Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study

Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.

Prediction of the risk of 3-year chronic kidney disease among elderly people: a community-based cohort study

OBJECTIVE

We conducted a community-based cohort study to predict the 3-year occurrence of chronic kidney disease (CKD) among population aged ≥60 years.

METHOD

Participants were selected from two communities through randomized cluster sampling in Jiading District of Shanghai, China. The two communities were randomly divided into a development cohort (n = 12012) and a validation cohort (n = 6248) with a 3-year follow-up. Logistic regression analysis was used to determine the independent predictors. A nomogram was established to predict the occurrence of CKD within 3 years. The area under the curve (AUC), the calibration curve and decision curve analysis (DCA) curve were used to evaluate the model.

RESULT

At baseline, participants in development cohort and validation cohort were with the mean age of 68.24 ± 5.87 and 67.68 ± 5.26 years old, respectively. During 3 years, 1516 (12.6%) and 544 (8.9%) new cases developed CKD in the development and validation cohorts, respectively. Nine variables (age, systolic blood pressure, body mass index, exercise, previous hypertension, triglycerides, fasting plasma glucose, glycated hemoglobin and serum creatinine) were included in the prediction model. The AUC value was 0.742 [95% confidence interval (CI), 0.728-0.756] in the development cohort and 0.881(95%CI, 0.867-0.895) in the validation cohort, respectively. The calibration curves and DCA curves demonstrate an effective predictive model.

CONCLUSION

Our nomogram model is a simple, reasonable and reliable tool for predicting the risk of 3-year CKD in community-dwelling elderly people, which is helpful for timely intervention and reducing the incidence of CKD.

Development of a nomogram for predicting myopia risk among school-age children: a case-control study

OBJECTIVES

To analyze the factors influencing myopia and construct a nomogram to forecast the risk of myopia among school-age children, providing a reference for identifying high-risk groups to aid prevention and control.

METHODS

This case-control study enrolled 3512 students from three primary schools in Shenzhen using random cluster sampling for a questionnaire survey, myopia screening and ocular biometric parameter measurement. Logistic regression was used to analyze the influencing factors of myopia, and a nomogram was constructed to forecast myopia risk. Bootstrap resampling was used to verify the practicability of the nomogram.

RESULTS

Older age (odds ratio[OR] = 1.164; 95% confidence interval [CI]: 1.111-1.219), female sex (OR = 2.405; 95% CI: 2.003-2.887), maternal myopia (OR = 1.331; 95% CI: 1.114-1.589), incorrect posture during reading and writing (OR = 1.283; 95% CI: 1.078-1.528) and axial length (OR = 7.708; 95% CI: 6.044-8.288) are risk factors for myopia, whereas an increase in corneal radius (OR = 0.036; 95% CI: 0.025-0.052) is a protective factor against myopia. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.857, and the net benefit was high when the risk threshold of the decision curve analyses (DCA) ranged from 0.20 to 1.00. The measured values were consistent with the prediction.

CONCLUSION

The nomogram was accurate in predicting the risk of myopia among schoolchildren. This study provides a reference for screening high-risk students and for individualized myopia prevention and control.

Nomogram for predicting postoperative ileus after radical cystectomy and urinary diversion: a retrospective single-center study

OBJECTIVE

To predict the incidence of postoperative ileus in bladder cancer patients after radical cystectomy.

METHODS

We retrospectively analyzed the perioperative data of 452 bladder cancer patients who underwent radical cystectomy with urinary diversion at the Second Hospital of Tianjin Medical University between 2016 and 2021. Univariate and multivariate logistic regression were used to identify the risk factors for postoperative ileus. Finally, a nomogram model was established and verified based on the independent risk factors.

RESULTS

Our study revealed that 96 patients (21.2%) developed postoperative ileus. Using multivariate logistic regression analysis, we found that the independent risk factors for postoperative ileus after radical cystectomy included age > 65.0 years, high or low body mass index, constipation, hypoalbuminemia, and operative time. We established a nomogram prediction model based on these independent risk factors. Validation by calibration curves, concordance index, and decision curve analysis showed a strong correlation between predicted and actual probabilities of occurrence.

CONCLUSION

Our nomogram prediction model provides surgeons with a simple tool to predict the incidence of postoperative ileus in bladder cancer patients undergoing radical cystectomy.

LASSO-derived model for the prediction of lean-non-alcoholic fatty liver disease in examinees attending a routine health check-up

BACKGROUND

Lean individuals with non-alcohol fatty liver disease (NAFLD) often have normal body size but abnormal visceral fat. Therefore, an alternative to body mass index should be considered for prediction of lean-NAFLD. This study aimed to use representative visceral fat links with other laboratory parameters using the least absolute shrinkage and selection operator (LASSO) method to construct a predictive model for lean-NAFLD.

METHODS

This retrospective cross-sectional analysis enrolled 2325 subjects with BMI < 24 kg/m2 from medical records of 51,271 examinees who underwent a routine health check-up. They were randomly divided into training and validation cohorts at a ratio of 1:1. The LASSO-derived prediction model used LASSO regression to select 23 clinical and laboratory factors. The discrimination and calibration abilities were evaluated using the Hosmer-Lemeshow test and calibration curves. The performance of the LASSO model was compared with the fatty liver index (FLI) model.

RESULTS

The LASSO-derived model included four variables-visceral fat, triglyceride levels, HDL-C-C levels, and waist hip ratio-and demonstrated superior performance in predicting lean-NAFLD with high discriminatory ability (AUC, 0.8416; 95% CI: 0.811-0.872) that was comparable with the FLI model. Using a cut-off of 0.1484, moderate sensitivity (75.69%) and specificity (79.86%), as well as high negative predictive value (95.9%), were achieved in the LASSO model. In addition, with normal WC subgroup analysis, the LASSO model exhibits a trend of higher accuracy compared to FLI (cut-off 15.45).

CONCLUSIONS

We developed a LASSO-derived predictive model with the potential for use as an alternative tool for predicting lean-NAFLD in clinical settings.

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study

BACKGROUND AND AIM

This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.

METHODS

This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups' demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

RESULTS

Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram's area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively.

CONCLUSION

APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD.

Construction and validation of an 18F-FDG-PET/CT-based prognostic model to predict progression-free survival in newly diagnosed multiple myeloma patients

OBJECTIVE

To investigate the relationship between 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) related parameters and the prognosis of multiple myeloma and to establish and validate a prediction model regarding the progression-free survival (PFS) of multiple myeloma.

METHODS

A retrospective analysis of 126 newly diagnosed multiple myeloma patients who attended Nanjing Drum Tower Hospital from 2014-2021. All patients underwent PET/CT before treatment and were divided into a training cohort (n = 75) and a validation cohort (n = 51). Multivariate Cox proportional hazard regression analysis incorporated PET/CT-related parameters and clinical indicators. A nomogram was established to individually predict PFS in MM patients. The model was evaluated by calculating the C-index and calibration curve.

RESULTS

Here, 4.2 was used as the cut-off value of SUVmax to divide patients into high and low groups. PFS significantly differed between patients in the high-SUVmax group and low-SUVmax group, and SUVmax was an independent predictor of PFS in newly diagnosed multiple myeloma (NDMM) patients. Univariate and multivariate cox regression analysis suggested that lactate dehydrogenase (LDH), bone marrow plasma cell (BMPC), and SUVmax affected PFS. These factors were incorporated to construct a nomogram model for predicting PFS at 1 and 2 years in NDMM patients. The C-index and calibration curves of the nomogram exhibited good accuracy and consistency, and the DCA curves suggested that the model had good clinical utility.

CONCLUSION

The PET/CT parameter SUVmax is closely related to the prognosis of myeloma patients. The nomogram constructed in this study based on PET/CT-related parameters and clinical indicators individually predicts the PFS rate of NDMM patients and enables further risk stratification of NDMM patients.

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

A predictive nomogram developed and validated for gastric cancer patients with triple-negative tumor markers

Aim: To predict the prognosis of gastric cancer patients with triple-negative tumor markers. Materials & methods: Prognostic factors of the nomogram were identified through univariate and multivariate Cox regression analyses. Calibration and receiver operating characteristic curves were used to assess accuracy. Decision curve analysis and concordance indexes were utilized to compare the nomogram with the pathological tumor, node, metastasis stage. Results: A nomogram incorporating log odds of positive lymph nodes, tumor size and lymphocyte-to-monocyte ratio was constructed. The calibration and receiver operating characteristic curves (area under the curve >0.85) showed high accuracy in predicting overall survival. The concordance indexes (0.832 vs 0.760; p < 0.001) and decision curve analysis demonstrated that the nomogram was superior to the pathological tumor, node, metastasis stage. Conclusion: A prediction and risk stratification nomogram has been developed and validated for gastric cancer patients with triple-negative tumor markers.

