Receiver Operating Characteristic Research Articles

Development and validation of a tumor size-stratified prognostic nomogram for patients with gastric signet ring cell carcinoma.

Gastric signet ring cell carcinoma (GSRC) is a rare malignancy without a commonly acknowledged prognostic assessment and treatment system. This study aimed to determine the optimal cut-off value of tumor size (TS), and construct a prognostic nomogram in combination with other independent prognostic factors (PFs) to predict 3year and 5year overall survival (OS) in GSRC patients. From the Surveillance, Epidemiology, and End Results (SEER) database, this study collected 4744 patients diagnosed with GSRC. These patients were randomized into a training cohort (n = 2320,) and a validation cohort (n = 1142). A restricted cubic spline (RCS) was used to determine the cut-off value for TS, and univariate and multivariate Cox regression analyses were performed in the training cohort to identified significant predictors. A prognostic nomogram was constructed to predict OS at 3 and 5years. Concordance index (C index), receiver operating characteristics curve (ROC curve), area under curve (AUC), and calibration curve were used to test the predictive accuracy of the model. A non-linear relationship was observed between TS and the risk of OS in GSRC, with TS thresholds at 4.4cm and 9.6cm. Survival was significantly lower in GSRC patients with TS > 4.4cm. Age, marriage, chemotherapy, surgery, TS, SEER stage, regional lymph node status, and total number were independent predictors of OS. The C index in the training cohort was 0.748, and the AUC values for both 3- and 5-year OS were higher than 0.80. Similar results were observed in the validation cohort. In addition, the calibration curves showed good agreement between the predicted 3year and 5year OS and the actual OS. TS is a key prognostic factor for patients with GSRC, and patients with a TS of 4.4-9.6cm and > 9.6cm may have a poorer prognosis than those with a TS of < 4.4cm. The TS-stratified nomogram we constructed and validated has favorable accuracy and calibration precision, and may be helpful in predicting the survival rate of patients.

Diagnosis of Enlarged Vestibular Aqueduct Using Wideband Tympanometry.

Background: Wideband tympanometry (WBT) has the potential to distinguish various mechanical middle ear and inner ear pathologies noninvasively. This study investigated the diagnostic value of WBT in the diagnosis of enlarged vestibular aqueduct (EVA). Methods: The absorbance and resonance frequency (RF) of patients with EVA (40 ears, 25 patients) and matched population controls (39 ears, 28 subjects) were compared, alongside receiver operating characteristic (ROC) analysis. Correlations between VA width and RF were also examined. Results: Patients with EVA had higher absorbance at low frequencies (226-917 Hz) and lower absorbance at high frequencies (2520-4896 Hz) compared to controls. The RF of the EVA group was significantly lower versus controls (751 [391-1165] vs. 933 [628-1346] Hz). The ROC analysis revealed area under the curve values of 0.771 and 0.801, respectively, for absorbance and RF. RF had a sensitivity, specificity, positive predictive value, and negative predictive value of 74.4%, 82.5%, 76.7%, and 80.6%, respectively, for diagnosing EVA. In the EVA group, the VA midpoint width (r = -0.334) and VA petrous width (r = -0.402) both significantly correlated with RF. Conclusions: Our findings support the utility of WBT for diagnosing EVA, with RF as the optimal index used.

Analyzing the Distribution Patterns of Endemic Quercus vulcanica (Boiss. et Heldr. ex) Kotschy in Türkiye Under Climate Change Using Ensemble Modeling

