Areas Under The ROC Curve Research Articles

The usefulness of ultrasound in identifying the underlying findings linked to pain in podagra patients.

To explore whether ultrasound (US) can be employed to identify the underlying characteristics associated with painin patients with podagra by evaluating the relationship between ultrasound findings and clinical pain. Patients with podagra were recruited and grouped into a pain group (G1, 82 patients) and a non pain group (G2, 123 patients).US features were collected and compared. US data were analyzed by binary logistic regression analysis and ROC analysis.Interobserver reliability was assessed, too. A total of 205 patients (196 male and 9 female) were enrolled in thisstudy. In multivariate analysis, the thickness of the synovium (OR=1.928, CI=1.074-3.463), CD (color Doppler) signal of thesynovium (OR=1.458, CI=1.011-2.103), and CD signal of the tophi (OR=1.576, CI=1.142-2.177) were identified as risk factorsfor clinical pain. Areas under the ROC curves (AUC) were 0.713, 0.686 and 0.641 for the three indicators, respectively.The best cutoff points were 1 mm for the thickness of the synovium, grade 1 for the CD signal of the synovium and grade 2for the CD signal of the tophi. Ultrasound can provide valuable information for determining underlying featuresassociated with pain in patients with podagra.

Development of a Predictive Model for Skin-to-Skin Contact Immediately after Birth: A Cross-Sectional Study.

The aim of this study was to develop and validate a predictive model for the establishment of skin-to-skin contact immediately after birth. A descriptive cross-sectional study was conducted during the last trimester of 2022 and the first trimester of 2023 with women who had given birth in Spain. A questionnaire containing sociodemographic, psychosocial, and health variables referring to the mother and the newborn, as well as the Bond and Attachment questionnaire (VAMF, for its name in Spanish) for the analysis of the mother-child bond and attachment, were administered. A multivariate analysis was performed, and areas under the ROC curve (AUC) with their 95% confidence intervals (CI) and the predictive characteristics of these models were estimated. In total, 1077 women participated. The prevalence of early skin-to-skin contact after delivery was 50.2% (468) in the derivation cohort and 49.8% (464) in the validation cohort. Multivariate analysis showed that prematurity, type of delivery, and birth experience were statistically significant, so they were included in the model (p ≤ 0.05). The predictive ability (AUC ROC) was good in both the derivation cohort, yielding 0.92 (95% CI: 0.89-0.95), and in the validation cohort, yielding 0.89 (95% CI: 0.85-0.93). This study developed a predictive model identifying factors facilitating early skin-to-skin contact between a mother and her newborn immediately after birth.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma

BackgroundThis study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA).ResultsThe PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit.ConclusionsThe radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Generalizability of PGS313 for breast cancer risk in a Los Angeles biobank

Polygenic scores (PGS) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PGSs in breast cancer to date is PGS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PGS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular EHR system, genotyping platform, and quality control process. We found that the PGS313 achieved overlapping Areas under the ROC Curve (AUCs) in females of Lantix (AUC, 0.68; 95 CI, 0.65-0.71) and European ancestry (AUC, 0.70; 95 CI, 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC, 0.61; 95 CI, 0.56-0.65; EAA: AUC, 0.64; 95 CI, 0.60-0.680). While PGS313 is associated with Hormone Positive (HR+) disease in European Americans (OR, 1.42; 95 CI, 1.16-1.64), this association is lost in African, Latinx, and Asian Americans. In summary, we found that PGS313 was significantly associated with breast cancer but with attenuated accuracy in women of African and Asian descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to clinical implementation of polygenic scores.

