The 2012 Briganti nomogram predicts disease progression in surgically treated intermediate-risk prostate cancer patients with favorable tumor grade group eventually associated with some adverse factors

To evaluate the prognostic potential of the 2012 Briganti nomogram for pelvic lymph node invasion on disease progression after surgery in intermediate-risk (IR) prostate cancer (PCa) patients with favorable tumor grade (International Society of Urological Pathology grade group 1 or 2), eventually associated with adverse clinical features as PSA between 10 and 20 ng/mL and/or clinical stage T2b. All IR PCa patients treated with robot-assisted radical prostatectomy and eventually extended pelvic lymph node dissection at the Department of Urology of the Integrated University Hospital of Verona between 2013 and 2021, with the abovementioned features, and available follow-up were considered. The 2012 Briganti nomogram score was assessed both as a continuous and dichotomous variable, where a mean risk score of 4% was used a threshold. The independent predictor status of the nomogram score on disease progression defined as the occurrence of biochemical recurrence and/or metastatic progression was evaluated using the Cox regression analysis. Overall, 348 patients were enrolled in the study. Median (interquartile range) follow-up was 98 (83.5-112.4) months. At multivariable Cox regression analysis, PCa progression, which occurred in 65 (18.7%) cases, was independently predicted only by the 2012 Briganti nomogram score evaluated as a continuous variable, among all considered clinical features (HR 1.16; 95%CI 1.08-1.24; p < 0.001). In addition, patients presenting with a nomogram score ≥ 4% were more likely to experience disease progression even after adjustment for clinical (HR 2.22, 95%CI 1.02-4.79; p = 0.043) and pathological (HR 1.80; 95%CI 1.06-3.05; p = 0.031) factors. In the examined patient population, the 2012 Briganti nomogram predicted PCa progression after surgery. Accordingly, as the risk score increased, patients were more likely to progress, independently by the occurrence of adverse pathology in the surgical specimen. The 2012 Briganti nomogram score categorized according to the mean value allowed to identify prognostic subgroups.

Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors

BACKGROUND

Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes.

MATERIALS AND METHODS

We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance.

RESULTS

By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score.

CONCLUSIONS

A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.

Diagnostic prediction of gastrointestinal graft-versus-host disease based on a clinical- CT- signs nomogram model

OBJECTIVE

Gastrointestinal graft-versus-host disease (GI-GVHD) is one of the complications that can easily occur after hematopoietic stem cell transplantation (HSCT). Timely diagnosis and treatment are pivotal factors that greatly influence the prognosis of patients. However, the current diagnostic method lacks adequate non-invasive diagnostic tools.

METHODS

A total of 190 patients who suspected GI-GVHD were retrospectively included and divided into training set (n = 114) and testing set (n = 76) according to their discharge time. Least absolute shrinkage and selection operator (LASSO) regression was used to screen for clinically independent predictors. Based on the logistic regression results, both computed tomography (CT) signs and clinically independent predictors were integrated in order to build the nomogram, while the testing set was verified independently. The receiver operating characteristic (ROC), area under the curve (AUC), decision curve, and clinical impact curve were used to measure the accuracy of prediction, clinical net benefit, and consistency of diagnostic factors.

RESULTS

Four key factors, including II-IV acute graft-versus-host disease (aGVHD), the circular target sign, multifocal intestinal inflammation, and an increased in total bilirubin, were identified. The combined model, which was constructed from CT signs and clinical factors, showed higher predictive performances. The AUC, sensitivity, and specificity of the training set were 0.867, 0.787, and 0.811, respectively. Decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) showed that the developed model exhibited a better prediction accuracy than the others.

CONCLUSIONS

This combined model facilitates timely diagnosis and treatment and subsequently improves survival and overall outcomes in patients with GI-GVHD.

CRITICAL RELEVANCE STATEMENT

GI-GVHD is one of the complications that can easily occur after HSCT. However, the current diagnostic approach lacks adequate non-invasive diagnostic methods. This non-invasive combined model facilitates timely treatment and subsequently improves patients with GI-GVHD survival and overall outcomes.

KEY POINTS

• There is currently lacking of non-invasive diagnostic methods for GI-GVHD. • Four clinical CT signs are the independent predictors for GI-GVHD. • Association between the CT signs with clinical factors may improve the diagnostic performance of GI-GVHD.

A Radiomics-clinical Nomogram based on CT Radiomics to Predict Acquired T790M Mutation Status in Non-small Cell Lung Cancer Patients

OBJECTIVE

To develop and validate a radiomics-clinical nomogram for the detection of the acquired T790M mutation in patients with advanced non-small cell lung cancer (NSCLC) with resistance after the duration of first-line epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment.

MATERIALS AND METHODS

Thoracic CT was collected from 120 advanced NSCLC patients who suffered progression on first- or second-generation TKIs. Radiomics signatures were retrieved from the entire tumor. Pearson correlation and the least absolute shrinkage and selection operator (LASSO) regression method were adopted to choose the most suitable radiomics features. Clinical and radiological factors were assessed using univariate and multivariate analysis. Three Machine Learning (ML) models were constructed according to three classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), and RandomForest (RF), combining clinical and radiomic features. A nomogram combining clinical features and the rad score signature was built. The predictive ability of the nomogram was assessed by the ROC curve, calibration curve, and decision curve analysis (DCA).

RESULTS

Multivariate regression analysis showed that two clinicopathological characteristics and two radiological features were highly correlated with the acquired T790M mutation, including the progression-free survival (PFS) of first-line EGFR TKIs (P = 0.029), the initial EGFR profile (P = 0.01), vascular convergence (P = 0.043), and air bronchogram (P = 0.030). The AUCs of clinical, radiomics, and combined models using RF classifiers for T790M mutation detection were 0.951 (95% confidence interval [CI] 0.911,0.991), 0.917 (95%CI 0.856,0.978), and 0.961 (95%CI 0.927,0.995) in the training cohort, respectively, higher than those of other classifier models.The calibration curve and Hosmer-Lemeshow Test showed good calibration power, and the DCA demonstrated a significant net benefit.

CONCLUSION

A radiomics-clinical nomogram based on CT radiomics proved valuable in non-invasively and efficiently predicting the acquired T790M mutation in patients who suffered progression on first-line TKIs.

A Novel Nomogram for Predicting Early Rebleeding After Endoscopic Treatment of Esophagogastric Variceal Hemorrhage

BACKGROUND

Early rebleeding is a significant complication of endoscopic treatment for esophagogastric variceal hemorrhage (EGVH). However, a reliable predictive model is currently lacking.

AIMS

To identify risk factors for rebleeding within 6 weeks and establish a nomogram for predicting early rebleeding after endoscopic treatment of EVGH.

METHODS

Demographic information, comorbidities, preoperative evaluation, endoscopic features, and laboratory tests were collected from 119 patients who were first endoscopic treatment for EGVH. Independent risk factors for early rebleeding were determined through least absolute shrinkage and selection operator logistic regression. The discrimination, calibration, and clinical utility of the nomogram were assessed and compared with the model for end-stage liver disease (MELD), Child-Pugh, and albumin-bilirubin (ALBI) scores using receiver-operating characteristic (ROC) curves, calibration plots, and decision curve analyses (DCA).

RESULTS

Early rebleeding occurred in 39 patients (32.8%) within 6 weeks after endoscopic treatment. Independent early rebleeding factors included gastric variceal hemorrhage (GVH), concomitant hepatocellular carcinoma (HCC), international normalized ratio (INR), and creatinine. The nomogram demonstrated exceptional calibration and discrimination capability. The area under the curve for the nomogram was 0.758 (95% CI 0.668-0.848), and it was validated at 0.71 through cross-validation and bootstrapping validation. The DCA and ROC curves demonstrated that the nomogram outperformed the MELD, Child-Pugh, and ALBI scores.

CONCLUSIONS

Compared with existing prediction scores, the nomogram demonstrated superior discrimination, calibration, and clinical applicability for predicting rebleeding in patients with EGVH after endoscopic treatment. Therefore, it may assist clinicians in the early implementation of aggressive treatment and follow-up.

Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis

BACKGROUND

Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis.

METHODS

Retrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer-Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted.

RESULTS

The research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems.

CONCLUSIONS

The nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.

