Nomogram-based Model Research Articles

The Vienna CATScore for predicting cancer-associated venous thromboembolism: an external validation across multiple time points.

Patients with cancer undergoing systemic therapies have a high risk for venous thromboembolism (VTE). Risk assessment models were developed to select high-risk subgroups that might benefit from primary thromboprophylaxis, yet currently available models reportedly underperform in contemporary cancer treatment populations and risk models across multiple time points throughout therapy are not available. We, therefore, aimed to validate the Vienna CATScore, a nomogram-based model including tumor type and continuous D-dimer levels, in a prospective cohort study of patients initiating contemporary systemic anticancer therapies. The validity of the model was tested at study inclusion, 3 weeks, and 3 months after start of therapy. Overall, 598 patients were included [49% women, median age 62 years (interquartile range 53-70 years)]. Most patients had stage IV disease (68.2%). The 6-month cumulative incidence of VTE was 9.2% [95% confidence interval (CI) 6.8% to 11.5%]. The Vienna CATScore demonstrated good discriminatory ability (c-statistics: 0.69, 95% CI 0.61-0.76) at study baseline and across all evaluated time points (c-statistics: 0.68, 95% CI 0.63-0.73). Applying a 6-month predicted VTE risk threshold of 8%, the CATScore effectively distinguished between low- and high-risk groups at study inclusion (7.1% versus 15.1% observed VTE risk, P= 0.004) and across all three time points (6.3% versus 13.6% observed VTE risk, P < 0.001). Assuming a 50% risk reduction with thromboprophylaxis, this threshold resulted in a number needed to treat (NNT) of 13 and 15, respectively, in the high-risk group, while the NNT was 28 and 32, respectively, in the low-risk group. This external validation of the Vienna CATScore confirms its effectiveness in predicting VTE risk in the initial months of state-of-the-art systemic anticancer therapies and across multiple time points.

Development and Validation of a Nomogram-Based Model to Predict Primary Hypertension Within the Next Year in Children and Adolescents: Retrospective Cohort Study.

Primary hypertension (PH) poses significant risks to children and adolescents. Few prediction models for the risk of PH in children and adolescents currently exist, posing a challenge for doctors in making informed clinical decisions. This study aimed to investigate the incidence and risk factors of PH in Chinese children and adolescents. It also aimed to establish and validate a nomogram-based model for predicting the next year's PH risk. A training cohort (n=3938, between January 1, 2008, and December 31, 2020) and a validation cohort (n=1269, between January 1, 2021, and July 1, 2023) were established for model training and validation. An independent cohort of 576 individuals was established for external validation of the model. The result of the least absolute shrinkage and selection operator regression technique was used to select the optimal predictive features, and multivariate logistic regression to construct the nomogram. The performance of the nomogram underwent assessment and validation through the area under the receiver operating characteristic curve, concordance index, calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. The PH risk factors that we have ultimately identified include gender (odds ratio [OR] 3.34, 95% CI 2.88 to 3.86; P<.001), age (OR 1.11, 95% CI 1.08 to 1.14; P<.001), family history of hypertension (OR 42.74, 95% CI 23.07 to 79.19; P<.001), fasting blood glucose (OR 6.07, 95% CI 4.74 to 7.78; P<.001), low-density lipoprotein cholesterol (OR 2.03, 95% CI 1.60 to 2.57; P<.001), and uric acid (OR 1.01, 95% CI 1.01 to 1.01; P<.001), while factor breastfeeding (OR 0.04, 95% CI 0.03 to 0.05; P<.001) has been identified as a protective factor. Subsequently, a nomogram has been constructed incorporating these factors. Areas under the receiver operating characteristic curves of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. Concordance indexes of the nomogram were 0.892 in the training cohort, 0.808 in the validation cohort, and 0.790 in the external validation cohort. The nomogram has been proven to have good clinical benefits and stability in calibration curves, decision curve analysis, clinical impact curves, and sensitivity analysis. Finally, we observed noteworthy differences in uric acid levels and family history of hypertension among various subgroups, demonstrating a high correlation with PH. Moreover, the web-based calculator of the nomogram was built online. We have developed and validated a stable and reliable nomogram that can accurately predict PH risk within the next year among children and adolescents in primary care and offer effective and cost-efficient support for clinical decisions for the risk prediction of PH.

