The Mobile Constant, a Self-Reported Method for Shoulder Function Evaluation: Development and Validation Study

BackgroundThere remains a demand for a practical method of identifying lipid-poor adrenal lesions.PurposeTo explore the predictive value of computed tomography (CT) features combined with demographic characteristics for lipid-poor adrenal adenomas and nonadenomas.Materials and MethodsWe retrospectively recruited patients with lipid-poor adrenal lesions between January 2015 and August 2021 from two independent institutions as follows: Institution 1 for the training set and the internal validation set and Institution 2 for the external validation set. Two radiologists reviewed CT images for the three sets. We performed a least absolute shrinkage and selection operator (LASSO) algorithm to select variables; subsequently, multivariate analysis was used to develop a generalized linear model. The probability threshold of the model was set to 0.5 in the external validation set. We calculated the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the model and radiologists. The model was validated and tested in the internal validation and external validation sets; moreover, the accuracy between the model and both radiologists were compared using the McNemar test in the external validation set.ResultsIn total, 253 patients (median age, 55 years [interquartile range, 47–64 years]; 135 men) with 121 lipid-poor adrenal adenomas and 132 nonadenomas were included in Institution 1, whereas another 55 patients were included in Institution 2. The multivariable analysis showed that age, male, lesion size, necrosis, unenhanced attenuation, and portal venous phase attenuation were independently associated with adrenal adenomas. The clinical-image model showed AUCs of 0.96 (95% confidence interval [CI]: 0.91, 0.98), 0.93 (95% CI: 0.84, 0.97), and 0.86 (95% CI: 0.74, 0.94) in the training set, internal validation set, and external validation set, respectively. In the external validation set, the model showed a significantly and non-significantly higher accuracy than reader 1 (84% vs. 65%, P = 0.031) and reader 2 (84% vs. 69%, P = 0.057), respectively.ConclusionsOur clinical-image model displayed good utility in differentiating lipid-poor adrenal adenomas. Further, it showed better diagnostic ability than experienced radiologists in the external validation set.

Objective: To investigate the value of CT-based tumor and peritumoral radiomics in predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading for non-metastatic clear cell renal cell carcinoma (ccRCC). Methods: A total of 503 patients with non-metastatic ccRCC were retrospectively enrolled from 7 tertiary hospitals between February 2017 and December 2023. Patients from 4 hospitals within Shandong Province were divided into a training set and an internal validation set in a 7∶3 ratio, while patients from 3 hospitals outside Shandong Province constituted the external validation set. Regions of interest (ROI) were manually delineated slice-by-slice along the tumor margin on contrast-enhanced CT images. Peritumoral regions were obtained by expanding 10 mm outward from the tumor boundary. Key radiomics features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression with five-fold cross-validation to build tumor models and peritumoral models for predicting the WHO/ISUP grading. Univariate logistic regression was used to screen clinical factors associated with the WHO/ISUP grading, which was included to construct a combined predictive model together with tumor and peritumoral features. The predictive performance of the models for WHO/ISUP grading was evaluated using receiver operating characteristic (ROC) curves, and the Youden index and optimal cutoff value were calculated for risk stratification. Calibration curves were used to validate model performance, and decision curve analysis (DCA) was employed to evaluate the clinical value of the models. Results: The median age [M(Q1,Q3)] of the 503 patients was 59.0 (52.0, 66.0) years, with 335 males and 168 females. Univariate logistic regression analysis showed there was a statistically significance in age between low and high WHO/ISUP grades (P=0.043). The AUC of the tumor model and peritumoral model in the internal validation set were 0.744 (95%CI: 0.700-0.788) and 0.742 (95%CI: 0.709-0.774), respectively. In the external validation set, the AUC of the tumor model and peritumoral model were 0.685 (95%CI: 0.651-0.720) and 0.655 (95%CI: 0.628-0.683), respectively. The combined model demonstrated the best predictive performance in both internal and external validation sets, with AUC of 0.760 (95%CI: 0.721-0.800) and 0.706 (95%CI: 0.675-0.737), respectively. Using the Youden index calculated from the ROC curve from the combined model, the optimal cutoff value was 0.504 for risk stratification, where 190 cases were classified as low-grade and 313 as high-grade, achieving a concordance rate of 0.718 (361/503) with the WHO/ISUP grading. Calibration curve tests indicated good fit for the combined model (internal validation set: P=0.932; external validation set: P=0.404). DCA showed that the combined model provided favorable clinical net benefit within the threshold probability range of 0.2-0.8. Conclusion: The combined model incorporating age, CT-based tumor features, and peritumoral features demonstrates good performance in predicting the WHO/ISUP grading for patients with non-metastatic ccRCC.