Enhancing precision medicine: a nomogram for predicting platinum resistance in epithelial ovarian cancer

BACKGROUND

This study aimed to develop a novel nomogram that can accurately estimate platinum resistance to enhance precision medicine in epithelial ovarian cancer(EOC).

METHODS

EOC patients who received primary therapy at the General Hospital of Ningxia Medical University between January 31, 2019, and June 30, 2021 were included. The LASSO analysis was utilized to screen the variables which contained clinical features and platinum-resistance gene immunohistochemistry scores. A nomogram was created after the logistic regression analysis to develop the prediction model. The consistency index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's performance.

RESULTS

The logistic regression analysis created a prediction model based on 11 factors filtered down by LASSO regression. As predictors, the immunohistochemical scores of CXLC1, CXCL2, IL6, ABCC1, LRP, BCL2, vascular tumor thrombus, ascites cancer cells, maximum tumor diameter, neoadjuvant chemotherapy, and HE4 were employed. The C-index of the nomogram was found to be 0.975. The nomogram's specificity is 95.35% and its sensitivity, with a cut-off value of 165.6, is 92.59%, as seen by the ROC curve. After the nomogram was externally validated in the test cohort, the coincidence rate was determined to be 84%, and the ROC curve indicated that the nomogram's AUC was 0.949.

CONCLUSION

A nomogram containing clinical characteristics and platinum gene IHC scores was developed and validated to predict the risk of EOC platinum resistance.

Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC.

METHODS

A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model.

RESULTS

A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC.

CONCLUSION

The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

Development and validation of a nomogram for predicting the prognosis in children with spinal cord injuries

AIMS

This research aims to construct and verify an accurate nomogram for forecasting the 3-, 5-, and 7-year outcomes in pediatric patients afflicted with spinal cord injury (SCI).

METHODS

Pediatric patients with SCI from multiple hospitals in China, diagnosed between Jan 2005 and Jan 2020, were incorporated into this research. Half of these patients were arbitrarily chosen for training sets, and the other half were designated for external validation sets. The Cox hazard model was employed to pinpoint potential prognosis determinants related to the American Spinal Injury Association (ASIA) and Functional Independence Assessment (FIM) index. These determinants were then employed to formulate the prognostic nomogram. Subsequently, the bootstrap technique was applied to validate the derived model internally.

RESULTS

In total, 224 children with SCI were considered for the final evaluation, having a median monitoring duration of 68.0 months. The predictive nomogram showcased superior differentiation capabilities, yielding a refined C-index of 0.924 (95% CI: 0.883-0.965) for the training cohort and a C-index of 0.863 (95% CI: 0.735-0.933) for the external verification group. Additionally, when applying the aforementioned model to prognostic predictions as classified by the FIM, it demonstrated a high predictive value with a C-index of 0.908 (95% CI: 0.863-0.953). Moreover, the calibration diagrams indicated a consistent match between the projected and genuine ASIA outcomes across both sets.

CONCLUSION

The crafted and verified prognostic nomogram emerges as a dependable instrument to foresee the 3-, 5-, and 7-year ASIA and FIM outcomes for children suffering from SCI.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study

Background

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery.

Methods

Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n = 128) and a validation cohort (n = 64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared.

Results

Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates.

Conclusion

The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

The inflammation score predicts the prognosis of gastric cancer patients undergoing Da Vinci robot surgery

Neutrophil-to-lymphocyte ratio (NLR), calculated from peripheral blood immune-inflammatory cell counts, is considered a predictor of survival in various cancers. Nevertheless, there is a lack of research into the predictive value of NLR specifically in gastric cancer patients following surgery using the Da Vinci robot. Investigate the objectives of this research, confirm the positive predictive value of NLR in the prognosis of gastric cancer patients undergoing Da Vinci robotic-assisted surgery by comparing its prognostic ability with other inflammation markers and tumor biomarkers. In this retrospective analysis, information from 128 individuals diagnosed with gastric cancer and treated with da Vinci robot-assisted surgery was examined. The study examined various markers in the peripheral blood, including neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), systemic immune-inflammatory index (SII) prognostic nutrition index (PNI), cancer antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 72-4 (CA72-4), carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP).To ascertain the prognostic ability and optimal cutoff values of each parameter, operating characteristic curves and the area under the curve were utilized in the analysis. For evaluation of independent prognostic factors, we utilized Kaplan-Meier curves and multifactorial Cox analysis. The variables from the multifactorial Cox analysis were used to construct a nomogram. NLR, LMR, CEA, AFP, primary location, largest tumor size and TNM stage were all found to be significant predictive elements for overall survival (OS). Multivariate Cox identified NLR (P = 0.005), LMR (P = 0.03) and AFP (P = 0.007) as the only separate predictive variables among hematological indicators. The nomogram built using NLR demonstrates excellent predictive performance at 1 year (AUC = 0.778), 3 years (AUC = 0.773), and 5 years (AUC = 0.781). Cross-validation demonstrates that this model has favorable predictive performance and discriminative ability. NLR is an uncomplicated yet potent marker for forecasting the survival result of individuals with gastric cancer following da Vinci robotic surgery, and it possesses considerable predictive significance. The nomogram based on NLR provides patients with a visual and accurate prognosis prediction.

Nomogram to predict overall survival of patients receiving radical gastrectomy and incomplete peri-operative adjuvant chemotherapy for stage II/III gastric cancer: a retrospective bi-center cohort study

BACKGROUND

To establish a nomogram to predict the probability of survival of patients with stage II/III gastric cancer (GC) who received incomplete peri-operative adjuvant chemotherapy (PAC).

METHODS

The medical records of stage II/III GC patients who received curative resection and 1 to 5 cycles of PAC from two tertiary hospitals were retrospectively reviewed. Patients were randomly classified into either a training group or validation group at a ratio of 7:3. The nomogram was constructed based on various prognostic factors using Cox regression analysis in the training cohort, and was validated by the validation group. Concordance index and calibration curves were used to evaluate the discrimination and calibration of the nomogram. Additionally, decision curve analysis (DCA) was used to compare the net clinical benefits of the nomogram and eighth version of TNM staging system.

RESULTS

A total of 1,070 consecutive patients were included and 749 patients were enrolled into the training group. Lower body mass index (< 18.5 kg/m2), total gastrectomy, stage III disease and fewer cycles of PAC were identified to be independent predictors for poorer survival. The area under the curve (AUC) values of receiver operating characteristics (ROC) curve predicting 5-year survival probabilities and C-index were 0.768 and 0.742, 0.700 (95%CI: 0.674-0.726) and 0.689 (95%CI: 0.646-0.732) in the training and validation groups, respectively. The calibration curves in the validation cohort showed good agreement between the prediction and observation of 1-, 3- and 5-year survival probabilities. Furthermore, DCA showed that our model has a better net benefit than that of TNM staging system.

CONCLUSIONS

The findings emphasize the value of completing PAC. The nomogram which was established to predict survival probability in patients with stage II/III GC receiving radical gastrectomy and incomplete PAC had good accuracy and was verified through both internal and external validation.

A nomogram for predicting the risk of heart failure with preserved ejection fraction

BACKGROUND

This study purposed to design and establish a nomogram to predict the risk of having heart failure with preserved ejection fraction.

METHOD

The clinical data of 1031 patients diagnosed with heart failure (HF) in the First Affiliated Hospital of Jinan University from January 2018 to December 2022 were retrospectively analyzed, among which 618 patients were diagnosed with heart failure with preserved ejection fraction (HFpEF). Patients were randomly divided into a training set (70%, n = 722) and a validation set (30%, n = 309). The prediction model of HFpEF was established by using clinical characteristic data parameters, and the risk of having HFpEF was predicted by using a nomogram. Single-factor analysis was used to select independent risk factors (P < 0.05), and then binary logistic regression was used to screen predictive variables (P < 0.05). The discrimination ability of the model was evaluated by the ROC curve and calculating the area under the curve (AUC). In addition, the predictive ability of the established nomogram was evaluated using calibration curves and the Hosmer-Lemeshow goodness of fit test (HL test), and the clinical net benefit was evaluated using decision curve analysis (DCA).