The correlation between systemic inflammatory markers and efficiency for advanced gastric cancer patients treated with ICIs combined with chemotherapy

Currently lacking research to explore the correlation between inflammatory markers and the efficacy of immune checkpoint inhibitors (ICIs) combined with chemotherapy in the treatment of advanced gastric cancer. This study is a retrospective study and included patients with advanced gastric cancer who receiving ICIs combined with chemotherapy from January 2020 to December 2022. We analysed the relationship between systemic inflammatory markers and the efficacy of ICIs combined chemotherapy and constructed a clinical prediction model. A nomogram was constructed based on the results of the bidirectional stepwise regression model. A total of 197 patients were enrolled in the training group, with a median follow-up period of time 26 months. Kaplan Meier analysis showed that the median OS of patients with low systemic immune-inflammatory index (SII) and low platelet to lymphocyte ratio (PLR) was superior to those with high SII and PLR. Univariate and multivariate Cox regression analysis showed that SII, NLR, PLR, and N stage as independent prognostic factors for OS. Adding SII to the conventional model improved the predictive ability of the 12-month OS. A total of 95 patients were included in the validation group, and external validation of the SII-based nomogram showed favourable predictive performance. Baseline SII, PLR, and N stage may serve as independent predictive factors for survival outcomes in advanced gastric cancer patients undergoing ICIs combined with chemotherapy. The SII-based nomogram can provide intuitive and accurate prognosis prediction of individual patients.

Incidence, risk factors, and a prognostic nomogram for distant metastasis in endometrial cancer: A SEER-based study

OBJECTIVE

To evaluate the metastatic pattern, identify the risk factors, and establish a nomogram for predicting prognosis of endometrial cancer (EC) with distant metastasis.

METHODS

A retrospective cohort study of women diagnosed with EC was conducted according to the Surveillance, Epidemiology, and End Results (SEER) database during 2010-2017. Multivariate logistic analysis and Cox analysis were performed to identify the risk factors in promoting distant metastasis and predictors associated with overall survival (OS) in this particular subpopulation. A nomogram was then constructed and validated by the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis.

RESULTS

A total of 2799 cases of distant metastasis in EC patients were identified, with an overall incidence rate of 3.74% from 2010 to 2017. Black race, unmarried status, non-endometrioid histologic types, and grade IV were significant risk factors for distant metastasis in EC patients. Meanwhile, race, histology, grade, metastasis status, surgery, lymphadenectomy, and chemotherapy were identified as independent prognostic factors for OS. A nomogram to predict 1-, 3-, and 5-year OS was established, and presented favorable accuracy and clinical applicability. Patients were further divided into high- and low-risk groups according to the model.

CONCLUSION

The nomogram was developed as a highly accurate, individualized tool to better predict the prognosis of EC patients with distant metastasis, which would help clinicians to identify high-risk patients, and adjust and tailor their treatment strategies.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study

INTRODUCTION

To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC).

METHODS

A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models.

RESULTS

Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives.

CONCLUSION

We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

Nomogram predicts survival and surgical benefits for patients with breast cancer with initial bone metastasis: A population-based study

BACKGROUND

Primary stage IV breast cancer is associated with a poor prognosis. At present, the value of local surgical treatment for patients with stage IV breast cancer remains uncertain; therefore, treatment principles remain controversial. Because of the high heterogeneity of these patients, it is often difficult to evaluate their prognoses. As a result, this study aimed to establish a prognostic nomogram to evaluate the prognosis of patients with breast cancer experiencing primary bone metastasis.

METHODS

The clinical characteristics and follow-up data of patients with primary breast cancer and bone metastasis from 2010 to 2018 were collected from the Surveillance, Epidemiology, and End Results database and from 2013 to 2021 at the Peking Union Medical College Hospital. Patients were divided into training and validation groups. Multivariate Cox regression analysis was used to identify the independent prognostic variables for predicting cancer-specific survival (CSS). On the basis of these independent risk factors, a nomogram was developed and used calibration curves to evaluate its accuracy. Patients were divided into three risk groups according to their scores and surgery-related survival curves plotted using the log-rank test.

RESULTS

Overall, 6372 patients were included, with 6319 from the Surveillance, Epidemiology, and End Results database and 53 from the Peking Union Medical College Hospital Breast Surgery Department. Multivariate analysis showed that age, race, marital status, grade, tumor stage, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor 2 status, and burden of other metastatic lesions were all associated with CSS. Based on these results, a nomogram that predicted the 1-, 3-, and 5-year CSS rates in patients with primary breast cancer and bone metastasis (concordance index > 0.69) was developed. After dividing patients into low-risk, high-risk, or super-high-risk groups based on nomogram scoring criteria, survival analysis revealed that patients in the low- and high-risk groups had significant survival benefits from primary focal surgery.

CONCLUSION

Independent risk factors for primary breast cancer in patients with bone metastasis were analyzed and a nomogram established to predict CSS. The prognostic tool derived in this study can assist clinicians in predicting the survival and surgical benefits of these patients through scoring, thereby providing further guidance for treatment strategies.

Triple-negative breast cancer survival prediction using artificial intelligence through integrated analysis of tertiary lymphoid structures and tumor budding

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly heterogeneous and clinically aggressive disease. Accumulating evidence indicates that tertiary lymphoid structures (TLSs) and tumor budding (TB) are significantly correlated with the outcomes of patients who have TNBC, but no integrated TLS-TB profile has been established to predict their survival. The objective of this study was to investigate the relationship between the TLS/TB ratio and clinical outcomes of patients with TNBC using artificial intelligence (AI)-based analysis.

METHODS

The infiltration levels of TLSs and TB were evaluated using hematoxylin and eosin staining, immunohistochemistry staining, and AI-based analysis. Various cellular subtypes within TLS were determined by multiplex immunofluorescence. Subsequently, the authors established a nomogram model, conducted calibration curve analyses, and performed decision curve analyses using R software.

RESULTS

In both the training and validation cohorts, the antitumor/protumor model established by the authors demonstrated a positive correlation between the TLS/TB index and the overall survival (OS) and relapse-free survival (RFS) of patients with TNBC. Notably, patients who had a high percentage of CD8-positive T cells, CD45RO-positive T cells, or CD20-positive B cells within the TLSs experienced improved OS and RFS. Furthermore, the authors developed a comprehensive TLS-TB profile nomogram based on the TLS/TB index. This novel model outperformed the classical tumor-lymph node-metastasis staging system in predicting the OS and RFS of patients with TNBC.

CONCLUSIONS

A novel strategy for predicting the prognosis of patients with TNBC was established through integrated AI-based analysis and a machine-learning workflow. The TLS/TB index was identified as an independent prognostic factor for TNBC. This nomogram-based TLS-TB profile would help improve the accuracy of predicting the prognosis of patients who have TNBC.

An ultrasound-based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation

INTRODUCTION

The staging and treatment of axillary nodes in breast cancer have become a focus of research. For breast cancer patients with fine-needle aspiration-or core needle biopsy-confirmed positive nodes, axillary lymph node dissection (ALND) after neoadjuvant chemotherapy (NAC) is still a standard treatment. However, some patients achieve an axillary pathologic complete response (pCR) after NAC. In this study, the authors sought to construct a model to predict axillary pCR in patients with positive axillary lymph nodes (cN+) breast cancer.

METHODS

Data from patients with pathologically proven cN+ breast cancer treated with NAC followed by ALND between January 2010 and April 2019 at the Peking University Cancer Hospital were reviewed. Axillary lymph node status was assessed using ultrasonography before and after NAC. The patient cohort was assigned to the construction and internal validation cohorts according to admission time. A nomogram was constructed based on the significant factors associated with axillary pCR. The predictive performance of the model was externally validated using data from Peking University First Hospital.

RESULTS

This study included 953 and 267 patients from Peking University Cancer Hospital and Peking University First Hospital, respectively. In the construction cohort, 39.7% (238 of 600) of patients achieved axillary pCR after NAC. The result of multivariate logistic regression analysis showed that tumor grade, clinical nodal response, NAC regimen, tumor pCR, lymphovascular invasion, and tumor biologic subtype were significant independent predictors of ypN0 (p < 0.05). The areas under the receiver operating characteristic curves for the construction, validation, and independent testing cohorts were 0.87 (95% confidence interval [CI], 0.84-0.90), 0.83 (95% CI, 0.79-0.87), and 0.84 (0.79-0.89), respectively.

CONCLUSIONS

A nomogram was constructed to predict the pCR of axillary lymph nodes after NAC for breast cancer. Validation of both the internal and external cohorts achieved good predictive performance, indicating that the model has preliminary clinical application prospects.

A Retrospective Study of Biological Risk Factors Associated with Primary Knee Osteoarthritis and the Development of a Nomogram Model

Aim

A high percentage of the elderly suffer from knee osteoarthritis (KOA), which imposes a certain economic burden on them and on society as a whole. The purpose of this study is to examine the risk of KOA and to develop a KOA nomogram model that can timely intervene in this disease to decrease patient psychological burdens.

Methods

Data was collected from patients with KOA and without KOA at our hospital from February 2021 to February 2023. Initially, a comparison was conducted between the variables, identifying statistical differences between the two groups. Subsequently, the risk of KOA was evaluated using the Least Absolute Shrinkage and Selection Operator method and multivariate logistic regression to determine the most effective predictive index and develop a prediction model. The examination of the disease risk prediction model in KOA includes the corresponding nomogram, which encompasses various potential predictors. The assessment of disease risk entails the application of various metrics, including the consistency index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, the calibration chart, the GiViTi calibration band, and the model for predicting KOA. Furthermore, the potential clinical significance of the model is explored through decision curve analysis (DCA) and clinical influence curve analysis.

