applying pre- and post-nCRT MRI data, respectively. Different merged models integrating clinical factors, Radscore and DL model were constituted. Their predictive performances were validated and compared by receiver operating characteristic (ROC) and decision curve analyses (DCA).ResultsMerged models were established integrating selected clinical factors, Radscore and DL model for pCR prediction. The areas under the ROC curves (AUCs) of the pre-nCRT merged model were 0.834 (95% CI: 0.737–0.931) and 0.742 (95% CI: 0.650–0.834) in test and validation sets, respectively. The AUCs of the post-nCRT merged model were 0.746 (95% CI: 0.636–0.856) and 0.737 (95% CI: 0.646–0.828) in test and validation sets, respectively. DCA showed that the pretreatment algorithm could yield enhanced clinically benefit than the post-nCRT approach.ConclusionsThe pre-nCRT merged model including clinical factors, Radscore and DL model constitutes an effective non-invasive tool for pCR prediction in LARC.

Iodine-Based Dual-Energy Computed Tomography After Mechanical Thrombectomy Predicts Secondary Neurologic Decline from Cerebral Edema After Severe Stroke.

Patients with severe stroke are at high risk of secondary neurologic decline (ND) from the development of malignant cerebral edema (MCE). However, early infarcts are hard to diagnose on conventional head computed tomography (CT). We hypothesize that high-energy (190keV) virtual monochromatic imaging (VMI) from dual-energy CT (DECT) imaging enables earlier detection of ND from MCE. Consecutive patients with severe stroke with National Institute of Health Stroke Scale (NIHSS) scores > 15 and DECT within 10h of mechanical thrombectomy from May 2020 to March 2022 were included. We excluded patients with parenchymal hematoma type 2 transformation. Retrospective analysis of clinical and novel variables included the VMI Alberta Stroke Program Early CT Score (ASPECTS), total iodine content, and VMI infarct volume. The primary outcome was secondary ND, defined using a composite outcome variable of clinical worsening (increase in NIHSS score ≥ 4 or decrease in Glasgow Coma Scale score > 2) or malignant radiographical edema (midline shift ≥ 5mm at the level of the septum pellucidum). Fisher's exact test and Wilcoxon's test were used for univariate analysis. Logistic regression was used to develop prediction models for categorical outcomes. Eighty-four patients with severe stroke with a median age of 67.5 (interquartile range [IQR] 57-78) years and an NIHSS score of 22 (IQR 18-25) were included. Twenty-nine patients had ND. The VMI ASPECTS, total iodine content, and VMI infarct volume were associated with ND. The VMI ASPECTS, VMI infarct volume, and total iodine content were predictors of ND after adjusting for age, sex, initial NIHSS score, and tissue plasminogen activator administration, with areas under the receiver operating characteristic curve (AUROC) of 0.691 (95% confidence interval [CI] 0.572-0.810), 0.877 (95% CI 0.800-0.954), and 0.845 (95% CI 0.750-0.940). By including all three predictors, the model achieved an AUROC of 0.903 (95% CI 0.84-0.97) and was cross-validated by the leave one out method, with an AUROC of 0.827. The VMI ASPECTS and VMI infarct volume from DECT are superior to the conventional CT ASPECTS and are novel predictors for secondary ND due to MCE after severe stroke. Clinical trial registration ClinicalTrials.gov identifier: NCT04189471.

Serum proteomic analysis identified ITIH4 as a potential novel biomarker for feline infectious peritonitis

Feline infectious peritonitis (FIP) is a fatal feline disease. At present, the reference standard for FIP diagnosis is immunohistochemistry (IHC) of organs, but this method involves high time-related costs, invasive sampling procedures and professional requirements. Serological detection is a common auxiliary method for diagnosing diseases. As a result, we assessed the changes in the serum proteome of FIP patients with the aim of identifying novel specific serum biomarkers that could aid in the clinical diagnosis of FIP. Pre- and postinfection groups were compared and 92 differentially expressed proteins (DEPs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEPs revealed that the enriched GO terms and KEGG pathways among the DEPs were immune activation, peptidase regulator activity and the complement and coagulation cascade pathways. The level of peptidase regulator interalpha-trypsin inhibitor heavy chain 4 (ITIH4) in cat serum was significantly correlated with FIP. The areas under the ROC curve (AUCs) of full-length ITIH4 (f-ITIH4) and cleaved ITIH4 (c-ITIH4) expression were 0.922 and 1.000, respectively, which allowed the discrimination of FIP cats from healthy cats. These results suggest that ITIH4 may be a potential serum biomarker for detecting early FIP. SignificanceFIP causes fatal disease in cats of almost all ages, and there is currently no effective vaccine or treatment for FIP. Therefore, early diagnosis is extremely important for disease prevention and control. The results of the model and clinical samples revealed that ITIH4 was significantly increased in the serum of FIP cats. This study is the first to propose ITIH4 as a diagnostic biomarker in cats with FIP and our results suggest that serum ITIH4 levels might identify cats with FIP during the early stage.

