Receiver Operating Characteristic Curve Curve Research Articles

The cardiothoracic ratio on postmortem CT as a screening tool for cardiomegaly in the Thai population.

IntroductionIn forensic radiology, the cardiothoracic ratio (CTR) calculated from postmortem computed tomography (PMCT) images can be used to detect cardiomegaly. In this study, a new measurement method is studied that involves measurement in the anteroposterior (AP) and transverse directions, with a reference level based on the Thai population. ObjectiveTo screen for cardiomegaly using the CTR calculated from PMCT images. Research methodA sample size of 116 deceased Thai individuals who underwent PMCT before autopsy was obtained. Individuals were divided into two groups: normal heart weight and overweight heart. Hearts heavier than the mean plus one standard deviation were categorized into the overweight group. The CTR was calculated in both the AP and transverse directions at six reference levels. Receiver operating characteristic curves (ROC) were calculated to determine the CTR cutoff point for the diagnosis of cardiomegaly. ResultsThe CTR cutoff values for diagnosing cardiomegaly were as follows: 1) CTR >0.5 in the transverse direction at the mid-vertebra of T7 (sensitivity, 75.6%; specificity, 70.6%; area under the ROC curve 0.81), 2) CTR >0.49 in the transverse direction at the mid-vertebra of T8 (sensitivity 71%, specificity 81.4%, area under the ROC curve 0.80). The selection of the cutoff values depended on the location of the heart. The area under the ROC curve in the AP direction was in the range 0.5-0.7, which is inferior compared to the transverse direction (0.7-0.9). ConclusionCalculating the CTR with a reference level on PMCT images can assist in the diagnosis of cardiomegaly.

Comparation between Fibroscan and Hepatus for detecting NAFLD in patients with metabolic dysregulation

Background The clinical application of the innovative instantaneous elasticity and fat attenuation measurement technology, Hepatus, is currently in the evaluation stage. This study aimed to compare the detection performance of Fibroscan and Hepatus in patients with metabolic dysregulation who are "at-risk" for non-alcoholic fatty liver disease (NAFLD).Methods Between January 2021 and April 2021, 149 patients were enrolled in this study. Clinical data were collected, and all patients underwent both Fibroscan and Hepatus assessments to determine liver stiffness measurement (LSM) and attenuation parameters. The correlation between the results obtained from the two transient elastography (TE) devices was analyzed. Receiver operating characteristic curves (ROC) were constructed to compare the diagnostic value of Fibroscan and Hepatus for Hepatic Steatosis Index (HSI)-based NAFLD.Results The detection success rate of Hepatus (100.0%) was higher than that of Fibroscan (96.0%). LSM (r = 0.663, P0.05) and attenuation parameters (r = 0.778, P0.05) obtained by Fibroscan and Hepatus were significantly correlated. Hepatus tended to produce a higher LSM (Hepatus vs. Fibroscan: 6.04 vs. 5.66 kPa, P=0.016) but a lower attenuation parameter than Fibroscan (Hepatus vs. Fibroscan: 264 vs. 277 dB/m, P0.001). The area under the ROC curve for detecting HSI-based NAFLD was 0.811 for Fibroscan and 0.832 for Hepatus.Conclusion Measurements obtained by Fibroscan and Hepatus are strongly correlated, and the diagnostic value of the two TE devices is comparable in detecting HSI-based NAFLD. Hepatus offer a potential TE alternative in NAFLD examination.

A comprehensive evaluation of serum circCSPP1 as a novel diagnostic and prognostic biomarker for gastric cancer

