Receiver Operating Characteristic Curve Research Articles

Roles of microRNA-486-5p in the diagnosis and the association with clinical symptoms of cervical cancer.

Aim: To explore the predictive value of miR-486-5p in early cervical cancer and the associations of miR-486-5p with different clinical symptoms.Materials & methods: A total of 185 women were recruited. The relative expression levels of serum miR-486-5p were analyzed by real-time polymerase chain reaction. The receiver operator characteristic curves were utilized to reflect the predictive performance of miR-486-5p and squamous cell carcinoma antigen for early cervical cancer. Univariate logistic regression and ranked logistic regression were used to explore the associations of miR-486-5p with different clinical symptoms of early cervical cancer, with odd ratios (ORs) and 95% confidence intervals.Results: Eighty-one women (44.26%) had early cervical cancer. The relative expression of serum miR-486-5p was 1.99-fold higher in early cervical cancer patients than that in controls (p<0.0001). The predictive performance of miR-486-5p for early cervical cancer was significantly superior to that of squamous cell carcinoma antigen, with an area under the curve of 0.865 (p<0.001), sensitivity of 1.000and specificity of 0.804. In addition, overexpressed miR-486-5p was associated with high odds of maximum tumor diameter increase (OR=1.30, 95%CI: [1.01-1.66]).Conclusion: MiR-486-5p may be a potential biomarker for the early cervical cancer diagnosis, and was linked to the risk of maximum tumor diameter.

Malnutrition is associated with severe outcome in elderly patients hospitalised with COVID-19

Some studies have identified influencing factors of COVID-19 illness in elderly, such as underlying diseases, but research on the effect of nutritional status is still lacking. This study retrospectively examined the influence of nutritional status on the outcome of elderly COVID-19 inpatients. A retrospective analysis of the clinical data of 4241 COVID-19 patients who were admitted to a third-class hospital of Nanchang between November 1, 2022 and January 31, 2023 was conducted. Nutritional status was assessed using the prognostic nutritional index (PNI) and controlling nutritional status score (CONUT). The influence of nutritional status on the outcome of COVID-19 patients was determined through multivariate adjustment analysis, restrictive cubic spline, and receiver operating characteristic curve (ROC). Compared with mild/no malnutrition, severe malnutrition substantially increased the critical outcome of COVID-19. A linear relationship was observed between the odds ratio (OR) and PNI and CONUT (P > 0.05). The area under the ROC curve indicated that PNI was the better predictor. The optimal cutoff value of PNI was 38.04 (95%CI: 0.797 ~ 0.836, AUC = 0.817), with a sensitivity of 70.7% and a specificity of 79.6%. The critical illness of elderly COVID-19 patients shows a linear relationship with malnutrition at admission. The use of PNI to assess the prognosis of COVID-19 eldeely patients is reliable, highlighting the importance for doctors to closely pay attention to the nutritional status of COVID-19 patients. Focusing on nutritional status in clinical practice can effectively reduce the critical illness of elderly COVID-19 patients.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Osteocalcin: may be a useful biomarker for early identification of rapidly progressive central precocious puberty in girls.