RESULTS

The results of binary logistic regression analysis showed that age, gender, hypertension, coronary heart disease, glycosylated hemoglobin, serum creatinine, E/e' septal, relative wall thickness (RWT), left ventricular mass index (LVMI) and pulmonary hypertension (PH) were independent influencing factors for the risk of having HFpEF (P < 0.05). Based on the results of logistic regression analysis, a nomogram was established and calibration curves were made. The prediction model showed that the AUC of the training dataset was 0.876 (95%CI, 0.851-0.902), and 0.837 (95%CI, 0.791-0.883) in the validation set. According to the calibration curves and HL test, the nomogram shows good calibration, and DCA shows that our model is clinically useful.

CONCLUSION

A nomogram prediction model was constructed to predict the patient's risk of having HFpEF. This prediction model indicated that the combination of creatinine, E/e', RWT, LVMI and PH may be valuable in the diagnosis of HFpEF.

Radiogenomic analysis based on lipid metabolism-related subset for non-invasive prediction for prognosis of renal clear cell carcinoma

PURPOSE

Multiple lipid metabolism pathways alterations are associated with clear cell renal cell carcinoma (ccRCC) development and aggressiveness. In this study, we aim to develop a novel radiogenomics signature based on lipid metabolism-related genes (LMRGs) that may accurately predict ccRCC patients' survival.

MATERIALS AND METHODS

First, 327 ccRCC were used to screen survival-related LMRGs and construct a gene signature based on The Cancer Genome Atlas (TCGA) database. Then, 182 ccRCC were analyzed to establish radiogenomics signature linking LMRGs signature to radiomic features in The Cancer Imaging Archive (TCIA) database included enhanced CT images and transcriptome sequencing data. Lastly, we validated the prognostic power of the identified radiogenomics signature using these patients of TCIA and the Third Xiangya Hospital.

RESULTS

We identified the LMRGs signature, consisting of 13 genes, which could efficiently discriminate between low-risk and high-risk patients and serve as an independent and reliable predictor of overall survival (OS). Radiogenomics signature, comprised of 9 radiomic features, was created and could accurately predict the expression level of LMRGs signature (low- or high-risk) for patients. The predictive performance of this radiogenomics signature was demonstrated through AUC values of 0.75 and 0.74 for the training and validation sets (at a ratio of 7:3), respectively. Radiogenomics signature was proven to be an independent risk factor for OS by multivariable analysis (HR = 4.98, 95 % CI:1.72-14.43, P = 0.003).

CONCLUSIONS

The LMRGs radiogenomics signature could serve as a novel prognostic predictor.

A Dynamic Online Nomogram Based on Gd-EOB-DTPA-Enhanced MRI and Inflammatory Biomarkers for Preoperative Prediction of Pathological Grade and Stratification in Solitary Hepatocellular Carcinoma: A Multicenter Study

BACKGROUND

Hepatocellular carcinoma (HCC) is an inflammatory cancer. We aimed to explore whether preoperative inflammation biomarkers compared to the gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI can add complementary value for predicting HCC pathological grade, and to develop a dynamic nomogram to predict solitary HCC pathological grade.

METHODS

331 patients from the Institution A were divided chronologically into the training cohort (n = 231) and internal validation cohort (n = 100), and recurrence-free survival (RFS) was determined to follow up after surgery. 79 patients from the Institution B served as the external validation cohort. Overall, 410 patients were analyzed as the complete dataset cohort. Least absolute shrinkage and selection operator (LASSO) and multivariate Logistic regression were used to gradually filter features for model construction. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance.

RESULTS

Five models of the inflammation, imaging, inflammation+AFP, inflammation+imaging and nomogram were developed. Adding inflammation to imaging model can improve the AUC in training cohort (from 0.802 to 0.869), internal validation cohort (0.827 to 0.870), external validation cohort (0.740 to 0.802) and complete dataset cohort (0.739 to 0.788), and obtain more net benefit. The nomogram had excellent performance for predicting high-grade HCC in four cohorts (AUCs: 0.882 vs. 0.869 vs. 0.829 vs. 0.806) with a good calibration, and accessed at https://predict-solitaryhccgrade.shinyapps.io/DynNomapp/. Additionally, the nomogram obtained an AUC of 0.863 (95% CI 0.797-0.913) for predicting high-grade HCC in the HCC≤ 3 cm. Kaplan-Meier survival curves demonstrated that the nomogram owned excellent stratification for HCC grade (P < 0.0001).

CONCLUSION

This easy-to-use dynamic online nomogram hold promise for use as a noninvasive tool in prediction HCC grade with high accuracy and robustness.

Construction and validation of a perioperative concomitant lower extremity deep vein thrombosis line graph model in patients with aneurysmal subarachnoid hemorrhage

Background

To develop and validate a nomogram for predicting the probability of deep venous thrombosis (DVT) in patients with aneurysmal subarachnoid hemorrhage (aSAH) during the perioperative period, using clinical features and readily available biochemical parameters.

Methods

The least absolute shrinkage and selection operator (LASSO) regression technique was employed for data dimensionality reduction and selection of predictive factors. A multivariable logistic regression analysis was conducted to establish a predictive model and nomogram for post-aSAH DVT. The discriminative ability of the model was determined by calculating the area under the curve (AUC).

Results

A total of 358 aSAH patients were included in the study, with an overall incidence of DVT of 20.9%. LASSO regression identified four variables, including age, modified Fisher grade, total length of hospital stay, and anticoagulation therapy, as highly predictive factors for post-aSAH DVT. The patients were randomly divided into a modeling group and a validation group in a 6:4 ratio to construct the nomogram. The AUCs of the modeling and validation groups were 0.8511 (95% CI, 0.7922-0.9099) and 0.8633 (95% CI, 0.7968-0.9298), respectively.

Conclusions

The developed nomogram exhibits good accuracy, discriminative ability, and clinical utility in predicting DVT, aiding clinicians in identifying high-risk individuals and implementing appropriate preventive and treatment measures.

Stereotactic hematoma puncture and drainage for primary pontine hemorrhage: Clinical outcomes and predictive model

Primary pontine hemorrhage (PPH) is a particularly grave form of hemorrhagic stroke, characterized by its significant mortality rate. stereotactic hematoma puncture and drainage is a procedure that has been shown to improve the prognosis of patients with PPH. However, there are currently no established criteria for selecting patients for this procedure. We contrasted the clinical outcomes of PPH patients treated with stereotactic hematoma puncture and drainage with those who received conservative treatment in this study. We conducted logistic regression analysis to identify the risk factors associated with postoperative mortality. A mortality risk nomogram was then constructed using these risk factors. A total of 127 conservatively treated patients and 96 patients who underwent stereotactic hematoma puncture and drainage were included in this study. In the surgical group, the 30-day mortality rate stood at 28.1%, significantly lower than the 43.3% observed in the control group (p = 0.02). Age, along with the Glasgow Coma Scale (GCS) score and hematoma size, were identified as independent risk factors associated with death within 30 days post-surgery. The mortality risk nomogram was well calibrated and discriminatory, with a c-index of 0.878 (95% CI 0.80-0.95) as validated by bootstrapping, and a c-index of 0.849. This study provides a predictive model for selecting patients who are most likely to benefit from stereotactic hematoma puncture and drainage. The results of this study could be helpful to neurosurgeons in their decision-making process. However, further external validation is necessary to confirm these findings.

A prognostic nomogram for predicting breast cancer survival based on mammography and AJCC staging

Rationale and objectives

To develop a prognostic nomogram using mammography data and AJCC staging to predict breast cancer survival.

Materials and methods

A prognostic nomogram was created using data from 1000 women diagnosed with breast cancer at a medical cancer center in Taiwan between 2011 and 2015. The variables included age at diagnosis (≤60 or > 60 years), mammography purpose (screening or diagnostic), mammography modality (digital mammogram or digital breast tomosynthesis), and the 7th American Joint Committee on Cancer (AJCC) stage. The outcome predicted was breast cancer-related mortality. The nomogram utilized Kaplan-Meier analysis for all subsets and Cox proportional hazards regression analysis for prediction. The nomogram's accuracy was internally validated using the concordance index and receiver operating characteristic (ROC) curve analysis, focusing on 3-year and 5-year survival predictions.

