Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis

Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease

OBJECTIVE

Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model incorporating machine learning (ML) for assessing kidney fibrosis severity using biochemical markers.

METHODS

This multi-center retrospective study included 598 patients with kidney fibrosis from four hospitals. A training cohort of 360 patients from Shanghai Tongji Hospital was used to develop a predictive nomogram and ML model, with fibrosis severity classified as mild or moderate-to-severe based on Banff scores. Logistic regression identified key predictors, which were incorporated into a nomogram and ML model. An external validation cohort of 238 patients from three additional hospitals was used for model evaluation.

RESULTS

Serum creatinine (Scr), estimated glomerular filtration rate (eGFR), parathyroid hormone (PTH), brain natriuretic peptide (BNP), and sex were identified as independent predictors of kidney fibrosis severity. The nomogram demonstrated superior discriminative ability in the training cohort (AUC: 0.89, 95% CI: 0.85-0.92) compared to eGFR (AUC: 0.83, 95% CI: 0.78-0.87) and Scr (AUC: 0.87, 95% CI: 0.83-0.91). Among ML models, the Random Forest (RF) model achieved the highest AUC (0.98). In external validation, the nomogram and RF models maintained robust performance with AUCs of 0.86 and 0.79, respectively.

CONCLUSION

This study presents a validated, noninvasive, multi-center Scr-based machine learning model for assessing kidney fibrosis severity in CKD. The integration of a clinical nomogram and ML approach offers a novel, practical alternative to biopsy for dynamic fibrosis evaluation.

GIMAP8 could serve as a potential prognostic factor for lung adenocarcinoma and is closely related to immunity

GTPase IMAP family member 8 (GIMAP8) plays a key role in pathophysiology of several malignancies. The objective of this current research endeavor was to investigate the prognosis value of GIMAP8 in lung adenocarcinoma and examine how it relates to immunity. Expression profiles associated with GIMAP8 and related clinical details were acquired from The Cancer Genome Atlas database, and we conducted survival analysis, enrichment analysis and immune infiltration studies. Additionally, we evaluated the effect of GIMAP8 on radiation resistance of tumor by in vivo and in vitro experiments. Our results showed that lung adenocarcinoma tumor tissues exhibited lower GIMAP8 levels compared to nearby normal tissues. Furthermore, decreased GIMAP8 expression strongly correlated with poorer OS. The expression of GIMAP8 is closely related to the formation of radiation resistance in tumor cells. GSEA identified multiple signaling pathways linked to GIMAP8, including immune-related, chemokine, cell adhesion molecule, and NF-κB signaling pathways. GIMAP8 expression strongly correlated with the expression of immune checkpoint molecules, tumor mutational burden, tumor neoantigen burden, immune cells, and tumor immune microenvironment. GIMAP8 was found to have an inhibitory effect on lung adenocarcinoma and was closely related to the immune response. Moreover, GIMAP8 may also influence radiation resistance in tumors.

A Nomogram for Predicting the Risk of Death in Patients with Prolonged Hospital Stays in Internal Medicine Wards: A Retrospective Study

Objective

Prolonged hospital length of stay (PLOS) is associated with adverse outcomes, including increased healthcare costs, higher risk of complications, and increased mortality. This study aimed to investigate the relationship between PLOS and mortality among patients hospitalized in internal medicine wards and to develop a nomogram to predict the risk of death in this patient population.

Methods

This retrospective study included patients hospitalized for more than 30 days in internal medicine wards between January 1, 2022, and December 31, 2022. Multivariate logistic regression analysis was used to identify independent risk factors for in-hospital mortality. The nomogram was constructed based on the independent factors. Calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of the nomogram, and decision curve analysis (DCA) was conducted to assess its clinical utility.

Results

A total of 1042 patients were included in this study, resulting in a mortality rate of 10.17%. Multivariate logistic regression analysis showed that age (OR=1.043, 95% CI: 1.026-1.061, P<0.001), tumor (OR=2.274, 95% CI: 1.441-3.589, P<0.001), blood transfusion (OR=4.667, 95% CI: 2.932-7.427, P<0.001), ADL score (OR=0.966, 95% CI: 0.952-0.981, P<0.001) and MNA-SF score (OR=0.825, 95% CI: 0.760-0.895, P<0.001) as independent risk factors for mortality among patients hospitalized in internal medicine wards. The nomogram constructed using these factors demonstrated well discriminatory ability, with an AUC of 0.803 (95% CI: 0.761-0.846). Decision curve analysis further validated the clinical utility of the nomogram, highlighting its potential to improve risk assessment and guide clinical decision-making.