Nomogram-Based model accurately predicts disease flare-ups in axial spondyloarthritis

Background: The accuracy of predicting axial spondyloarthritis (axSpA) flares based on clinical experience is limited. Objective: The aim of this study was to evaluate the efficacy of the previously designed nomogram prediction model in forecasting disease flares among rheumatologists and medical students. Methods: Patients who met the classification criteria for axSpA were enrolled in the study. Once a low ankylosing spondylitis disease activity score (ASDAS ≤ 2.1) was achieved, patients were monitored for 12 months to observe any disease flare-ups. Investigators assessed the likelihood of axSpA recurrence using the nomogram prediction model and their clinical experience, respectively. This allowed for a comparison of the predictive efficacy of both methods among the specialists and students. Results: The accuracy, sensitivity, specificity, and Youden index in which disease flare-ups were predicted by the rheumatologist using clinical experience were slightly lower than those obtained using the nomogram prediction model, but the difference was not statistically significant ( P &gt; 0.05). In contrast, the indicators above by medical students using clinical experience were significantly lower compared to those predicted by the nomogram prediction model ( P &lt; 0.05). Conclusion: The nomogram prediction model is effective in predicting the probability of disease remission and flare-ups in axSpA patients with low disease activity, demonstrating good clinical practicality and usability. Medical students can also use this model to significantly enhance the accuracy of predicting axSpA flares.

A preoperative nomogram with MR elastography in identifying cytokeratin 19 status of hepatocellular carcinoma.

Developing a nomogram integrating MR elastography (MRE)-based tumor stiffness and contrast-enhanced MRI in identifying cytokeratin 19 (CK19) status of hepatocellular carcinoma (HCC) preoperatively. 120 CK19-negative HCC and 39 CK19-positive HCC patients undergoing curative resection were prospectively evaluated. All received MRE and contrast-enhanced MRI. Clinical and MRI tumor features were compared. Univariate and multivariate logistic regression analyses identified independent predictors for CK19 status. Receiver operating characteristic curve analysis evaluated diagnostic performance. A nomogram was established with calibration and decision curve analysis. Multivariate analysis revealed serum alpha fetoprotein (AFP) level (P < 0.001), targetoid appearance (P = 0.007), and tumor stiffness (P = 0.011) as independent significant variables for CK19-positive HCC. The area under the curve for tumor stiffness was 0.729 (95% CI 0.653, 0.796). Combining these features, a nomogram-based model achieved an area under the curve value of 0.844 (95% CI 0.778, 0.897), with sensitivity, specificity, and accuracy of 76.92%, 85.00%, and 83.02%, respectively. Calibration and decision curve analyses demonstrated good agreement and optimal net benefit. MRE-measured tumor stiffness aids in predicting CK19 status in HCC. The combined nomogram incorporating tumor stiffness, targetoid appearance, and AFP provides a reliable biomarker for CK19-positive HCC. MRE-measured tumor stiffness can be used to predict CK19 status in HCC. The nomogram, which integrates tumor stiffness, targetoid appearance, and AFP levels, has shown enhanced diagnostic performance. It offers a comprehensive preoperative tool for clinical decision-making, further advancing personalized treatment strategies in HCC management.

Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

PurposeTo develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.MethodsThis is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility.ResultsThe T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram.ConclusionA nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

Development and validation of a nomogram-based model to predict combined esophageal injury post-esophagoscopy for esophageal foreign bodies.