Results

Participants' mean age at breast cancer diagnosis was 54 years (SD = 11.2 years). The 1-year, 3-year, and 5-year overall survival (OS) rates were found to be 99.7%, 95.3%, and 91.4%, respectively. The bootstrap-corrected concordance indices indicated the following: nomogram, 0.807 and AJCC, 0.759. A significant difference was observed between the nomogram's area under the curve (AUC) and the AJCC stage in predicting the probability of 5-year survival (p = 0.005). A nomogram, constructed based on mammography and AJCC, demonstrated excellent calibration through internal validation using bootstrapping.

Conclusion

The utilization of a nomogram that incorporates mammography data and the AJCC registry data has been demonstrated to be a reliable predictor of breast cancer survival.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

PURPOSE

To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.

METHODS

The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.

RESULTS

The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).

CONCLUSION

The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

Construction of a depression risk prediction model for type 2 diabetes mellitus patients based on NHANES 2007-2014

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prevalent global health issue that has been linked to an increased risk of depression. The objective of this study was to construct a nomogram model for predicting depression in T2DM patients.

METHODS

A total of 4280 patients with T2DM were included in this study from the 2007-2014 NHANES. The entire dataset was split randomly into training set comprising 70 % of the data and a validation set comprising 30 % of the data. LASSO and multivariate logistic regression analyses identified predictors significantly associated with depression, and the nomogram was constructed with these predictors. The model was assessed by C-index, calibration curve, the hosmer-lemeshow test and decision curve analysis (DCA).

RESULTS

The nomogram model comprised of 9 predictors, namely age, gender, PIR, BMI, education attainment, smoking status, LDL-C, sleep duration and sleep disorder. The C-index of the training set was 0.780, while that of the validation set was 0.752, indicating favorable discrimination for the model. The model exhibited excellent clinical applicability and calibration in both the training and validation datasets. Moreover, the cut-off value of the nomogram is 223.

LIMITATIONS

This study has shortcomings in data collection, lack of external validation, and results non-extrapolation.

CONCLUSIONS

Our nomogram exhibits high clinical predictability, enabling clinicians to utilize this tool in identifying high-risk depressed patients with T2DM. It has the potential to decrease the incidence of depression and significantly improve the prognosis of patients with T2DM.

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma: Machine learning model based on contrast-enhanced computed tomography

BACKGROUND

Recently, vessels encapsulating tumor clusters (VETC) was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner, and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC).

AIM

To develop and validate a preoperative nomogram using contrast-enhanced computed tomography (CECT) to predict the presence of VETC+ in HCC.

METHODS

We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers. Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase. Radiomics features, essential for identifying VETC+ HCC, were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set. The model's performance was validated on two separate test sets. Receiver operating characteristic (ROC) analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets. The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features. ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features, the radiomics features and the radiomics nomogram.

RESULTS

The study included 190 individuals from two independent centers, with the majority being male (81%) and a median age of 57 years (interquartile range: 51-66). The area under the curve (AUC) for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825, 0.788, and 0.680 in the training set and the two test sets. A total of 13 features were selected to construct the Rad-score. The nomogram, combining clinical-radiological and combined radiomics features could accurately predict VETC+ in all three sets, with AUC values of 0.859, 0.848 and 0.757. Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.

CONCLUSION

This study demonstrates the potential utility of a CECT-based radiomics nomogram, incorporating clinical-radiological features and combined radiomics features, in the identification of VETC+ HCC.

Factors and predictive model for malnutrition in poststroke disabled patients: A multicenter cross-sectional study

BACKGROUND

Although malnutrition has been shown to influence the clinical outcome of poststroke disabled patients, the associated factors and the prediction model have yet to be uncovered.

OBJECTIVES

This study aims to assess the current prevalence and factors associated with malnutrition in poststroke disabled patients and establish a prediction model.

METHODS

A multicenter cross-sectional survey among Chinese poststroke disabled patients (≥18 y old) was conducted in 2021. Information on patients' basic data, medical history, Barthel Index, dysphagia, and nutritional status was collected. A multivariable logistic regression model was used to identify the factors that influence malnutrition. Nomogram was developed and internal validation was conducted using 5-fold cross-validation. External validation was performed using the data from a preliminary survey. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to analyze the predictive value of the nomogram.

RESULTS

Four hundred fifty-seven cases were enrolled, with the prevalence of malnutrition as 71.77%. Age (aOR = 1.039, 95% CI: 1.006-1.078), pulmonary infection (aOR = 4.301, 95% CI: 2.268-14.464), dysphagia (aOR = 24.605, 95% CI: 4.966-191.058), total intake volume (aOR = 0.997, 95% CI: 0.995-0.999), Barthel Index (aOR = 0.965, 95% CI: 0.951-0.980), and nasogastric tube (aOR = 16.529, 95% CI: 7.418-52.518) as nutrition support mode (compared to oral intake) were identified as the associated factors of malnutrition in stroke-disabled patients (P < 0.05). ROC analysis showed that the area under the curve (AUC) for nomogram was 0.854 (95% CI: 0.816-0.892). Fivefold cross-validation showed the mean AUC as 0.829 (95% CI: 0.784-0.873). There were no significant differences between predicted and actual probabilities. The DCA revealed that the model exhibited a net benefit when the risk threshold was between 0 and 0.4.

CONCLUSIONS

Age, pulmonary infection, dysphagia, nutrition support mode, total intake volume, and Barthel Index were factors associated with malnutrition in stroke-related disabled patients. The nomogram based on the result exhibited good accuracy, consistency and values.

Predicting individual outcomes after radical cystectomy in urothelial variants with Cancer of the Bladder Risk Assessment (COBRA) score

OBJECTIVE

To validate the Cancer of the Bladder Risk Assessment (COBRA) score in patients with urothelial variants.

METHODS

Epidemiological, clinical, radiological, and anatomopathological data were collected from patients with urothelial carcinoma who underwent radical cystectomy at the Institute of Cancer of São Paulo between May 2008 and December 2022. Patients with the presence of at least 10% of any urothelial variants in the radical cystectomy specimens' anatomopathological exam were included in the study. The COBRA score and derivatives were applied and correlated with oncological outcomes.

RESULTS

A total of 680 patients [482 men (70.9%) and 198 women (29.1%)]; 66 years (IQR 59-73) underwent radical cystectomy for bladder tumor, and of these patients, a total of 167 patients presented any type of urothelial variant. The median follow-up time was 28.77 months (IQR 12-85). The three most prevalent UV were squamous differentiation (50.8%), glandular differentiation (31.3%), and micropapillary differentiation (11.3%). The subtypes with the worst prognosis were sarcomatoid with a median survival of 8 months (HR 1.161; 95% CI 0.555-2.432) and plasmacytoid with 14 months (HR 1.466; 95% CI 0.528-4.070). The COBRA score for patients with micropapillary variants demonstrated good predictive accuracy for OS (log-rank P = 0.009; 95% IC 6.78-29.21) and CSS (log-rank P = 0.002; 95% IC 13.06-26.93).

CONCLUSIONS

In our study, the COBRA score proved an effective risk stratification tool for urothelial histological variants, especially for the micropapillary urothelial variant. It may be helpful in the prognosis evaluation of UV patients after radical cystectomy.

Nomogram predicting the efficacy of transurethral surgery in benign prostatic hyperplasia patients

PURPOSE

This study aimed to develop and validate a nomogram for predicting the efficacy of transurethral surgery in benign prostatic hyperplasia (BPH) patients.

METHODS

Patients with BPH who underwent transurethral surgery in the West China Hospital and West China Shang Jin Hospital were enrolled. Patients were retrospectively involved as the training group and were prospectively recruited as the validation group for the nomogram. Logistic regression analysis was utilized to generate nomogram for predicting the efficacy of transurethral surgery. The discrimination of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots were applied to evaluate the calibration of the nomogram.

RESULTS

A total of 426 patients with BPH who underwent transurethral surgery were included in the study, and they were further divided into a training group (n = 245) and a validation group (n = 181). Age (OR 1.07, 95% CI 1.02-1.15, P < 0.01), the compliance of the bladder (OR 2.37, 95% CI 1.20-4.67, P < 0.01), the function of the detrusor (OR 5.92, 95% CI 2.10-16.6, P < 0.01), and the bladder outlet obstruction (OR 2.21, 95% CI 1.07-4.54, P < 0.01) were incorporated in the nomogram. The AUC of the nomogram was 0.825 in the training group, and 0.785 in the validation group, respectively.