Conclusion

This nomogram effectively evaluates the risk of death for prolonged hospitalization of patients in internal medicine wards and holds significant potential for promotion in clinical practice.

Development and validation of a prognostic model for post-surgical overall survival in Asian colon cancer patients: a real-world population-based study

Objective

This study aimed to identify the determinants of postoperative overall survival in Asian patients with colon cancer and to establish a prognostic nomogram model.

Methods

The study included colon cancer cases diagnosed between 2010 and 2015, sourced from the SEER database as well as an external cohort from Yixing No.2 People's Hospital. Records with incomplete data on predetermined variables were excluded. The SEER dataset of eligible Asian postoperative colon cancer cases was split into a training set and a validation set with a 7:3 ratio. Prognostic factors affecting overall survival were identified using univariate and multivariate Cox regression analyses on the training set. A prognostic nomogram was developed with the R software package, and its predictive accuracy was evaluated in training, validation and external cohorts using ROC curves and calibration plots. Concordance index (C-index) and area under curves (AUCs) were also calculated, while decision curve analysis (DCA) was performed to examine the clinical utility.

Results

Based on the criteria, 8738 cases from the SEER database were deemed suitable for analysis, and were divided into a training set (6118 cases) and a validation set (2620 cases) with a 7:3 ratio. An external cohort consisting of 73 cases with colon cancer was collected for external validation. The Cox regression analysis revealed that factors such as age, gender, marital status, histological type, grade classification, AJCC_T stage, AJCC_N stage, AJCC_M stage, CEA levels, and chemotherapy significantly influenced OS (P<0.05). These factors were incorporated into the nomogram, which demonstrated a C-index of 0.775 (95% CI: 0.766-0.784) for predicting OS in the training set, a C-index of 0.774 (95% CI: 0.760-0.787) in the validation set, and a C-index of 0.763 (95% CI: 0.698-0.828) in the external cohort. The nomogram was validated with good accuracy and clinical utility across three datasets.

Conclusion

This study identified several independent prognostic factors influencing the postoperative overall survival of Asian colon cancer patients, including age, gender, marital status, histological type, grade classification, AJCC_T, AJCC_N, and AJCC_M stages, CEA levels, and chemotherapy. The constructed prognostic model showed good discrimination and accuracy, offering clinicians an individualized tool for survival predictions.

Individualized estimation of conditional survival for patients with spinal chordoma

Background

Unlike traditional survival analysis methods, conditional survival (CS) provides enhanced insight by offering a personalized prognosis estimation as time advances for tumor patients. This study aimed to estimate CS and devised a novel CS-nomogram for real-time prediction of 10-year CS for patients with spinal chordoma.

Methods

Patients diagnosed with spinal chordoma from 2000 to 2019, as documented in the Surveillance, Epidemiology, and End Results (SEER) database, were included in this study. CS represents the likelihood of surviving an additional y years given that the patient has already survived x years. It is computed using the equation CS(x|y) = S(x + y)/S(x), where S(x) denotes the patient's survival rate at x years. The univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO) analysis and best subset regression (BSR) methods were employed for variable selection. Based on these selected factors, the CS-based nomogram and a risk classification system were developed. Finally, several approaches were used to validate the performance of our model.

Results

Between 2000 and 2019, the SEER database identified 730 patients with spinal chordoma, distributed into 510 in the training group and 220 in the validation group. CS analysis showed that patients experienced a gradual augmentation in their 10-year survival rates over the course of each additional year post-diagnosis. We also successfully created a CS-based nomogram model for forecasting 3-, 5-, and 10-year overall survival, along with 10-year CS. The CS-based nomogram incorporating age, tumor size, tumor extension, multiple primary tumors, and surgery demonstrated robust predictive capabilities. Moreover, a novel risk classification system was constructed to aid in tailored management strategies and personalized treatment decisions for spinal chordoma patients.