Quercus vulcanica (Boiss. et Heldr. ex) Kotschy (Kasnak oak), one of the 18 Quercus species naturally distributed in Anatolia, is an endemic species with a restricted distribution range. In accordance with the International Union for the Conservation of Nature (IUCN) Red List of Endangered Species classification, Quercus vulcanica is designated as a species of low risk (LC: Least Concern). However, it is predicted that the habitat of Quercus vulcanica will narrow and that the species will become endangered as a result of potential climate change scenarios in the future. The aim of this study was to estimate the temporal and spatial distribution of Quercus vulcanica in Anatolia during the LGM, as well as to examine the impact of present and future climate changes on the species. In this context, principal component analysis was applied to 19 bioclimatic variables of the Community Climate System Model Version 4 (CCSM4) climate model, with nine variables identified for use in modeling. Habitat suitability was estimated using the Biodiversity Modeling (BIOMOD) ensemble modeling method, which combines the results of nine different algorithms through the R package ‘biomod2’, applying both committee averaging and weighted average approaches. To evaluate the performance of the models, the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC), True Skill Statistics (TSS), KAPPA and Boyce Index were calculated. The contributions of the environmental variables were determined on a per-algorithm-model basis. The results of the analyses show that the bioclimatic variables that contribute the most to the distribution of the species are Bio8. The modeling results show that Quercus vulcanica is capable of occupying suitable habitat areas across the majority of Anatolia during the Last Glacial Maximum (LGM). It is anticipated that future projections will indicate a notable reduction in the extent of suitable habitat for the species, with the remaining areas confined to the vicinity of the Ilgaz Mountains, Köroğlu Mountains and Bolkar Mountains. Given the increasing destruction that Quercus vulcanica, an endemic plant, will be adversely affected by as a result of human impacts and climate change, it is of the highest importance to develop adaptation strategies with a view to protecting the species’ habitat and the sustainability of the species.

Prognostic significance of creatine kinase in resected pancreatic cancer.

Creatine kinase (CK) levels decrease with cancer progression and muscle wasting, but its association with pancreatic ductal adenocarcinoma (PDAC) remains unclear. The aim of this study was to investigate CK as a prognostic biomarker and surrogate marker for muscle mass in patients with PDAC. A retrospective analysis of 476 patients with PDAC was conducted. CK levels were categorized into low and high groups using receiver-operating characteristic (ROC) curve analysis. Among the 476 patients, 200 (42.0%) and 276 (58.0%) were classified into the low and high CK groups, respectively. The low CK group had significantly poorer overall survival (p < .001) and recurrence-free survival (p < .001) compared to the high CK group. Multivariate analysis identified low CK as an independent poor prognostic factor (p < .001). The low CK group had significantly lower skeletal muscle index (p = .048) than the high CK group; however, the difference was slight and not significantly associated with sarcopenia. Additionally, combined risk assessment incorporating CK and resectability facilitated a more nuanced prognostic stratification. CK served as a reliable prognostic marker independent from resectability but was less effective as a marker for sarcopenia in PDAC.

Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on 18F-FDG PET/CT

ObjectiveTo investigate the value of 18F-FDG PET/CT-based intratumoral and peritumoral radiomics in predicting the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer.Methods190 patients who met the inclusion and exclusion criteria from 2017 to 2022 were studied. Features were extracted from the PET/CT intratumoral and peritumoral regions, feature selection was performed through the correlation analysis, t-tests, and least absolute shrinkage and selection operator regression (LASSO). Four classifiers, support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), and naive bayes (NB) were used to build the prediction models. The receiver operating characteristic (ROC) curves were plotted to measure the predictive performance of the models. Concurrent stratified analysis was conducted to establish subtype-specific features for each molecular subtype.ResultsCompared to intratumoral features alone, intratumoral + peritumoral features achieved higher AUC values in each classifier. The SVM model constructed with intratumoral + peritumoral features achieved the highest AUC values in both the train and test set (train set: 0.95 and test set: 0.83). Subtype-specific features improve performance in predicting the efficacy of NAC (luminal group: 0.90; HER2 + group: 0.86; triple negative group: 0.92).ConclusionIntratumoral and peritumoral radiomics models based on 18F-FDG PET/CT can reliably forecast the efficacy of NAC, thereby assisting clinical decision-making.