PurposeGastric cancer (GC) has high incidence and mortality due to its low early screening efficiency. Circular RNAs (CircRNAs) are a new class of non-coding RNAs which is closely related to GC. Nevertheless, the clinical application value of circRNAs in GC are largely unknown. Therefore, we studied the role of a novel circRNA named circCSPP1 in patients with GC. MethodsCircRNA sequencing was performed to screen out the target molecule. Real-time fluorescent quantitative PCR (RT-qPCR) was utilized to detect the expression level of circCSPP1 in GC tissues, cells, and serum. Gel and Sanger sequencing were utilized to verify the ring structure of circCSPP1. RNase R enzyme digestion experiment and actinomycin D experiment were verifed the advantage of circCSPP1 as a diagnostic biomarker in patients with GC when that compared with linear RNA. The correlation between the expression level of serum circCSPP1 and clinicopathological data of GC patients was further analyzed. Receiver operating characteristic curve (ROC) and the area under ROC curve (AUC) were utilied to evaluate the diagnostic performance. ResultsCircCSPP1 has a circular structure which with resistance to RNA exonuclease digestion and long half-life compared with linear RNA. In our study, circCSPP1 was first found up-regulated in patients with GC. Serum circCSPP1 level was decreased significantly after surgical resection whereas increased after recurrence. High expression of circCSPP1 was associated with poor survival rates. The expression level of circCSPP1 was significantly correlated to tumor size, T stage, lymph node metastasis, and TNM stage. The AUC of serum circCSPP1 was 0.834, with high sensitivity and specificity in discriminating patients with GC from healthy donors. More importantly, the combined diagnosis of circCSPP1, CEA, and CA19–9 achieved the superior AUC of 0.882, with the highest specificity. ConclusionSerum circCSPP1 may prove to be a potential non-invasive auxiliary diagnostic biomarker for patients with GC.

Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study

BackgroundDeep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos.MethodsA total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test.ResultsAt the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880–0.958) for caries detection and 0.902 (95% CI, 0.853–0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection.ConclusionsThe proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

Construction and validation of clinical prediction model of acupuncture and moxibustion for Bell's palsy

To establish and validate a clinical prediction model of acupuncture and moxibustion for Bell's palsy so as to provide a tool for predicting the effect of acupuncture and moxibustion on Bell's palsy. A total of 269 patients with Bell's palsy were collected from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital, neurology department, Shenxian County Central Hospital and department of rehabilitation medicine, Dongying Municipal Hospital of TCM from June 2018 to June 2023. All of these cases were treated with acupuncture and moxibustion. Of them, 182 cases, from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital and neurology department, Shenxian County Central Hospital, were randomized into a training group (128 cases) and an internal validation group (54 cases); 87 cases from department of rehabilitation medicine, Dongying Municipal Hospital of TCM were assigned to an external validation group. The clinical data of all of the cases were extracted from the electronic medical record information platform. Using SPSS25.0 and R4.2.3, through univariate and multivariate Logistic regression analysis, the independent factors influencing the effects of acupuncture and moxibustion on Bell's palsy were identified. By means of internal and external validations, the receiver operating characteristic curve (ROC), the goodness-of-fit curve (GFC) and the decision curve analysis (DCA) were plotted. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the model were calculated; and its comprehensive performance was evaluated. The results of the multivariate Logistic regression analysis showed that the independent factors for the unsatisfactory effect on Bell's palsy were advanced age, severe symptoms before treatment, no use of steroids within 72 h of onset, and lack of acupuncture-moxibustion therapy during the acute phase or single acupuncture-moxibustion protocol (P<0.05, P<0.01). Based on these factors, nomogram model and online columnar plot prediction tool (https://bmuchen.shinyapps.io/dynnomapp/) were established. The area under the ROC curve of the model was 0.921 (95% CI: 0.877, 0.966), 0.876 (95% CI: 0.787, 0.966), and 0.846 (95% CI: 0.766, 0.926) in the training group, the internal validation group, and the external validation group, respectively, indicating good predictive value. The model showed a satisfactory calibration curve alignment. The decision threshold in the range of 0 to 0.8 provided clinical benefits for participants. The model exhibited the sensitivity from 65.9% to 88.0%, the specificity ranging from 77.3% to 90.7%, the accuracy from 77.8% to 85.9%, the positive predictive value from 83.3% to 90.1%, and the negative predictive value from 70.8% to 78.7%. The comprehensive evaluation indicated a satisfactory clinical application value of the model. The clinical prediction model of acupuncture and moxibustion for Bell's palsy is valuable in its practice and promotion to a certain extent. The predicted results are conductive to clinicians' judgement of the effect of acupuncture and moxibustion for this disease and making effective and high-quality clinical decisions, as well as formulating the optimal therapeutic regimen.