CONCLUSION

The nomogram we developed included age, the compliance of the bladder, the function of the detrusor, and the severity of bladder outlet obstruction. The discrimination and calibration of the nomogram were confirmed by internal and external validation.

Timing of first abdominal operation in Crohn's disease based on a diagnostic model

This study aims to develop a clinical diagnostic model for assessing the need for initial abdominal surgery in patients diagnosed with Crohn's disease (CD) and create a nomogram to facilitate clinical decision-making. A total of 164 surgical CD patients and 230 control CD patients were included in this retrospective analysis. Least Absolute Shrinkage and Selection Operator (Lasso) regression and binomial logistic regression were employed to select clinical variables. The 394 CD patients were randomly allocated to a training set and a validation set in a 7:3 ratio. The filtered variables were used to establish a diagnostic model and nomogram in the training set, subsequently validated in the testing set. Decision Curve Analysis (DCA) and clinical impact curve were constructed to validate the clinical applicability of the model. Binomial logistic regression analysis identified seven clinical variables with a p-value less than 0.01, including Biomarker (B), Waist-to-Height Ratio (WHtR), Intestinal obstruction, Albumin (ALB), Crohn's Disease Activity Index (CDAI), Myocardial Flow Index (MFI), and C-reactive protein (CRP). These variables were utilized to establish the diagnostic model. Calibration curves showed good alignment, with a C-index of 0.996 in the training set and 0.990 in the testing set. DCA and clinical impact curve demonstrated that the diagnostic model had good clinical efficiency and could provide clinical benefits. A validated diagnostic model for determining the timing of the first abdominal operation in CD patients was established and evaluated, showing high discriminative ability, calibration, and clinical efficiency. It can be utilized by clinicians to assess the optimal timing for transitioning CD patients from medical treatment to surgical intervention, providing valuable references for individualized treatment decisions for CD patients.

A risk prediction nomogram for resistant hypertension in patients with obstructive sleep apnea

Patients with obstructive sleep apnea (OSA) are liable to have resistant hypertension (RH) associated with unfavorable cardiovascular events. It is of necessity to predict OSA patients who are susceptible to resistant hypertension. Hence, we conducted a retrospective study based on the clinical records of OSA patients admitted to Yixing Hospital Affiliated to Jiangsu University from January 2018 to December 2022. According to different time periods, patients diagnosed between January 2018 and December 2021 were included in the training set (n = 539) for modeling, and those diagnosed between January 2022 and December 2022 were enrolled into the validation set (n = 259) for further assessment. The incidence of RH in the training set and external validation set was comparable (P = 0.396). The related clinical data of patients enrolled were collected and analyzed through univariate analysis and least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a nomogram. Finally, five variables were confirmed as independent risk factors for OSA patients with RH, including smoking, heart disease, neck circumference, AHI and T90. The nomogram established on the basis of variables above was shown to have good discrimination and calibration in both the training set and validation set. Decision curve analysis indicated that the nomogram was useful for a majority of OSA patients. Therefore, our nomogram might be useful to identify OSA patients at high risk of developing RH and facilitate the individualized management of OSA patients in clinical practice.

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BACKGROUND

The recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.

METHODS

We extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan-Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.

RESULTS

Seven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675-0.690), 0.689 (95% CI: 0.677-0.701), and 0.823 (95% CI: 0.786-0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.

CONCLUSION

The nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting

BACKGROUND AND OBJECTIVE

Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer.

METHODS

A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors.

RESULTS

The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8.

CONCLUSIONS

Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.

Construction and validation of a nomogram model to predict the poor prognosis in patients with pulmonary cryptococcosis

Background

Patients with poor prognosis of pulmonary cryptococcosis (PC) are prone to other complications such as meningeal infection, recurrence or even death. Therefore, this study aims to analyze the influencing factors in the poor prognosis of patients with PC, so as to build a predictive nomograph model of poor prognosis of PC, and verify the predictive performance of the model.

Methods

This retrospective study included 410 patients (78.1%) with improved prognosis of PC and 115 patients (21.9%) with poor prognosis of PC. The 525 patients with PC were randomly divided into the training set and validation set according to the ratio of 7:3. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to screen the demographic information, including clinical characteristics, laboratory test indicators, comorbidity and treatment methods of patients, and other independent factors that affect the prognosis of PC. These factors were included in the multivariable logistic regression model to build a predictive nomograph. The receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to verify the accuracy and application value of the model.

Results

It was finally confirmed that psychological symptoms, cytotoxic drugs, white blood cell count, hematocrit, platelet count, CRP, PCT, albumin, and CD4/CD8 were independent predictors of poor prognosis of PC patients. The area under the curve (AUC) of the predictive model for poor prognosis in the training set and validation set were 0.851 (95% CI: 0.818-0.881) and 0.949, respectively. At the same time, calibration curve and DCA results confirmed the excellent performance of the nomogram in predicting poor prognosis of PC.

Conclusion

The nomograph model for predicting the poor prognosis of PC constructed in this study has good prediction ability, which is helpful for improving the prognosis of PC and further optimizing the clinical management strategy.

Predicting Ki-67 expression in hepatocellular carcinoma: nomogram based on clinical factors and contrast-enhanced ultrasound radiomics signatures

PURPOSE

To develop a contrast-enhanced ultrasound (CEUS) clinic-radiomics nomogram for individualized assessment of Ki-67 expression in hepatocellular carcinoma (HCC).

METHODS

A retrospective cohort comprising 310 HCC individuals who underwent preoperative CEUS (using SonoVue) at three different centers was partitioned into a training set, a validation set, and an external test set. Radiomics signatures indicating the phenotypes of the Ki-67 were extracted from multiphase CEUS images. The radiomics score (Rad-score) was calculated accordingly after feature selection and the radiomics model was constructed. A clinic-radiomics nomogram was established utilizing multiphase CEUS Rad-score and clinical risk factors. A clinical model only incorporated clinical factors was also developed for comparison. Regarding clinical utility, calibration, and discrimination, the predictive efficiency of the clinic-radiomics nomogram was evaluated.

RESULTS

Seven radiomics signatures from multiphase CEUS images were selected to calculate the Rad-score. The clinic-radiomics nomogram, comprising the Rad-score and clinical risk factors, indicated a good calibration and demonstrated a better discriminatory capacity compared to the clinical model (AUCs: 0.870 vs 0.797, 0.872 vs 0.755, 0.856 vs 0.749 in the training, validation, and external test set, respectively) and the radiomics model (AUCs: 0.870 vs 0.752, 0.872 vs 0.733, 0.856 vs 0.729 in the training, validation, and external test set, respectively). Furthermore, both the clinical impact curve and the decision curve analysis displayed good clinical application of the nomogram.

CONCLUSION

The clinic-radiomics nomogram constructed from multiphase CEUS images and clinical risk parameters can distinguish Ki-67 expression in HCC patients and offer useful insights to guide subsequent personalized treatment.

Transcriptome analysis reveals the clinical significance of CXCL13 in Pan-Gyn tumors

BACKGROUND

Gynecologic and breast tumors (Pan-Gyn) exhibit similar characteristics, and the role of CXCL13 in anti-tumor immunity and it's potential as a biomarker for immune checkpoint blockade (ICB) therapy have been gradually revealed. However, the precise role of CXCL13 in Pan-Gyn remains unclear, lacking a systematic analysis.

METHODS

We analyzed 2497 Pan-Gyn samples from the TCGA database, categorizing them into high and low CXCL13 expression groups. Validation was conducted using tumor expression datasets sourced from the GEO database. Correlation between CXCL13 and tumor immune microenvironment (TIME) was evaluated using multiple algorithms. Finally, we established nomograms for 3-year and 5-year mortality.

RESULTS

High expression of CXCL13 in Pan-Gyn correlates with a favorable clinical prognosis, increased immune cell infiltration, and reduced intra-tumor heterogeneity. Model was assessed using the C-index [BRCA: 0.763 (0.732-0.794), UCEC: 0.821 (0.793-0.849), CESC: 0.736 (0.684-0.788), and OV: 0.728 (0.707-0.749)], showing decent prediction of discrimination and calibration.