To assess serum osteocalcin (OC) as a potential biomarker for the early detection of rapidly progressive central precocious puberty (RP-CPP) in girls. Serum OC levels were quantified using enzyme-linked immunosorbent assays (ELISAs). In the retrospective analysis, receiver operating characteristic (ROC) curve analysis was employed to evaluate the ability of OC to identify RP-CPP. A prospective study and screening tests were utilized to assess the potential of OC for use in the early prediction of RP-CPP. Variable selection in the multivariate analysis was conducted using the Bayesian Information Criterion (BIC) and binary logistic regression was employed to construct the diagnostic prediction model. Girls with RP-CPP had significantly higher serum OC levels compared to girls with non-rapidly progressive central precocious puberty (NRP-CPP) (149.04±40.50 vs. 89.10±31.83 ng/mL, P < 0.001). The optimal OC cut-off point for differentiating RP-CPP from NRP-CPP was 107.05 ng/mL, the area under the ROC curve (AUC) was 0.90 (95%CI: 0.851-0.949; P < 0.001), with a sensitivity of 91.1% and specificity of 70.7%. The results of the prospective study indicated that changes in OC precede alterations in estradiol (E2) and bone age (BA). A diagnostic prediction model that includes duration of breast development, BA, OC, high-density lipoprotein cholesterol (HDL-C), and uterine length achieved an AUC of 0.961, with a sensitivity of 94.1% and specificity of 91.5% for the detection of RP-CPP. If OC is excluded from the model, the AUC decreases to 0.894, with sensitivity and specificity declining to 80.5% and 83.1%, respectively. Serum OC levels may serve as a promising biomarker for the early differentiation between RP-CPP and NRP-CPP in girls. The diagnostic prediction model that incorporates duration of breast development, BA, OC, HDL-C, and uterine length effectively identifies girls with RP-CPP.

Evaluation of albumin and lactate dehydrogenase in comparison with cytokeratin 19 fragments and neuron‐specific enolase as diagnostic biomarkers for non‐small cell lung cancer

AbstractBackgroundThe diagnosis of non‐small cell lung cancer (NSCLC) is a clinical issue that requires attention, and more practical and effective biomarkers need to be selected to assist in diagnosis. This study aimed to examine the diagnostic value of serum albumin (ALB), lactate dehydrogenase (LDH), cytokeratin 19 fragments (CYFRA21‐1), and neuron‐specific enolase (NSE) for NSCLC.MethodsThe clinical data of 1048 NSCLC patients and 1125 healthy subjects were extracted from electronic medical records. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic significance of ALB, LDH, CYFRA21‐1, and NSE for NSCLC. The Cancer Genome Atlas (TCGA) data, which included mRNA profiles for ALB and LDH expression, were acquired from TCGA Program. Finally, interactive survival scatter plots and survival analyses for NSCLC patients were evaluated using the Human Protein Atlas and Kaplan–Meier Plotter.ResultsSignificant differences were noted in the levels of ALB and LDH between NSCLC patients and healthy controls. The areas under the ROC curves (AUCs) for ALB and LDH were 0.754 (95% CI: 0.734–0.774) and 0.681 (95% CI: 0.658–0.704), respectively. Moreover, the combination of ALB and LDH raised the AUC to 0.804 (95% CI: 0.785–0.823), and the incorporation of CYFRA21‐1 and NSE further increased the AUC to 0.903 (95% CI: 0.890–0.916). Notably, ALB and LDH might be related to the overall survival of NSCLC patients.ConclusionThis study revealed that ALB and LDH in NSCLC patient serum could improve the diagnostic accuracy of conventional biomarkers for NSCLC.

Diagnostic Value of Osteopontin, Lymphocyte-to-Monocyte Ratio, and CA-125 in Ovarian Carcinoma Suspect

Osteopontin (OPN) is an important protein in cancer development and progressivity. Lymphocyte-to-Monocyte Ratio (LMR) as a systemic immunity and inflammatory indicator could be an ideal predictor biomarker because of its method’s simplicity and availability. Elevated CA-125 and OPN as well as decreased LMR were reported as signs of ovarian malignancy. Limited studies about OPN and LMR as diagnostic biomarkers, as well as various specificity and sensitivity of CA-125 intrigued the researcher to prove OPN, LMR, and CA-125 as diagnostic biomarkers for ovarian carcinoma. This study aimed to measure the diagnostic value of OPN, LMR, and CA-125 levels against histopathology results for ovarian carcinoma diagnosis. Eighty patients involved with suspected ovarian carcinoma who were referred to Dr. Kariadi Hospital, Semarang. Osteopontin and CA-125 levels were measured using ELISA, and LMR was calculated from absolute lymphocyte and monocyte counts using an automated hematology analyzer. The receiver operating characteristics curve was used to determine the cut-off and 2x2 table. The cut-off values for OPN, LMR, and CA-125 were 124 ng/mL, 3.7 and 45.4 U/mL, respectively. The sensitivity for OPN, LMR, and CA-125 was 67.24%, 62.07% and 60.34%. Specificity for OPN, LMR, and CA-125 were 68.18%, 54.55% and 59.09%. Osteopontin is the best parameter for determining the diagnosis of ovarian carcinoma but it is still not sufficient because OPN cut-off was still within the normal reference value.