The aim of this study is to create and validate a nomogram model for predicting complications of esophageal injury post-esophagoscopy in patients with esophageal foreign bodies (EFB). We examined 303 patients who underwent esophagoscopy from January 2019 to December 2022 at a leading hospital in Anhui, known for its expertise in otorhinolaryngology-head and neck surgery. The patients were split into a modeling group and a validation group in a 7:3 ratio. Logistic regression analysis was employed to determine the risk factors for esophageal injury after undergoing esophagoscopy in patients with EFB. Based on these factors, a nomogram risk prediction model was developed and assessed using a goodness of fit test. Logistic regression analysis revealed that the type of foreign body, failure of gastroscopic retrieval, duration of lodgment, lodgment site, and presence of combined cardiovascular and cerebrovascular diseases were significant (p < 0.05) independent risk factors for esophageal injury following esophagoscopy for EFB. The area under the ROC curve for the training set was 0.850, and the Hosmer-Lemeshow goodness of fit test resulted in a p value of 0.908. For the validation set, the area under the ROC curve was 0.848, and the Hosmer-Lemeshow test gave a p value of 0.665. The calibration curve showed a close alignment between the predicted and observed values. The type of foreign body, duration of lodgment, lodgment site, previous failure of gastroscopic retrieval, and history of combined cardiovascular and cerebrovascular diseases are significant risk factors for esophageal injury following EFB esophagoscopy. This model accurately quantifies the risk of esophageal injury after EFB esophagoscopy.

Lymph node ratio–based model for predicting survival and assessing the benefit of adjuvant chemotherapy in postoperative duodenal adenocarcinoma

BackgroundThis study aimed to evaluate the prognostic significance of lymph node ratio in postoperative duodenal adenocarcinoma and develop a nomogram-based model for prognosis assessment and treatment optimization. MethodsClinical information of patients with duodenal adenocarcinoma were retrieved from the Surveillance, Epidemiology, and End Results database, and prognostic factors were identified by univariate and multivariable analyses. Prognostic factors influencing patient outcomes were identified using univariate and multivariable Cox analyses. Subsequently, a novel nomogram and risk stratification system were developed based on these identified factors. ResultsA total of 943 eligible patients were included, with 656 in the training cohort and 287 in the validation cohort. Lymph node ratio ≥0.12 were associated with poorer overall survival (hazard ratio 1.562, 95% confidence interval 1.195–2.041, and P = .001 for lymph node ratio = 0.12–0.30; hazard ratio 2.431, 95% confidence interval 1.847–3.199, and P < .001 for lymph node ratio >0.30). Prognostic factors including age at diagnosis, race, T stage, lymph node ratio, and tumor size were integrated into the nomogram. Patients in the low-risk group demonstrated significantly better overall survival compared with those in the high-risk group. Additionally, adjuvant chemotherapy significantly improved overall survival in the high-risk subgroup, whereas low-risk patients might be exempt from adjuvant chemotherapy. ConclusionsThis study represented the pioneering endeavor in introducing a lymph node ratio–based nomogram model for prognosis stratification and adjuvant chemotherapy decision-making protocol for patients with duodenal adenocarcinoma, thereby guiding personalized treatment strategies and minimizing overtreatment.

463 A Predictive Nomogram-Based Model for Lower Extremity Compartment Syndrome After Trauma

463 A Predictive Nomogram-Based Model for Lower Extremity Compartment Syndrome After Trauma

Nomogram predicting the cardiovascular disease mortality for older patients with colorectal cancer: A real-world population-based study.

Cardio-oncology has received increasing attention especially among older patients with colorectal cancer (CRC). Cardiovascular disease (CVD)-specific mortality is the second-most frequent cause of death. The risk factors for CVD-specific mortality among older patients with CRC are still poorly understood. To identify the prognostic factors and construct a nomogram-based model to predict the CVD-specific mortality among older patients with CRC. The data on older patients diagnosed with CRC were retrieved from The Surveillance, Epidemiology, and End Results database from 2004 to 2015. The prognostic factors and a nomogram-based model predicting the CVD-specific mortality were assessed using least absolute shrinkage and selection operator and Cox regression. A total of 141251 eligible patients with CRC were enrolled, of which 41459 patients died of CRC and 12651 patients died of CVD. The age at diagnosis, sex, marital status, year of diagnosis, surgery, and chemotherapy were independent prognostic factors associated with CVD-specific mortality among older patients with CRC. We used these variables to develop a model to predict CVD-specific mortality. The calibration curves for CVD-specific mortality probabilities showed that the model was in good agreement with actual observations. The C-index value of the model in the training cohort and testing cohort for predicting CVD-specific mortality was 0.728 and 0.734, respectively. The proposed nomogram-based model for CVD-specific mortality can be used for accurate prognostic prediction among older patients with CRC. This model is a potentially useful tool for clinicians to identify high-risk patients and develop personalized treatment plans.