Pancreatic adenocarcinoma (PaC) patients with positive lymph nodes (PLNs) have a dismal prognosis and lack a specific prognostic stage. This study aimed to construct a nomogram for the prediction of overall survival (OS) in these patients. A total of 1,340 patients screened from the Surveillance, Epidemiology, and End Results database were included and randomly divided at a ratio of 7:3 into a training set (n=940) and an internal validation set (n=400). Cox regression analyses were conducted to select independent predictors in the training set, and a nomogram was constructed. The model was verified in the internal validation set and in an external validation set, which comprised 64 patients from a Chinese institute. Six independent prognostic factors (age at diagnosis, tumor grade, lymph node ratio, T stage, radiotherapy, and chemotherapy) were identified in PaC patients with PLNs and were entered into the nomogram. The final model had a higher C-index for predicting OS than the American Joint Committee on Cancer-8th edition staging system (training set: 0.658 vs. 0.546; internal validation set: 0.661 vs. 0.546; external validation set: 0.691 vs. 0.581). The 1-, 2-, and 3-year area under the receiver operating characteristic curve values indicated better discrimination power for the established nomogram with respect to the prediction of OS in the training, internal validation, and external validation sets than for the American Joint Committee on Cancer-8th edition staging system. Furthermore, the nomogram performed well in both calibration and decision curve analyses (DCA) of clinical applicability. OS in PaC patients with PLNs was significantly distinguished among the three risk groups stratified according to the nomogram score (P<0.001). The well-calibrated nomogram was determined to be extremely efficient in predicting survival, and defining a high-risk population based on the nomogram score among PaC patients with PLNs after surgery.

ObjectivesTo evaluate the value of quantitative parameters and radiomic features based on dual-layer spectral detector CT (DLCT) in predicting spread through air spaces (STAS) of lung adenocarcinoma (LUAD).MethodsThis study analyzed 266 patients with pathologically confirmed LUAD from two medical centers. Patients from center 1 were divided into training (n = 131) and internal validation (n = 57) sets, while center 2 (n = 78) formed the external validation set. Clinical data, conventional imaging features, and DLCT quantitative parameters were analyzed to develop a clinical-radiological model. Radiomic features were extracted from venous-phase images, including conventional images, virtual monoenergetic images (VMI) at 40 keV, 65 keV, and 100 keV, along with iodine density maps, effective atomic number (Zeff) maps, and electron density (ED) maps. The best-performing radiomics model was combined with clinical-radiological predictors to create a nomogram. Model performance was evaluated through ROC analysis, calibration curves, and decision curve analysis.ResultsMultivariate analysis revealed that tumor-lung interface and ED values were independent predictive factors in the clinical-radiological model. The optimal radiomics model was constructed based on VMI 40 keV, demonstrating AUCs of 0.899, 0.835, and 0.828 in the training, internal validation, and external validation sets, respectively. The nomogram, which incorporated the VMI 40 keV radiomics signature along with tumor-lung interface and ED values, outperformed the clinical-radiological model in the training set (AUC = 0.910 vs. 0.870; P = 0.018) and the internal validation set (AUC = 0.868 vs. 0.798; P = 0.046). While the improvement in the external validation set was not statistically significant (AUC = 0.848 vs. 0.819; P = 0.184).ConclusionThe nomogram, which integrates conventional imaging features, DLCT quantitative parameters and VMI 40 keV radiomic features, demonstrates promising performance and represents a potential valuable non-invasive tool for the preoperative assessment of STAS in LUAD.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-025-15436-7.