Construction of vulnerable plaque prediction model based on multimodal vascular ultrasound parameters and clinical risk factors

The rupture of vulnerable plaque (VP) are significant pathogenic factors leading to cardiovascular and cerebrovascular diseases. This study aims to construct a vulnerable plaque prediction model (VPPM) by combining multimodal vascular ultrasound parameters and clinical risk factors, and to validate it. A total of 196 atherosclerotic patients who underwent carotid endarterectomy (CEA) from January 2017 to December 2023 were collected and divided into a modeling group (n = 137) and a validation group (n = 59). Clinical information including: hypertension, diabetes, smoking history, and body mass index (BMI) was included in the analysis. All patients underwent carotid ultrasound and contrast-enhanced ultrasound (CEUS) examination after admission, with main ultrasound parameters including thickness, echogenicity types, stenosis degree, and CEUS neovascularization grading of plaques. Independent risk factors for VP in CEA patients were screened through binary Logistic regression analysis, and a prediction model was established along with a nomogram. The calibration curve, receiver-operating characteristic curve (ROC), and decision curve analysis (DCA) were employed to assess the calibration, diagnostic efficacy, and clinical utility of the VPPM model. There were no significant statistical differences in multimodal vascular ultrasound parameters and clinical risk factors between the modeling and validation groups (P > 0.05). Binary Logistic regression analysis identified plaque thickness, echo type, CEUS neovascularization grading, BMI, and smoking history as 5 variables entering the prediction model. The VPPM model showed good diagnostic efficacy, with an area under the ROC curve of 0.959 (95% CI 0.915–0.999). Using the nomogram with a VPPM risk assessment score of 135.42 as the diagnostic cutoff value in the modeling group, the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and Youden index were 88.1%, 94.1%, 14.98, 0.126, and 82.2%, respectively. In the DCA curve, the VPPM model curve was significantly better than two extreme lines, indicating good clinical utility. The VPPM model constructed by integrating multimodal ultrasound parameters and clinical key risk factors has high diagnostic efficacy and is expected to be an auxiliary tool for clinical diagnosis of vulnerable plaques.

Preoperative discrimination of invasive and non-invasive breast cancer using machine learning based on automated breast volume scanning (ABVS) radiomics and virtual touch quantification (VTQ)

PurposeEvaluating the efficacy of machine learning for preoperative differentiation between invasive and non-invasive breast cancer through integrated automated breast volume scanning (ABVS) radiomics and virtual touch quantification (VTQ) techniques.MethodsWe conducted an extensive retrospective analysis on a cohort of 171 breast cancer patients, differentiating them into 124 invasive and 47 non-invasive cases. The data was meticulously divided into a training set (n = 119) and a validation set (n = 52), maintaining a 70:30 ratio. Several machine learning models were developed and tested, including Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM). Their performance was evaluated using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), and visualized the feature contributions of the optimal model using Shapley Additive Explanations (SHAP).ResultsThrough both univariate and multivariate logistic regression analyses, we identified key independent predictors in differentiating between invasive and non-invasive breast cancer types: coronal plane features, Shear Wave Velocity (SWV), and Radscore. The AUC scores for our machine learning models varied, ranging from 0.625 to 0.880, with the DT model demonstrating a notably high AUC of 0.874 in the validation set.ConclusionOur findings indicate that machine learning models, which integrate ABVS radiomics and VTQ, are significantly effective in preoperatively distinguishing between invasive and non-invasive breast cancer. Particularly, the DT model stood out in the validation set, establishing it as the primary model in our study. This highlights its potential utility in enhancing clinical decision-making processes.

