Novel screening tool for psychiatric samples with major depressive disorder: a two-centre study

Individualized Prediction and Risk Factors of Recurrence in Chinese Patients with Sebaceous Carcinoma: A Multicenter Study of 418 Patients

To develop and validate a computed tomography (CT) radiomics nomogram from multicentre datasets for preoperative prediction of perineural invasion (PNI) in colorectal cancer. A total of 299 patients with histologically confirmed colorectal cancer from three hospitals were enrolled in this retrospective study. Radiomic features were extracted from the whole tumour volume. The least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomics signature construction. Finally, a radiomics nomogram combining the radiomics score and clinical predictors was established. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomics nomogram in the training cohort, internal validation and external validation cohorts. Twelve radiomics features extracted from the whole tumour volume were used to construct the radiomics model. The area under the curve (AUC) values of the radiomics model in the training cohort, internal validation cohort, external validation cohort 1, and external validation cohort 2 were 0.82 (0.75-0.90), 0.77 (0.62-0.92), 0.71 (0.56-0.85), and 0.73 (0.60-0.85), respectively. The nomogram, which combined the radiomics score with T category and N category by CT, yielded better performance in the training cohort (AUC = 0.88), internal validation cohort (AUC = 0.80), external validation cohort 1 (AUC = 0.75), and external validation cohort 2 (AUC = 0.76). DCA confirmed the clinical utility of the nomogram. The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with colorectal cancer.

Objective To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). Materials and methods Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. Results Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. Conclusion This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

Objective To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). Materials and methods Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. Results Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. Conclusion This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

BackgroundAlthough erectile dysfunction (ED) often occurs simultaneously with depression, not all patients with ED suffer major depression (MD), with a PHQ-9 score ≥15 indicating MD. Because the PHQ-9 questionnaire includes phrases such as “I think I am a loser” and “I want to commit suicide,” the psychological burdens of ED patients are likely to increase inevitably after using the PHQ-9, which, in turn, may affect ED therapeutic effects. Accordingly, we endeavored to develop a nomogram to predict individual risk of PHQ-9 score ≥15 in these patients.MethodsThe data of 1,142 patients with ED diagnosed in Xijing Hospital and Northwest Women and Children's Hospital from January 2017 to May 2020 were analyzed. While the Least Absolute Shrinkage and Selection Operator regression was employed to screen PHQ-9 score ≥15 related risk factors, multivariate logistic regression analysis was performed to verify these factors and construct the nomogram. The training cohort and an independent cohort that comprised 877 prospectively enrolled patients were used to demonstrate the efficacy of the nomogram.ResultsThe IIEF-5 score, PEDT score, physical pain score, frequent urination, and feeling of endless urination were found to be independent factors of PHQ-9 score ≥15 in patients with ED. The nomogram developed by these five factors showed good calibration and discrimination in internal and external validation, with a predictive accuracy of 0.757 and 0.722, respectively. The sensitivity and specificity of the nomogram in the training cohort were 0.86 and 0.52, respectively. Besides, the sensitivity and specificity of the nomogram in the validation cohort were 0.73 and 0.62, respectively. Moreover, based on the nomogram, the sample was divided into low-risk and high-risk groups.ConclusionThis study established a nomogram to predict individual risk of PHQ-9 score ≥15 in patients with ED. It is deemed that the nomogram may be employed initially to avoid those with a low risk of MD completing questionnaires unnecessarily.

BackgroundVessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.MethodsA total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell’s concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.ResultsA prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.ConclusionsThe VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