CONCLUSION

Overall, this study provides comprehensive insights into the commonalities and differences of CXCL13 in Pan-Gyn, potentially opening new avenues for personalized treatment.

Elaboration and validation of a novelty nomogram for the prognostication of anxiety susceptibility in individuals suffering from low back pain

Low back pain (LBP) constitutes a distressing emotional ordeal and serves as a potent catalyst for adverse emotional states, notably anxiety. We dedicated to discerning methodologies for identifying patients who are predisposed to heightened levels of anxiety and pain. A self-assessment questionnaire was administered to patients afflicted with LBP. The pain scores were subjected to analysis in conjunction with anxiety scores, and a clustering procedure was executed using the scientific k-means methodology. Subsequently, six machine learning algorithms, including Logistics Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB), were employed. Next, five pertinent variables were identified, namely Age, Course, Body Mass Index (BMI), Education, and Marital status. Furthermore, a LR model was utilized to construct a nomogram, which was subsequently subjected to assessment for discrimination, calibration, and evaluation of its clinical utility. As a result, 599 questionnaires were valid (effective rate: 99 %). The correlation analysis revealed a significant association between anxiety and pain scores (r = 0.31, P < 0.001). LBP patients could be divided into two clusters, Cluster1 had higher pain scores (P < 0.05) and SAS scores (P < 0.001). The proposed nomogram demonstrated an area under the receiver operating characteristics curve (ROC) of 0.841 (95 %CI: 0.804-0.878) and 0.800 (95 %CI: 0.733-0.867) in the training and test groups, respectively. Briefly, the established nomogram has demonstrated remarkable proficiency in discerning individuals afflicted with LBP who are at a heightened risk of experiencing anxiety.

Development of a risk estimation model for condomless sex among college students in Zhuhai, China: a cross-sectional study

BACKGROUND

Condom use at last intercourse is an effective indicator for human immunodeficiency virus (HIV) prevention. To identify at-risk individuals and improve prevention strategies, this study explored factors associated with condomless sex at last intercourse in the last year and developed a risk estimation model to calculate the individual possibility of condomless sex among college students in Zhuhai, China.

METHODS

A cross-sectional study was conducted among 1430 college students who had sex in the last year from six universities in Zhuhai. The least absolute shrinkage and selection operator (LASSO) and logistic regression were performed to explore the predictors of condomless sex. The nomogram was constructed to calculate the individual possibility of condomless sex. Discrimination and calibration of the nomogram were evaluated using the area under the receiver-operator characteristic curve (AUROC) and the calibration curve.

RESULTS

The proportion of students who had condomless sex at last intercourse was 18.2% (260/1430). Students who had experienced more types of intimate partner violence (aOR, 1.58; 95% CI, 1.31 ~ 1.92) and had anal sex (aOR, 1.75; 95% CI, 1.06 ~ 2.84) were more likely to have condomless sex. Students who had heterosexual intercourse (aOR, 0.37; 95% CI, 0.21 ~ 0.70), used condoms at first sex (aOR, 0.20; 95% CI, 0.14 ~ 0.27), had high attitudes towards condom use (aOR, 0.87; 95% CI, 0.80 ~ 0.95) and self-efficacy for condom use (aOR, 0.84; 95% CI, 0.78 ~ 0.90) were less likely to have condomless sex. The nomogram had high accuracy with an AUROC of 0.83 and good discrimination.

CONCLUSIONS

Intimate partner violence, anal sex, condom use at first sex, attitude towards condom use, and self-efficacy for condom use were associated with condomless sex among college students. The nomogram was an effective and convenient tool for calculating the individualized possibility of condomless sex among college students. It could help to identify individuals at risk and help universities and colleges to formulate appropriate individualized interventions and sexual health education programs.

A nomogram based on radiomics and clinical information to predict prognosis in percutaneous balloon compression for the treatment of trigeminal neuralgia

OBJECTIVE

To develop a clinical-radiomics nomogram based on clinical information and radiomics features to predict the prognosis of percutaneous balloon compression (PBC) for the treatment of trigeminal neuralgia (TN).

METHODS

The retrospective study involved clinical data from 149 TN patients undergoing PBC at Zhongnan Hospital, Wuhan University from January 2018 to January 2022. The free open-source software 3D Slicer was used to extract all radiomic features from the intraoperative X-ray balloon region. The relationship between clinical information and TN prognosis was analyzed by univariate logistic analysis and multivariate logistic analysis. Using R software, the optimal radiomics features were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. A prediction model was constructed based on the clinical information and radiomic features, and a nomogram was visualized. The performance of the clinical radiomics nomogram in predicting the prognosis of PBC in TN treatment was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

A total of 149 patients were eventually included. The clinical factors influencing the prognosis of TN in univariate analysis were compression severity score and TN type. The lasso algorithm Max-Relevance and Min-Redundancy(mRMR) was used to select two predictors from 13 morphology-related radiomics features, including elongation and surface-volume ratio. A total of 4 predictors were used to construct a prediction model and nomogram. The AUC was 0.886(95% confidence interval (CI), 0.75 to 0.96), indicating that the model's good predictive ability. DCA demonstrated the nomogram's high clinical applicability.

CONCLUSION

Clinical-radiomics nomogram constructed by combining clinical information and morphology-related radiomics features have good potential in predicting the prognosis of TN for PBC treatment. However, this needs to be further studied and validated in several independent external patient populations.

Prognostic value of dynamic changes of pre- and post-operative tumor markers in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) prognosis assessment is vital for personalized treatment plans. This study investigates the prognostic value of dynamic changes of tumor markers CEA, CA19-9, CA125, and AFP before and after surgery and constructs prediction models based on these indicators.

METHODS

A retrospective clinical study of 2599 CRC patients who underwent radical surgery was conducted. Patients were randomly divided into training (70%) and validation (30%) datasets. Univariate and multivariate Cox regression analyses identified independent prognostic factors, and nomograms were constructed.

RESULTS

A total of 2599 CRC patients were included in the study. Patients were divided into training (70%, n = 1819) and validation (30%, n = 780) sets. Univariate and multivariate Cox regression analyses identified age, total number of resected lymph nodes, T stage, N stage, the preoperative and postoperative changes in the levels of CEA, CA19-9, and CA125 as independent prognostic factors. When their postoperative levels are normal, patients with elevated preoperative levels have significantly worse overall survival. However, when the postoperative levels of CEA/CA19-9/CA125 are elevated, whether their preoperative levels are elevated or not has no significance for prognosis. Two nomogram models were developed, and Model I, which included CEA, CA19-9, and CA125 groups, demonstrated the best performance in both training and validation sets.

CONCLUSION

This study highlights the significant predictive value of dynamic changes in tumor markers CEA, CA19-9, and CA125 before and after CRC surgery. Incorporating these markers into a nomogram prediction model improves prognostic accuracy, enabling clinicians to better assess patients' conditions and develop personalized treatment plans.

Development of a Multi-Parametric ultrasonography nomogram for prediction of invasiveness in ductal carcinoma in situ

OBJECTIVE

To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment.

METHODS

Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established.

RESULTS

A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867.

CONCLUSION

Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization.

A prognostic nomogram for recurrence survival in post-surgical patients with varicose veins of the lower extremities

Varicose veins of the lower extremities (VVLEs) are prevalent globally. This study aims to identify prognostic factors and develop a prediction model for recurrence survival (RS) in VVLEs patients after surgery. A retrospective analysis of VVLEs patients from the Third Hospital of Nanchang was conducted between April 2017 and March 2022. A LASSO (Least Absolute Shrinkage and Selection Operator) regression model pinpointed significant recurrence predictors, culminating in a prognostic nomogram. The model's performance was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The LASSO regression identified seven predictors for the nomogram predicting 1-, 2-, and 5-year RS. These predictors were age, body mass index (BMI), hypertension, diabetes, the Clinical Etiological Anatomical Pathophysiological (CEAP) grade, iliac vein compression syndrome (IVCS), and postoperative compression stocking duration (PCSD). The nomogram's C-index was 0.716, with AUCs (Area Under the Curve scores) of 0.705, 0.725, and 0.758 for 1-, 2-, and 5-year RS, respectively. Calibration and decision curve analyses validated the model's predictive accuracy and clinical utility. Kaplan-Meier analysis distinguished between low and high-risk groups with significant prognostic differences (P < 0.05). This study has successfully developed and validated a nomogram for predicting RS in patients with VVLEs after surgery, enhancing personalized care and informing clinical decision-making.