Quantitative ultrasound assessment of fatty infiltration of the rotator cuff muscles using backscatter coefficient

BackgroundTo prospectively evaluate ultrasound backscatter coefficients (BSCs) of the supraspinatus and infraspinatus muscles and compare with Goutallier classification on magnetic resonance imaging (MRI).MethodsFifty-six participants had shoulder MRI exams and ultrasound exams of the supraspinatus and infraspinatus muscles. Goutallier MRI grades were determined and BSCs were measured. Group means were compared and the strength of relationships between the measures were determined. Using binarized Goutallier groups (0–2 versus 3–4), areas under the receiver operating characteristic curves (AUROCs) were calculated. The nearest integer cutoff value was determined using Youden’s index.ResultsBSC values were significantly different among most Goutallier grades for the supraspinatus and infraspinatus muscles (both p < 0.001). Strong correlations were found between the BSC values and Goutallier grades for the supraspinatus (τb = 0.72, p < 0.001) and infraspinatus (τb = 0.79, p < 0.001) muscles. BSC showed excellent performance for classification of the binarized groups (0–2 versus 3–4) for both supraspinatus (AUROC = 0.98, p < 0.0001) and infraspinatus (AUROC = 0.98, p < 0.0001) muscles. Using a cutoff BSC value of −17 dB, sensitivity, specificity, and accuracy for severe fatty infiltration were 87.0%, 90.0%, and 87.5% for the supraspinatus muscle, and 93.6%, 87.5%, and 92.7% for the infraspinatus muscle.ConclusionBSC can be applied to the rotator cuff muscles for assessment of fatty infiltration. For both the supraspinatus and infraspinatus muscles, BSC values significantly increased with higher Goutallier grades and showed strong performance in distinguishing low versus high Goutallier grades.Relevance statementFatty infiltration of the rotator cuff muscles can be quantified using BSC values, which are higher with increasing Goutallier grades.Key PointsUltrasound BSC measurements are reliable for the quantification of muscle fatty infiltration.BCS values increased with higher Goutallier MRI grades.BCS values demonstrated high performance for distinguishing muscle fatty infiltration groups.Graphical

Siglec-15 expression in diffuse gliomas and its correlation with MRI morphologic features and apparent diffusion coefficient.

Sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) enhances tumor immune escape and leads to tumor growth. To investigate the expression of Siglec-15 in diffuse gliomas and its correlation with tumor magnetic resonance imaging (MRI) features. This study included 57 patients with gliomas. Morphological MRI features, including the largest tumor diameter, enhancement category, location, calcification, cysts, and hemorrhage, were visually rated. Apparent diffusion coefficient (ADC) values were calculated in tumor region. MRI morphologic features and ADC were compared between patients with positive and negative Siglec-15 expression. Receiver operating characteristic (ROC) curves were further constructed to assess the diagnostic performance. Siglec-15 was expressed in immunocytes, such as macrophages in the peritumoral area. Siglec-15 expression was positive in 20/57 (35.09%) patients, with higher expression in patients with IDH-mutant gliomas and lower grade gliomas. The tumor diameter was significantly smaller in patients with positive Siglec-15 expression than in those with negative expression for all patients (P = 0.017) and for patients with IDH-mutant gliomas (P = 0.020). Moreover, ADC values of the tumor were significantly higher in patients with positive Siglec-15 expression than in those with negative expression for all patients (P = 0.027). The areas under the ROC curve (AUCs) of the diameter and ADC were 0.702 and 0.686, respectively. A combination of these two parameters generated an improved AUC of 0.762. Siglec-15 was expressed in immunocytes such as macrophages in the peritumoral area, with a positive rate of 35.09%. Positive Siglec-15 expression in diffuse gliomas was correlated with smaller tumor size and higher ADC values.