Conclusions

In contrast to traditional survival assessment methods, our analysis of CS yielded more dynamic and real-time outcomes for spinal chordoma patients. Via our CS-based nomogram model and risk classification system, we have provided more precise prognostic insights for these patients, aiding in treatment planning and follow-up strategy formulation in clinical settings.

Nomogram based on burn characteristics and the National Early Warning Score to predict survival in severely burned patients

BACKGROUND

Extensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients.

METHODS

A retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of Changhai Hospital, the First Affiliated Hospital of Naval Medical University. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling.

RESULTS

The results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, inhalation tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923(95 % CI 0.892-0.953), compared to NEWS which had a C-index of 0.699 (95 % CI 0.628-0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group.

CONCLUSION

This study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

Impact of serum lipid on recurrence of uterine fibroids: a single center retrospective study

BACKGROUND

We aimed to analyze the correlation between serum lipid levels [total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C)] and recurrence after uterine fibroids (UF) resection, and explore the predictive value of serum lipid levels in determining recurrence after myomectomy.

METHODS

In this retrospective cohort study, 323 patients undergoing first myomectomy who came from Li Huili Hospital, Ningbo Medical Center between December 2019 and January 2023 were included. The primary endpoint was the recurrence of UF within 12 months following surgery. Univariate and multivariate logistic regression analyses were adopted to evaluate the association between four serum lipid parameters and the risk of UF recurrence. All included patients were randomly assigned to the training group for nomogram development and the testing group for nomogram validation, with a ratio of 7:3. Receiver operator characteristic, calibration curves, and decision curve analysis were used to assess the predicting performance of constructed nomograms.

RESULTS

Totally, 98 developed the recurrence of UF within 12 months following surgery. Multivariate logistic regression analyses indicated that high levels of TC [odds ratio (OR) = 9.98, 95% confidence interval (CI): 4.28-23.30], LDL-C (OR = 11.31, 95% CI: 4.66-27.47) and HDL-C (OR = 2.37, 95% CI: 1.21-4.64) were associated with recurrence of UF risk. The association between TG level and UF recurrence risk did not statistical significance (P > 0.05). Four online prediction nomograms by integrating serum lipid levels and clinical features for predicting the risk of recurrence of UF were developed (TC-model, TG-model, LDL-C-model and HDL-C-model). Through verification, these models may have good prediction performance for predicting the recurrence of UF risk.

CONCLUSION

This study developed and validated prediction nomograms for predicting the risk of UF recurrence. These nomograms can provide individual risk assessment for UF recurrence.

Nomogram based on the log odds of negative lymph node/T stage can predict the prognosis of patients with colorectal cancer: a retrospective study based on SEER database and external validation in China

OBJECTIVES

This study investigated the prognostic role of log odds of negative lymph node/T stage (LONT) and established a nomogram based on LONT to predict the prognosis in colorectal cancer (CRC) patients.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

We enrolled 80 518 CRC patients from the Surveillance, Epidemiology and End Results database between 2010 and 2015. The dataset was split into a training cohort (56 364 patients) and a validation cohort (24 154 patients) at a ratio of 7:3. Furthermore, 500 CRC patients who underwent surgery in the Tenth Affiliated Hospital of Southern Medical University between 1 January 2017 and 20 December 2018, were recruited as the external validation set.

OUTCOME MEASURES

1-, 3- and 5-year cancer-specific survival (CSS).

METHODS

The univariate and multivariate Cox regression analyses were carried out to identify the significant independent prognostic factors of CSS. A nomogram was established based on LONT to predict the prognosis. The performance of the nomogram was comprehensively assessed via the time-dependent receiver operating characteristic curve, concordance index (C-index), calibration curve and decision curve analysis (DCA) comprehensively. Moreover, Kaplan-Meier curves were performed to assess the CSS of the three risk subgroups.

RESULT

LONT was a significant independent prognostic factor for CSS (LONT1 vs LONT2, HR=0.670, 95% CI 0.642 to 0.698, p<0.001; LONT1 vs LONT3, HR=0.443, 95% CI 0.420 to 0.467, p<0.001). LONT, age, sex, race, subsite, differentiation, histology, tumour size, T stage, N stage, M stage and chemotherapy were included in the nomogram. The 1-, 3- and 5-year survival area under the curve were 0.856, 0.862 and 0.852, respectively. The C-index of the model was 0.809 (95% CI 0.825 to 0.839) in the model. The calibration curve and DCA verified the favourable predictive performance and clinical application of the nomogram.