Sepsis-associated acute kidney injury (SA-AKI) is a common and serious complication in critically ill patients, leading to increased morbidity and mortality. This study aims to develop a model for early identification and risk assessment of SA-AKI based on contrast-enhanced ultrasound (CEUS) and biomarkers. This retrospective observational study included 152 septic patients admitted to the Surgical Intensive Care Unit (SICU) of Zhongshan Hospital from January 2021 to June 2022. Patients were divided into training and validation cohorts. Clinical data, CEUS-derived parameters, and biomarkers were collected within 24 h of admission. A risk assessment model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. Model performance was assessed by the area under the receiver operating characteristic curve (AUROC) and calibration. The primary outcome was AKI occurring within 48 h of ICU admission. SA-AKI occurred in 86 patients (56.6%). Four independent predictors—urinary neutrophil gelatinase-associated lipocalin (NGAL), serum cystatin C, renal resistive index (RRI), and medullary wash-in slope (WIS)—were identified. The model demonstrated high discriminative performance, with AUROCs of 0.96 in the training cohort and 0.93 in the validation cohort. Sensitivity and specificity were 82.8% and 91.1% in the training cohort, and 75.0% and 88.9% in the validation cohort. Decision curve analysis (DCA) demonstrated favorable net clinical benefit across a wide range of thresholds. We developed a noninvasive model integrating CEUS-derived microcirculatory metrics and renal biomarkers for early identification and risk assessment of SA-AKI at ICU admission. This model may help clinicians prioritize surveillance and renal-protective strategies in the early phase of sepsis care. Given the single-center retrospective design and internal validation, external validation in independent multicenter cohorts is essential before any clinical implementation, and the generalizability of these findings should not be assumed. SA-AKI significantly increases mortality and morbidity in sepsis patients. This study aimed to develop an early risk assessment model for SA-AKI using conventional ultrasound, CEUS, and biomarkers. Independent risk factors identified include RRI, urinary NGAL, cystatin C, and medullary WIS. The model showed high discriminative performance with an AUROC of 0.96 in the training cohort and 0.93 in the validation cohort. Early identification of SA-AKI may help clinicians prioritize surveillance and kidney-protective care during the initial ICU phase.

Aspiration is a common complication of poststroke dysphagia (PSD) and is associated with poor prognosis and mortality. There is no uniform criterion for determining aspiration associated with dysphagia. The aim of this study was to identify early predictors of aspiration, leading to the development of a simple nomogram for identifying aspiration risk associated with dysphagia in hospitalized patients after stroke. Demographic information and clinical characteristics of 330 patients with PSD in the training cohort were utilized to develop a nomogram. The LASSO regression method was used to screen variables, and logistic regression was used to construct the nomogram. Internal validation was performed with bootstrap in the training cohort, and external validation was performed in the validation cohort of another 82 patients. The area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Seven variables were selected based on LASSO and multivariate logistic regression. The AUC of the nomogram was 0.834 (95% CI, 0.790-0.878) in the training cohort, 0.806 (95% CI, 0.791-0.880) in the internal validation cohort, and 0.882 (95% CI, 0.810-0.954) in the external validation cohort, which indicated that the model had good discrimination. The calibration and DCA curves showed that the nomogram had good accuracy and clinical utility. In this study, we established a nomogram that can be used to identify the risk of aspiration associated with dysphagia after stroke, and patients may benefit from early screening and preventive care.

MRI-Based Radiomics and Delta-Radiomics Models of the Patella Predict the Radiographic Progression of Osteoarthritis: Data From the FNIH OA Biomarkers Consortium

We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid-enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19-positive (+) hepatocellular carcinoma (HCC) and patients' prognosis. A two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan-Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort. By combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (-) group were 81% and 77%, and 80% and 74%, respectively. Kaplan-Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients. The combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment.

BackgroundPost-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram to preoperatively predict grade B/C PHLF defined by the International Study Group on Liver Surgery Grading (ISGLS) in HCC patients undergoing major hepatectomy.Study DesignThe consecutive HCC patients who underwent major hepatectomy at the Eastern Hepatobiliary Surgery Hospital between 2008 and 2013 served as a training cohort to develop a preoperative nomogram, and patients from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify preoperative predictors of grade B/C PHLF. Multivariable logistic regression was utilized to establish a nomogram model. Internal and external validations were used to verify the performance of the nomogram. The accuracy of the nomogram was also compared with the conventional scoring models, including MELD and ALBI score.ResultsA total of 880 patients who underwent major hepatectomy (668 in the training cohort and 192 in the validation cohort) were enrolled in this study. The independent risk factors of grade B/C PHLF were age, gender, prothrombin time, total bilirubin, and CSPH, which were incorporated into the nomogram. Good prediction discrimination was achieved in the training (AUROC: 0.73) and validation (AUROC: 0.72) cohorts. The calibration curve also showed good agreement in both training and validation cohorts. The nomogram has a better performance than MELD and ALBI score models.ConclusionThe proposed nomogram showed more accurate ability to individually predict grade B/C PHLF after major hepatectomy in HCC patients than MELD and ALBI scores.