Neutrophil gelatinase-associated lipocalin (NGAL) levels for early prediction and ruling out future contrast nephropathy in patients undergoing primary coronary intervention

Abstract Funding Acknowledgements None. Background Neutrophil gelatinase-associated lipocalin (NGAL), is an early marker of acute kidney injury(AKI) . We aimed to investigate the diagnostic utility of sequential NGAL measurements for both prediction and ruling out of contrast induced AKI (CI-AKI) among patients with ST segment elevation (STEMI) undergoing primary coronary intervention (PCI). Methods We included 133 patients with STEMI treated with PCI. Blood samples for plasma NGAL were drawn immediately before PCI (NGAL 0) and 24 hours after PCI (NGAL 24). Optimal threshold for the development of CI-AKI were determined using receiver operating characteristic curves (ROC). Patients were also assessed for the dynamics between NGAL 0 and NGAL 24 levels. Results Mean age was 62 ±13 years and 78% were men, and a total of 20 (15%) developed CI-AKI . Patients with CI-AKI demonstrated higher plasma NGAL 0 levels (164 ± 42 vs. 95 ± 30 ng/mL; p &amp;lt; 0.001) as well as NGAL 24 levels (142 ± 41 vs. 93 ± 36 ng/mL; p &amp;lt; 0.001). There were no significant changes between NGAL 0 &amp; 24 hours within the 2 groups( figure 1). According to the ROC curve analysis (figure 2), the optimal NGAL 0 level for CI-AKI prediction was &amp;gt;125 ng/mL (AUC 0.841, 95% CI 0.801–0.961, p &amp;lt; 0.001), with a sensitivity of and specificity of 70% and 84% respectively, while the negative predictive value of NGAL 0&amp;lt;125 ng/mL was 94%. Conclusions Among STEMI patients, NGAL levels before primary PCI may be utilized both for ruling out and for the prediction of CI-AKI.NGAL 0 vs 24 according to CI-AKINGAL AUC

A Prognostic Model Based on the Log Odds Ratio of Positive Lymph Nodes Predicts Prognosis of Patients with Rectal Cancer.

This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging. Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification. While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata. Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer.

Establishment of a mortality risk nomogram for predicting in-hospital mortality of sepsis: cohort study from a Chinese single center.

To establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population. Data were obtained from the medical records of sepsis patients enrolled at the Affiliated Huadu Hospital, Southern Medical University, between 2019 and 2021. A total of 696 sepsis patients were initially included in our research, and 582 cases were finally enrolled after screening and divided into the survival group (n = 400) and the non-survival group (n = 182) according to the incidence of mortality during hospitalization. Twenty-eight potential sepsis-related risk factors for mortality were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. We used binary logistic regression to build a model for predicting mortality from the variables based on LASSO regression selection. Binary logistic regression was used to establish a nomogram based on independent mortality risk factors. To validate the prediction accuracy of the nomogram, receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and restricted cubic spline (RCS) analysis were employed. Eventually, the Hosmer-Lemeshow test and calibration curve were used for nomogram calibration. LASSO regression identified a total of ten factors, namely, chronic heart disease (CHD), lymphocyte count (LYMP), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), C reactive protein (CRP), Procalcitonin (PCT), lactic acid, prothrombin time (PT), alanine aminotransferase (ALT), total bilirubin (Tbil), interleukin-6 (IL6), that were incorporated into the multivariable analysis. Finally, a nomogram including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6 was established by multivariable logistic regression. The ROC curves of the nomogram in the training and validation sets were 0.9836 and 0.9502, respectively. DCA showed that the nomogram could be applied clinically if the risk threshold was between 29.52 and 99.61% in the training set and between 31.32 and 98.49% in the testing set. RCS showed that when the value of independent risk factors from the predicted model exceeded the median, the mortality hazard ratio increased sharply. The results of the Hosmer-Lemeshow test (χ2 = 0.1901, df = 2, p = 0.9091) and the calibration curves of the training and validation sets showed good agreement with the actual results, which indicated good stability of the model. Our nomogram, including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6, exhibits good performance for predicting mortality risk in adult sepsis patients.

Circulating Memory T Cells and TCF1+ T Cells Aid in Diagnosis and Monitor Disease Activity in Vitiligo