Comparing ultrasound-derived fat fraction and MRI-PDFF for quantifying hepatic steatosis: a real-world prospective study.

To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard. This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis. A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5-18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0-18.0) and 9.0% (IQR: 5.0-18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934-0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852-0.930). The Bland-Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, -8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively. UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively. Question Is ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)? Findings UDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively. Clinical relevance UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.

White Blood Cell and Peripheral Blast Counts in Leukemia Patients with Non-Graying WBC-Diff Scattergram

Graying scattergram of White Blood Cell differential (WBC-diff) is often accompanied by blasts in the blood and/or leukocytosis in leukemia patients. Therefore, monitoring White Blood Cell (WBC) and peripheral blast count is crucial for patient management. In contrast, patients with non-graying WBC-diff scattergrams may have varying WBC counts and peripheral blasts. This study aimed to establish the cut-off values of WBC and peripheral blast counts that could predict a non-graying scattergram in leukemia patients. A retrospective descriptive study with a cross-sectional design analyzed secondary data from 21 leukemia patients. The acute and chronic leukemia diagnosis were based on bone marrow aspirates, which simultaneously showed a graying WBC-diff scattergram on Complete Blood Count (CBC). Receiver Operating Characteristic (ROC) curve analysis was conducted when the patient’s CBC displayed a non-graying scattergram. The cut-off value for WBC count was 16,380 x 109/L with an Area Under Curve (AUC) of 0.932, a sensitivity of 85.7%, p = 0.001, and the cut-off for peripheral blast count was 35.5% with an AUC of 0.872, the sensitivity of 76.2%, p=0.001. The results suggest that a non-graying scattergram of the WDF channel in leukemia is more likely associated with WBC counts below 16,380 x 109/L and peripheral blast counts below 35.5%, which have excellent and good predictive value.

Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks

ABSTRACT Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthier life. This study proposed a procedure to fine-tune the Convolutional Neural Networks (CNN) model with data preprocessing and augmentation in classifying mammogram images called the Hybrid Mammogram Classification and Detection Pipeline (HMCaD). After using CNN for classification because it brings higher confidence in classifying tasks, the YOLOv8 has been applied for localization subtask to detect abnormal positions with predicted bounding boxes. The database is provided by the Mammographic Image Analysis Society (MIAS) and is protected by the United Kingdom. It comprises 330 samples, including 79 benign, 54 malignant, and 207 normal images. As a result, the classification in our model based on the custom EfficientNetB3 model and seam carving technique received a great validation accuracy, test accuracy, and F1 score throughout three scenarios at 96.73%, 97.59%, and 97.58%, respectively. Furthermore, the area under the Receiver Operating Characteristic (ROC) curve also has a surprise result of 0.96 (i.e. AUC = 0.96 ). Moreover, YOLOv8 for detecting abnormal positions in our study achieved 83.22% in Intersection over Union (IoU). This led to the research reaching good results in classifying and detecting breast cancer by considering several performance metrics.

Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue.

The distinction between noncancerous and cancerous breast tissues is challenging in clinical settings, and discovering new proteomics-based biomarkers remains underexplored. Through a pilot proteomic study (discovery cohort), we aimed to identify a protein signature indicative of breast cancer for subsequent validation using six published proteomics/transcriptomics data sets (validation cohorts). Sequential window acquisition of all theoretical (SWATH)-based mass spectrometry revealed 370 differentially abundant proteins between noncancerous tissue and breast cancer. Protein-protein interaction-based networks and enrichment analyses revealed dysregulation in pathways associated with extracellular matrix organization, platelet degranulation, the innate immune system, and RNA metabolism in breast cancer. Through multivariate unsupervised analysis, we identified a four-protein signature (OGN, LUM, DCN, and COL14A1) capable of distinguishing breast cancer. This dysregulation pattern was consistently verified across diverse proteomics and transcriptomics data sets. Dysregulation magnitude was notably higher in poor-prognosis breast cancer subtypes like Basal-Like and HER2 compared to Luminal A. Diagnostic evaluation (receiver operating characteristic (ROC) curves) of the signature in distinguishing breast cancer from noncancerous tissue revealed area under the curve (AUC) ranging from 0.87 to 0.9 with predictive accuracy of 80% to 82%. Upon stratifying, to solely include the Basal-Like/Triple-Negative subtype, the ROC AUC increased to 0.922-0.959 with predictive accuracy of 84.2%-89%. These findings suggest a potential role for the identified signature in distinguishing cancerous from noncancerous breast tissue, offering insights into enhancing diagnostic accuracy.