Results

The study included a total of 582 patients, consisting of 392 patients with KOA and 190 patients without KOA. The nomogram utilized age, haematocrit, platelet count, apolipoprotein a1, potassium, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, and estimated glomerular filtration rate as predictors. The C index, AUC, calibration plot, Giviti calibration band, DCA and clinical influence KOA indicated the ability of nomogram model to differentiate KOA.

Conclusion

Using nomogram based on disease risk, high-risk KOA can be identified directly without imaging.

Prognosis and immunotherapy efficacy in dMMR&MSS colorectal cancer patients and an MSI status predicting model

The inconsistency between mismatch repair (MMR) protein immunohistochemistry (IHC) and microsatellite instability PCR (MSI-PCR) methods has been widely reported. We aim to investigate the prognosis and the effect of immunotherapy in dMMR by IHC but MSS by MSI-PCR (dMMR&MSS) colorectal cancer (CRC) patients. A microsatellite instability (MSI) predicting model was established to help find dMMR&MSS patients. MMR and MSI states were detected by the IHC and MSI-PCR in 1622 CRC patients (ZS6Y-1 cohort). Logistic regression analysis was used to screen clinical features to construct an MSI-predicting nomogram. We propose a new nomogram-based assay to find patients with dMMR&MSS, in which the MSI-PCR assay only detects dMMR patients with MSS predictive results. We applied the new strategy to a random cohort of 248 CRC patients (ZS6Y-2 cohort). The consistency of MMR IHC and MSI-PCR in the ZS6Y-1 cohort was 95.7% (1553/1622). Both pMMR&MSS and dMMR&MSS groups experienced significantly shorter overall survival (OS) than those in dMMR by IHC and MSI-H by MSI-PCR (dMMR&MSI-H) group (hazard ratio [HR] = 2.429, 95% confidence interval [CI]: 1.89-3.116, p < .01; HR = 21.96, 95% CI: 7.24-66.61, p < .01). The dMMR&MSS group experienced shorter OS than the pMMR&MSS group, but the difference did not reach significance (log rank test, p = .0686). In the immunotherapy group, the progression-free survival of dMMR&MSS patients was significantly shorter than that of dMMR&MSI-H patients (HR = 13.83, 95% CI: 1.508-126.8, p < .05). The ZS6Y-MSI-Pre nomogram (C-index = 0.816, 95% CI: 0.792-0.841, already online) found 66% (2/3) dMMR&MSS patients in the ZS6Y-2 cohort. There are significant differences in OS and immunotherapy effect between dMMR&MSI-H and dMMR&MSS patients. Our prediction model provides an economical way to screen dMMR&MSS patients.

A Nomogram Including Total Cerebral Small Vessel Disease Burden Score for Predicting Mild Vascular Cognitive Impairment in Patients with Type 2 Diabetes Mellitus

Background

Total cerebral small vessel disease (CSVD) burden score is an important predictor of vascular cognitive impairment (VCI). However, few predictive models of VCI in type 2 diabetes mellitus (T2DM) patients have included the total CSVD burden score, especially in the early stage of VCI.

Objective

To develop and validate a nomogram that includes the total CSVD burden score to predict mild VCI in patients with T2DM.

Methods

A total of 322 eligible participants with T2DM who were divided into mild and normal cognitive groups were enrolled in this retrospective study. Demographic data, laboratory data and imaging markers of CSVD were collected. The total CSVD burden score was calculated by combining the different CSVD markers. Step-backward multivariable logistic regression analysis with the Akaike information criterion was applied to select significant predictors and develop a best-fit predictive nomogram. The performance of the nomogram was assessed in terms of discriminative ability, calibrated ability, and clinical usefulness.

Results

The nomogram model consisted of five variables: age, education, hemoglobin A1c level, serum homocysteine level, and total CSVD burden score. A nomogram with these variables showed good discriminative ability (area under the receiver operating characteristic curve was 0.801 in internal verification). In addition, the Hosmer-Lemeshow test (χ2 =9.226, P=0.417) and bootstrap-corrected calibration plot indicated that the nomogram had good calibration. The Brier score of the predictive model was 0.178. Decision curve analysis demonstrated that when the threshold probability ranged between 16% and 98%, the use of the nomogram to predict mild VCI in patients with T2DM provide a greater net benefit.

Conclusions

The nomogram, composed of age, education, stroke, HbA1c level, Hcy level, and total CSVD burden score, had good predictive accuracy and may provide clinicians with a practical tool for predicting the risk of mild VCI in T2DM patients.

A novel exosome-related prognostic risk model for thyroid cancer

AIM

The aim was to build an exosome-related gene (ERG) risk model for thyroid cancer (TC) patients.

METHODS

Note that, 510 TC samples from The Cancer Genome Atlas database and 121 ERGs from the ExoBCD database were obtained. Differential gene expression analysis was performed to get ERGs in TC (TERGs). Functional enrichment analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on the TERGs. Then we constructed a model based on LASSO Cox regression analysis. Kaplan-Meier survival analysis was applied and a Nomogram model was also built. The immune landscape was evaluated by CIBERSORT.

RESULTS

Thirty-eight TERGs were identified and their functions were enriched on 591 GO terms and 30 KEGG pathways. We built a Risk Score model based on FGFR3, ADRA1B, and POSTN. Risk Scores were significantly higher in T4 than in other stages, meanwhile, it didn't significantly differ in genders and TNM N or M classifications. The nomogram model could reliably predict the overall survival of TC patients. The mutation rate of BRAF and expression of cytotoxic T-lymphocyte-associated protein 4 were significantly higher in the high-risk group than in the low-risk group. The risk score was significantly correlated to the immune landscape.

CONCLUSION

We built a Risk Score model using FGFR3, ADRA1B, and POSTN which could reliably predict the prognosis of TC patients.

Establishment and validation of a nomogram model for predicting ovulation in the PCOS women

BACKGROUND

The mechanisms underlying ovulatory dysfunction in PCOS remain debatable. This study aimed to identify the factors affecting ovulation among PCOS patients based on a large sample-sized randomized control trial.

METHODS

Data were obtained from a multi-centered randomized clinical trial, the PCOSAct, which was conducted between 2011 and 2015. Univariate and multivariate analysis using binary logistic regression were used to construct a prediction model and nomogram. The accuracy of the model was assessed using receiver operating characteristic (ROC) curves and calibration curves.

RESULTS

The predictive variables included in the training dataset model were luteinizing hormone (LH), free testosterone, body mass index (BMI), period times per year, and clomiphene treatment. The ROC curve for the model in the training dataset was 0.81 (95% CI [0.77, 0.85]), while in the validation dataset, it was 0.7801 (95% CI [0.72, 0.84]). The model showed good discrimination in both the training and validation datasets. Decision curve analysis demonstrated that the nomogram designed for ovulation had clinical utility and superior discriminative ability for predicting ovulation.

CONCLUSIONS

The nomogram composed of LH, free testosterone, BMI, period times per year and the application of clomiphene may predict the ovulation among PCOS patients.

Development and validation of a nomogram for predicting depressive symptoms in dentistry patients: A cross-sectional study

Depressive symptoms are frequently occur among dentistry patients, many of whom struggle with dental anxiety and poor oral conditions. Identifying the factors that influence these symptoms can enable dentists to recognize and address mental health concerns more effectively. This study aimed to investigate the factors associated with depressive symptoms in dentistry patients and develop a clinical tool, a nomogram, to assist dentists in predicting these symptoms. Methods: After exclusion of ineligible participants, a total of 1355 patients from the dentistry department were included. The patients were randomly assigned to training and validation sets at a 2:1 ratio. The LASSO regression method was initially employed to select highly influrtial features. This was followed by the application of a multi-factor logistic regression to determine independent factors and construct a nomogram. And it was evaluated by 4 methods and 2 indicators. The nomograms were formulated based on questionnaire data collected from dentistry patients. Nomogram2 incorporated factors such as medical burden, personality traits (extraversion, conscientiousness, and emotional stability), life purpose, and life satisfaction. In the training set, Nomogram2 exhibited a Concordance index (C-index) of 0.805 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.805 (95% CI: 0.775-0.835). In the validation set, Nomogram2 demonstrated an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.810 (0.768-0.851) and a Concordance index (C-index) of 0.810. Similarly, Nomogram1 achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.816 (0.788-0.845) and a Concordance index (C-index) of 0.816 in the training set, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.824 (95% CI: 0.784-0.864) and a Concordance index (C-index) of 0.824 in the validation set. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) indicated that Nomogram1, which included oral-related factors (oral health and dental anxiety), outperformed Nomogram2. We developed a nomogram to predict depressive symptoms in dentistry patients. Importantly, this nomogram can serve as a valuable psychometric tool for dentists, facilitating the assessment of their patients' mental health and enabling more tailored treatment plans.