External validation of two clinical prediction models for mortality in COVID-19 patients (4C and NEWS2), in three centers in Medellín, Colombia: Assessing the impact of vaccination over time

ObjectivesExternal validation of the 4C and NEWS2 scores for the prediction of in-hospital mortality in COVID-19 patients, and evaluation of its operational performance in two time periods: before and after the start of the vaccination program in Colombia. MethodsRetrospective cohort in three high complexity hospitals in the city of Medellín, Colombia, between June 2020 and April 2022. ResultsThe areas under the ROC curve (AUC) for the 4C mortality risk score and the NEWS2 were 0.75 (95% CI 0.73–0.78) and 0.68 (95% CI 0.66–0.71), respectively. For the 4C score, the AUC for the first and second periods was 0.77 (95% CI 0.74–0.80) and 0.75 (95% CI 0.71–0.78); whilst for the NEWS2 score, it was 0.68 (95% CI 0.65–0.71) and 0.69 (95% CI 0.64–0.73). The calibration for both scores was adequate, albeit with reduced performance during the second period. ConclusionsThe 4C mortality risk score proved to be the more adequate predictor of in-hospital mortality in COVID-19 patients in this Latin American population. The operational performance during both time periods remained similar, which shows its utility notwithstanding major changes, including vaccination, as the pandemic evolved.

Preparation and validation of a predictive model of breastfeeding initiation in the first hour of life

ObjectiveThe objective was to develop and validate a predictive model of initiation of breastfeeding in the first hour after delivery. MethodsRetrospective cohort study on women who gave birth between 2013 and 2018 in Spain. For data collection, an ad hoc questionnaire was designed to be filled in by the mothers, which was distributed to the different Spanish breastfeeding associations which, in turn, shared it with their associate partners. The development of the predictive model was made on a cohort of 3218 women (2/3) and was validated on a cohort of 1609 women (1/3). Mothers whose children were admitted to hospital at the time of birth were excluded. A multivariate analysis was performed by means of logistic regression, and predictive ability was determined by areas under the ROC curve (AUC). Results81.0 % (2608) women started breastfeeding in the first hour in the derivation cohort, and 80.1 % (1289) in the validation cohort. The predictive factors in the final model were: the highest number of children and skin-to-skin contact at birth as flattering factors, while dystocic delivery reduced the likelihood of the onset of breastfeeding. The predictive ability (ROC AUC) in the derivation cohort was 0.89 (CI 95 %: 0.87–0.90), while in the validation cohort it was 0.89 (CI 95 %: 0.87–0.92). ConclusionsThis three-variable predictive model has excellent predictive ability in both the derivation cohort and the validation cohort. This model can identify women who are at high risk of non-initiating breastfeeding within the first hour after delivery.

PREDICTING LENGTH OF STAY OF JOINT REPLACEMENT PATIENTS AT ASHFORD AND ST. PETER'S HOSPITAL USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Arthroplasties are widely performed to improve mobility and quality of life for symptomatic knee/hip osteoarthritis patients. With increasing rates of Total Joint Replacements in the United Kingdom, predicting length of stay is vital for hospitals to control costs, manage resources, and prevent postoperative complications. A longer Length of stay has been shown to negatively affect the quality of care, outcomes and patient satisfaction. Thus, predicting LOS enables us to make full use of medical resources.Clinical characteristics were retrospectively collected from 1,303 patients who received TKA and THR. A total of 21 variables were included, to develop predictive models for LOS by multiple machine learning (ML) algorithms, including Random Forest Classifier (RFC), K-Nearest Neighbour (KNN), Extreme Gradient Boost (XgBoost), and Na¯ve Bayes (NB). These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance. A feature selection approach was used to identify optimal predictive factors. Based on the ROC of Training result, XgBoost algorithm was selected to be applied to the Test set.The areas under the ROC curve (AUCs) of the 4 models ranged from 0.730 to 0.966, where higher AUC values generally indicate better predictive performance. All the ML-based models performed better than conventional statistical methods in ROC curves. The XgBoost algorithm with 21 variables was identified as the best predictive model. The feature selection indicated the top six predictors: Age, Operation Duration, Primary Procedure, BMI, creatinine and Month of Surgery.By analysing clinical characteristics, it is feasible to develop ML-based models for the preoperative prediction of LOS for patients who received TKA and THR, and the XgBoost algorithm performed the best, in terms of accuracy of predictive performance. As this model was originally crafted at Ashford and St. Peters Hospital, we have naturally named it as THE ASHFORD OUTCOME.