Erythrocytic α-Synuclein in Parkinson’s Disease and Progressive Supranuclear Palsy—A Pilot Study

Background/Objectives: The current research examines the accuracy of α-synuclein in RBCs as a diagnostic biomarker for PD and PSP, despite their distinct molecular etiologies. Methods: We used ELISA to measure total, oligomeric, and p129-α-synuclein levels in erythrocytes from 8 PSP patients, 19 PD patients, and 18 healthy controls (HCs). The classification performances of RBC α-synuclein levels were investigated by receiver operator characteristic (ROC) curve. We also evaluated a possible correlation between RBC α-synuclein level and the biological and clinical features of our cohorts. Results: RBC total α-synuclein was higher in PSP patients compared to both PD patients and HCs, achieving good classification performance (AUC: 0.853) in distinguishing PSP patients from PD patients, with a sensitivity of 100% and a specificity of 70.6%; moreover, the levels of this biomarker positively correlated with disease severity in PSP group. Regarding oligomeric α-synuclein and p129-α-synuclein, the latter was slightly increased in RBCs from PSP patients compared to HCs, but no correlations were detected. Conclusions: Although these findings need to be confirmed in larger studies, our pilot work suggests that RBC total α-synuclein may represent a potential molecular biomarker for the differential diagnosis and clinical staging of PSP.

Development and validation of a model for predicting who can benefit from multiple TACE in HCC patients

This study was to develop and validate a model for predicting who can benefit from multiple transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) patients.228 and 98 patients were included in the development and validation sets, respectively. The primary clinical endpoint was benefiting from consecutive multiple TACE treatments. Logistic regression analysis was used to screen the independent risk factors for the clinical endpoint. The independent risk factors were then used to construct the predictive model. The area under receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves were used to evaluate the predictive ability of the model.Multivariate Logistic regression analysis showed that complete envelope, hepatic lopes, tumor number, and alpha-fetoprotein (AFP) were independent risk factors for benefiting from multiple TACE in HCC patients. The area under the curve (AUC) of the model constructed by using independent risk factors in the development and validation sets was 0.843 (95% confidence interval [CI]: 0.784–0.902) and 0.828 (95%CI: 0.739–0.916), respectively. The calibration curves and clinical decision curves showed that the model had good predictive ability.The model established in this study has a good predictive effect on HCC patients who can benefit from multiple TACE.

Validation of the adult asthma epidemiological score: a secondary analysis of the EPI-ASTHMA population-based study.

The A2 score is an eight-question patient-reported outcome measure that has been validated for ruling in (score ≥4) and ruling out (score 0-1) asthma. However, this screening tool has been validated in a cohort similar to the derivation cohort used. This study aims to validate the predictive accuracy of the A2 score in a primary care population against general practitioner (GP) clinical assessment and to determine whether the proposed cut-offs are the most appropriate. This accuracy study is a secondary analysis of the EPI-ASTHMA population-based study. Primary care centres in Portugal. Random adult participants answered the A2 score by phone interview. Those with an A2 score ≥1 (plus 5% with an A2 score of 0) were invited to a diagnostic visit carried out by a GP to confirm or not a diagnosis of asthma. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves. A total of 1283 participants (median 54 (p25-p75 43-66) years; 60% women) were analysed. The A2 score showed high discriminatory power in identifying asthma, with an area under the ROC curve of 82.9% (95% CI 80.4% to 85.4%). The proposed cut-off ≥4 was the most appropriate to rule in asthma (specificity 83.1%, positive predictive value 62.4%, accuracy 78%). Similarly, the proposed cut-off<2 was the most suitable for excluding asthma (sensitivity 92.7%, negative predictive value 93.7%, accuracy 60.5%). The A2 score is a useful tool to identify patients with asthma in a primary care population. NCT0516961.

Novel radiological predictors for the progression of proximal junctional kyphosis in osteoporotic vertebral compression fracture with kyphosis following posterior corrective surgery.