A Nomogram Model Based on the Inflammation-Immunity-Nutrition Score (IINS) and Classic Clinical Indicators for Predicting Prognosis in Extranodal Natural Killer/T-Cell Lymphoma

Background

Systemic inflammation, immunity, and nutritional status are closely related to patients' outcomes in several kinds of cancers. This study aimed to establish a new nomogram based on inflammation-immunity-nutrition score (IINS) to predict the prognosis of extranodal natural killer/T-cell lymphoma (ENKTL) patients.

Methods

The clinical data of 435 patients with ENTKL were retrospectively reviewed and randomly assigned to training cohort (n=305) and validation cohort (n=131) at a ratio of 7:3. Cox regression analysis was employed to identify independent prognostic factors and develop a nomogram in the training cohort. Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were employed to assess the performance of the nomogram and compare it with traditional prognostic systems (PINK, IPI, KPI). Internal validation was performed using 1000 bootstrap resamples in the validation cohort. Kaplan-Meier survival analyses were conducted to compare the overall survival (OS) of patients in different risk groups.

Results

In the training cohort, in addition to several classic parameters, IINS was identified as an independent prognostic factor significantly associated with the OS of patients. The nomogram established based on the independent prognostic indicators showed superior survival prediction efficacy, with C-index of 0.733 in the training cohort and 0.759 in the validation cohort compared to the PINK (0.636 and 0.737), IPI (0.81 and 0.707), and KPI (0.693 and 0.639) systems. Furthermore, compared with PINK, IPI, and IPI systems, the nomogram showed relatively superior calibration curves and more powerful prognostic discrimination ability in predicting the OS of patients. DCA curves revealed some advantages in terms of clinical applicability of the nomogram compared to the PINK, IPI, and IPI systems.

Conclusion

Compared with traditional prognostic systems, the nomogram showed promising prospects for risk stratification in ENKTL patient prognosis, providing new insights into the personalized treatment.

Development and Validation of a Nomogram for Patients Undergoing Transarterial Chemoembolization for Recurrent Hepatocellular Carcinoma After Hepatectomy

Purpose

This study aims to establish a prognostic nomogram for patients who underwent transarterial chemoembolization (TACE) for recurrent hepatocellular carcinoma (HCC) after hepatectomy.

Patients and Methods

Patients who underwent TACE for recurrent early- and middle-stage HCC after hepatectomy between 2009.01 and 2015.12 were included. Enrolled patients were randomly divided into training (n=345) and validation (n=173) cohorts according to a computer-generated randomized number. Independent factors for overall survival (OS) were determined and included in the nomogram based on the univariate and multivariate analyses of the training group. The nomogram was validated and compared to other prognostic models. Discriminative ability and predictive accuracy were determined using the Harrell C index (C-index), area under the receiver operating characteristic curve (AUROC), and calibration curve.

Results

The final nomogram was established based on four parameters including resection-to-TACE time interval, recurrent tumor diameter, recurrent tumor number, and AFP level. The C-indexes of the nomogram for predicting OS were 0.67 (95% CI 0.63-0.70) and 0.71 (95% CI 0.68-0.74) in the training and validation cohort respectively. The AUROCs for predicting the 1-year, 2-year and 3-year OS based on the nomogram were also superior to those of the other models. The calibration curve for 3-year survival showed a high congruence between the predicted and actual survival probabilities. According to the scores calculated by the nomogram, patients were stratified into three subgroups: high-risk (scoring ≥53 points), middle-risk (scoring ≥26 and <53 points), and low-risk (scoring <26 points) subgroups with a median OS of 10.1 (95% CI 0.63-0.70), 20.3 (95% CI 17.5-22.5) and 47.0 (95% CI 34.2-59.8) months, respectively.

Conclusion

The proposed nomogram served as a new tool to predict individual survival in patients who underwent TACE for recurrent HCC after hepatectomy, with favorable performance and discrimination. For high-risk patients, treatment should be optimized beyond TACE alone based on the nomogram.

A combined pre- and intra-operative nomogram in evaluation of degrees of liver cirrhosis predicts post-hepatectomy liver failure: a multicenter prospective study

Background

Adequate evaluation of degrees of liver cirrhosis is essential in surgical treatment of hepatocellular carcinoma (HCC) patients. The impact of the degrees of cirrhosis on prediction of post-hepatectomy liver failure (PHLF) remains poorly defined. This study aimed to construct and validate a combined pre- and intra-operative nomogram based on the degrees of cirrhosis in predicting PHLF in HCC patients using prospective multi-center's data.

Methods

Consecutive HCC patients who underwent hepatectomy between May 18, 2019 and Dec 19, 2020 were enrolled at five tertiary hospitals. Preoperative cirrhotic severity scoring (CSS) and intra-operative direct liver stiffness measurement (DSM) were performed to correlate with the Laennec histopathological grading system. The performances of the pre-operative nomogram and combined pre- and intra-operative nomogram in predicting PHLF were compared with conventional predictive models of PHLF.

Results

For 327 patients in this study, histopathological studies showed the rates of HCC patients with no, mild, moderate, and severe cirrhosis were 41.9%, 29.1%, 22.9%, and 6.1%, respectively. Either CSS or DSM was closely correlated with histopathological stages of cirrhosis. Thirty-three (10.1%) patients developed PHLF. The 30- and 90-day mortality rates were 0.9%. Multivariate regression analysis showed four pre-operative variables [HBV-DNA level, ICG-R15, prothrombin time (PT), and CSS], and one intra-operative variable (DSM) to be independent risk factors of PHLF. The pre-operative nomogram was constructed based on these four pre-operative variables together with total bilirubin. The combined pre- and intra-operative nomogram was constructed by adding the intra-operative DSM. The pre-operative nomogram was better than the conventional models in predicting PHLF. The prediction was further improved with the combined pre- and intra-operative nomogram.

Conclusions

The combined pre- and intra-operative nomogram further improved prediction of PHLF when compared with the pre-operative nomogram.

Trial Registration

Clinicaltrials.gov Identifier: NCT04076631.

A nomogram based on contrast-enhanced ultrasound for evaluating the glomerulosclerosis rate in transplanted kidneys

Background

A high rate of glomerulosclerosis serves as an important signal of poor response to treatment and a high risk of disease progression or adverse prognosis in transplanted kidneys. We hypothesized that contrast-enhanced ultrasound (CEUS) could serve as a novel imaging biomarker in the early prediction of glomerulosclerosis rate by evaluating renal allograft microcirculation.

Methods

A retrospective analysis was performed on 143 transplanted kidney recipients with confirmed pathology, including 100 in the training group and 43 in the validation group. All patients underwent conventional ultrasound (CUS) and CEUS examinations. The patients were divided into two groups: those with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis. The nomograms derived from independent predictors identified by multivariate logistic analysis were assessed using receiver operating characteristic (ROC) curve analysis, 1,000 bootstrap resamples, calibration curves, and decision curve analysis (DCA).

Results

The patients with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis showed statistically significant differences in CEUS parameters, including in peak intensity (PI) (25 vs. 30; P<0.001), absolute time to peak (ATTP) (10 vs. 9; P=0.004), and time to peak (TTP) (22 vs. 19.5; P=0.026). Multivariate analysis revealed that PI [odds ratio (OR) =0.852; 95% confidence interval (CI): 0.737-0.986], peak systolic velocity (PSV) of the interlobar artery (OR =0.850; 95% CI: 0.758-0.954), cortical echogenicity (OR =38.429; 95% CI: 3.695-399.641), and time since transplantation (OR =1.017; 95% CI: 1.006-1.028) were independent predictors of whether the glomerulosclerosis rate was >50% and were incorporated into the construction of a nomogram. The area under the curve (AUC) of the nomogram in the training and validation groups was 0.914 (95% CI: 0.840-0.960) and 0.909 (95% CI: 0.781-0.975), respectively, with a bootstrap resampling AUC of 0.877. The calibration curve and DCA confirmed the diagnostic performance of the nomogram model.

Conclusions

The nomogram, which combined CUS, CEUS, and clinical indicators, exhibited notable predictive efficacy for the glomerulosclerosis rate in transplanted kidneys, thereby demonstrating the potential to improve clinical decision-making.

A Novel Clinical Tool to Predict Cancer-specific Survival in Postoperative Patients With Primary Spinal and Pelvic Sarcomas: A Large Population-Based Retrospective Cohort Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

Primary osseous sarcomas originating from the spine and pelvis are rare and usually portend inferior prognoses. Currently, the standard treatment for spinal and pelvic sarcomas is surgical resection, but the poor prognosis limits the benefits to postoperative patients. This study aims to identify the independent prognostic factors of cancer-specific survival (CSS) in postoperative patients with primary spinal and pelvic sarcomas and construct a nomogram for predicting these patients' 3-, 5-, and 10-year CSS probability.

METHODS

A total of 452 patients were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. They were divided into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to identify these patients' CSS-related independent prognostic factors. Then, those factors were used to construct a prognostic nomogram for predicting the 3-, 5-, and 10-year CSS probability, whose predictive performance and clinical value were verified by the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was constructed.