Comparison of blood ΒHBA measurement devices for diagnosis of subclinical pregnancy toxaemia in sheep: A field study.

Pregnancy toxaemia is a serious disease that occurs during the last trimester of pregnancy in sheep. Yet, in most cases, the disease may have a subclinical course. This study was aimed at comparing blood βHBA measurement devices for diagnosis of subclinical pregnancy toxaemia in late pregnant sheep. In the study, a total of 50 blood samples were collected from Romanov (n = 30) and cross-bred Hamdani (n = 20) sheep. Blood βHBA levels were measured using TaiDoc TD-4235 and CentriVet βHBA hand-held meter. Randox βHBA (Ranbut) assay was used as a reference laboratory method to compare hand-held meters. βHBA value of 0.8 mmol/L was set as the cut-off value for diagnosis of subclinical pregnancy toxaemia. Statistical analyses were carried out using Minitab 21 and Jamovi software. In the study, the correlation of Randox-TaiDoc TD-4235 and Randox-CentriVet was .822 (p < .001) and .728 (p < .001), respectively. Based on the Ranbut assay, nine ewes out of 50 were diagnosed with subclinical pregnancy toxaemia. Specificity (detection of healthy ewes) and sensitivity (detection of ewes with subclinical pregnancy toxaemia) for TaiDoc TD-4235 and CentriVet hand-held meters were 100%, 77.8%, and 100%, 66.7%, respectively. In the receiver operating characteristic (ROC) analysis, areas under the ROC curve (AUC) were 0.976 and 0.920 for TaiDoc and CentriVet, respectively. Bland-Altman analysis revealed a bias of 0.092 mmol/L for TaiDoc and a bias of 0.132 mmol/L for CentriVet. TaiDoc hand-held meter shows a better correlation with the Randox Ranbut assay and greater sensitivity compared to the CentriVet hand-held meter. In conclusion, both TaiDoc and CentriVet hand-held meters can be securely used in the diagnosis of subclinical pregnancy toxaemia in sheep. For these reasons, subclinical pregnancy toxaemia and these devices will be evaluated within the scope of herd management programme in the sheep industry. It should also be taken into account that these conditions will affect the future fertility of the mother and offspring.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. Retrospective. 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared. DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%). The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. Stage 2.

Risk factors and early detection of joint damage in patients with psoriasis: a case-control study.

Our aim was to target the unsatisfied need for early detection of the at-risk population and determine the subgroup of patients whose psoriasis (PsO) could transform into psoriatic arthritis (PsA). A retrospective and longitudinal case-control study was conducted at Beijing Chao-yang Hospital. It included 75 patients who were clinically diagnosed with PsA in the case group and 345 who solely suffered from PsO without PsA in the control group. A variety of baseline covariates were gathered from every patient with PsO. Univariate and multivariate analyses and receiver operating characteristic (ROC) curves were used to identify underlying risk factors and determine whether it was necessary to examine the imaging of PsO patients. In multivariate logistic regression analysis, age ≥40 (odds ratio (OR): 1.04, 95% confidence interval (CI): 1.02-1.06, P < 0.01), nail involvement (OR: 1.17, 95% CI: 1.09-1.32, P < 0.01), erythrocyte sedimentation rate (ESR) (OR: 1.03, 95% CI: 1.01-1.06, P < 0.05) and elevated high-sensitivity C-reactive protein (hs-CRP) (OR: 1.31, 95% CI: 1.13-1.53, P < 0.01) were perceived to be risk factors for the transformation from PsO into clinical PsA. By combining magnetic resonance imaging (MRI)-detected enthesitis with tenosynovitis, combined predictors demonstrated better diagnostic efficacy, with an improvement in specificity (94.3% vs. 69%) and similarities in sensitivity (89% vs. 84.6%). The areas under the ROC curve (AUCs) amounted to 0.925 (95% CI: 0.882-0.967, P < 0.01) and 0.858 (95% CI: 0.814-0.903, P < 0.01). It was identified that age ≥40, nail involvement, as well as an elevated ESR, and hs-CRP served as independent risk factors for PsO transforming into PsA. Additionally, MRI provides additional value for the early recognition of PsA.