A predictive nomogram-based model for lower extremity compartment syndrome after trauma in the United States: a retrospective case-control study.

The aim of this study was to utilize the American College of Surgeons Trauma Quality Improvement Program (TQIP) database to identify risk factors associated with developing acute compartment syndrome (ACS) following lower extremity fractures. Specifically, a nomogram of variables was constructed in order to propose a risk calculator for ACS following lower extremity trauma. A large retrospective case-control study was conducted using the TQIP database to identify risk factors associated with developing ACS following lower extremity fractures. Multivariable regression was used to identify significant risk factors and subsequently, these variables were implemented in a nomogram to develop a predictive model for developing ACS. Novel risk factors identified include venous thromboembolism prophylaxis type particularly unfractionated heparin (odds ratio [OR], 2.67; 95% confidence interval [CI], 2.33-3.05; P<0.001), blood product transfusions (blood per unit: OR 1.13 [95% CI, 1.09-1.18], P<0.001; platelets per unit: OR 1.16 [95% CI, 1.09-1.24], P<0.001; cryoprecipitate per unit: OR 1.13 [95% CI, 1.09-1.22], P=0.003). This study provides evidence to believe that heparin use and blood product transfusions may be additional risk factors to evaluate when considering methods of risk stratification of lower extremity ACS. We propose a risk calculator using previously elucidated risk factors, as well as the risk factors demonstrated in this study. Our nomogram-based risk calculator is a tool that will aid in screening for high-risk patients for ACS and help in clinical decision-making.

Nomograms based on ratio indexes to predict severity and prognosis in immune checkpoint inhibitors-related myocarditis: a retrospective analysis

PurposeImmune checkpoint inhibitors-related myocarditis (ICI-M) is one of the immune-related adverse events (irAEs), which is rare and highly lethal. This study aimed to establish nomograms based on ratio biomarkers to predict the severity and prognosis of ICI-M.MethodsWe retrospectively examined patients with advanced cancers who were also diagnosed with ICI-M at the Fourth Hospital of Hebei Medical University. The patients of ICI-M were divided into mild and severe groups and a 40-day following up was carried out. The major adverse cardiovascular events(MACEs) were regarded as the endpoint. Nomogram-based models were established and validated.ResultsSeventy-seven patients were involved, including 31 severe cases(40.3%). Lactate dehydrogenase-to-albumin ratio(LAR) combined with the change rate from baseline to onset of LAR(▵\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ riangle$$\\end{document}LAR) which performed best to diagnose the severe ICI-M was identified to establish the nomogram-based model. The bootstrap-corrected concordance index [0.752 95% confidence interval (CI): 0.635-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}0.866] and calibration plot with good degree of fitting confirmed this diagnostic model. Neutrophil-to-high-density lipoprotein cholesterol ratio(NHR) and LAR were also screened into the nomogram-based model for 40-day MACEs after ICI-M, which performed well by validating for concordance index(0.779 95% CI: 0.677-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}0.865)and calibration plots after being bootstrap-corrected. Moreover, a ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document}101% increase in LAR significantly separated patients in MACE-free survival.ConclusionRatio indexes at onset and their change rates from baseline showed good diagnostic value for the severity of ICI-M and prognostic value for subsequent MACEs, particularly LAR, NHR and their change rates. The nomogram-based models of ratio indexes could provide a potential choice for early detection and monitor of the severe ICI-M and subsequent MACEs.Graphical abstract