Ki-67 labeling index (Ki-67 LI) is a proliferation marker that is correlated with aggressive behavior and prognosis of pituitary adenomas (PAs). Dynamic contrast-enhanced MRI (DCE-MRI) may potentially contribute to the preoperative assessment of Ki-67 LI. To investigate the feasibility of assessing Ki-67 LI of PAs preoperatively using delta-radiomics based on DCE-MRI. Retrospective. 605 PA patients (female = 47.1%, average age = 52.2) from two centers (high Ki-67 LI (≥ 3%) = 229; low Ki-67 LI (< 3%) = 376), divided into a training set (n = 313), an internal validation set (n = 196), and an external validation set (n = 96). 1.5-T and 3-T, DCE-MRI. This study developed a non-delta-radiomics model based on the non-delta-radiomic features directly extracted from four phases, a delta-radiomics model based on the delta-radiomic features, and a combined model integrating clinical parameters (Knosp grade and tumor diameter) with delta-radiomic features. U test, recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) regression were utilized to select important radiomic features. Support vector machine (SVM), XGBoost (XGB), logistic regression (LR), and Gaussian naive Bayes (GNB) were utilized to develop the models. Receiver operating characteristic (ROC) curve. Calibration curve. Decision curve analysis (DCA). Intraclass correlation coefficients (ICC). DeLong test for ROC curves. U test or t test for numerical variables. Fisher's test or Chi-squared test for categorical variables. A p-value < 0.05 was considered statistically significant. The combined model demonstrated the best performance in preoperatively assessing the Ki-67 LI of PAs, achieving AUCs of 0.937 and 0.897 in the internal and external validation sets, respectively. The models based on delta-radiomic features outperformed the non-delta-radiomic model. A delta-radiomics-based model using DCE-MRI may show high diagnostic performance for preoperatively assessing the Ki-67 LI status of PAs. 3 Technical Efficacy: Stage 2.

BackgroundThe diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.ObjectivesThe present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.MethodsA total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.ResultsSeven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942–0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932–0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956–0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.ConclusionsThe present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

Rationale and ObjectivesThe human epidermal growth factor receptor 2 (HER2) gene status is crucial for determining treatment efficacy. This study assessed preoperative HER2 classification in breast cancer using machine learning based on clinicopathological and MRI characteristics.Materials and MethodsThis retrospective study involved 1,015 patients (1,030 lesions) across two centers. Patients were divided into training, internal validation, and external validation sets. Nomograms were developed using clinicopathological and MRI features. Predictive models were constructed using decision trees (DT), support vector machines (SVM), k-nearest neighbors (k-NN), artificial neural networks (ANN), and multivariable logistic regression (LR). Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and calibration curves. Model interpretability was achieved by developing nomograms and employing SHAP (SHapley Additive exPlanations) analysis.ResultsKey variables for distinguishing HER2-positive from HER2-negative cases included regional N category, estrogen receptor, PR (progesterone receptor) status, Ki-67 status, lesion number, distribution quadrant, and accompanying signs. The SVM model achieved the highest AUC of 0.86 (95% confidence interval (CI): 0.81–0.90) in the training set, while the ANN model had an AUC of 0.77 (95% CI: 0.67–0.86) in the internal validation set. In the external validation set, the LR model achieved the highest AUC of 0.66 (95% CI: 0.56–0.76), although the overall performance was modest. For HER2-low versus HER2-zero differentiation, Ki-67 status, lesion number, distribution quadrant, mass shape, early enhancement rate, and ADC (apparent diffusion coefficient) were significant. The SVM model attained the highest AUC of 0.87 (95% CI: 0.83–0.91) in the training set, while the LR model demonstrated superior generalizability, yielding the highest AUCs in both the internal and external validation sets (internal: 0.67, 95% CI: 0.58–0.76; external: 0.74, 95% CI: 0.65–0.83). Radiologists benefited from the nomogram for improved diagnostic accuracy, especially junior radiologists. SHAP analysis revealed that PR status was paramount for HER2-positive classification, whereas mass shape and ADC values were dominant for identifying HER2-low status.ConclusionIntegrating machine learning with clinicopathological and MRI characteristics improves the accuracy of HER2 status classification in breast cancer and enhances diagnostic capabilities for radiologists in clinical practice.