RESULTS

Sex, histological type, tumor stage, and tumor grade were identified as CSS-related independent prognostic factors. A nomogram with high predictive performance and good clinical value to predict the 3-, 5-, and 10-year CSS probability was constructed, on which a mortality risk stratification system was constructed based to divide these patients into 3 mortality risk subgroups effectively.

CONCLUSIONS

This study constructed and validated a clinical nomogram to predict CSS in postoperative patients with primary spinal and pelvic sarcomas. It could assist clinicians in classifying these patients into different mortality risk subgroups and realize sarcoma-specific management.

Development and internal validation of a nomogram based on peripheral blood inflammatory markers for predicting prognosis in nasopharyngeal carcinoma

BACKGROUND

Inflammatory markers, including the product of neutrophil count, platelet count, and monocyte count divided by lymphocyte count (PIV) and the platelet-to-white blood cell ratio (PWR), have not been previously reported as prognostic factors in nasopharyngeal carcinoma (NPC) patients. In order to predict overall survival (OS) in NPC patients, our goal was to create and internally evaluate a nomogram based on inflammatory markers (PIV, PWR).

METHODS

A retrospective study was done on patients who received an NPC diagnosis between January 2015 and December 2018. After identifying independent prognostic indicators linked to OS using Cox proportional hazards regression analysis, we created a nomogram with the factors we had chosen.

RESULTS

A total of 630 NPC patients in all were split into training (n = 441) and validation sets (n = 189) after being enrolled in a population-based study in 2015-2018 and monitored for a median of 5.9 years. In the training set, the age, PIV, and PWR, selected as independent predictors for OS via multivariate Cox's regression model, were chosen to develop a nomogram. Both training and validation cohorts had C-indices of 0.850 (95% confidence interval [CI]: 0.768-0.849) and 0.851 (95% CI: 0.765-0.877). Furthermore, compared with traditional TNM staging, our nomogram demonstrated greater accuracy in predicting patient outcomes. The risk stratification model derived from our prediction model may facilitate personalized treatment strategies for NPC patients.

CONCLUSION

Our findings confirmed the prognostic significance of the PWR and PIV in NPC. High PIV levels (>363.47) and low PWR (≤36.42) values are associated with worse OS in NPC patients.

Nomograms for predicting overall survival and cancer-specific survival of endometrioid ovarian carcinoma: A retrospective cohort study from the SEER database

OBJECTIVE

Endometrioid ovarian cancer (EnOC) accounts for approximately 10%-15% of epithelial ovarian cancer cases. There are no effective tools for predicting the prognosis of EnOC in clinical work. The aim of this study was to construct and validate a nomogram to predict overall survival and cancer-specific survival (CSS) in patients with EnOC.

METHODS

Data regarding patients diagnosed with primary EnOC between 2004 and 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. LASSO Cox regression and Cox regression analyses were performed to screen for prognostic factors, which were used to construct nomograms. In addition, we performed subgroup analyses of the prognostic value of chemotherapy and lymph node surgery.

RESULTS

In total, 3957 patients with primary EnOC were included in the analysis: 2770 in a training cohort and 1187 in a validation cohort. Age, stage, grade, lymph node surgery, and race were significantly and independently correlated with overall survival and CSS. Nomograms were constructed to predict 3- and 5-year overall survival and CSS. Nomograms have good predictive ability and clinical practicability. Subgroup analysis showed that lymph node surgery improved the prognosis of patients with EnOC (P < 0.05) except for patients with grade III-IV and Stage I disease (overall survival P = 0.272, CSS P = 0.624). Chemotherapy did not improve survival time in most patients (P > 0.05) except for patients with grade I-II and Stage II-IV disease (overall survival P = 0.008, CSS P = 0.046).

CONCLUSION

We constructed predictive nomograms and a risk classification system to evaluate overall survival and CSS in EnOC patients. For most patients with EnOC, chemotherapy did not improve the prognosis. In contrast to chemotherapy, lymph node surgery improved prognosis in most patients with EnOC.

Development of a joint prediction model based on both the radiomics and clinical factors for preoperative prediction of circumferential resection margin in middle-low rectal cancer using T2WI images

OBJECTIVES

A circumferential resection margin (CRM) is an independent risk factor for local recurrence, distant metastasis, and poor overall survival of rectal cancer. In this study, we developed and validated a radiomics prediction model to predict perioperative surgical margins in patients with middle and low rectal cancer following neoadjuvant treatment and for decisions about treatment plans for patients.

METHODS

This study retrospectively analyzed 275 patients from center 1(training cohort) and 120 patients from center 2(verification cohort) with rectal cancer diagnosed at two centers from July 2020 to July 2022 who underwent neoadjuvant therapy and had their CRM status confirmed by preoperative high-resolution magnetic resonance imaging (MRI) scans. Radiomics signatures were extracted and screened from MRI images and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model, which was combined with clinical signatures to construct a nomogram. The receiver operating characteristic (ROC) curve and area under the curve (AUC) value, sensitivity, specificity, positive predictive value, negative predictive value, and calibration curve were used to evaluate the predictive performance of the model.

RESULTS

In our research, the combined model has the best performance. In the training group, the radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI), clinical model and combined model demonstrated an AUC of 0.819 (0.802-0.833), 0.843 (0.822-0.861), and 0.910 (0.880-0.940), respectively. In the validation group, they demonstrated an AUC of 0.745 (0.715-0.788), 0.827 (0.798-0.850), and 0.848 (0.779-0.917), respectively. The calibration curve confirmed the clinical applicability of the model.

CONCLUSIONS

The individualized prediction model established by combining radiomics signatures and clinical signatures can efficiently and objectively predict perioperative margin invasion in patients with middle and low rectal cancer.

MRI-based Multiregional Radiomics for Pretreatment Prediction of Distant Metastasis After Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

To develop and validate a nomogram based on intratumoral and peritumoral radiomics signatures for pretreatment prediction of distant metastasis-free survival (DMFS) in patients after neoadjuvant chemoradiotherapy (NCRT) with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This retrospective study included 230 patients (161 training cohort; 69 validation cohort) with LARC who underwent NCRT and surgery. Radiomics features were extracted on T2-weighted images from gross tumor volume (GTV) and volumes of 4-mm, 6-mm, and 8-mm peritumoral regions (PTV4, PTV6, and PTV8). The least absolute shrinkage and selection operator (LASSO)-Cox analysis were used for features selection and models construction. The performance of each model in predicting DMFS was evaluated by the Concordance index (C-index) and time-independent receiver operating characteristic curve (ROC).

RESULTS

The PTV4 radiomics model demonstrated superior performance compared to the PTV6 and PTV8 radiomics models, with C-indexes of 0.750 and 0.703 in the training and validation cohorts, respectively. The nomogram was constructed by integrating the GTV radiomics signature, PTV4 radiomics signature, and relevant clinical characteristics, including CA19-9 level, clinical T stage, and clinical N stage. The nomogram achieved C-indexes of 0.831 and 0.748, with corresponding AUCs of 0.872 and 0.808 for 5-year DMFS in the training and validation cohorts, respectively. Kaplan-Meier analysis revealed that a cut-off value of 1.653 effectively stratified patients into high- and low-risk groups for DM (P < 0.001).

CONCLUSION

The intra-peritumoral radiomics nomogram is a favorable tool for clinicians to develop personalized systemic treatment and intensive follow-up strategies to improve patient prognosis.

Constructing and validating a risk model based on neutrophil-related genes for evaluating prognosis and guiding immunotherapy in colon cancer

BACKGROUND

Colon cancer is one of the most common digestive tract malignancies. Although immunotherapy has brought new hope to colon cancer patients, there is still a large proportion of patients who do not benefit from immunotherapy. Studies have shown that neutrophils can interact with immune cells and immune factors to affect the prognosis of patients.

METHODS

We first determined the infiltration level of neutrophils in tumors using the CIBERSORT algorithm and identified key genes in the final risk model by Spearman correlation analysis and subsequent Cox analysis. The risk score of each patient was obtained by multiplying the Cox regression coefficient and the gene expression level, and patients were divided into two groups based on the median of risk score. Differences in overall survival (OS) and progression-free survival (PFS) were assessed by Kaplan-Meier survival analysis, and model accuracy was validated in independent dataset. Differences in immune infiltration and immunotherapy were evaluated by immunoassay. Finally, immunohistochemistry and western blotting were performed to verify the expression of the three genes in the colon normal and tumor tissues.

RESULTS

We established and validated a risk scoring model based on neutrophil-related genes in two independent datasets, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, with SLC11A1 and SLC2A3 as risk factors and MMP3 as a protective factor. A new nomogram was constructed and validated by combining clinical characteristics and the risk score model to better predict patients OS and PFS. Immune analysis showed that patients in the high-risk group had immune cell infiltration level, immune checkpoint level and tumor mutational burden, and were more likely to benefit from immunotherapy.

CONCLUSIONS

The low-risk group showed better OS and PFS than the high-risk group in the neutrophil-related gene-based risk model. Patients in the high-risk group presented higher immune infiltration levels and tumor mutational burden and thus may be more responsive to immunotherapy.

Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study

BACKGROUND

Deep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate optimal therapy decision.

PURPOSE

To develop a nomogram to identify DSI in cervical AC/ASC.

STUDY TYPE

Retrospective.

POPULATION

Six hundred and fifty patients (mean age of 48.2 years) were collected from center 1 (primary cohort, 536), centers 2 and 3 (external validation cohorts 1 and 2, 62 and 52).

FIELD STRENGTH/SEQUENCE

5-T, T2-weighted imaging (T2WI, SE/FSE), diffusion-weighted imaging (DWI, EPI), and contrast-enhanced T1-weighted imaging (CE-T1WI, VIBE/LAVA).

ASSESSMENT

The DSI was defined as the outer 1/3 stromal invasion on pathology. The region of interest (ROI) contained the tumor and 3 mm peritumoral area. The ROIs of T2WI, DWI, and CE-T1WI were separately imported into Resnet18 to calculate the DL scores (TDS, DDS, and CDS). The clinical characteristics were retrieved from medical records or MRI data assessment. The clinical model and nomogram were constructed by integrating clinical independent risk factors only and further combining DL scores based on primary cohort and were validated in two external validation cohorts.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U test, or Chi-squared test were used to compare differences in continuous or categorical variables between DSI-positive and DSI-negative groups. DeLong test was used to compare AU-ROC values of DL scores, clinical model, and nomogram.

RESULTS

The nomogram integrating menopause, disruption of cervical stromal ring (DCSRMR), DDS, and TDS achieved AU-ROCs of 0.933, 0.807, and 0.817 in evaluating DSI in primary and external validation cohorts. The nomogram had superior diagnostic ability to clinical model and DL scores in primary cohort (all P < 0.0125 [0.05/4]) and CDS (P = 0.009) in external validation cohort 2.

DATA CONCLUSION

The nomogram achieved good performance for evaluating DSI in cervical AC/ASC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Clinical Features, Risk Factors, and Prediction Nomogram for Primary Spinal Osteosarcoma: A Large-Cohort Retrospective Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

The goal of this study was to determine the clinical characteristics of patients with primary spinal osteosarcoma and to construct a practical clinical prediction model for patients to achieve an accurate prediction of overall survival.

METHODS

This study included 230 patients diagnosed between 2004-2015 from the Surveillance, Epidemiology, and End Results database. Independent risk factors were screened in the training set using Cox regression algorithms, and a prognostic model was developed. Internal and external validation sets were used to test the nomogram model's calibration, discrimination, and clinical utility. A risk classification system based on the nomogram was developed and validated.

RESULTS

Four independent prognostic factors were identified, and based on this a nomogram model was developed for predicting patient prognosis. The C-index of the training set was .737, while that of the validation set was .693. The time-varying area under the curve values was greater than .720 in both cohorts. The calibration curves proved that the prediction model has high prediction accuracy. The decision curve analysis showed that the nomogram is clinically useful. A risk classification system was established, which allows all patients to be divided into two different risk groups.

CONCLUSIONS

A nomogram and risk classification system was developed for patients with primary spinal osteosarcoma to accurately predict overall patient survival and achieve risk stratification of patient mortality. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions.

Prognostic value of diffuse reduction of spleen density on postoperative survival of pancreatic ductal adenocarcinoma: A retrospective study

PURPOSE

It is difficult to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) before radical operation. The purpose of this study was to explore the connection between the diffuse reduction of spleen density on computed tomography (DROSD) and the postoperative prognosis of patients with PDAC.

PATIENTS AND METHODS

A total of 160 patients with PDAC who underwent radical surgery in the First Affiliated Hospital of Wenzhou Medical University were enrolled. Cox regression analysis was used to cast the overall survival (OS) and evaluate the prognostic factors. Nomogram was used to forecast the possibility of 1-year, 3-year, and 5-year OS. The prediction accuracy and clinical net benefit are performed by concordance index (C-index), calibration curve, time-dependent receiver operating characteristics (tdROC), and decision curve analysis.

RESULTS

In multivariable Cox analysis, DROSD is independently related to OS. Advanced age, TNM stage, neutrophil/lymphocyte ratio, and severe complications were also independent prognostic factors. The calibration curves of nomogram showed optimal agreement between prediction and observation. The C-index of nomogram is 0.662 (95%CI, 0.606-0.754). The area under tdROC curve for a 3-year OS of nomogram is 0.770.

CONCLUSION

DROSD is an independent risk factor for an OS of PDAC. We developed a nomogram that combined imaging features, clinicopathological factors, and systemic inflammatory response to provide a personalized risk assessment for patients with PDAC.

The value of radiographic features in predicting postoperative facial nerve function in vestibular schwannoma patients: A retrospective study and nomogram analysis

OBJECTIVE

The purpose of this study was to identify significant prognostic factors associated with facial paralysis after vestibular schwannoma (VS) surgery and develop a novel nomogram for predicting facial nerve (FN) outcomes.

METHODS

Retrospective data were retrieved from 355 patients who underwent microsurgery via the retrosigmoid approach for VS between December 2017 and December 2022. Univariate and multivariate logistic regression analysis were used to construct a radiographic features-based nomogram to predict the risk of facial paralysis after surgery.

RESULTS

Following a thorough screening process, a total of 185 participants were included. The univariate and multivariate logistic regression analysis revealed that tumor size (p = 0.005), fundal fluid cap (FFC) sign (p = 0.014), cerebrospinal fluid cleft (CSFC) sign (p < 0.001), and expansion of affected side of internal auditory canal (IAC) (p = 0.033) were independent factors. A nomogram model was constructed based on these indicators. When applied to the validation cohort, the nomogram demonstrated good discrimination and favorable calibration. Then we generated a web-based calculator to facilitate clinical application.

CONCLUSION

Tumor size, FFC and CSFC sign, and the expansion of the IAC, serve as good predictors of postoperative FN outcomes. Based on these factors, the nomogram model demonstrates good predictive performance.

Prognostic factors and novel nomograms for overall survival and cancer specific survival of malignant ovarian cancer patients with bone metastasis: A SEER-based study

OBJECTIVE

Ovarian cancer (OC) is a frequent and fatal disease in women, and bone metastasis of ovarian cancer (OCBM) leads to a poor survival trend. This study aimed to determine the factors which influence overall survival (OS) and cancer-specific survival (CSS) of OCBM patients and to develop prognostic predictive models.

METHODS

Data of OCBM patients were stratified from the Surveillance, Epidemiology and End Results database from 2010 to 2017 and were randomly divided into training and testing datasets (7:3). Prognostic factors were identified by Cox regression analyses and nomograms were then developed. Nomogram models were examined on the discriminative ability and accuracy by calibration plots, Brier score (BS), and time-dependent receiver operating characteristic (ROC) curves. Decision curve analyses (DCA) was used for estimation of the clinical benefit of nomogram models.

RESULTS

Grade, tumor size, tumor metastasis (liver, lung), primary site surgery, chemotherapy, and systemic therapy were realized as independent prognostic factors for OS and CSS, respectively. Agreement between the actual and predicted outcomes was proved by calibration plots. Nomograms performed well in OS and CSS predictions, as shown by area under the ROC curves (AUCs) and BSs for testing dataset as follows: for OS, 3-/6-/12-month AUCs and BSs were 0.778/0.788/0.822 and 19.0/18.5/15.4, respectively; for CSS, 3-/6-/12-month AUCs and BSs were 0.799/0.806/0.832 and 18.1/18.0/15.4, respectively. DCA suggested an agreeable clinical benefit of both nomograms.

CONCLUSION

The nomograms developed for OCBM patients' survival prediction were proved to be accurate, efficient, and clinically beneficial, which were further deployed as web-based calculators to help in clinical decision making and future studies.

A Nomogram of MRI Features to Assess Muscle Invasion in VI-RADS 2 Tumors With Stalk

BACKGROUND

Vesical Imaging-Reporting and Data System (VI-RADS) is widely used to assess the muscle-invasive status of bladder cancer. However, the current classification efficacy of VI-RASD 2 tumors of stalk is unsatisfactory.

PURPOSE

To develop a nomogram to assess muscle-invasive bladder cancer (MIBC) in VI-RADS 2 tumors with stalk.

STUDY TYPE

Retrospective.

POPULATION

A total of 186 patients (age: 67.8 ± 12.7 years) with 15.1% females, divided randomly into a training cohort (N = 130) and validation cohort (N = 56).

FIELD STRENGTH/SEQUENCE

3-T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin-echo), and dynamic contrast-enhanced imaging (gradient-echo).

ASSESSMENT

Twenty-one MRI features of tumors and stalks were developed from training cohort. The mean apparent diffusion coefficient (ADC) values of the tumor, stalk, and psoas muscles were calculated from the three circular regions of interest. The normalized T value = mean ADC tumor mean ADC muscle . The normalized ST value = mean ADC stalk mean ADC tumor . Three readers assessed the morphology of tumors and stalks.

STATISTICAL TESTS

The final features of nomogram were selected by univariable logistic and the least absolute shrinkage and selection operator (LASSO) regression. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis.