CONCLUSION

CRC patients with a high LONT had a low incidence of CSS. The nomogram based on LONT could effectively predict the CSS of CRC.

Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning

BACKGROUND

Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil-lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in stage II/III colorectal cancer remains unclear. This study developed a nomogram to predict long-term prognosis for these patients.

METHODS

This retrospective study included 1540 patients (training set) from the First Affiliated Hospital of Kunming Medical University and 152 patients (testing set) from The Honghe Third People's Hospital. Cox regression identified independent prognostic factors, and machine learning established predictive models. Model performance was evaluated by the C-index, area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

In the training set, a total of 1540 patients with stage II/III colorectal cancer were included. More than 70 years old (HR = 1.830, p = 0.000); SIS = 2 (HR = 1.693, p = 0.002); Preoperative CEA more than 5 ng/mL (HR = 1.614, p = 0.000); and Moderately differentiated (HR = 1.438, p = 0.011); or Low/undifferentiated (HR = 2.126, p = 0.000); The pN1 (HR = 2.040, p = 0.000) and pN2 (HR = 3.297, p = 0.000) stages were considered independent prognostic risk factors of stage II/III colorectal cancer. Negative perineural invasion (HR = 0.733, p = 0.014) and NLR less than 4 (HR = 0.696, p = 0.022) were considered independent prognostic protective factors of stage II/III colorectal cancer. A nomogram was established based on SIS, NLR, and the clinicopathological results for predicting and validating the overall survival in the training and testing sets. The C-index of the training set was 0.746, and the C-index of the testing set was 0.708, indicating the high prediction efficiency of the nomogram.

CONCLUSIONS

A nomogram combining SIS, NLR, and clinicopathological factors provides an effective, cost-efficient tool for predicting the prognosis of stage II/III colorectal cancer. Future studies will validate its long-term predictive performance in larger, multicenter cohorts.

Development and validation of a prognostic nomogram including inflammatory indicators for overall survival in hepatocellular carcinoma patients treated primarily with surgery or loco-regional therapy: A single-center retrospective study

Hepatocellular carcinoma (HCC) is among the most prevalent malignant tumors, but the current staging system has limited efficacy in predicting HCC prognosis. The authors sought to develop and validate a nomogram model for predicting overall survival (OS) in HCC patients primarily undergoing surgery or loco-regional therapy. Patients diagnosed with HCC from January 2017 to June 2023 were enrolled in the study. The data were randomly split into a training cohort and a validation cohort. Utilizing univariate and multivariate Cox regression analyses, independent risk factors for OS were identified, and a nomogram model was constructed to predict patient survival. Therapy, body mass index, portal vein tumor thrombus, leukocyte, γ-glutamyl transpeptidase to platelet ratio, monocyte to lymphocyte ratio, and prognostic nutritional index were used to build the nomogram for OS. The nomogram demonstrated strong predictive ability, with high C-index values (0.745 for the training cohort and 0.650 for the validation cohort). ROC curves, calibration plots, and DCA curves all indicated satisfactory performance of the nomogram. Kaplan-Meier curve analysis showed a significant difference in prognosis between patients in the low- and high- risk groups. This nomogram provides precise survival predictions for HCC patients and helps identify individuals with varying prognostic risks, emphasizing the need for individualized follow-up and treatment plans.

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients

BACKGROUND

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.

METHODS

A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor.

RESULTS

There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively.

CONCLUSION

We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Nomogram model for predicting long-term survival in esophageal cancer patients with metastasis after treatment: a SEER-based study

Background

There is a large variance in the long-term survival of esophageal cancer (EC) patients with metastasis after treatment. This study was designed to analyze long-term survival of metastatic EC patients after surgery, radiotherapy and chemotherapy.