A simple and practicable strategy for predicting the risk of breast cancer (BC) urgently needs to be established. This study aimed to construct a nomogram model based on age and ultrasound (US) features to predict BC. Consecutive adult females with a breast mass who underwent breast US followed by biopsy or surgery (Breast Imaging Reporting and Data System (BI-RADS) categories III-V) or who received follow-up US more than 6 months after the initial US (BI-RADS category II) from August 2020 to November 2023 were enrolled in this prospective multicenter study. The participants were allocated to three groups (the training set, internal validation set, and external validation set). A logistic regression analysis of the training set was performed to identify the independent variables associated with BC, based on which the breast nomogram model (B-NM) was constructed. The performance of the B-NM was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve and calibration diagrams. The training set comprised 306 females (43.0±11.9 years), the internal validation set comprised 45 females (46.0±12.6 years), and the external validation set comprised 114 females (40.6±11.4 years). Age and four US variables (mass size, orientation, margin, and vascularity) were found to be independently associated with BC. The B-NM combining these variables demonstrated relatively good performance {AUC [95% confidence interval (CI)]: 0.914 (0.882-0.946) vs. 0.905 (0.827-0.982) vs. 0.846 (0.750-0.940)} in the training, internal, and external validation sets, respectively. The calibration diagram showed that the nomogram's predicted probabilities were highly consistent with the observed values. The B-NM incorporating age and US variables demonstrated favorable discrimination and calibration in predicting the risk of BC.

To construct a nomogram based on sonogram features and radiomics features to differentiate granulomatous lobular mastitis (GLM) from invasive breast cancer (IBC). A retrospective collection of 213 GLMs and 472 IBCs from three centers was divided into a training set, an internal validation set, and an external validation set. A radiomics model was built based on radiomics features, and the RAD score of the lesion was calculated. The sonogram radiomics model was constructed using ultrasound features and RAD scores. Finally, the diagnostic efficacy of the three sonographers with different levels of experience before and after combining the RAD score was assessed in the external validation set. The RAD score, lesion diameter, orientation, echogenicity, and tubular extension showed significant differences in GLM and IBC (p < 0.05). The sonogram radiomics model based on these factors achieved optimal performance, and its area under the curve (AUC) was 0.907, 0.872, and 0.888 in the training, internal, and external validation sets, respectively. The AUCs before and after combining the RAD scores were 0.714, 0.750, and 0.830 and 0.834, 0.853, and 0.878, respectively, for sonographers with different levels of experience. The diagnostic efficacy was comparable for all sonographers when combined with the RAD score (p > 0.05). Radiomics features effectively enhance the ability of sonographers to discriminate between GLM and IBC and reduce interobserver variation. The nomogram combining ultrasound features and radiomics features show promising diagnostic efficacy and can be used to identify GLM and IBC in a noninvasive approach.

BackgroundDespite the success of antiretroviral therapy (ART) for human immunodeficiency virus (HIV) in China, immune non-response (INR) remains critical for the long-term quality of life of people living with HIV (PLWH). Although the consensus on diagnosis and management of immunological non-responders in HIV infection (Version 2023) was published in China to standardize diagnostic criteria, prediction models for INR based on these criteria remain scarce. This study aims to develop and validate a nomogram model for early identification of INR risk based on the diagnostic criteria in this consensus, so as to facilitate clinical intervention.MethodsIn this retrospective study, the primary cohort included 615 PLWH who initiated ART and completed over 4 years of follow-up at Zhongnan Hospital of Wuhan University (January 2016 to May 2025). They were randomly split into a training set (n=433) and an internal validation set (n=182) in a 7:3 ratio. An external validation set comprised 213 PLWH from Xishui County People’s Hospital (January 2012 to August 2025). Least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to identify independent predictors of INR, and a nomogram was constructed. The receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were employed to evaluate the discrimination, calibration, and clinical utility of the model in the training set and validation sets, respectively.ResultsLASSO regression identified seven candidate variables: age at ART initiation, baseline CD4+ T cell count, body mass index (BMI), white blood cell count (WBC), hemoglobin (Hb), aspartate aminotransferase (AST), and World Health Organization (WHO) clinical stage. Subsequent multivariable logistic regression confirmed baseline CD4+ T cell count (p < 0.001), age at ART initiation (p = 0.001), and AST level (p = 0.014) as independent INR predictors. The resulting nomogram demonstrated area under the curve (AUC) values of 0.896, 0.903, and 0.766 in the training, internal validation, and external validation sets, respectively. The calibration curve and DCA further indicated satisfactory consistency and clinical net benefit.ConclusionThe developed nomogram effectively predicts INR risk in PLWH initiating ART, providing clinicians a practical tool for individualized management to improve patient prognosis.