Vitiligo is an immune memory skin disease. T-cell factor 1 (TCF1) is essential for maintaining the maintenance of the memory T-cell pool. There is an urgent need to investigate the characteristics of peripheral memory T-cell profile and TCF1+ T-cell frequencies in patients with vitiligo patients. In this study, 31 patients with active vitiligo (AV), 22 with stable vitiligo (SV), and 30 healthy controls (HCs) were included. We measured circulating memory and TCF1+ T-cell frequencies using by flow cytometry. The Spearman’s rank test was used to evaluate the correlation correlations between cell frequencies and disease characteristics. Receiver operating characteristic curves (ROC) were constructed to investigate the discriminative power of the cell subpopulations. Circulating CD4+ and CD8+ terminally differentiated effector memory T-cell (TEMRA) frequencies were significantly higher in the AV group than in compared to HCs (P < 0.05). TCF1+ T-cell subpopulations were widespread increased in patients with vitiligo patients (P < 0.05). After adjusting adjustment for potential confounders, CD8+ and CD4+ central memory (TCM) cells, and CD8+ TEMRA were correlated with disease activity (P < 0.05). The combined diagnostic value of the four (naïve naive, effector memory, TCM, and TEMRA) CD8+TCF1+ T-cell subsets was relatively high (area under the ROC curve [AUC]AUC = 0.804, sensitivity = 71.70%, specificity = 83.34%), and the CD8+ T-cell subsets combination performed well in discriminating disease activity (AUC = 0.849, sensitivity = 70.97%, specificity = 90.91%). We demonstrated an altered circulating memory T-cell profile and increased TCF1+ T-cell percentage percentages in patients with vitiligo patients. T-cell subpopulations had a strong value values for vitiligo diagnosis and activity evaluation. This evidence presents a potential new pharmacological target for inhibiting to inhibit the autoimmunity that leads leading to vitiligo disease activity.

Human circular RNA hsa_circ_0000231 clinical diagnostic effectiveness as a new tumor marker in gastric cancer.

Owing to the subtlety of initial symptoms associated with gastric cancer (GC), the majority of patients are diagnosed at later stages. Given the absence of reliable diagnostic markers, it is imperative to identify novel markers that exhibit high sensitivity and specificity. Circular RNA, a non-coding RNA, plays an important role in tumorigenesis and development and is well expressed in body fluids. In this study, we aimed to identify hsa_circ_0000231 as a new biomarker for the diagnosis of GC and to assess its clinical diagnostic value in serum. The stability and correctness of hsa_circ_0000231 was determined by agarose gel electrophoresis, Rnase R assay and Sanger sequencing. Real-time quantitative polymerase chain reaction (qRT-PCR) was designed to discover the expression level of hsa_circ_0000231 and whether it has dynamic serum monitoring capability. The correlation between hsa_circ_0000231 and clinicopathological parameters was analyzed by collecting clinical and pathological data from GC patients. In addition, diagnostic efficacy was assessed by constructing receiver operating characteristic curves (ROC). Hsa_circ_0000231 exhibits a stable and consistently expressed structure. In GC serum, cells, and tissues, it demonstrates reduced expression levels. Elevated expression levels observed postoperatively suggest its potential for dynamic monitoring. Additionally its expression level correlates with TNM staging and neuro/vascular differentiation. The area under ROC curve (AUC) for hsa_circ_0000231 is 0.781, indicating its superior diagnostic value compared to CEA, CA19-9, and CA72-4. The combination of these four indicators enhances diagnostic accuracy, with an AUC of 0.833. The stable expression of hsa_circ_0000231 in the serum of gastric cancer patients holds promise as a novel biomarker for both the diagnosis and dynamic monitoring of GC.

Postoperative serum mir-28-5p level has predictive value for the prognosis after endovascular abdominal aortic aneurysm repair

BackgroundWe explored the clinical significance of miR-28-5p pre- and post-endovascular abdominal aortic aneurysm repair (EVAR) in abdominal aortic aneurysm (AAA) patients.MethodsSubjects included AAA patients receiving EVAR and non-AAA people without statistical differences from AAA patient in comorbidities/Framingham risk score. Fasting elbow venous blood (4 mL) was collected in the morning of the day of EVAR surgery and in the morning of 3 months post-EVAR. Pre-/post-EVAR serum miR-28-5p expression, AAA maximum diameter alterations, CD3+/CD4+/CD8+/TC/TG pre-/post-EVAR, and the correlations between miR-28-5p and AAA maximum diameter were investigated. Prediction of miR-28-5p on post-EVAR mortality, prognosis, and independent factors of post-EVAR death were analyzed using receiver operating characteristic curve (ROC)/Kaplan-Meier curve/univariable and multivariable Cox regression. According to the cut-off value of ROC curve for postoperative miR-28-5p was the cut-off value, and the patients were classified into the miR-28-5p high- and low-expression groups. The survival or death of both groups were compared after 48-month follow-up.ResultsSerum miR-28-5p levels in AAA patients dropped post-EVAR. AAA patients showed notable differences in CD3+/CD4+/CD8+/TC/TG levels pre-/post-EVAR. The miR-28-5p low-expression group exhibited higher CD3+/CD4+ and lower CD8+/TC/TG levels. We observed a positive correlation between post-EVAR miR-28-5p and AAA maximum diameter and between the pre-/post-EVAR miR-28-5p fold change and the AAA maximum diameter change. Postoperative miR-28-5p demonstrated good predictive value for postoperative death. Hypertension, Framingham risk score, TC, TG, and miR-28-5p were independent influencing factors of post-EVAR death.ConclusionEVAR decreased serum miR-28-5p expression in AAA patients. Post-operative miR-28-5p level and pre-/post-operative fold change level are positively-correlated with AAA diameter.