Cornea endothelial cell loss before and after explantation of Artisan and Artiflex iris-fixated phakic intraocular lenses

PurposeTo investigate the risk factors for corneal endothelial cell density (ECD) loss of two types of iris-fixated phakic intraocular lenses (pIOL) and indicate the cutoff values for preventing irreversible ECD loss after explantation. DesignThe retrospective, comparative, clinical cohort study MethodsTotal 114 eyes of 61 patients were included in this study. Fifty-five eyes with Artisan pIOL and 59 eyes with Artiflex pIOL had undergone explantation of pIOLs due to continuous ECD loss. Correlation analysis was performed to assess the ocular parameters associated with ECD loss. Receiver operating characteristic (ROC) curve was performed to set cutoff values of ocular parameters to prevent ECD loss after explantation. ResultThe mean ECD before explantation was 1637 cells/mm2 in Artisan group and 1769 cells/mm2 in Artiflex group. The distance of cornea endothelium to IOL center and edges were all significantly higher in Artisan group (p <0.05). The risk factor of decreased ECD in Artisan group were small anterior chamber angle (ACA), and distance of nasal edge of IOL to endothelium and iris fixation to nasal limbus distance, and differences of fixation length. In Artiflex group, ACA, distance of nasal edge of IOL to endothelium, both limbus to IOL fixation length, fixation length, iris pigmentation grading were the risk factors. At 1 year after the pIOL removal, the population demonstrating ΔECD >10% were significantly higher in Artisan group than Artiflex group (p <0.05). In ROC curve analysis, the cutoff value of preoperative ECD to prevent significant post-explantation ECD loss was 1683.5 cells/mm2 in Artisan group and 1648 cells/mm2 in Artiflex group. ConclusionThis study examined the existing explantation guidelines for these lenses. In analyzing the longterm prognosis of cornea ECD with Iris-fixated phakic intraocular lens, it is important to identify the relevant anatomical factors. Small ACA, and short distances from IOL to endothelium and from limbus to iris fixation especially in the nasaSSSl region were significant risk factors to continuous ECD loss in both lenses. To minimize irreversible ECD loss after explantation, it is important to determine the optimal period of explantation by observing both ECD and structural changes.

The predictive value of stress-induced hyperglycemia parameters for delayed healing after tibial fracture post-surgery

PurposeThe objective of this retrospective cohort study was to investigate the predictive value of stress-induced hyperglycemia parameters for delayed healing after tibial fracture post-surgery.MethodsA cohort of 108 participants who underwent surgical intervention for tibial fractures caused by trauma was included in this retrospective study. Data collected from electronic medical records encompassed demographic characteristics, bone healing assessments, stress-induced hyperglycemia parameters, inflammatory markers, and stress-related hormones. Comparative analyses, correlation analyses, univariate logistic regression analyses, and receiver operating characteristic (ROC) curve analyses were conducted to assess the predictive value of the studied parameters.ResultsThe delayed healing group exhibited higher levels of fasting blood glucose, postprandial glucose, and HbA1c, as well as elevated levels of inflammatory markers and stress-related hormones compared to the normal healing group. Correlation analysis and logistic regression demonstrated positive associations between stress-induced hyperglycemia parameters, inflammatory markers, stress-related hormones, and delayed union of tibial fractures (R2: 0.183 ~ 0.403;OR: 1.091 ~ 16.332). ROC curve analysis revealed high area under the curve (AUC = 0.911) values for stress-induced hyperglycemia parameters, indicating their potential as predictive markers for delayed healing. Multivariate regression analysis further substantiated the predictive capability of stress-induced hyperglycemia parameters.ConclusionThe study findings highlight the complex interplay between stress-induced hyperglycemia, inflammatory response, and bone healing outcomes in patients undergoing surgical intervention for tibial fractures. The identification of stress-induced hyperglycemia parameters as potential predictive markers for delayed healing after tibial fracture surgery offers insights for risk assessment and patient management, emphasizing the need for comprehensive understanding of these factors to optimize postoperative recovery in orthopedic patients.