This study aimed to identify the effect of some novel risk factors associated with L1 vertebrae and parameters closely related to the sagittal alignment for the occurrence of proximal junctional kyphosis (PJK) following surgery for patients with osteoporotic vertebral compression fractures (OVCF)kyphosis. 74 OVCF patients undergoing posterior corrective surgery between January 2008 and June 2021 with a minimum 2-year follow-up were included. These patients were divided into PJK and non-PJK groups. Spinopelvic parameters, includingthoracolumbar slope (TLS)and the L1 plumb line (L1PL) were measured preoperatively, postoperatively, and at follow-up. Multivariate logistic analysis was performed on various risk factors and Global Alignment and Proportion (GAP) scores. Associations between novel parameters and PJK were analyzed using receiver operating characteristic(ROC) analysis. PJK was identified in 28.4% of patients. The mean age and follow-up were 63.45years and 38.17months, respectively. There was no difference between the PJK and the non-PJK groups in baseline demographics, pre-operative and immediate post-operative pelvic incidence-lumbar lordosis mismatch. Multiple comparisonsshowed that the proportion of PJK in the severely disproportioned group(the group with the highest GAP scores) and that of the other two groups with lower GAP scores were statistically different (P < 0.001). Potential risk factors for PJK included preoperative thoracic kyphosis (TK) (P < 0.001), TLS (P = 0.016), postoperative TLS (P < 0.001), and L1PL (P < 0.001). Postoperative TLS and L1PL were respectively independent risk factors for PJK, with the cut-off values set at 8.6° and 10.4mm to predict the occurrence of PJK. TLS and L1PL can be used to predict the occurrence of PJK in patients undergoing surgery for OVCF and are crucial for preventing the progression of PJK. Achieving a proportionate GAP Score postoperatively seems to be a viable option as higher GAP scores were associated with higher rates of PJK.

Diagnostic accuracy of the point-of-care standard G6PD test™ (SD Biosensor) for glucose-6-phosphate dehydrogenase deficiency: a systematic review and meta-analysis

BackgroundGlucose-6-Phosphate Dehydrogenase deficiency (G6PDd) is a common genetic enzymopathy that can induce haemolysis triggered by various factors, including some anti-malarial drugs. Although many Point-of-Care (PoC) tests, such as Standard G6PD™ are available to detect G6PDd, its pooled diagnostic test accuracy (DTA) remains unknown.MethodsTo estimate the DTA of StandG6PD-BS at various thresholds of G6PDd, a systematic review with a DTA meta-analysis were conducted, searching EMBASE, MEDLINE, and SciELO databases up to April 4, 2024.The included studies were those that measured G6PD activity using StandG6PD-BS (reference test) and spectrophotometry (gold standard) in patients suspected of having G6PDd. The risk of bias (RoB) of the studies was assessed using the QUADAS-2 tool and the certainty of evidence (CoE) with the GRADE approach. For the estimation of within-study DTA, a random-effect bivariate meta-analysis was performed to determine the pooled sensitivity and specificity for 30%, 70%, and 80% enzyme levels’ thresholds, and a graphical analysis of the heterogeneity using crosshair and Confidence Regions on receiver operating characteristic (ROC) space plots.ResultsAfter screening 2496 reports, four studies were included with 7864 participants covering all thresholds. Two studies had high RoB in QUADAS-2 domains 2 and 3, and the others had low RoB, with low, moderate, and high heterogeneity at the 30%, 70%, and 80% thresholds, respectively. The pooled sensitivity was 99.1%, 95.7%, and 90% for 30%, 70%, and 80% thresholds, respectively. The pooled specificity was 97.4%; 92.9%; and 89.0% for 30%, 70%, and 80% thresholds, respectively.ConclusionStandG6PD-BS is a PoC test with high sensitivity and specificity to detect G6PDd at different thresholds.