RESULTS

In VI-RADS 2 tumors with stalk, tumor size over 3 cm, increased stalk width, stalk morphology, decreased normalized T value, and increased normalized ST value were selected as the risk factors for MIBC. The AUC, accuracy, sensitivity, and specificity of the nomogram to assess MIBC were 0.969 (95% CI: 0.941-0.997), 92.3%, 94.1%, and 92.0% in training cohort and 0.940 (95% CI: 0.859-1.000), 89.3%, 75.0%, and 91.7% in validation cohort.

DATA CONCLUSION

This study constructed a nomogram for preoperative assessment of MIBC and modifying the current VI-RADS.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Developing and validating a nomogram for penile cancer survival: A comprehensive study based on SEER and Chinese data

OBJECTIVE

The primary aim of this study was to create a nomogram for predicting survival outcomes in penile cancer patients, utilizing data from the Surveillance, Epidemiology, and End Results (SEER) and a Chinese organization.

METHODS

Our study involved a cohort of 5744 patients diagnosed with penile cancer from the SEER database, spanning from 2004 to 2019. In addition, 103 patients with penile cancer from Sun Yat-sen Memorial Hospital of Sun Yat-sen University were included during the same period. Based on the results of regression analysis, a nomogram is constructed and validated internally and externally. The predictive performance of the model was evaluated by concordance index (c-index), area under the curve, decision curve analysis, and calibration curve, in internal and external datasets. Finally, the prediction efficiency is compared with the TNM staging model.

RESULTS

A total of 3154 penile patients were randomly divided into the training group and the internal validation group at a ratio of 2:1. Nine independent risk factors were identified, including age, race, marital status, tumor grade, histology, TNM stage, and the surgical approach. Based on these factors, a nomogram was constructed to predict OS. The nomogram demonstrated relatively better consistency, predictive accuracy, and clinical relevance, with a c-index over 0.73 (in the training cohort, the validation cohort, and externally validation cohort.) These evaluation indexes are far better than the TNM staging system.

CONCLUSION

Penile cancer, often overlooked in research, has lacked detailed investigative focus and guidelines. This study stands as the first to validate penile cancer prognosis using extensive data from the SEER database, supplemented by data from our own institution. Our findings equip surgeons with an essential tool to predict the prognosis of penile cancer better suited than TNM, thereby enhancing clinical decision-making processes.

A Modification of the American Joint Committee on Cancer Nomogram for Undifferentiated Sarcoma With External Validation and Risk Stratification

BACKGROUND

The aim of this study is to establish a model to predict the overall survival (OS) and stratify the risk of postoperative patients with undifferentiated sarcoma.

METHODS

A total of 452 postoperative patients with undifferentiated sarcoma in the trunk and extremity from the Surveillance, Epidemiology, and End Results database were enrolled as the training cohort. We collected a group of 163 undifferentiated sarcoma patients from our center as the external validation cohort. Cox proportional hazards regression model was used to screen survival-associated factors for the construction of the nomogram. Concordance-indexes (C-indexes), calibration curves, and receiver operating characteristics (ROCs) curves were applied for the discrimination and calibration of the nomogram. The cutoff value of nomogram-based total points was applied to stratify the risk of patients.

RESULTS

A nomogram was developed incorporating four independent factors: age, tumor site, eighth AJCC stage, and radiotherapy. The nomogram showed good prognostic accuracy and excellent agreement in the training and validation cohort, with C-indexes of .701 (95% confidence interval [CI]: .683-.719) and .700 (95% CI: 0.659-.741), respectively. Furthermore, we identified the best cutoff value of nomogram total points (103.2) as the predicted risk and divided the patients into a high-risk group and a low-risk group. Significant differences in OS between the two groups were indicated in the training cohort and external validation cohort, showing the appreciable clinical validity and clinical utility of the nomogram (P < .001).

CONCLUSION

This nomogram provides an insightful and applicable tool for individual evaluations and the distinguishment of risk for patients with undifferentiated sarcoma.

Prognostic factors and predictive models for patients with lung large cell neuroendocrine carcinoma: Based on SEER database

BACKGROUND

Lung Large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive, high-grade neuroendocrine carcinoma with a poor prognosis, mainly seen in elderly men. To date, we have found no studies on predictive models for LCNEC.

METHODS

We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database of confirmed LCNEC from 2010 to 2018. Univariate and multivariate Cox proportional risk regression analyses were used to identify independent risk factors, and then we constructed a novel nomogram and assessed the predictive effectiveness by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 2546 patients with LCNEC were included, excluding those diagnosed with autopsy or death certificate, tumor, lymph node, metastasis (TNM) stage, tumor grade deficiency, etc., and finally, a total of 743 cases were included in the study. After univariate and multivariate analyses, we concluded that the independent risk factors were N stage, intrapulmonary metastasis, bone metastasis, brain metastasis, and surgical intervention. The results of ROC curves, calibration curves, and DCA in the training and validation groups confirmed that the nomogram could accurately predict the prognosis.

CONCLUSIONS

The nomogram obtained from our study is expected to be a useful tool for personalized prognostic prediction of LCNEC patients, which may help in clinical decision-making.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database

OBJECTIVE

We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma.

METHODS

Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms.

RESULTS

We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification.

CONCLUSION

The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single-center cross-sectional study

BACKGROUND

To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy.

METHODS

In this cross-sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis.

RESULTS

A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high- and low-risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility.

CONCLUSIONS

The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

Construction and validation of a prognostic model for stemness-related genes in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with poor overall prognosis. Early identification of high-risk patients and individualized treatment can help extend the survival time of patients. This study aimed to construct and validate a prognostic prediction least absolute shrinkage and selection operator (LASSO) model for stemness-related genes in LUAD.

Methods

Firstly, LUAD RNA-sequencing data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor stemness index based on mRNA expression (mRNAsi) was calculated, and the relationship between mRNAsi and the survival prognosis as well as clinical features of LUAD patients was analyzed. Then, the weighted gene co-expression network analysis (WGCNA) method was used to screen for gene modules highly correlated with mRNAsi, and functional annotation [Gene Ontology (GO) analysis] and pathway enrichment analysis [Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis] were performed for the selected stemness-related gene module. Furthermore, prognosis-associated genes were determined from the stemness-related genes through univariate Cox analysis, and a prognostic model was constructed using LASSO analysis. Finally, a series of validations including survival curve analysis, receiver operating characteristic (ROC) curve analysis, and risk analysis were conducted for the prognostic model, and nomogram based on the risk model and various clinicopathological features were constructed.

Results

LUAD patients with high mRNAsi had a higher mortality rate than those with low mRNAsi. GO analysis showed that stemness-related genes were mainly involved in mRNA processing and extracellular matrix organization, while KEGG analysis revealed their involvement in cell cycle and PI3K-Akt signaling pathways. A prognostic model based on 12 stemness-related genes was constructed using LASSO regression. Validation of the prognostic model demonstrated its good accuracy in predicting the prognosis of LUAD patients.

Conclusions

mRNAsi plays an important role in the occurrence and development of LUAD. This study successfully constructed a prognostic prediction LASSO model for stemness-related genes in LUAD, which can serve as a novel prognostic indicator for LUAD and may be an effective complement to the current Tumor Node Metastasis (TNM) clinical staging of LUAD.

Development and validation of a nomogram superior to CHADS2 and CHA2DS2-VASc models for predicting left atrial appendage dense spontaneous echo contrast/left atrial appendage thrombus

Background

Atrial fibrillation (AF) is one of the most frequently encountered arrhythmias in clinical practice, with stroke triggered by detachment of left atrial appendage thrombus (LAAT) after AF being its most critical complication. The purpose of this study was to construct a nomogram model for forecasting left atrial appendage (LAA) dense spontaneous echo contrast (SEC) and LAAT to accurately identify patients at high risk for stroke.

Methods

A retrospective analysis was conducted on 433 patients with AF receiving transesophageal echocardiography (TEE) in the First Affiliated Hospital of Soochow University from October 2019 to July 2022. These patients were assigned into a non-dense SEC/LAAT group or a dense SEC/LAAT group. We constructed a nomogram model dependent on the odds ratios (ORs) of logistic regression and subsequently compared its performance with two models, CHADS2 and CHA2DS2-VASc.

Results

Female gender, high D-dimer level, low left ventricular ejection fraction, low left atrial ejection fraction, and low left atrial reservoir strain rate were found to be independent factors for predicting LAA SEC/LAAT, with OR values and 95% confidence intervals of 2.811 (1.445-5.469), 2.460 (1.230-4.921), 0.961 (0.927-0.996), 0.950 (0.932-0.967), and 0.173 (0.035-0.848), respectively. The consistency statistic of the nomogram based on these given predictive factors was 0.921, and the calibrated consistency statistic was 0.903. According to receiver operation curve analysis and decision curve analysis, the nomogram was demonstrated to be superior to the CHADS2 and CHA2DS2-VASc models in predicting LAA dense SEC/LAAT. The net reclassification improvement and integrated discrimination improvement of the nomogram were 0.449 (0.324-0.575) and 0.461 (0.408-0.515), when compared with the CHADS2 model, and were 0.521 (0.411-0.632), and 0.432 (0.400-0.504), respectively, when compared with the CHA2DS2-VASc models.