Cardiovascular mortality risk in patients with ovarian cancer: a population-based study

ObjectivesOvarian cancer (OC) can occur at different ages and is affected by a variety of factors. In order to evaluate the risk of cardiovascular mortality in patients with ovarian cancer, we included influencing factors including age, histological type, surgical method, chemotherapy, whether distant metastasis, race and developed a nomogram to evaluate the ability to predict occurrence. At present, we have not found any correlation studies on cardiovascular death events in patients with ovarian cancer. This study was designed to provide targeted measures for effective prevention of cardiovascular death in patients with ovarian cancer.MethodsKaplan–Meier analysis and multivariable Cox proportional model were performed to evaluate the effectiveness of cardiovascular diseases on overall survival (OS) and ovarian cancer‐specific survival (OCSS). We compared multiple groups including clinical, demographic, therapeutic characteristics and histological types. Cox risk regression analysis, Kaplan–Meier survival curves, and propensity score matching were employed for analyzing the data.ResultsA total of 88,653 ovarian cancer patients were collected, of which 2,282 (2.57%) patients died due to cardiovascular-related diseases. Age, chemotherapy and whether satisfactory cytoreduction surgery is still the most important factors affecting the prognosis of ovarian cancer patients, while different histological types, diagnosis time, and race also have a certain impact on the prognosis. The newly developed nomogram model showed excellent predictive performance, with a C-index of 0.759 (95%CI: 0.757–0.761) for the group. Elderly patients with ovarian cancer are still a high-risk group for cardiovascular death [HR: 21.07 (95%CI: 5.21–85.30), p < 0.001]. The calibration curve showed good agreement from predicted survival probabilities to actual observations.ConclusionThis study found that age, histology, surgery, race, chemotherapy, and tumor metastasis are independent prognostic factors for cardiovascular death in patients with ovarian cancer. The nomogram-based model can accurately predict the OS of ovarian cancer patients. It is expected to inform clinical decision-making and help develop targeted treatment strategies for this population.

Prediction of survival and analysis of prognostic factors for patients with AFP negative hepatocellular carcinoma: a population-based study

PurposeHepatocellular carcinoma (HCC) has a poor prognosis, and alpha-fetoprotein (AFP) is widely used to evaluate HCC. However, the proportion of AFP-negative individuals cannot be disregarded. This study aimed to establish a nomogram of risk factors affecting the prognosis of patients with AFP-negative HCC and to evaluate its diagnostic efficiency.Patients and methodsData from patients with AFP-negative initial diagnosis of HCC (ANHC) between 2004 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and validation. We randomly divided overall cohort into the training or validation cohort (7:3). Univariate and multivariate Cox regression analysis were used to identify the risk factors. We constructed nomograms with overall survival (OS) and cancer-specific survival (CSS) as clinical endpoint events and constructed survival analysis by using Kaplan-Meier curve. Also, we conducted internal validation with Receiver Operating Characteristic (ROC) analysis and Decision curve analysis (DCA) to validate the clinical value of the model.ResultsThis study included 1811 patients (1409 men; 64.7% were Caucasian; the average age was 64 years; 60.7% were married). In the multivariate analysis, the independent risk factors affecting prognosis were age, ethnicity, year of diagnosis, tumor size, tumor grade, surgery, chemotherapy, and radiotherapy. The nomogram-based model related C-indexes were 0.762 (95% confidence interval (CI): 0.752–0.772) and 0.752 (95% CI: 0.740–0.769) for predicting OS, and 0.785 (95% CI: 0.774–0.795) and 0.779 (95% CI: 0.762–0.795) for predicting CSS. The nomogram model showed that the predicted death was consistent with the actual value. The ROC analysis and DCA showed that the nomogram had good clinical value compared with TNM staging.ConclusionThe age(HR:1.012, 95% CI: 1.006–1.018, P-value < 0.001), ethnicity(African-American: HR:0.946, 95% CI: 0.783–1.212, P-value: 0.66; Others: HR:0.737, 95% CI: 0.613–0.887, P-value: 0.001), tumor diameter(HR:1.006, 95% CI: 1.004–1.008, P-value < 0.001), year of diagnosis (HR:0.852, 95% CI: 0.729–0.997, P-value: 0.046), tumor grade(Grade 2: HR:1.124, 95% CI: 0.953–1.326, P-value: 0.164; Grade 3: HR:1.984, 95% CI: 1.574–2.501, P-value < 0.001; Grade 4: HR:2.119, 95% CI: 1.115–4.027, P-value: 0.022), surgery(Liver Resection: HR:0.193, 95% CI: 0.160–0.234, P-value < 0.001; Liver Transplant: HR:0.102, 95% CI: 0.072–0.145, P-value < 0.001), chemotherapy(HR:0.561, 95% CI: 0.471–0.668, P-value < 0.001), and radiotherapy(HR:0.641, 95% CI: 0.463–0.887, P-value:0.007) were independent prognostic factors for patients with ANHC. We developed a nomogram model for predicting the OS and CSS of patients with ANHC, with a good predictive performance.