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network. Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector. Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system. An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

A novel scoring system for predicting disease severity without CT imaging in acute diverticulitis

PurposeClinical scoring could help physicians identify patients with suspected acute diverticulitis who would benefit from further evaluation using computed tomography imaging. The aim of the study was to identify risk factors for complicated acute diverticulitis and create a risk score to predict disease severity in acute diverticulitis.MethodsPatients diagnosed with CT-verified acute diverticulitis between 2015 and 2017 were included. Data on patients’ clinical and laboratory findings and medical histories were collected retrospectively. Risk factors for complicated acute diverticulitis were identified using univariate and multivariate analyses. Continuous laboratory values were categorised by cut-off points determined using receiver operating characteristic (ROC) analysis. The Acute Diverticulitis Severity Score was formulated using logistic regression analysis.ResultsOf the total 513 patients included in the study, 449 (88%) had UAD, and 64 (12%) had CAD. Older age, significant comorbidities, C-reactive protein level, leucocyte count, vomiting, and body temperature were found to be independently associated with a higher risk for CAD. The novel Acute Diverticulitis Severity Score could reliably detect patients with CAD. The area under the ROC curve was 0.856 (p < 0.001) in discriminating disease severity. While higher scores indicate radiological studies, patients with low scores face an almost non-existent risk for complicated disease, making such studies possibly redundant.ConclusionsThe Acute Diverticulitis Severity Score accurately separated patients with uncomplicated disease from those at risk for complicated disease. This score can be applied in daily clinical practice to select patients requiring further investigation, consequently reducing healthcare costs and burdens.

Diagnostic value of baseline 18F-FDG PET/CT and peripheral blood inflammatory markers for aggressive lymphoma in non-Hodgkin's lymphoma.

This study aims to investigate the diagnostic value of baseline F18 fluorodeoxyglucose (FDG) PET/computed tomography (CT) parameters and peripheral blood inflammatory markers in aggressive lymphoma of non-Hodgkin lymphoma (NHL) and the correlation between peripheral blood inflammatory markers and maximum standardized uptake value (SUVmax). We conducted a retrospective analysis including 121 patients with NHL. Patients were divided into aggressive lymphoma group and indolent lymphoma group. Mann-Whitney U test, chi-square test and multivariate stepwise logistic regression were used to analyse. Subsequently, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance. Additionally, Spearman correlation analysis was utilized to explore the correlation between peripheral blood inflammatory markers and SUVmax. Leptin mass criterion uptake value (SUL)max, SUVmax, SUVavg, SUVpeak, focal SUVmax/liver SUVmax, focal SUVmax/ mediastinal SUVmax, SULavg, SULpeak, systemic immune-inflammation, neutrophil ratio, total lesion glycolysis, neutrophils versus lymphocyte ratio, platelet-to-lymphocyte ratio, hemoglobin-to-white blood cell ratio, lactate dehydrogenase and lymphocyte ratio between two groups were statistically significant (P < 0.05). SUVmax was an independent influencing factor, and the area under the ROC curve was 0.862. There was a positive correlation between the platelet-to-lymphocyte ratio and SUVmax (r = 0.239; P = 0.008). PET/CT parameters and peripheral blood inflammatory markers have certain value in the diagnosis of aggressive lymphoma in NHL, among which SUVmax is an independent influencing marker and is positively correlated with PLR.