Deep learning (DL) algorithms could improve the classification of ovarian tumours assessed with ultrasound (US) images and clinical information. This study aimed to develop a DL model for diagnosing ovarian tumours based on US images and clinical information, and to compare the performance of the DL model with radiologist assessment. This study retrospectively collected data from four hospitals involving women who underwent US examinations for ovarian tumours. Additionally, data were prospectively randomly collected from two other hospitals. Histopathological analysis served as the reference standard. The retrospective dataset was divided into training, test and internal validation sets. The prospective dataset was used as an external validation set. A DL model was developed. The performance of the DL model was compared with the eleven radiologists’ assessments in the internal and external validation sets. The performance of radiologists' assessments with or without DL model were also compared. A total of 1518 women (6432 images) with ovarian tumours were collected, including 364 women in the internal validation dataset and 340 women in the external validation dataset. In the external validation dataset, the accuracy for the DL model's classification of benign, borderline, and malignant ovarian tumours were 85.5%, which were comparable to the expert assessments (85.3%, p > 0.05), but significantly higher than the average level of eleven different-experience radiologists (72.5%, p < 0.05). The diagnostic performance of junior and mid-level experienced radiologists were significantly improved after applying the DL model. The DL model developed using US images and clinical information can effectively classify benign, malignant, and borderline ovarian tumours with diagnostic performance comparable to the expert assessment and improving junior and mid-level experienced radiologists.

PurposeSarcopenia is a common complication of diabetes. Nevertheless, precise evaluation of sarcopenia risk among patients with diabetes is still a big challenge. The objective of this study was to develop a nomogram model which could serve as a practical tool to diagnose sarcopenia in patients with diabetes.MethodsA total of 783 participants with diabetes from China Health and Retirement Longitudinal Study (CHARLS) 2015 were included in this study. After oversampling process, 1,000 samples were randomly divided into the training set and internal validation set. To mitigate the overfitting effect caused by oversampling, data of CHARLS 2011 were utilized as the external validation set. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were employed to explore predictors. Subsequently, a nomogram was developed based on the 9 selected predictors. The model was assessed by area under receiver operating characteristic (ROC) curves (AUC) for discrimination, calibration curves for calibration, and decision curve analysis (DCA) for clinical efficacy. In addition, machine learning models were constructed to enhance the robustness of our findings and evaluate the importance of the predictors.Results9 factors were selected as predictors of sarcopenia for patients with diabetes. The nomogram model exhibited good discrimination in training, internal validation and external validation sets, with AUC of 0.808, 0.811 and 0.794. machine learning models revealed that age and hemoglobin were the most significant predictors. Calibration curves and DCA illustrated excellent calibration and clinical applicability of this model.ConclusionThis comprehensive nomogram presented high clinical predictability, which was a promising tool to evaluate the risk of sarcopenia in patients with diabetes.

Purpose:Deep learning is a newer and advanced subfield in artificial intelligence (AI). The aim of our study is to validate a machine-based algorithm developed based on deep convolutional neural networks as a tool for screening to detect referable diabetic retinopathy (DR).Methods:An AI algorithm to detect DR was validated at our hospital using an internal dataset consisting of 1,533 macula-centered fundus images collected retrospectively and an external validation set using Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (MESSIDOR) dataset. Images were graded by two retina specialists as any DR, prompt referral (moderate nonproliferative diabetic retinopathy (NPDR) or above or presence of macular edema) and sight-threatening DR/STDR (severe NPDR or above) and compared with AI results. Sensitivity, specificity, and area under curve (AUC) for both internal and external validation sets for any DR detection, prompt referral, and STDR were calculated. Interobserver agreement using kappa value was calculated for both the sets and two out of three agreements for DR grading was considered as ground truth to compare with AI results.Results:In the internal validation set, the overall sensitivity and specificity was 99.7% and 98.5% for Any DR detection and 98.9% and 94.84%for Prompt referral respectively. The AUC was 0.991 and 0.969 for any DR detection and prompt referral respectively. The agreement between two observers was 99.5% and 99.2% for any DR detection and prompt referral with a kappa value of 0.94 and 0.96, respectively. In the external validation set (MESSIDOR 1), the overall sensitivity and specificity was 90.4% and 91.0% for any DR detection and 94.7% and 97.4% for prompt referral, respectively. The AUC was. 907 and. 960 for any DR detection and prompt referral, respectively. The agreement between two observers was 98.5% and 97.8% for any DR detection and prompt referral with a kappa value of 0.971 and 0.980, respectively.Conclusion:With increasing diabetic population and growing demand supply gap in trained resources, AI is the future for early identification of DR and reducing blindness. This can revolutionize telescreening in ophthalmology, especially where people do not have access to specialized health care.