Conclusions

The nomogram model constructed in this study demonstrated excellent performance in predicting LAA dense SEC/LAAT, displaying a superior ability to that of the CHADS2 and CHA2DS2-VASc models.

Development of a haematological indices-based nomogram for prognostic prediction and immunotherapy response assessment in primary pulmonary lymphoepithelioma-like carcinoma patients

Background

Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare yet aggressive malignancy. This study aims to investigate a deep learning model based on hematological indices, referred to as haematological indices-based signature (HIBS), and propose multivariable predictive models for accurate prognosis prediction and assessment of therapeutic response to immunotherapy in PPLELC.

Methods

This retrospective study included 117 patients with PPLELC who received immunotherapy and were randomly divided into a training (n=82) and a validation (n=35) cohort. A total of 41 hematological features were extracted from routine laboratory tests and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to establish the HIBS. Additionally, we developed a nomogram using the HIBS and clinical characteristics through multivariate Cox regression analysis. To evaluate the nomogram's predictive performance, we used calibration curves and calculated the time-dependent area under the curve (AUC). Kaplan-Meier survival analysis was performed to estimate progression-free survival (PFS) in both cohorts.

Results

The proposed HIBS comprised 14 hematological features and showed that patients who experienced disease progression had significantly higher HIBS scores compared to those who did not progress (P<0.001). Five prognostic factors, including HIBS, tumor-node-metastasis (TNM) stage, presence of bone metastasis and the specific immunotherapy regimen, were found to be independent factors and were used to construct a nomogram, which effectively categorized PPLELC patients into a high-risk and a low-risk group, with patients in the high-risk patients demonstrating worse PFS (7.0 vs. 18.0 months, P<0.001) and lower overall response rates (22.2% vs. 52.7%, P<0.001). The nomogram showed satisfactory discrimination for PFS, with AUC values of 0.837 and 0.855 in the training and validation cohorts, respectively.

Conclusions

The HIBS-based nomogram could effectively predict the PFS and response of patients with PPLELC regarding immunotherapy and serve as a valuable tool for clinical decision making.

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

Background

Hepatitis B surface antigen (HBsAg) clearance is associated with improved long-term outcomes and reduced risk of complications. The aim of our study was to identify the effects of levels of HBsAg in HCC patients undergoing TACE and sequential ablation. In addition, we created a nomogram to predict the prognosis of HCC patients with high levels of HBsAg (≥1000U/L) after local treatment.

Method

This study retrospectively evaluated 1008 HBV-HCC patients who underwent TACE combined with ablation at Beijing Youan Hospital and Beijing Ditan Hospital from January 2014 to December 2021, including 334 patients with low HBsAg levels and 674 patients with high HBsAg levels. The high HBsAg group was divided into the training cohort (N=385), internal validation cohort (N=168), and external validation cohort (N=121). The clinical and pathological features of patients were collected, and independent risk factors were identified using Lasso-Cox regression analysis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Patients were classified into high-risk and low-risk groups based on the risk scores of the nomogram.

Result

After PSM, mRFS was 28.4 months (22.1-34.7 months) and 21.9 months (18.5-25.4 months) in the low HBsAg level and high HBsAg level groups (P<0.001). The content of the nomogram includes age, BCLC stage, tumor size, globulin, GGT, and bile acids. The C-index (0.682, 0.666, and 0.740) and 1-, 3-, and 5-year AUCs of the training, internal validation, and external validation cohorts proved good discrimination of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classification of patients with high HBsAg levels into low-risk and high-risk groups according to the risk of recurrence. There was a statistically significant difference in RFS between the two groups in the training, internal validation, and external validation cohorts (P<0.001).

Conclusion

High levels of HBsAg were associated with tumor progression. The nomogram developed and validated in the study had good predictive ability for patients with high HBsAg levels.

A Nomogram for Predicting the Risk of Deep Vein Thrombosis in Patients With Acute Ischemic Stroke During the COVID-19

Deep vein thrombosis (DVT) is an important complication of stroke. As coronavirus disease 2019 (COVID-19) enters the stage of persistent and long-term management, the clinical management of DVT in stroke patients may require adjustment. The present study evaluated whether there was an increased risk of DVT in stroke patients during the COVID-19 period. Furthermore, we analyzed the possible risk factors and developed an easy-to-use nomogram to predict DVT in stroke patients during the long-term management of COVID-19. A total of 7087 stroke patients during the COVID-19 period and 14,174 patients with age, sex, and National Institutes of Health Stroke Scale (NIHSS) scores matched before the period from four centers were included. The incidence of DVT in stroke patients during the COVID-19 period (20.5%) was significantly higher than that before this period (15.9%, P < .001). Age, body mass index, smoking, D-dimer, physical activity level, NIHSS score, and intermittent pneumatic compression were significant predictors of DVT during the COVID-19 period (P < .05). A nomogram was constructed; internal and external validations showed high accuracy, and decision curve analysis showed excellent clinical applicability. This nomogram could evaluate the risk of DVT after stroke and assist in its early prevention during the long-term management of COVID-19.

Linking preoperative and early intensive care unit data for prolonged intubation prediction

Objectives

Prolonged intubation (PI) is a frequently encountered severe complication among patients following cardiac surgery (CS). Solely concentrating on preoperative data, devoid of sufficient consideration for the ongoing impact of surgical, anesthetic, and cardiopulmonary bypass procedures on subsequent respiratory system function, could potentially compromise the predictive accuracy of disease prognosis. In response to this challenge, we formulated and externally validated an intelligible prediction model tailored for CS patients, leveraging both preoperative information and early intensive care unit (ICU) data to facilitate early prophylaxis for PI.

Methods

We conducted a retrospective cohort study, analyzing adult patients who underwent CS and utilizing data from two publicly available ICU databases, namely, the Medical Information Mart for Intensive Care and the eICU Collaborative Research Database. PI was defined as necessitating intubation for over 24 h. The predictive model was constructed using multivariable logistic regression. External validation of the model's predictive performance was conducted, and the findings were elucidated through visualization techniques.

Results

The incidence rates of PI in the training, testing, and external validation cohorts were 11.8%, 12.1%, and 17.5%, respectively. We identified 11 predictive factors associated with PI following CS: plateau pressure [odds ratio (OR), 1.133; 95% confidence interval (CI), 1.111-1.157], lactate level (OR, 1.131; 95% CI, 1.067-1.2), Charlson Comorbidity Index (OR, 1.166; 95% CI, 1.115-1.219), Sequential Organ Failure Assessment score (OR, 1.096; 95% CI, 1.061-1.132), central venous pressure (OR, 1.052; 95% CI, 1.033-1.073), anion gap (OR, 1.075; 95% CI, 1.043-1.107), positive end-expiratory pressure (OR, 1.087; 95% CI, 1.047-1.129), vasopressor usage (OR, 1.521; 95% CI, 1.23-1.879), Visual Analog Scale score (OR, 0.928; 95% CI, 0.893-0.964), pH value (OR, 0.757; 95% CI, 0.629-0.913), and blood urea nitrogen level (OR, 1.011; 95% CI, 1.003-1.02). The model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI, 0.840-0.865) in the training cohort, 0.867 (95% CI, 0.853-0.882) in the testing cohort, and 0.704 (95% CI, 0.679-0.727) in the external validation cohort.

Conclusions

Through multicenter internal and external validation, our model, which integrates early ICU data and preoperative information, exhibited outstanding discriminative capability. This integration allows for the accurate assessment of PI risk in the initial phases following CS, facilitating timely interventions to mitigate adverse outcomes.

Predictive factors for lung metastasis in pediatric differentiated thyroid cancer: a clinical prediction study

OBJECTIVES

The objective of this study was to develop and evaluate the efficacy of a nomogram for predicting lung metastasis in pediatric differentiated thyroid cancer.

METHODS

The SEER database was utilized to collect a dataset consisting of 1,590 patients who were diagnosed between January 2000 and December 2019. This dataset was subsequently utilized for the purpose of constructing a predictive model. The model was constructed utilizing a multivariate logistic regression analysis, incorporating a combination of least absolute shrinkage feature selection and selection operator regression models. The differentiation and calibration of the model were assessed using the C-index, calibration plot, and ROC curve analysis, respectively. Internal validation was performed using a bootstrap validation technique.

RESULTS

The results of the study revealed that the nomogram incorporated several predictive variables, namely age, T staging, and positive nodes. The C-index had an excellent calibration value of 0.911 (95 % confidence interval: 0.876-0.946), and a notable C-index value of 0.884 was achieved during interval validation. The area under the ROC curve was determined to be 0.890, indicating its practicality and usefulness in this context.

CONCLUSIONS

This study has successfully developed a novel nomogram for predicting lung metastasis in children and adolescent patients diagnosed with thyroid cancer. Clinical decision-making can be enhanced by assessing clinicopathological variables that have a significant predictive value for the probability of lung metastasis in this particular population.