Development and validation of a nomogram for predicting overall survival of head and neck adenoid cystic carcinoma

This study aimed to develop and validate a nomogram using clinical variables to guide personalized treatment strategies for adenoid cystic carcinoma of the head and neck (ACCHN). Data from 1069 patients with ACCHN diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were used to construct the nomogram. External validation was performed using an independent cohort of 70 patients from Fujian Cancer Hospital. Multivariate Cox regression analysis was conducted using IBM SPSS version 26.0 and R Software version 4.2.3. The concordance index (C-index) and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the nomogram. Age, tumor site, surgery, N stage, M stage, and TNM stage were identified as independent prognostic factors through univariate and multivariate Cox analyses. The nomogram demonstrated superior predictive performance compared to the TNM staging system, effectively stratifying patients into high-risk and low-risk groups. This nomogram offers a valuable tool for predicting overall survival in patients with ACCHN and tailoring individualized treatment approaches.

Enhancing preoperative diagnosis of pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis: a study on normalized conventional MR imaging parameters.

To assess the utility of signal intensity ratio (SIR) in distinguishing between mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), thereby reducing unnecessary pancreatectomies or delayed diagnosis brought by misdiagnosis. This retrospective study included 170 participants (34 with MFCP and 136 with PDAC) who underwent radical pancreatic surgery and were diagnosed via specimen pathology. The study group was carefully selected with a 1:4 ratio matching for sex, age, and operation time between two entities. T1 SIR, T2 SIR, arterial phase (AP) SIR, portal venous phase (VP) SIR, delay phase (DP) SIR, DWI0-50 SIR, and DWI500-1000 SIR, were calculated by dividing the signal intensity of lesions by that of the paraspinal muscle, serving as a reference organ. Intraclass Correlation Coefficient (ICC) was estimated to evaluate the intraobserver and interobserver reliability. Wilcoxon tests were employed for univariate analysis, and receiver operating characteristic (ROC) curves were generated to determine optimal cutoff points and AUC values for selected predictors. A tenfold cross-validation method was applied to validate the robustness of the results. The ICC demonstrated excellent correlation for both intraobserver and interobserver(ICCs > 0.8). T1 SIR, AP SIR, VP SIR, and DP SIR were significantly lower in the PDAC group compared to the MFCP group, and exhibited good independent predictive properties with the sensitivities of 61.8, 61.8, 70.6, and 73.5%, specificities of 66.2, 68.4, 59.6, and 55.9%, and AUCs of 0.620, 0.659, 0.670, and 0.668, respectively, hovering around 0.7. The tenfold cross-validation confirmed the reliability and robustness of our findings, with consistent AUC, sensitivity, specificity, and 95% confidence intervals over 1000 iterations. T1 SIR, AP SIR, VP SIR, and DP SIR show promise as potential imaging biomarkers for distinguishing between MFCP and PDAC.

Effects of Licorice Functional Components Intakes on Blood Pressure: A Systematic Review with Meta-Analysis and NETWORK Toxicology.

To investigate the effects of licorice functional ingredient intake on blood pressure, explore its potential mechanisms of action, and provide safety information for personalized nutritional interventions in special populations and for the application of licorice-derived functional foods. PubMed, Cochrane Library, Medline, Embase, EBSCO, ScienceDirect, and Web of Science databases were searched from inception to 31 August 2024. Randomized controlled trials (RCTs) investigating the intake of licorice or its functional components were included. The range of continuous variables was assessed using the weighted mean difference (WMD) with 95% confidence intervals. Genes associated with hypertension were screened using an online database. Machine learning, receiver operating characteristic(ROC) curve analysis, molecular docking, and gene set enrichment analysis (GSEA) were employed to explore the potential mechanisms underlying licorice-induced blood pressure fluctuations. Eight RCTs (541 participants) were included in the meta-analysis, which indicated interventions containing glycyrrhizic acid (GA) as the main component increased systolic blood pressure (SBP) and diastolic blood pressure (DBP) (SBP: WMD [95% CI] = 3.48 [2.74, 4.21], p < 0.001; DBP: WMD [95% CI] = 1.27 [0.76, 1.78], p < 0.001). However, interventions dominated by licorice flavonoids(LF) had no significant effect on SBP or DBP (SBP: WMD [95% CI] = 0.58 [-1.15, 2.31], p = 0.511; DBP: WMD [95% CI] = 0.17 [-1.53, 1.88], p = 0.843). Three machine learning algorithms identified five biomarkers associated with hypertension: calmodulin 3 (CALM3), cluster of differentiation 9 (CD9), growth factor independence 1B transcriptional repressor (GFI1B), myosin light chain kinase (MYLK), and Ras suppressor-1 (RSU1). After removing biomarkers with lower validity and reliability, GFI1B, MYLK, and RSU1 were selected for subsequent analysis. The network toxicology results suggested that GA and its metabolite glycyrrhetinic acid may act on GFI1B, MYLK, and RSU1, influencing blood pressure fluctuations by modulating nitrogen metabolism signaling pathways. There were distinct differences in the effects of licorice functional components on blood pressure. Functional constituents dominated by GA were shown to increase both SBP and DBP, whereas those dominated by LF did not exhibit significant effects on blood pressure. The hypertensive mechanism of GA may involve the modulation of GFI1B, MYLK, and RSU1 to regulate nitrogen metabolic pathways.