Analysis of factors affecting intraoperative hemorrhage during percutaneous nephrolithotomy and establishment of nomogram model.

Intraoperative hemorrhage is an important factor affecting intraoperative safety and postoperative patient recovery in percutaneous nephrolithotomy (PCNL). This study aimed to identify the factors that influence intraoperative hemorrhage during PCNL and develop a predictive nomogram model based on these factors.A total of 118 patients who underwent PCNL at the Department of Urology, The Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University from January 2021 to September 2023 was included in this study. The patients were divided into a hemorrhage group (58 cases) and a control group (60 cases) based on the decrease in hemoglobin levels after surgery. The clinical data of all patients were collected, and both univariate analysis and multivariate logistic regression analysis were conducted to identify the independent risk factors for intraoperative hemorrhage during PCNL. The independent risk factors were used to construct a nomogram model using R software. Additionally, receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were utilized to evaluate the model.Multivariate logistic regression analysis revealed that diabetes, long operation time and low psoas muscle mass index (PMI) were independent risk factors for intraoperative hemorrhage during PCNL (P < 0.05). A nomogram model was developed incorporating these factors, and the areas under the ROC curve (AUCs) in the training set and validation set were 0.740 (95% CI: 0.637-0.843) and 0.742 (95% CI: 0.554-0.931), respectively. The calibration curve and Hosmer-Lemeshow test (P = 0.719) of the model proved that the model was well fitted and calibrated. The results of the DCA showed that the model had high value for clinical application.Diabetes, long operation time and low PMI were found to be independent risk factors for intraoperative hemorrhage during PCNL. The nomogram model based on these factors can be used to predict the risk of intraoperative hemorrhage, which is beneficial for perioperative intervention in high-risk groups to improve the safety of surgery and reduce the incidence of postoperative complications.

Integrated clinical and genomic models using machine-learning methods to predict the efficacy of paclitaxel-based chemotherapy in patients with advanced gastric cancer

BackgroundPaclitaxel is commonly used as a second-line therapy for advanced gastric cancer (AGC). The decision to proceed with second-line chemotherapy and select an appropriate regimen is critical for vulnerable patients with AGC progressing after first-line chemotherapy. However, no predictive biomarkers exist to identify patients with AGC who would benefit from paclitaxel-based chemotherapy.MethodsThis study included 288 patients with AGC receiving second-line paclitaxel-based chemotherapy between 2017 and 2022 as part of the K-MASTER project, a nationwide government-funded precision medicine initiative. The data included clinical (age [young-onset vs. others], sex, histology [intestinal vs. diffuse type], prior trastuzumab use, duration of first-line chemotherapy), and genomic factors (pathogenic or likely pathogenic variants). Data were randomly divided into training and validation sets (0.8:0.2). Four machine learning (ML) methods, namely random forest (RF), logistic regression (LR), artificial neural network (ANN), and ANN with genetic embedding (ANN with GE), were used to develop the prediction model and validated in the validation sets.ResultsThe median patient age was 64 years (range 25–91), and 65.6% of those were male. A total of 288 patients were divided into the training (n = 230) and validation (n = 58) sets. No significant differences existed in baseline characteristics between the training and validation sets. In the training set, the areas under the ROC curves (AUROC) for predicting better progression-free survival (PFS) with paclitaxel-based chemotherapy were 0.499, 0.679, 0.618, and 0.732 in the RF, LR, ANN, and ANN with GE models, respectively. The ANN with the GE model that achieved the highest AUROC recorded accuracy, sensitivity, specificity, and F1-score performance of 0.458, 0.912, 0.724, and 0.579, respectively. In the validation set, the ANN with GE model predicted that paclitaxel-sensitive patients had significantly longer PFS (median PFS 7.59 vs. 2.07 months, P = 0.020) and overall survival (OS) (median OS 14.70 vs. 7.50 months, P = 0.008). The LR model predicted that paclitaxel-sensitive patients showed a trend for longer PFS (median PFS 6.48 vs. 2.33 months, P = 0.078) and OS (median OS 12.20 vs. 8.61 months, P = 0.099).ConclusionsThese ML models, integrated with clinical and genomic factors, offer the possibility to help identify patients with AGC who may benefit from paclitaxel chemotherapy.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

VEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) and 166 094 non-IED 4-second video-EEG segments. The video data is processed by the proposed patient detection method, with frame difference and Simple Keypoints (SKPS) capturing patients’ movements. EEG data is processed with EfficientNetV2. The video and EEG features are fused via a multilayer perceptron. We developed a comparative model, termed nEpiNet, to test the effectiveness of the video feature in vEpiNet. The 10-fold cross-validation was used for testing. The 10-fold cross-validation showed high areas under the receiver operating characteristic curve (AUROC) in both models, with a slightly superior AUROC (0.9902) in vEpiNet compared to nEpiNet (0.9878). Moreover, to test the model performance in real-world scenarios, we set a prospective test dataset, containing 215 h of raw video-EEG data from 50 patients. The result shows that the vEpiNet achieves an area under the precision–recall curve (AUPRC) of 0.8623, surpassing nEpiNet’s 0.8316. Incorporating video data raises precision from 70% (95% CI, 69.8%–70.2%) to 76.6% (95% CI, 74.9%–78.2%) at 80% sensitivity and reduces false positives by nearly a third, with vEpiNet processing one-hour video-EEG data in 5.7 min on average. Our findings indicate that video data can significantly improve the performance and precision of IED detection, especially in prospective real clinic testing. It suggests that vEpiNet is a clinically viable and effective tool for IED analysis in real-world applications.

Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study

BackgroundPrediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance.MethodsFrom September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity.ResultsIn total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance.ConclusionsThe AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists.Trial registrationWe registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.

The relationship between serum astroglial and neuronal markers and AQP4 and MOG autoantibodies

BackgroundCertain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case.MethodsTo elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed.ResultsGFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80.ConclusionThe 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and seronegativity for αAQP4 and αMOG.

Development and validation of a nomogram for predicting lymph node metastasis in ductal carcinoma in situ with microinvasion: A SEER population-based study.

Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607-0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.

The relationship of testicular stiffness with Johnsen score and sperm retrieval outcome in men with non-obstructive azoospermia.

The pathological Johnsen score (JS) is a quantitative histological scoring system used to assess spermatogenesis in men with nonobstructive azoospermia (NOA), while elastic modulus derived from shear wave elastography (SWE) is a diagnostic tool for evaluating spermatogenic dysfunction. In this prospective observational study, we aimed to investigate whether testicular stiffness measured by SWE could serve as a substitute for JS in predicting sperm retrieval outcomes in men with NOA. This prospective cohort study analyzed 140 testes from 115 consecutive outpatient participants with NOA who had sought treatment at the reproductive medical center of a tertiary care hospital between January 2018 and October 2021. Testicular volume, elastic modulus, JS, and sperm retrieval outcomes were calculated. Statistical differences in parameters between the positive and negative sperm retrieval groups were determined using the Mann-Whitney test. Spearman rank correlation analysis was performed to determine the correlations between JS and either testicular volumes or elastic modulus. Receiver operating characteristic (ROC) curves were drawn to evaluate the diagnostic performance of the testicular elastic modulus and testicular volume. The JS correlated positively with testicular volume and negatively with the maximum elastic modulus (Emax) and mean elastic modulus (Emean), with correlation coefficients of 0.804, -0.686, and -0.456, respectively (P<0.01). There were significant differences in JS, testicular volume, and Emax between participants with positive and negative sperm retrieval of microdissection testicular sperm extraction (micro-TESE) (P<0.01). ROC curves were plotted for JS, testicular volume, and Emax to distinguish between participants with positive and negative sperm retrieval. The areas under the ROC curves (AUCs) were 0.783 [95% confidence interval (CI): 0.707-0.859; P<0.01], 0.737 (95% CI: 0.651-0.823; P<0.01), and 0.729 (95% CI: 0.643-0.814; P<0.01), respectively. When the cutoff value of JS was 4.5, its sensitivity and specificity were 60.3% and 89.6%, respectively. When the cutoff value of Emax was 3.75 kPa, its sensitivity and specificity were 79.1% and 64.4%, respectively. The sensitivity and specificity were 68.5% and 83.6%, respectively when the cutoff value of testicular volume was 8.17 mL. Emax combined with testicular volume improved this diagnostic value, with an AUC of 0.742 (95% CI: 0.657-0.828; P<0.01), and sensitivity and specificity were 83.6% and 68.5%, respectively. Our study suggests that the combination of testicular stiffness and volume measurements may serve as a viable alternative to pathological JS in predicting the likelihood of successful sperm retrieval prior to micro-TESE procedures.