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

BACKGROUND

Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures.

RESULTS

The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03-0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21-5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22-0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74-6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60-6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08-3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712-0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13% to 85%, reaffirming the nomogram's reliability through rigorous internal validation.

CONCLUSION

We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

Development and validation of a prognostic nomogram model in locally advanced NSCLC based on metabolic features of PET/CT and hematological inflammatory indicators

BACKGROUND

We combined the metabolic features of 18F-FDG-PET/CT and hematological inflammatory indicators to establish a predictive model of the outcomes of patients with locally advanced non-small cell lung cancer (LA-NSCLC) receiving concurrent chemoradiotherapy.

RESULTS

A predictive nomogram was developed based on sex, CEA, systemic immune-inflammation index (SII), mean SUV (SUVmean), and total lesion glycolysis (TLG). The nomogram presents nice discrimination that yielded an AUC of 0.76 (95% confidence interval: 0.66-0.86) to predict 1-year PFS, with a sensitivity of 63.6%, a specificity of 83.3%, a positive predictive value of 83.7%, and a negative predictive value of 62.9% in the training set. The calibration curves and DCA suggested that the nomogram had good calibration and fit, as well as promising clinical effectiveness in the training set. In addition, survival analysis indicated that patients in the low-risk group had a significantly longer mPFS than those in the high-risk group (16.8 months versus 8.4 months, P < 0.001). Those results were supported by the results in the internal and external test sets.

CONCLUSIONS

The newly constructed predictive nomogram model presented promising discrimination, calibration, and clinical applicability and can be used as an individualized prognostic tool to facilitate precision treatment in clinical practice.

HFpEF as systemic disease, insight from a diagnostic prediction model reminiscent of systemic inflammation and organ interaction in HFpEF patients

Systemic inflammation and reciprocal organ interactions are associated with the pathophysiology of heart failure with preserved ejection fraction (HFpEF). However, the clinical value, especially the diagnositc prediction power of inflammation and extra-cardiac organ dysfunction for HfpEF is not explored. In this cross-sectional study, 1808 hospitalized patients from January 2014 to June 2022 in ChiHFpEF cohort were totally enrolled according to inclusion and exclusion criteria. A diagnostic model with markers from routine blood test as well as liver and renal dysfunction for HFpEF was developed using data from ChiHFpEF-cohort by logistic regression and assessed by receiver operating characteristic curve (ROC) and Brier score. Then, the model was validated by the tenfold cross-validation and presented as nomogram and a web-based online risk calculator as well. Multivariate and LASSO regression analysis revealed that age, hemoglobin, neutrophil to lymphocyte ratio, AST/ALT ratio, creatinine, uric acid, atrial fibrillation, and pulmonary hypertension were associated with HFpEF. The predictive model exhibited reasonably accurate discrimination (ROC, 0.753, 95% CI 0.732-0.772) and calibration (Brier score was 0.200). Subsequent internal validation showed good discrimination and calibration (AUC = 0.750, Brier score was 0.202). In additoin to participating in pathophysiology of HFpEF, inflammation and multi-organ interactions have diagnostic prediction value for HFpEF. Screening and optimizing biomarkers of inflammation and multi-organ interactions stand for a new field to improve noninvasive diagnostic tool for HFpEF.

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

PURPOSE

Hepatocellular 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 METHODS

Data 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.

RESULTS

This 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.

CONCLUSION

The 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.

Development and validation of a novel preoperative clinical model for predicting lymph node metastasis in perihilar cholangiocarcinoma

BACKGROUD

We aimed to develop a novel preoperative nomogram to predict lymph node metastasis (LNM) in perihilar cholangiocarcinoma (pCCA) patients.

METHODS

160 pCCA patients were enrolled at Lihuili Hospital from July 2006 to May 2022. A novel nomogram model was established to predict LNM in pCCA patients based on the independent predictive factors selected by the multivariate logistic regression model. The precision of the nomogram model was evaluated through internal and external validation with calibration curve statistics and the concordance index (C-index). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate and determine the clinical utility of the nomogram.

RESULTS

Multivariate logistic regression demonstrated that age (OR = 0.963, 95% CI: 0.930-0.996, P = 0.030), CA19-9 level (> 559.8 U/mL vs. ≤559.8 U/mL: OR = 3.162, 95% CI: 1.519-6.582, P = 0.002) and tumour diameter (OR = 1.388, 95% CI: 1.083-1.778, P = 0.010) were independent predictive factors of LNM in pCCA patients. The C-index was 0.763 (95% CI: 0.667-0.860) and 0.677 (95% CI: 0.580-0.773) in training cohort and validation cohort, respectively. ROC curve analysis indicated the comparative stability and adequate discriminative ability of nomogram. The sensitivity and specificity were 0.820 and 0.652 in training cohort and 0.704 and 0.649 in validation cohort, respectively. DCA revealed that the nomogram model could augment net benefits in the prediction of LNM in pCCA patients.

CONCLUSIONS

The novel prediction model is useful for predicting LNM in pCCA patients and showed adequate discriminative ability and high predictive accuracy.

Who would be the winner? A prognostic nomogram for predicting the benefit of postoperative radiotherapy ± chemotherapy in patients with locally advanced gastric cancer: TROD-02-01 study

OBJECTIVES

We aimed to develop a basic, easily applicable nomogram to improve the survival prediction of the patients with stage II/III gastric cancer (GC) and to select the best candidate for postoperative radiotherapy (RT).

METHODS

In this multicentric trial, we retrospectively evaluated the data of 1597 patients with stage II/III GC after curative gastrectomy followed by postoperative RT ± chemotherapy (CT). Patients were divided into a training set (n = 1307) and an external validation set (n = 290). Nomograms were created based on independent predictors identified by Cox regression analysis in the training set. The consistency index (C-index) and the calibration curve were used to evaluate the discriminative ability and accuracy of the nomogram. A nomogram was created based on the predictive model and the identified prognostic factors to predict 5-year cancer-specific survival (CSS) and progression-free survival (PFS).

RESULTS

The multivariate Cox model recognized lymph node (LN) involvement status, lymphatic dissection (LD) width, and metastatic LN ratio as covariates associated with CSS. Depth of invasion, LN involvement status, LD width, metastatic LN ratio, and lymphovascular invasion were the factors associated with PFS. Calibration of the nomogram predicted both CSS and PFS corresponding closely with the actual results. In our validation set, discrimination was good (C-index, 0.76), and the predicted survival was within a 10% margin of ideal nomogram.

CONCLUSIONS

In our relatively large cohort, we created and validated both CSS and PFS nomograms that could be useful for underdeveloped or developing countries rather than Korea and Japan, where the D2 gastrectomy is routinely performed. This could serve as a true map for oncologists who must make decisions without an experienced surgeon and a multidisciplinary tumor board.

Models predicting mortality risk of patients with burns to ≥ 50% of the total body surface

BACKGROUND

Several models predicting mortality risk of burn patients have been proposed. However, models that consider all such patients may not well predict the mortality of patients with extensive burns.

METHOD

This retrospective multicentre study recruited patients with extensive burns (≥ 50% of the total body surface area [TBSA]) treated in three hospitals of Eastern China from 1 January 2016 to 30 June 2022. The performances of six predictive models were assessed by drawing receiver operating characteristic (ROC) and calibration curves. Potential predictors were sought via "least absolute shrinkage and selection operator" regression. Multivariate logistic regression was employed to construct a predictive model for patients with burns to ≥ 50% of the TBSA. A nomogram was prepared and the performance thereof assessed by reference to the ROC, calibration, and decision curves.

RESULT

A total of 465 eligible patients with burns to ≥ 50% TBSA were included, of whom 139 (29.9%) died. The FLAMES model exhibited the largest area under the ROC curve (AUC) (0.875), followed by the models of Zhou et al. (0.853) and the ABSI model (0.802). The calibration curve of the Zhou et al. model fitted well; those of the other models significantly overestimated the mortality risk. The new nomogram includes four variables: age, the %TBSA burned, the area of full-thickness burns, and blood lactate. The AUCs (training set 0.889; internal validation set 0.934; external validation set 0.890) and calibration curves showed that the nomogram exhibited an excellent discriminative capacity and that the predictions were very accurate.