Patients' Nonattendance in Outpatient Specialist Consultations: A National Cohort Analysis of a Health System.

Analyzing patients' nonattendance at medical appointments helps address an issue impacting the management and sustainability of health systems globally, providing valuable insights for healthcare managers. This study aims to identify factors at both patient and health system levels that contribute to understanding missed appointments. The analysis was conducted using data from secondary care consultations within the Brazilian Unified Health System between April 2018 and March 2020. Primary care includes general medical consultations, while secondary care involves specialized services provided by doctors with advanced expertise. We examined demographic factors (age, sex, race/color, socioeconomic level) and health system practices (waiting time, hospitalization, distance to service, medical specialty, and severity of clinical condition) to assess their impact on patient attendance. A weighted analysis and receiver operating characteristic (ROC) analysis were applied to determine the relative risk of nonattendance. Of 5,003,159 consultations, 435,523 (8.7%) were missed. Nonattendance was highest among patients facing long distances to the service (13.3%, [RRR] 1.227), younger age (16-30 years: 11.8%, [RRR] 1.041), and waiting times (>30: 10.9%, [RRR] 1.738). Socially vulnerable patients were more likely to miss appointments (9.6%, [RRR] 1.055) compared to less vulnerable groups (8.6%). Practice-level factors had a slightly greater impact on nonattendance (ROC: 0.621) than patient-level factors (ROC: 0.5674). The overall predictive model achieved a C statistic of 0.6228, resulting in a fair predictive ability. However, the model showed only modest prediction of no-shows, indicating the need for more detailed data to improve accuracy. Gauging which group suffers the highest risk of nonattendance was a secondary goal of this analysis. Young, socially vulnerable patients with long commutes and extended waiting times are at higher risk of nonattendance. Effective management of these risk factors and targeted preventive actions are essential to reduce absenteeism and improve health system efficiency.

Associations between Chinese Visceral Adiposity Index and incident stroke among community populations in Pudong New Area, Shanghai