Construction of a nomogram to predict the probability of new vertebral compression fractures after vertebral augmentation of osteoporotic vertebral compression fractures: a retrospective study.

This study aimed to develop and validate a new nomogram model that can predict new vertebral fractures after surgery for osteoporotic compression fractures to optimize surgical plans and reduce the incidence of new vertebral compression fractures. 420 patients with osteoporotic vertebral compression fractures were randomly sampled using a computer at a fixed ratio; 80% of the patients were assigned to the training set, while the remaining 20% were assigned to the validation set. The least absolute shrinkage and selection operator (LASSO) regression method was applied to screen the factors influencing refracture and construct a predictive model using multivariate logistic regression analysis. The results of the multivariate logistic regression analysis showed a significant correlation between bone cement leakage, poor cement dispersion, the presence of fractures in the endplate, and refractures. The receiver operating characteristic curve (ROC) results showed that the area under the ROC curve (AUC) of the training set was 0.974 and the AUC of the validation set was 0.965, which proves that this prediction model has a good predictive ability. The brier score for the training set and validation set are 0.043 and 0.070, respectively, indicating that the model has high accuracy. Moreover, the calibration curve showed a good fit with minimal deviation, demonstrating the model's high discriminant ability and excellent fit. The decision curve indicated that the nomogram had positive predictive ability, indicating its potential as a practical clinical tool. Cement leakage, poor cement dispersion, and presence of fractures in the endplate are selected through LASSO and multivariate logistic regressions and included in the model development to establish a nomogram. This simple prediction model can support medical decision-making and maybe feasible for clinical practice.

Diagnostic value of whole blood cell parameters logistic regression model for radiation injury on radiation workers

Objective: To explore the diagnostic value of whole blood cell parameters logistic regression model for radiation injury on radiation workers by comparing the differences of whole blood cell parameters between occupational radiation injury population and occupational health examination population. Methods: In February 2023, 184 radiation workers who received occupational health examinations in our hospital and occurrenced chromosome aberration from July 2021 to July 2022 were retrospectively selected as the radiation injury group. And other 184 radiation workers encountered in the same period without chromosome aberration occurrence were selected as the control group. Collected whole blood cell parameters from two groups of research subjects, conducted comparative analysis, constructed a logistic regression model, and evaluated the diagnostic value of the logistic regression model for radiation injury on radiation workers by receiver operating characteristic curve (ROC) and area under curve (AUC) . In addition, with the same standard, 60 radiation workers with chromosome aberration and 60 radiation workers without chromosome aberration from August 2022 to January 2023 were included in the validation queue to validate the logistic regression model. Results: Neu_X, Neu_Y, Neu_Z, Lym_X, Lym_Y, Lym_Z, Mon_X, Mon_Y, Mon_Z, Micro, MCHC in the radiation injury group were significantly higher than those in the control group, and the difference was statistically significant (P<0.05) . And MCV and Macro in the radiation injury group were lower than those in the control group, and the difference was statistically significant (P<0.05) . Moreover, logistic regression analysis showed that Lym_X, Lym_Y, Lym_Z, MCHC, Micro were all independent risk factors for diagnosing radiation injury on radiation workers (OR=1.08、1.02、0.99、1.06、51.32, P<0.05) . ROC curve analysis showed that the AUC, sensitivity, specificity, and accuracy of the logistic regression model based by Lym_X, Lym_Y, Lym_Z, MCHC and Micro in diagnosing radiation injury on radiation workers were 0.80, 85.9%, 65.8% and 75.9% respectively. The validation queue verified the logistic regression model and the AUC, sensitivity, specificity, and accuracy of the logistic regression model were 0.80, 81.7%, 71.7% and 76.7% respectively, the model fitted well. Conclusion: Radiation damage can cause changes in multiple whole blood cell parameters of radiation workers. The logistic regression model based by Lym_X, Lym_Y, Lym_Z, MCHC and Micro showed good diagnosis ability and can be used for the screening of radiation injury on radiation workers.

Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers.

Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

Simplified S1 vertebral bone quality score independently predicts proximal junctional kyphosis after surgery for degenerative lumbar scoliosis

ObjectiveOur study aimed to assess the effectiveness of the simplified S1 vertebral bone quality (VBQ) score in predicting the incidence of proximal junctional kyphosis (PJK) after surgery for degenerative lumbar scoliosis (DLS).MethodsWe reviewed 122 patients with DLS who underwent posterior lumbar decompression and long-segment fusion surgery in our hospital from January 2016 to December 2020. The patients were classified into PJK group and non-PJK group. S1 VBQ scores are determined by signal intensity measurements taken from the mid-sagittal plane of T1-weighted non-contrast MRI. Logistic regression analysis was used to identify factors associated with PJK. Receiver-operating characteristic curve (ROC) analysis was used to evaluate the value of S1 VBQ score in predicting pedicle PJK after DLS.Results122 DLS patients (90 females and 32 males) met the inclusion criteria. In addition, 27 patients (22.13%) had PJK at the time of last follow-up. VBQ was higher in PJK patients than non-PJK patients (3.58 ± 0.67 vs. 3.08 ± 0.54, p < 0.001). Preoperatively, patients in the PJK group had a greater TLK than those in the non-PJK group (20.00 ± 6.22 vs. 16.86 ± 5.38, p = 0.011). After surgery, patients in the PJK group had greater TLK (p < 0.001) and PJA (p < 0.001) compared with the non-PJK group. At final FU, patients in the PJK group had greater TK (p = 0.002), TLK (p < 0.001), SVA (p < 0.001), and PJA (p < 0.001) than patients in the non-PJK group (Table 4). In multivariate logistic regression analysis, higher VBQ score (OR 4.565, 95% CI 1.43–14.568, p = 0.010), advanced age (OR 1.119, 95% CI 1.021–1.227, p = 0.016), and larger TLK (OR 1.191, 95% CI 1.041–1.362, p = 0.011) were significant predictors of postoperative PJK in patients with DLS (Table 6). A statistically significant positive correlation existed between VBQ score and PJA change (r = 0.370, p < 0.001). We created ROC curves for VBQ scores as predictors of PJK with a diagnostic accuracy of 72.1% (95% CI 60.15–82.9%.The ideal limit for the VBQ score was 3.205 (sensitivity: 77.8%, specificity: 81.4%).ConclusionTo the best of our knowledge, this is the first study to evaluate the effectiveness of the S1 VBQ score in predicting postoperative PJK in DLS. Our study included major risk factors and found that S1 VBQ score was a significant predictor of PJK in patients undergoing DLS surgery. The higher the S1 VBQ score, the higher the probability of PJK.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

BackgroundThe timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).MethodsThis study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).ResultsThe DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.ConclusionsThe US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

A nomogram clinical prediction model for predicting urinary infection stones: development and validation in a retrospective study.

This study aimed to develop a nomogram prediction model to predict the exact probability of urinary infection stones before surgery in order to better deal with the clinical problems caused by infection stones and take effective treatment measures. We retrospectively collected the clinical data of 390 patients who were diagnosed with urinary calculi by imaging examination and underwent postoperative stone analysis between August 2018 and August 2023. The patients were randomly divided into training group (n = 312) and validation group (n = 78) using the "caret" R package. The clinical data of the patients were evaluated. Univariate and multivariate logistic regression analysis were used to screen out the independent influencing factors and construct a nomogram prediction model. The receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA) and clinical impact curves were used to evaluate the discrimination, accuracy, and clinical application efficacy of the prediction model. Gender, recurrence stones, blood uric acid value, urine pH, and urine bacterial culture (P < 0.05) were independent predictors of infection stones, and a nomogram prediction model ( https://zhaoyshenjh.shinyapps.io/DynNomInfectionStone/ ) was constructed using these five parameters. The area under the ROC curve of the training group was 0.901, 95% confidence interval (CI) (0.865-0.936), and the area under the ROC curve of the validation group was 0.960, 95% CI (0.921-0.998). The results of the calibration curve for the training group showed a mean absolute error of 0.015 and the Hosmer-Lemeshow test P > 0.05. DCA and clinical impact curves showed that when the threshold probability value of the model was between 0.01 and 0.85, it had the maximum net clinical benefit. The nomogram developed in this study has good clinical predictive value and clinical application efficiency can help with risk assessment and decision-making for infection stones in diagnosing and treating urolithiasis.