CONCLUSION

For patients with burns to ≥ 50％of the TBSA, the Zhou et al. and FLAMES models demonstrate relatively high predictive ability for mortality. The new nomogram is sensitive, specific, and accurate, and will aid rapid clinical decision-making.

Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model

PURPOSE

To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL). In addition, 110 patients with simultaneous CT and MRI data were divided into a training cohort (n = 66) and a validation cohort (n = 44). We input the features extracted from DenseNet121 into an extreme learning machine (ELM) classifier to construct a classification model.

RESULTS

The area under the curve (AUC) of the MDL model was 0.844, which was superior to that of the single-phase CT (AUC = 0.706-0.776, P < 0.05), single-sequence MRI (AUC = 0.706-0.717, P < 0.05), single-modality DL model (AUCall-phase CT = 0.722, AUCall-sequence MRI = 0.731; P < 0.05), clinical (AUC = 0.648, P < 0.05), but not to that of the delay phase (DP) and in-phase (IP) MRI and portal venous phase (PVP) CT models. The MDL model achieved better performance than models described above (P < 0.05). When combined with clinical features, the AUC of the MDL model increased from 0.844 to 0.871. A nomogram, combining deep learning signatures (DLS) and clinical indicators for MDL models, demonstrated a greater overall net gain than the MDL models (P < 0.05).

CONCLUSION

The MDL model is a valuable noninvasive technique for preoperatively predicting MVI in HCC.

Chemoradiotherapy vs radiotherapy for non-surgical locally advanced laryngeal squamous cell carcinoma patients: a propensity score-matched study and practical nomogram construction

OBJECTIVE

To compare the cancer-specific survival (CSS) among patients with locally advanced laryngeal squamous cell carcinoma (LSCC) receiving chemoradiotherapy (CRT) and radiotherapy (RT) treatment, as well as to establish a prognostic nomogram for survival prediction in patients receiving CRT.

METHOD

Using data from the Surveillance, Epidemiology, and End Results (SEER) database, patients with laryngeal cancer were identified between 2010 and 2015, with follow-up up to 2018. Propensity score matching (PSM) was performed to minimize disproportionate distributions of the potential confounding. Cox regression models were used to evaluate the CSS of two treatment groups. A prognostic nomogram for patients receiving CRT was then developed and evaluated.

RESULTS

Totally 1085 non-surgical patients with locally advanced LSCC were included in this study (median [IQR] age, 62 [55-69] years; 829 [76.41%] males), of which 913 receiving CRT and 172 receiving RT. After PSM, significantly improved CSS was observed in locally advanced LSCC patients receiving CRT when compared to RT (HR: 0.62 [95% CI 0.42-0.92]; P = 0.014). Then, in the group of 639 locally advanced LSCC patients receiving CRT, a prognostic nomogram based on age, tumor size, N category, and marital status were developed and validated, of which the predictive performance was superior to that of TNM staging system (7th edition).

CONCLUSION

CSS shows a statistically significant improvement in locally advanced LSCC patients who receipt of CRT when compared with RT. Furthermore, a prognostic nomogram for locally advanced LSCC patients receiving CRT was established, which shows a good calibration and identification accuracy.

Robot-assisted versus laparoscopic pheochromocytoma resection and construction of a nomogram to predict perioperative hemodynamic instability

BACKGROUND

Despite recent improvements in perioperative outcomes after pheochromocytoma resection, hemodynamic instability (HI) remained of high concern. The emergence of robot-assisted surgery may bring different results to pheochromocytoma surgery. The purposes of this study were to investigate whether robot-assisted retroperitoneal pheochromocytoma resection promotes hemodynamic instability compared with laparoscopic retroperitoneal pheochromocytoma resection and construct a nomogram to predict perioperative hemodynamic instability.

METHODS

The clinical data of 221 patients who underwent pheochromocytoma resection were analyzed retrospectively. The patients were divided into two groups according to the mode of operation. Stepwise logistic regression was used to determine the independent risk factors of perioperative hemodynamic instability and to construct a visual prediction model. The final model was visualized via a nomogram.

RESULTS

124 (56.1 %) out of 221 patients experienced HI. The variables that were eventually included in the model were tumor size (OR1.363(1.143-1.646), P < 0.001), abnormal blood glucose (OR3.381(1.534-7.903), P = 0.003), preoperative SBP(OR1.04(1.014-1.067),P = 0.002), robot-assisted surgery(OR0.241(0.108-0.513),P < 0.001), and catecholamines(OR4.567(2.424-8.834),P < 0.001). The receiver operating characteristic curve showed the area under curve was 0.816(95 %CI 0.761-0.871).

CONCLUSION

We developed a nomogram for successful prediction of perioperative hemodynamics based on five independent risk factors. Clinicians can leverage this easy-to-use nomogram to perform personalized risk predictions for HI and develop preventive interventions to improve patient safety and surgical outcomes.

The predictive significance of chromobox family members in prostate cancer in humans

PURPOSE

The Chromobox (CBX) family proteins are crucial elements of the epigenetic regulatory machinery and play a significant role in the development and advancement of cancer. Nevertheless, there is limited understanding regarding the role of CBXs in development or progression of prostate cancer (PCa). Our objective is to develop a unique prognostic model associated with CBXs to improve the accuracy of predicting outcomes of patients with PCa.

METHODS

Data from TCGA and GEO databases were analyzed to assess differential expression, prognostic value, gene pathway enrichment, and immune cell infiltration. COX regression analysis was utilized to identify the independent prognostic factors that impact disease-free survival (DFS). The expression of CBX2 and FOXP3+ cells infiltration was verified by immunohistochemical staining of clinical tissue sections. In vitro proliferation, migration and invasion assay were conducted to examine the function of CBX2. RNA-seq was employed to examine the CBX2 related pathway enrichment.

RESULTS

CBX2, CBX3, CBX4, and CBX8 were upregulated, while CBX6 and CBX7 were downregulated in PCa tissues. CBXs expression varied by stage and grade. Elevated expression of CBX1, CBX2, CBX3, CBX4 and CBX8 is correlated with poor outcome. CBX2 expression, T stage, and Gleason score were independent prognostic factors. The expression level of CBX2 in PCa tissues was significantly higher than that in adjacent normal tissues. More Treg infiltration was observed in the group with high CBX2 expression. CBX2 expression affected PCa cell growth, migration, and invasion.

CONCLUSIONS

CBX2 is involved in the development and advancement of PCa, suggesting its potential as a reliable prognostic indicator for PCa patients.

Competing Risk Model to Determine the Prognostic Factors for Patients with Gliosarcoma

BACKGROUND

Gliosarcoma (GSM) is a highly aggressive variant of brain cancer with an extremely unfavorable prognosis. Prognosis is not feasible by traditional methods because of a lack of staging criteria, and the present study aims to screen more detailed demographic factors to predict the prognostic factors of the tumors.

METHODS

For this study, we extracted data of patients diagnosed with GSM from the SEER (Surveillance Epidemiology and End Results) database between 2000 and 2019. To account for the influence of competing risks, we used a Cumulative Incidence Function. Subsequently, univariate analysis was conducted to evaluate the individual variables under investigation. Specifically for patients with GSM, we generated cumulative risk curves for specific mortality outcomes and events related to competing risks. In addition, we used both univariate and multivariate Cox analysis to account for non-GSM-related deaths that may confound our research.

RESULTS

The competing risk model showed that age, marital status, tumor size, and adjuvant therapy were prognostic factors in GSM-related death. The analysis results showed that older age (60-70 years, ≥71 years) and larger tumor size (≥5.3 cm) significantly increased the risk of GSM-related death. Conversely, surgical intervention, chemotherapy, and being single were identified as protective factors against GSM-related death.

CONCLUSIONS

Our study using a competing risk model provided valuable insights into the prognostic factors associated with GSM-related death. Further research and clinical interventions targeted at minimizing these risk factors and promoting the use of protective measures may contribute to improved outcomes and reduced mortality for patients with GSM.