PurposeAs a coreceptor in Wnt and HGF signaling, glypican-3 (GPC-3) promotes the progression of tumor and is associated with a poor prognosis in hepatocellular carcinoma (HCC). GPC-3 has evolved as a target molecule in various immunotherapies, including chimeric antigen receptor T cell. However, its evaluation still relies on invasive histopathologic examination. Therefore, we aimed to develop an easy-to-use and noninvasive risk score integrating preoperative gadoxetic acid–enhanced magnetic resonance imaging (EOB-MRI) and clinical indicators to predict positive GPC-3 expression in HCC.Methods and materialsConsecutive patients with surgically-confirmed solitary HCC who underwent preoperative EOB-MRI between January 2016 and November 2021 were retrospectively included. EOB-MRI features were independently evaluated by two masked abdominal radiologists and the expression of GPC-3 was determined by two liver pathologists. On the training dataset, a predictive scoring system for GPC-3 was developed against pathology via logistical regression analysis. Model performances were characterized by computing areas under the receiver operating characteristic curve (AUCs).ResultsA total of 278 patients (training set, n=156; internal validation set, n=39; external validation set, n=83) with solitary HCC (208 [75%] with positive GPC-3 expression) were included. Serum alpha-fetoprotein >10 ng/ml (AFP, odds ratio [OR]=2.3, four points) and five EOB-MR imaging features, including tumor size >3.0cm (OR=0.5, -3 points), nonperipheral “washout” (OR=3.0, five points), infiltrative appearance (OR=9.3, 10 points), marked diffusion restriction (OR=3.3, five points), and iron sparing in solid mass (OR=0.2, -7 points) were significantly associated with positive GPC-3 expression. The optimal threshold of scoring system for predicting GPC-3 positive expression was 5.5 points, with AUC 0.726 and 0.681 on the internal and external validation sets, respectively.ConclusionBased on serum AFP and five EOB-MRI features, we developed an easy-to-use and noninvasive risk score which could accurately predict positive GPC-3 HCC, which may help identify potential responders for GPC-3-targeted immunotherapy.

BackgroundSpontaneous bacterial peritonitis (SBP) is a common and life-threatening infection in patients with decompensated cirrhosis (DC), and it is accompanied with high mortality and morbidity. However, early diagnosis of spontaneous bacterial peritonitis (SBP) is not possible because of the lack of typical symptoms or the low patient compliance and positivity rate of the ascites puncture test. We aimed to establish and validate a non-invasive diagnostic nomogram to identify SBP in patients with DC.MethodData were collected from 4,607 patients with DC from July 2015 to December 2019 in two tertiary hospitals in Chongqing, China (A and B). Patients with DC were divided into the SBP group (995 cases) and the non-SBP group (3,612 cases) depending on whether the patients had SBP during hospitalization. About 70% (2,685 cases) of patients in hospital A were randomly selected as the traindata, and the remaining 30% (1,152 cases) were used as the internal validation set. Patients in hospital B (770 cases) were used as the external validation set. The univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to screen variables, and logistic regression was used to determine independent predictors to construct a nomogram to identify patients with SBP. Area under curve (AUC), calibration curve, and dynamic component analysis (DCA) were carried out to determine the effectiveness of the nomogram.ResultThe nomogram was composed of seven variables, namely, mean red blood cell hemoglobin concentration (odds ratio [OR] = 1.010, 95% CI: 1.004–1.016), prothrombin time (OR = 1.038, 95% CI: 1.015–1.063), lymphocyte percentage (OR = 0.955, 95% CI: 0.943–0.967), prealbumin (OR = 0.990, 95% CI: 0.987–0.993), total bilirubin (OR = 1.003 95% CI: 1.002–1.004), abnormal C-reactive protein (CRP) level (OR = 1.395, 95% CI: 1.107–1.755), and abnormal procalcitonin levels (OR = 1.975 95% CI: 1.522–2.556). Good discrimination of the model was observed in the internal and external validation sets (AUC = 0.800 and 0.745, respectively). The calibration curve result indicated that the nomogram was well-calibrated. The DCA curve of the nomogram presented good clinical application ability.ConclusionThis study identified the independent risk factors of SBP in patients with DC and used them to construct a nomogram, which may provide clinical reference information for the diagnosis of SBP in patients with DC.