The Combination of circEPSTI1 and MIF Offers Diagnostic Value for Endometrial Cancer.

Circular RNAs (circRNAs) exhibit unique patterns of expression and high levels of stability in patient plasma samples such that they represent ideal non-invasive biomarkers that can be leveraged to detect a wide array of diseases including endometrial cancer (EC). This study was designed to identify circRNAs with potential diagnostic utility in serum samples from EC patients while also evaluating the utility of macrophage migration inhibitory factor (MIF) as a biomarker when screening for this form of cancer in the clinic. Levels of circEPSTI1 and MIF were assessed in the plasma of EC patients and healthy subjects (n=186 each) through qPCR and ELISAs. The diagnostic utility of these biomarkers was assessed with receiver operating characteristic curve (ROC) analyses. Relative to healthy subjects, EC patient serum contained significantly elevated circEPSTI1 and MIF. An association was noted between circEPSTI1 expression in stages, histologic grade, and residual tumor. ROC curves confirmed that serum circEPSTI1 levels distinguished between controls and patients with EC with an Area of 0.835 and serum MIF levels distinguished between controls and patients with EC with an Area of 0.6646. When instead diagnosing patients based on the combination of MIF and circEPSTI1, the Area further rose to 0.8604. Assessing the combination of circEPSTI1 and MIF may be a viable approach to reliably diagnosing EC.

Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.

Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.

Construction and verification of risk prediction model of osteoporotic fractures in patients with osteoporosis in China.

To explore the influencing factors of osteoporotic fractures (OPF) in patients with osteoporosis, construct a prediction model, and verify the model internally and externally, so as to provide reference for early screening and intervention of OPF in patients with osteoporosis. Osteoporosis patients in the First Affiliated Hospital of Soochow University were selected, and the medical records of patients were consulted through the Hospital Information System (HIS) and the data management platform of osteoporosis patients, so as to screen patients who met the criteria for admission and discharge and collect data. SPSS 26.0 software was used for single factor analysis to screen statistically significant variables (p < 0.05). The influencing factors of OPF were determined by multivariate analysis, and a binary Logistic regression model was established according to the results of multivariate analysis. Hosmer-Lemeshow (H-L) goodness of fit and receiver operating characteristic curve (ROC) were used to test the model's efficiency, and Stata 16.0 software was used to verify the Bootstrap model, draw the model calibration curve, clinical applicability curve and nomogram. In this study, the data of modeling set and verification set were 1,435 and 580, respectively. There were 493 (34.4%) cases with OPF and 942 (65.6%) cases without OPF in the modeling set. There were 204 (35.2%) cases with OPF and 376 (64.8%) cases without OPF. The variables with statistically significant differences in univariate analysis are Age, BMI, History of falls, Usage of glucocorticoid, ALP, Serum Calcium, BMD of lumbar, BMD of feminist neck, T value of feminist neck, BMD of total hip and T value of total hip. The area under ROC curve of the risk prediction model constructed this time is 0.817 [95%CI (0.794 ~ 0.839)], which shows that the model has a good discrimination in predicting the occurrence of OPF. The optimal threshold of the model is 0.373, the specificity is 0.741, the sensitivity is 0.746, and the AUC values of the modeling set and the verification set are 0.8165 and 0.8646, respectively. The results of Hosmer and Lemeshow test are modeling set: (χ2 = 6.551, p = 0.586); validation set: [(χ2 = 8.075, p = 0.426)]. The calibration curve of the model shows that the reference line of the fitted curve and the calibration curve is highly coincident, and the model has a good calibration degree for predicting the occurrence of fractures. The net benefit value of the risk model of osteoporosis patients complicated with OPF is high, which shows that the model is effective. In this study, a OPF risk prediction model is established and its prediction efficiency is verified, which can help identify the high fracture risk subgroup of osteoporosis patients in order to choose stronger intervention measures and management.