BackgroundIntramyocardial hemorrhage (IMH) after ST-segment elevation myocardial infarction (STEMI) reperfusion increases the risk of major adverse cardiovascular events (MACE). We aimed to integrate a coronary angiography-derived index of microcirculatory resistance (caIMR) with clinical features in a nomogram model for predicting IMH in patients with STEMI following primary percutaneous coronary intervention (PCI).MethodsA retrospective study included 309 STEMI patients admitted at Xuzhou Medical University for primary PCI from 2022 to 2023 in training and validation cohorts. Their caIMR was calculated from coronary angiography images and IMH was assessed by cardiac Magnetic Resonance (CMR). A nomogram was constructed through logistic regression analyses. Predictive accuracy, calibration, and clinical usefulness were validated by an area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA), respectively. All statistical tests were two-sided.ResultsA total of 247 patients were assigned to the training cohort and 62 to the validation cohort randomly. A nomogram was established using two independent predictors of IMH derived from multivariable analysis: caIMR and left ventricular ejection fraction (LVEF). The AUC for the nomogram was 0.844 (95% confidence interval (CI): 0.793–0.896) in the training cohort and 0.834 (95% CI: 0.724–0.944) in the validation cohort. The predicted and actual estimates were significantly correlated in both cohorts, indicating good calibration of the nomogram. The nomogram was clinically useful for the prediction of IMH, within a 10–88% and 10–74% threshold probability in the training and validation cohorts respectively, exhibited by DCA. Furthermore, the nomogram showed superior discriminative ability for MACE prediction compared to CMR-confirmed IMH (AUC 0.726 vs. 0.584, P < 0.001).ConclusionsThis novel caIMR-based nomogram resulted in high accuracy of prediction for IMH and MACE in STEMI patients undergoing primary PCI. It may become a convenient tool to predict IMH immediately after the PCI procedure, and help with prognostic judgment and early clinical intervention.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12872-025-05057-0.

Prediction of programmed death-1 expression status in non–small cell lung cancer based on intratumoural and peritumoral computed tomography (CT) radiomics nomogram

Urinary Cysteine-Rich Protein 61 and Trefoil Factor 3 as Diagnostic Biomarkers for Colorectal Cancer

We aimed to develop an effective model to identify the risk of concurrent bacterial infections in older patients with acute-on-chronic liver disease (AoCLD). Data from 809 individuals aged 60-80 sourced from the CATCH-LIFE cohort were analyzed. Participants were randomly assigned to training and internal validation groups at a ratio of 7:3. An independent cohort of 336 older inpatients with AoCLD from Xiangya Hospital, Central South University was used to conduct an external validation of the model. Independent risk factors were identified using LASSO and logistic regression analysis in the training cohort and were subsequently used to develop a user-friendly model. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis in the internal and external validation cohorts. Two different cutoff values were determined to stratify infection risk in older patients with AoCLD. The infection rate among older patients with AoCLD was 30.28%. Pulmonary infections were predominant, accounting for 93% of all infections. Gram-negative bacteria were the most frequently isolated pathogens, representing 64% of cases in this population. The novel model developed to identify bacterial infections included three variables: cirrhosis, absolute neutrophil count, and C-reactive protein (CRP) level. The AUC for the training, internal, and external validation datasets demonstrated high accuracy in identifying bacterial infections (AUC of the training dataset = 0.805, AUC of the internal validation dataset = 0.848, and AUC of the external validation dataset = 0.838). The model significantly outperformed neutrophil count, CRP level, and procalcitonin level alone in detecting bacterial infections among older patients with AoCLD. To facilitate clinical decision-making, we defined two cutoff values of prediction probability: a low cutoff of 32.2% to rule out bacterial infections and a high cutoff of 47.9% to confidently confirm bacterial infections. Our model aids in the early and precise diagnosis of bacterial infections in older patients with AoCLD, thereby facilitating prompt interventions to prevent adverse outcomes.