BackgroundTo analyze the relationship between Chinese Visceral Adiposity Index (CVAI) and stroke in Pudong New Area, and to provide a scientific basis for the prevention and treatment of this condition. Materials and MethodsBased on the Follow-up Cohort Program of Chronic Disease Risk Factors in Pudong New Area, a total of 7,194 residents from 12 townships and 35 village committees or neighborhood committees were selected. The cohort data in 2016 served as the baseline, and a follow-up was conducted on 5462 individuals from 2019 to 2020. ResultsIn the analysis of stroke incidence, 5,462 subjects were included, with 616 stroke events recorded. The incidence density of stroke was 33.41 per 1000 person-years, while the China standard rate and the world standard rate were 13.78 and 12.52 per 1000 person-years, respectively. The incidence density of stroke rose with increasing quartiles of CVAI. After adjusting for potential confounders, the risk of stroke for males and females with CVAI ≥ 132.11 was 2.492 times (95% CI: 1.457 ∼ 4.260) and 1.759 times (95% CI: 1.070-2.891), respectively, compared to the groups with CVAI < 81.91. Among the eight obesity indicators, including CVAI, Visceral Adiposity Index (VAI), Body Mass Index (BMI), Waist Circumference (WC), Waist Hip Ratio (WHR), Waist Height Ratio (WHtR), Body Adiposity Index (BAI), A Body Shape Index (ABSI),and Body Roundness Index (BRI), CVAI has the highest diagnostic accuracy for female stroke using Receiver Operator Characteristic (ROC) curves (the Area under the ROC curve/AUC = 0.619). Subgroup analysis showed that smoking and dyslipidemia significantly modified the association between CVAI and stroke risk in females (interaction P < 0.05). ConclusionsCVAI is identified as a significant risk factor for stroke and serves as a valuable predictor of stroke, particularly in females.

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities.Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Survival Benefits of Postoperative Chemotherapy in Patients With Colorectal Mucinous Adenocarcinoma: An Analysis Utilizing Propensity Score Matching From the Surveillance, Epidemiology, and End Results Database.

This study aimed to investigate the characteristics of patients with colorectal mucinous adenocarcinoma (MAC) who benefit from postoperative chemotherapy (POCT) and to develop effective postoperative survival nomograms for predicting overall survival (OS) in colorectal MAC patients. Data of colorectal MAC patients who underwent surgery from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020 were collected. Patients were grouped based on POCT, and intergroup analysis was performed using 1:1 propensity score matching (PSM). Kaplan-Meier (K-M) curves were used to compare the prognosis between the 2 groups. Cox analysis was employed to identify factors associated with OS in patients with colorectal MAC who underwent POCT. The variance inflation factor (VIF) and bilateral stepwise regression were used to determine factors included in the model. Additionally, a nomogram was constructed to predict postoperative survival outcomes for patients. The discriminative ability of the nomograms was evaluated using the C-index and calibration curve analysis, the decision curve analysis (DCA) assessed the clinical utility of the nomogram, and the receiver operating characteristic (ROC) curve evaluated the nomograms' performance. This study encompassed 6829 patients with colorectal MAC, among whom 2258 received POCT, and 4571 did not. Whether pre or post PSM, patients in the POCT group consistently exhibited a superior median OS compared to those in the postoperative non-chemotherapy group (P < .0001). For colorectal MAC patients undergoing POCT, OS was correlated with factors such as patient age, carcinoembryonic antigen levels, tumor deposits, perineural invasion (PNI), lymph node examination count, T staging, and Grade staging. Notably, a significant chemotherapy advantage was observed in patients without perineural invasion, those with lymph node examination counts exceeding 12, and patients with moderately differentiated tumors. The overall colorectal MAC patient postoperative OS predictive nomogram demonstrated a C-index of .74, with a calibration curve near the diagonal and a DCA curve indicating positive net benefits. In comparison to TNM staging, the ROC curves of the nomogram at 1 year, 3 years, and 5 years demonstrated superior predictive capabilities (AUC: .80 vs .71, .78 vs .71, .77 vs .70). This study revealed the characteristics of colorectal MAC patients who benefit from POCT and established effective prognostic nomograms, which can aid clinicians in designing personalized treatment plans for individual patients and promote precision medicine.