Methods

A retrospective cohort of EC patients with metastasis received surgery, radiotherapy and chemotherapy from 2004 to 2015 was obtained from the Surveillance, Epidemiology and End Results (SEER) database. Univariate Cox and complete subset regression analyses were performed to select prognostic factors. Nomograms were established to predict 3-, 5-, and 8-year overall survival (OS), and their performance was evaluated by receiver operating characteristic (ROC) curve and calibration curve.

Results

Age at diagnosis [hazard ratio (HR): 1.01; 95% confidence interval (CI): 1.00, 1.02; P=0.04], EC of other sites (HR: 1.78; 95% CI: 1.29, 2.45; P<0.001), lymph node involvement (HR: 1.37; 95% CI: 1.08, 1.37; P=0.009), and poorly differentiated or undifferentiated (grade III or IV) (HR: 1.39; 95% CI: 1.20, 1.76; P=0.006) was the independent risk factors for poor OS in EC patients. Female (HR: 0.58; 95% CI: 0.38, 0.88; P=0.01) showed reduced risks of showing poor OS compared with male population. The established nomograms based on these predictors showed satisfactory discrimination efficacy for predicting 3-, 5-, and 8-year OS in metastatic EC patients after treatment.

Conclusions

The nomograms showed good efficacy in predicting 3-, 5-, and 8-year OS among metastatic EC patients after surgery, radiotherapy and chemotherapy.

A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer

OBJECTIVES

To propose a nomogram-based survival prediction model for esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy using pretreatment computed tomography (CT), positron emission tomography (PET) radiomics and dosiomics features, and common clinical factors.

METHODS

Radiomics and dosiomics features were extracted from CT and PET images and dose distribution from 2 institutions. The least absolute shrinkage and selection operator (LASSO) with logistic regression was used to select radiomics and dosiomics features by calculating the radiomics and dosiomics scores (Rad-score and Dos-score), respectively, in the training model. The model was trained in 81 patients and validated in 35 patients at Center 1 using 10-fold cross validation. The model was externally tested in 26 patients at Center 2. The predictive clinical factors, Rad-score, and Dos-score were identified to develop a nomogram model.

RESULTS

Using LASSO Cox regression, 13, 11, and 19 CT, PET-based radiomics, and dosiomics features, respectively, were selected. The clinical factors T-stage, N-stage, and clinical stage were selected as significant prognostic factors by univariate Cox regression. In the external validation cohort, the C-index of the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were 0.74, 0.82, and 0.92, respectively. Significant differences in overall survival (OS) in the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were observed between the high- and low-risk groups (P = 0.019, 0.038, and 0.014, respectively).

CONCLUSION

The dosiomics features have a better predicter for OS than CT- and PET-based radiomics features in ESCC treated with radiotherapy.

CLINICAL RELEVANCE STATEMENT

The current study predicted the overall survival for esophageal squamous cell carcinoma patients treated with definitive chemoradiotherapy. The dosiomics features have a better predicter for overall survival than CT- and PET-based radiomics features.

Nomogram Predicts Prognostic Factors for Head and Neck Cutaneous Melanoma: A Population-Based Analysis

BACKGROUND

The head and neck cutaneous melanoma (HNCM) accounts for 20% of newly diagnosed melanoma. Research on prognostic models for their survival yet remains largely unexplored. This study employed a nomogram approach to develop and validate a predictive model for both overall survival (OS) and disease-specific survival (DSS) in patients with HNCM.

METHODS

This study analyzed the HNCM patients diagnosed between 2004 and 2014 from Surveillance, Epidemiology, and End Results database. To identify independent prognostic factors for HNCM, we integrated results from univariate Cox regression analysis, random survival forests, and LASSO regression with cross-validation. A nomogram was designed and validated based on the identified characteristics to predict the 3-, 5-, and 8-year OS and DSS of patients with HNCM.

RESULTS

Age, Stage, Ulceration, Thickness, Chemotherapy, lymph node metastasis, and Radiation were identified as independent prognostic factors. The nomogram achieved a satisfactory performance with C-indices of 0.824(DSS) and 0.757(OS) in the training cohort and 0.827(DSS) and 0.749(OS) in the validation cohort, respectively. The area under the curves for the OS at 3, 5, and 8 years were 0.789, 0.788, and 0.794 for the training cohort, and 0.778, 0.776, and 0.795 for the validation cohort, respectively. For DSS, the area under the curves at 3, 5, and 8 years were 0.859, 0.842, and 0.828 in the training cohort, and 0.864, 0.844, and 0.834 in the validation cohort, respectively. The calibration curve showed that there was a strong correlation between the observed outcomes and the predicted survival probability.