The value of varying diffusion curvature MRI for assessing the microvascular invasion of hepatocellular carcinoma.

Varying diffusion curvature (VDC) MRI is an emerging diffusion-weighted imaging (DWI) technique that can capture non-Gaussian diffusion behavior and reflect tissue heterogeneity. However, its clinical utility has hardly been evaluated. We aimed to investigate the value of the VDC technique in noninvasively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC). 74 patients with HCCs, including 39 MVI-positive and 35 MVI-negative HCCs were included into this prospective study. Quantitative metrics between subgroups, clinical risk factors, as well as diagnostic performance were evaluated. The power analysis was also carried out to determine the statistical power. MVI-positive HCCs exhibited significantly higher VDC-derived structural heterogeneity measure, D1 (0.680 ± 0.100 × 10-3 vs 0.572 ± 0.148 × 10-3mm2/s, p = 0.001) and lower apparent diffusion coefficient (ADC) (1.350 ± 0.166 × 10-3 vs 1.471 ± 0.322 × 10-3mm2/s, p = 0.0495) compared to MVI-negative HCCs. No statistical significance was observed for VDC-derived diffusion coefficient, D0 between the subgroups (p = 0.562). Tumor size (odds ratio (OR) = 1.242) and alpha-fetoprotein (AFP) (OR = 2.527) were identified as risk factors for MVI. A predictive nomogram was constructed based on D1, ADC, tumor size, and AFP, which exhibited the highest diagnostic accuracy (AUC = 0.817), followed by D1 (AUC = 0.753) and ADC (AUC = 0.647). The diagnostic performance of the nomogram-based model was also validated by the calibration curve and decision curve. VDC can aid in the noninvasive and preoperative diagnosis of HCC with MVI, which may result in the clinical benefit in terms of prognostic prediction and clinical decision-making.

A novel web-based online nomogram to predict advanced liver fibrosis in patients with autoimmune hepatitis-primary biliary cholangitis overlap syndrome

BackgroundPatients with autoimmune hepatitis-primary biliary cholangitis (AIH-PBC) overlap syndrome have a worse prognosis compared to AIH or PBC alone and accurately predicting the severity and dynamically monitoring the progression of disease are therefore essential. We aimed to develop a nomogram-based model to predict advanced liver fibrosis in patients with AIH-PBC overlap syndrome. MethodsA total of 121 patients with AIH-PBC overlap syndrome were retrospectively included and randomly assigned to a development set and a validation set. Backward stepwise regression's best model with the lowest AIC was employed to create a nomogram. Diagnose accuracy was evaluated using the area under the receiver operator characteristic curve (AUROC), calibration analysis, and decision curve analysis (DCA) and was compared with aspartate aminotransferase-to-platelet ratio (APRI) and fibrosis index based on four factors-4 (FIB-4) score. ResultsThe median age of patients was 53.0 years (IQR: 46.0–63.0), and female patients accounted for 95.0 %. Platelets, globulin, total bilirubin, and prothrombin time were associated with advanced fibrosis (≥S3) and used to construct an AIH-PBC overlap syndrome fibrosis (APOSF)-nomogram (available online at https://ndth-zzy.shinyapps.io/APOSF-nomogram/). The AUROCs of APOSF-nomogram were 0.845 (95 % CI: 0.754–0.936) and 0.843 (95 % CI: 0.705–0.982) in development set and validation set respectively, which was significantly better than APRI and FIB-4. Calibration revealed that the estimated risk fits well with biopsy-proven observation. DCA outperformed APRI and FIB4 in terms of net benefit, demonstrating clinical utility. ConclusionThis novel non-invasive web-based online APOSF-nomogram provided a convenient tool for identifying advanced fibrosis in patients with AIH-PBC overlap syndrome. Further prospective, multicenter studies with large sample size are necessary to validate the applicability of APOSF-nomogram.