Establishment and validation of a risk score model based on EUS: assessment of lymph node metastasis in early gastric cancer

Lymph node metastasis significantly affects the prognosis of early gastric cancer patients. Endoscopic ultrasonography (EUS) plays a crucial role in the preoperative assessment of early gastric cancer. This study evaluated the efficacy of EUS in identifying lymph node metastasis in early gastric cancer patients and developed a risk score model to aid in choosing the best treatment options. We retrospectively analyzed the effectiveness of EUS for detecting lymph node metastasis in early gastric cancer patients. A risk score model for predicting lymph node metastasis preoperatively was created using independent risk factors identified through binary logistic regression analysis and subsequently validated. Receiver operating characteristic (ROC) curves were generated for both the development and validation cohorts. The overall accuracy of EUS in identifying lymph node metastasis was 85.3%, although its sensitivity (29.2%) and positive predictive value (38.7%) were relatively low. Patients were categorized based on preoperative risk factors for lymph node metastasis, including tumor size ≥20 mm, lymph nodes ≥10 mm, BMI ≥24 kg/m2, and lymph node metastasis on CT scans. A 7-point risk score model was developed to assess the likelihood of lymph node metastasis. The areas under the ROC curve (AUCs) for the development and validation sets were 0.842 and 0.837, respectively, with sensitivities of 64% and 79%, respectively. We developed a practical risk score model based on preoperative factors to help EUS predict lymph node metastasis in early gastric cancer patients, guiding the selection of optimal treatment approaches for these patients.

The diagnostic efficiency of artificial intelligence based 2 hours Holter monitoring in premature ventricular and supraventricular contractions detection.

Electrocardiography (ECG) and 24 hours Holter monitoring (24 h-Holter) provided valuable information for premature ventricular and supraventricular contractions (PVC and PSVC). Currently, artificial intelligence (AI) based 2 hours single-lead Holter (2 h-Holter) monitoring may provide an improved strategy for PSVC/PVC diagnosis. AI combined with single-lead Holter monitoring improves PSVC/PVC detection. In total, 170 patients were enrolled between August 2022 and 2023. All patients wore both devices simultaneously; then, we compared diagnostic efficiency, including the sensitivity/specificity/positive predictive-value (PPV) and negative predictive-value (NPV) in detecting PSVC/PVC by 24 h-Holter and 2 h-Holter. The PPV and NPV in patients underwent 2 h-Holter were 76.00%/87.50% and 96.35%/98.55, respectively, and the sensitivity and specificity were 79.17%/91.30%, and 95.65%/97.84% in PSVC/PVC detection compared with 24 h-Holter. The areas under the ROC curves (AUCs) for PSVC and PVC were 0.885 and 0.741, respectively (p < .0001). The potential advantages of the 2 h-Holter were shortened wearing period, improved convenience, and excellent consistency of diagnosis.