CONCLUSIONS

This study established and validated predictive nomograms for HNCM patients with robust predictive performance.

Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning

BACKGROUND

The 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox regression to predict the prognostic value of tumor deposits in NMLP-CA.

METHODS

Patient data from the SEER registry (2010-2019) was used to develop CSS nomograms based on prognostic factors identified via multivariate Cox regression. Model performance was evaluated by c-index, dynamic calibration, and Schmid score. Shapley additive explanations (SHAP) were used to explain the selected models.

RESULTS

The study included 16,548 NMLP-CA patients, randomized 7:3 into training (n = 11,584) and test (n = 4964) sets. Multivariate Cox analysis identified TD, age, marital status, primary site, grade, pT stage, and pN stage as prognostic for cancer-specific survival (CSS). In the test set, the gradient boosting machine (GBM) model achieved the best C-index (0.733) for CSS prediction, while the Cox model and GAMBoost model optimized dynamic calibration(6.473) and Schmid score (0.285), respectively. TD ranked among the top 3 most important features in the models, with increasing predictive significance over time.

CONCLUSIONS

Positive tumor deposit status confers worse prognosis in NMLP-CA patients. Tumor deposits may confer higher TNM staging. Furthermore, TD could play a more significant role in the staging system.

Development and validation of a prediction model for hypoproteinemia after traumatic spinal cord injury: A multicenter retrospective clinical study

A multicenter retrospective analysis of conventionally collected data. To identify the potential causes of hypoproteinemia after traumatic spinal cord injury (TSCI) and provide a diagnostic model for predicting an individual likelihood of developing hypoproteinemia. Hypoproteinemia is a complication of spinal cord injury (SCI), an independent risk factor for respiratory failure in elderly patients with SCI, and a predictor of outcomes in patients with cervical SCI. Few nomogram-based studies have used clinical indicators to predict the likelihood of hypoproteinemia following TSCI. This multicenter retrospective clinical analysis included patients with TSCI admitted to the First Affiliated Hospital of Guangxi Medical University, Wuzhou GongRen Hospital, and Dahua Yao Autonomous County People Hospital between 2016 and 2020. The data of patients from the First Affiliated Hospital of Guangxi Medical University were used as the training set, and those from the other 2 hospitals were used as the validation set. All patient histories, diagnostic procedures, and imaging findings were recorded. To predict whether patients with TSCI may develop hypoproteinemia, a least absolute shrinkage and selection operator regression analysis was conducted to create a nomogram. The model was validated by analyzing the consequences using decision curve analysis, calibration curves, the C-index, and receiver operating characteristic curves. After excluding patients with missing data, 534 patients were included in this study. Male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, urinary tract infection (UTI), hyponatremia, fever, hypotension, and tracheostomy were identified as independent risk factors of hypoalbuminemia. A simple and easy-to-replicate clinical prediction nomogram was constructed using these factors. The area under the curve was 0.728 in the training set and 0.881 in the validation set. The predictive power of the nomogram was satisfactory. Hypoalbuminemia after TSCI may be predicted using the risk factors of male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, UTI, hyponatremia, fever, hypotension, and tracheostomy.

The construction of a nomogram to predict the prognosis and recurrence risks of UPJO

Objective

This study was conducted to explore the risk factors for the prognosis and recurrence of ureteropelvic junction obstruction (UPJO).

Methods

The correlation of these variables with the prognosis and recurrence risks was analyzed by binary and multivariate logistic regression. Besides, a nomogram was constructed based on the multivariate logistic regression calculation. After the model was verified by the C-statistic, the ROC curve was plotted to evaluate the sensitivity of the model. Finally, the decision curve analysis (DCA) was conducted to estimate the clinical benefits and losses of intervention measures under a series of risk thresholds.