Development of a nomogram-based model combining intra- and peritumoral ultrasound radiomics with clinical features for differentiating benign from malignant in Breast Imaging Reporting and Data System category 3-5 nodules.

The differences in benign and malignant breast tumors are not only within the nodules but also involve changes in the surrounding tissues. Radiomics can reveal many details that are not discernible to the naked eye. This study aimed to distinguish between benign and malignant breast nodules using an ultrasound-based intra- and peritumoral radiomics model. This study retrospectively collected the information from 379 patients with Breast Imaging Reporting and Data System (BI-RADS) category 3-5 nodules and clear pathological diagnosis of breast nodules screened by routine ultrasound examination in the Sixth People's Hospital Affiliated to Medical College of Shanghai Jiao Tong University from January 2017 to December 2022. The largest dimension of the lesion on the 2D ultrasound image was selected to outline the area of interest which was conformally and outwardly expanded automatically by 5 mm to extract intra- and peritumor radiomics features. The included cases were randomly divided into training sets and test sets in a ratio of 7:3. The optimal features of the included models were retained by statistical and machine learning methods of dimensionality reduction, and logistic regression was used as the classifier to build an intratumoral model and a combined intratumoral-peritumoral radiomics model, respectively; through single-factor and multifactor logistic regression, the optimal features that could predict benign and malignant breast tumors were screened. The clinical and imaging models were established by selecting independent risk factors as clinical and imaging features through univariate and multifactorial logistic regression. Among 379 BI-RADS category 3-5 breast nodules, there were 124 malignant nodules and 255 benign nodules; patients were aged 14 to 88 (46.22±15.51) years, and the age differences, radiomics score, and mass diameter between the training and test sets were not statistically significant (P>0.05). The intra- and peritumor radiomics model had an area under the curve (AUC) of 0.840 [95% confidence interval (CI): 0.766-0.914] in the test set. The model with intra- and peritumoral ultrasound radiomics features combined with clinical features had an AUC value of 0.960 (95% CI: 0.920-0.999). The nomogram, developed using intratumoral and peritumoral radiomics features combined with clinical risk features, demonstrated superior performance in distinguishing between benign and malignant BI-RADS 3-5 lesions.

Prediction of placenta accreta spectrum in patients with placenta previa using a clinical, US and MRI combined model: A retrospective study with external validation

PurposeTo build and validate a predictive model of placental accreta spectrum (PAS) in patients with placenta previa (PP) combining clinical risk factors (CRF) with US and MRI signs. MethodOur retrospective study included patients with PP from two institutions. All patients underwent US and MRI examinations for suspicion of PAS. CRF consisting of maternal age, cesarean section number, smoking and hypertension were retrieved. US and MRI signs suggestive of PAS were evaluated. Logistic regression analysis was performed to identify CRF and/or US and MRI signs associated with PAS considering histology as the reference standard. A nomogram was created using significant CRF and imaging signs at multivariate analysis, and its diagnostic accuracy was measured using the area under the binomial ROC curve (AUC), and the cut-off point was determined by Youden’s J statistic. ResultsA total of 171 patients were enrolled from two institutions. Independent predictors of PAS included in the nomogram were: 1) smoking and number of previous CS among CRF; 2) loss of the retroplacental clear space at US; 3) intraplacental dark bands, focal interruption of the myometrial border and placental bulging at MRI. A PAS-prediction nomogram was built including these parameters and an optimal cut-off of 14.5 points was identified, showing the highest sensitivity (91%) and specificity (88%) with an AUC value of 0.95 (AUC of 0.80 in the external validation cohort). ConclusionA nomogram-based model combining CRF with US and MRI signs might help to predict PAS in PP patients, with MRI contributing more than US as imaging evaluation.