Utilizing artificial intelligence to determine bone mineral density using dual-layer spectral detector CT

BackgroundDual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring dedicated software or calibration. Artificial intelligence (AI) has been utilized for the diagnosis of osteoporosis in routine CT scans but has rarely been used in DECT. This study investigated the diagnostic performance of an AI system for osteoporosis screening using DECT images with reference quantitative CT (QCT) images. MethodsThis prospective study included 120 patients who underwent DECT and QCT scans from August to December 2023. Two convolutional neural networks, 3D RetinaNet, and 3D U-Net, were employed for automated vertebral body segmentation. The accuracy of the BMD measurements was assessed with relative measurement error (RME%). Linear regression and Bland–Altman analyses were performed to compare the BMD values between the AI and manual systems with those of the QCT. The diagnostic performance of the AI and manual systems for osteoporosis and low BMD was evaluated using receiver operating characteristic (ROC) curve analysis. ResultsThe overall mean RMEs for the AI and manual systems were − 15.93 ± 12.05 % and − 25.47 ± 14.83 %, respectively. BMD measurements using the AI system achieved greater agreement with the QCT results than those using the manual system (R2 = 0.946, 0.898, p < 0.001; mean bias, 23.27, 35.71 mg/cm3; 95 % LoA, −9.72 to 56.26, −11.45 to 82.87 mg/cm3). The areas under the curve for the AI and manual systems were 0.933–0.979 for detecting osteoporosis and 0.980–0.991 for low BMD. ConclusionThis AI system could achieve relatively high accuracy for automated BMD measurement on DECT scans, providing great potential for the follow-up of BMD in osteoporosis screening.

Systemic Immune-Inflammation Index and Systemic Inflammation Response Index Predict the Response to Radioiodine Therapy for Differentiated Thyroid Cancer.

This research sought to evaluate the clinical value of systemic immune-inflammation index and systemic inflammation response index in predicting the response to radioactive iodine (RAI) therapy in individuals diagnosed with differentiated thyroid cancer. This retrospective study included 406 patients with differentiated thyroid cancer who received initial RAI therapy and follow-up from December 2019 to December 2023. Patients were divided into two groups based on imaging and serum indicators to evaluate the response to radioactive iodine treatment: the ER group (excellent response) and the non-ER group (suboptimal response). Systemic immune-inflammation index and systemic inflammation response index were calculated based on peripheral blood cell counts before treatment. Multivariable logistic regression analysis was used to assess the independent associations of these indices with the therapeutic response to radioiodine treatment. Receiver operating characteristic (ROC) curves were graphed and the area under the curve (AUC) was calculated to evaluate their predictive ability. Compared to the ER group, patients in the non-ER group had significantly elevated systemic immune-inflammation index and systemic inflammation response index levels (p < 0.001). After adjusting for confounding factors, there was a significant association between these indices and the response to radioactive iodine treatment in patients with differentiated thyroid cancer. The optimal cutoff values for predicting the response to RAI treatment were 668.91 for systemic immune-inflammation index (AUC=0.692, sensitivity 58.2%, specificity 73.1%, 95% CI: 0.639-0.745, p < 0.001) and 0.47 for systemic inflammation response index (AUC=0.664, sensitivity 85.6%, specificity 42.7%, 95% CI: 0.612-0.717, p < 0.001). Systemic immune-inflammation index and systemic inflammation response index could be valuable for predicting the response to RAI treatment in individuals diagnosed with differentiated thyroid cancer. Further research is needed to explore their practical utility, and these novel inflammation markers could serve as adjunct tools in clinical practice.

Research on Predicting Survival in Heart Failure Patients of Logistic Regression Models

Abstract. The purpose of this paper is to study a logistic regression model for predicting the survival of patients with heart failure. Heart failure is a serious clinical syndrome that usually develops from multiple heart diseases, and its inadequate pumping function of the heart poses a significant threat to the life and health of the patient and a significant economic burden on the healthcare system. Using medical record data from 299 patients with heart failure in the UCI database, this research utilized logistic regression models to identify the critical factors influencing the survival of heart failure patients and to forecast their survival outcomes. This paper found that age, ejection fraction, serum creatinine concentration and follow-up period are significant factors affecting survival in patients with heart failure. By adopting backward elimination method to optimize the model, the accuracy of prediction is further improved. The optimized logistic regression model yields an area under the Receiver Operating Characteristic (ROC) curve of 0.845, showing high prediction accuracy. The conclusions of this study provide a new perspective for the early diagnosis, risk assessment and personalized treatment of heart failure.