Results

Preoperative automated peritoneal dialysis (APD), preoperative urinary tract infection (UTI), preoperative renal parenchymal thickness (RPT), Mayo adhesive probability (MAP) score, and surgeon proficiency were the high-risk factors for the prognosis and recurrence of UPJO. In addition, a nomogram was constructed based on the above 5 variables. The area under the curve (AUC) was 0.8831 after self cross-validation, which validated that the specificity of the model was favorable.

Conclusion

The column chart constructed by five factors has good predictive ability for the prognosis and recurrence of UPJO, which may provide more reasonable guidance for the clinical diagnosis and treatment of this disease.

Prognostic model for predicting the survival benefit of adjuvant chemotherapy for elderly patients with stage II colon cancer: a population-based study

OBJECTIVES

Adjuvant chemotherapy benefits in elderly patients with stage II colon cancer (CC) remain controversial. We aimed to construct a nomogram to estimate the chemotherapy survival benefits in elderly patients.

METHODS

The training and testing cohort were patients with stage II CC older than 70 years from the Surveillance, Epidemiology, and End Results (SEER) database, while the external validation cohort included patients from the National Cancer Center (NCC). Cox proportional hazard models were used to determine the covariates associated with overall survival (OS). Using the risk factors identified by Cox proportional hazards regression, a nomogram was developed to predict OS. Nomogram precision was assessed using receiver operating characteristic and calibration curves.

RESULTS

The present study recruited 42 097 and 504 patients from the SEER database and NCC, respectively. The OS of patients who underwent surgery plus adjuvant chemotherapy was considerably longer than patients who underwent surgery alone. The nomogram included variables related to OS, including age, year of diagnosis, sex, AJCC T stage, tumor location, tumor size, harvested lymph nodes, and chemotherapy. According to the nomogram score, the elderly patients were separated into high- and low-risk groups, with high-risk group nomogram scores being greater than the median value, and vice versa. Patients in the high-risk group witnessed worse prognosis and were more likely to benefit from postoperative chemotherapy.

CONCLUSION

This nomogram can be regarded as a useful clinical tool for assessing the potential adjuvant chemotherapy benefits and for predicting survival in elderly patients with stage II CC.

Preoperative blood markers and intra-abdominal infection after colorectal cancer resection

BACKGROUND

Colorectal cancer (CRC) has one of the highest morbidity and mortality rates among digestive tract tumors. Intra-abdominal infection (IAI) is a common postoperative complication that affects the clinical outcomes of patients with CRC and hinders their rehabilitation process. However, the factors influencing abdominal infection after CRC surgery remain unclear; further, prediction models are rarely used to analyze preoperative laboratory indicators and postoperative complications.

AIM

To explore the predictive value of preoperative blood markers for IAI after radical resection of CRC.

METHODS

The data of 80 patients who underwent radical resection of CRC in the Anorectal Surgery Department of Suzhou Hospital affiliated with Anhui Medical University were analyzed. These patients were categorized into IAI (n = 15) and non-IAI groups (n = 65) based on whether IAI occurred. Influencing factors were compared; general data and laboratory indices of both groups were identified. The relationship between the indicators was assessed. Further, a nomogram prediction model was developed and evaluated; its utility and clinical applicability were assessed.

RESULTS

The risk factors for IAI after radical resection of CRC were neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and carcinoembryonic antigen (CEA) levels. NLR was correlated with PLR and SII (r = 0.604, 0.925, and 0.305, respectively), while PLR was correlated with SII (r = 0.787). The nomogram prediction model demonstrated an area under the curve of 0.968 [95% confidence interval (CI): 0.948-0.988] in the training set (n = 60) and 0.926 (95%CI: 0.906-0.980) in the validation set (n = 20). The average absolute errors of the calibration curves for the training and validation sets were 0.032 and 0.048, respectively, indicating a good model fit. The decision curve analysis curves demonstrated high net income above the 5% threshold, indicating the clinical practicality of the model.

CONCLUSION

The nomogram model constructed using NLR, PLR, SII, and CEA levels had good accuracy and reliability in predicting IAI after radical resection of CRC, potentially aiding clinical treatment decision-making.

Design and clinical application of a risk prediction model for diabetic foot

OBJECTIVE

To construct and evaluate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes based on their clinical data, and to assist clinical healthcare professionals in identifying high-risk factors and developing targeted intervention measures.