Preoperatively Identify the Microvascular Invasion of Hepatocellular Carcinoma with the Restricted Spectrum Imaging

To noninvasively and preoperatively identify the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) with the restricted spectrum imaging (RSI). 62 patients were included into this prospective study and underwent the RSI examination with a 3.0-T scanner. Mono-exponential diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) and RSI-derived metrics including f1 (fraction of restricted diffusion), f2 (fraction of hindered diffusion), f3 (fraction of free diffusion), and f1f2 (the multiply of f1 and f2) were calculated. Univariate and multivariate logistic regression were used to select the independent risk factors. Nomogram-based model was constructed with the selected indexes. Receiver operative characteristics analysis and calibration curve were used to evaluate the diagnostic accuracy. MVI-positive HCC showed significantly higher f1 and lower ADC values (ADC: 1.549±0.228×10-3 vs 1.365±0.239×10-3 mm2/s, P= .003; f1: 0.1633±0.0341 vs 0.2221±0.0491, P<.001). Tumor size and f1 were selected as independent risk factors for MVI. The nomogram-based model was then constructed with tumor size and f1. Nomogram-based model (area under ROC curve [AUC]=0.856) yielded the best diagnostic accuracy followed by f1 (AUC=0.842) and ADC (AUC=0.708). The AUC of both the f1 and nomogram model were significantly higher than that of ADC. RSI-derived metrics can be utilized to noninvasively and efficiently identify the MVI of HCC. Considering the importance of MVI as a significant prognostic factor for HCC, the utilization of RSI has the potential to assist in prognostic prediction and clinical management.

A Nomogram-Based Model for Predicting the Risk of Severe Acute Cholangitis Occurrence.

Acute cholangitis is a severe inflammatory disease associated with an infection of the biliary system, which can lead to complications and adverse outcomes. The existing nomogram-based risk assessment methods largely rely on a limited set of clinical features and laboratory indicators, and are mostly constructed using univariable models, which have limitations in predicting the severity. This study aims to develop a nomogram-based model that integrates multiple variables to improve risk prediction for acute cholangitis. Data were retrospectively collected from 152 patients with acute cholangitis who attended the People's Hospital of Jiangsu University between January 2019 and March 2022, and were graded as having mild to moderate versus severe cholangitis according to the 2018 Tokyo guidelines. Univariate and multivariate analyses were employed to discern independent risk factors associated with severe acute cholangitis, which were subsequently integrated into a nomogram model. The efficacy of the model was appraised by leveraging Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). Aspartate to alanine transaminase ratio (Transaminase ratio or TR), Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and D-dimer (DD) levels were independent risk factors for severe acute cholangitis. A nomogram model was constructed based on these 4 risk factors. ROC and calibration curves were well differentiated and calibrated. DCA had a high net gain in the range of 7% to 83%. The above model was tested internally. According to the nomogram model when patients using characteristic curve critical values were divided into a low-risk group and a high-risk group, the incidence in the high-risk group was significantly higher than in the low-risk group. This nomogram model may provide clinicians with an effective tool to predict the potential risk of severe acute cholangitis in patients and guide informed intervention measures and treatment decisions.

The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram-based model to detect mild cognitive impairment (MCI) in T2DM patients. Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well-qualified investigators conducted face-to-face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM-related information, and history of depression and anxiety. Cognitive function was assessed using the Mini-Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%-38.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815-0.883), and the threshold probability ranged from 35.0% to 85.0%. Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.