Predictive values and correlation of CRIB Score II and perfusion index in assessment of severity of illness in sick preterm neonates: An observational study.

Perfusion index (PI) and CRIB (Clinical risk index of babies) Score II both are minimally invasive tools for prediction of mortality and morbidity in sick neonates. This study aims to know the predictive values of both PI and CRIB Score II in assessment of severity of illness in preterm neonates of 28 to 32 weeks and their correlation. PI and CRIB II Score of 125 sick preterm neonates admitted in NICU were assessed within 12 hours of admission. Severity noted in form of outcome. Correlation between PI and CRIB Score II were observed. Receiver operating characteristic (ROC) curve were plotted for PI and CIRB Score II with outcome (discharged vs expired). Area under curve for perfusion index was 0.776 at 95% confidence interval. Optimum cutoff point based on ROC curve was 1.65 with sensitivity of 84%., specificity of 37.2% with positive predictive value of 70% and negative predictive value of 57%. Area under curve for CRIB Score II was 0.622 (p value < 0.028), optimum cut off point based on ROC curve for CRIB II score was 3.5 with sensitivity 79.10% of specificity of 40% positive predictive value is 43.03% and negative predictive value of 76.9%. Spearman's correlation coefficient between PI and CRIB II score was -0.272 and p value is 0.002. This study concluded that perfusion index (p < 0.001) is better for predicting the severity compared to CRIB II score (p < 0.028). A weak negative correlation exists between PI and CRIB II score (r = -0.272).

Multi-gene mutations in ultrasound-guided fine-needle aspiration specimens of thyroid micronodules and diagnostic value in thyroid microcarcinomas using next-generation sequencing

Objective: To explore the detection of BRAF, RAS, TERT promoter, and TP53 gene mutations in solitary thyroid micronodule (TMN) specimens obtained by ultrasound-guided fine-needle aspiration (US-FNA) using next-generation sequencing (NGS) technology and assess its diagnostic value in thyroid microcarcinomas (TMC). Methods: On-site recruitment of 428 patients with single suspicious TMC who underwent thyroid ultrasound examination, US-FNA, and NGS from September 2018 to July 2021 at Beijing Tongren Hospital affiliated to Capital Medical University. A total of 147 patients were finally included. NGS was used to detect mutations in the BRAF, RAS, TERT promoter, and TP53 genes in the US-FNA specimens. Comparisons were made between patients with and without gene mutations in terms of age, gender, and the maximum diameter of nodules. The diagnostic efficiency of BRAF mutation for TMC was calculated using the receiver operating characteristic (ROC) curve, with postoperative pathology as the gold standard. Results: The age [M (Q1, Q3)] of the 147 patients was 43.0 (32.0, 51.0) years, and 37 were male (25.2%). Among the 147 US-FNA specimens, 97 (66.0%) were detected with BRAF gene mutations, all of which were p.V600E point mutation; 6 (4.1%) were detected with RAS gene mutation, and no TERT promoter or TP53 gene mutations were detected. Postoperative pathology confirmed that 136 cases were TMC, all of which were papillary thyroid microcarcinomas (PTMC); 11 cases (7.5%)were benign. Among 136 TMC samples, BRAF gene mutations were detected in 97 cases (71.3%). There were no statistically significant differences in age, gender, and maximum nodule diameter between patients with and without BRAF gene mutations (all P>0.05). The sensitivity and specificity of BRAF gene mutation in diagnosing TMC were 71.3% and 100.0%, respectively, with an area under the ROC curve (AUC) (95%CI) of 0.857 (0.789-0.925). For nodules classified as Bethesda Ⅲ-Ⅴ, the sensitivity and specificity were 63.0% and 100.0%, respectively, with an AUC (95%CI) of 0.815 (0.680-0.950). Conclusions: NGS technology can successfully detect multiple gene mutations in US-FNA specimens from TMN patients, especially BRAF gene mutation, and BRAF gene mutation has certain value in diagnosing TMC.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.