METHODS

We retrospectively collected clinical data from 478 hospitalized patients with type 2 diabetes at the First Affiliated Hospital of Shantou University Medical College from January 2019 to December 2021. The patients were divided into a diabetic foot group (n=312) and a non-diabetic foot group (n=166) based on whether they had diabetic foot. The baseline data of both groups were collected. Univariate and multivariate analyses as well as logistic regression analysis were conducted to explore the risk factors for diabetic foot. A nomogram prediction model was established using the package "rms" version 4.3. The model was internally validated using the area under the receiver operating characteristic curve (AUC). Additionally, the decision curve analysis (DCA) was performed to evaluate the performance of the nomogram model.

RESULTS

The results from the logistic regression analysis revealed that being male, smoking, duration of diabetes, glycated hemoglobin, hyperlipidemia, and atherosclerosis were influencing factors for diabetic foot (all P<0.05). The AUC of the model in predicting diabetic foot was 0.804, with a sensitivity of 75.3% and specificity of 74.4%. Harrell's C-index of the nomogram prediction model for diabetic foot was 0.804 (95% CI: 0.762-0.844), with a threshold value of >0.675. The DCA findings demonstrated that the nomogram model provided a net clinical benefit.

CONCLUSION

The nomogram prediction model constructed in this study showed good predictive performance and can provide a basis for clinical workers to prevent and intervene in diabetic foot, thereby improving the overall diagnosis and treatment.

Significant Prognostic Factor at Age Cut-off of 73 Years for Advanced Ovarian Serous Cystadenocarcinoma Patients: Insights from Real-World Study

Objective

The objective of this research was to determine the age cut-off for worse prognosis and investigate age-related differentially expressed genes (DEGs) in patients with advanced ovarian serous cystadenocarcinoma (AOSC).

Methods

In this research, we included a cohort of 20,846 patients diagnosed with AOSC, along with RNA-seq data from 374 patients in publicly available databases. Then we used the X-tile software to determine the age cut-off and stratified the patients into young and old groups. We utilized propensity score matching (PSM) to balance baseline between the young and old groups. Furthermore, we conducted an enrichment analysis of DEGs between the two age groups using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology (GO) to identify dysregulated pathways. To evaluate the potential prognostic value of the DEGs, we performed survival analysis, such as Kaplan-Meier analysis and Log rank test.

Results

We stratified the patients into young group (n=16,336) and old group (n=4510) based on the cut-off age of 73 years by X-tile software. Age over 73 years was identified as an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). Next, we identified 436 DEGs and found that the neurotrophin signaling pathway and translation factor activity were associated with prognosis outcomes. Among the top 10 hub genes (RELA, NFKBIA, TRAF6, IRAK2, TAB3, AKT1, TBP, EIF2S2, MAPK10, and SUPT3H), RELA, TAB3, AKT1, TBP, and SUPT3H were found to be significantly associated with poor prognosis in old patients with AOSC.

Conclusion

Our study determined 73 years as the cutoff value for age in patients with AOSC. RELA, TAB3, AKT1, TBP, and SUPT3H were identified as age-related DEGs that could contribute to the poor prognosis of older patients with AOSC.

A predictive model for early death in elderly colorectal cancer patients: a population-based study

Purpose

The purpose of this study is to determine what variables contribute to the early death of elderly colorectal cancer patients (ECRC) and to generate predictive nomograms for this population.

Methods

This retrospective cohort analysis included elderly individuals (≥75 years old) diagnosed with colorectal cancer (CRC) from 2010-2015 in the Surveillance, Epidemiology, and End Result databases (SEER) databases. The external validation was conducted using a sample of the Chinese population obtained from the China-Japan Union Hospital of Jilin University. Logistic regression analyses were used to ascertain variables associated with early death and to develop nomograms. The nomograms were internally and externally validated with the help of the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The SEER cohort consisted of 28,111 individuals, while the Chinese cohort contained 315 cases. Logistic regression analyses shown that race, marital status, tumor size, Grade, T stage, N stage, M stage, brain metastasis, liver metastasis, bone metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for all-cause and cancer-specific early death in ECRC patients; The variable of sex was only related to an increased risk of all-cause early death, whereas the factor of insurance status was solely associated with an increased risk of cancer-specific early death. Subsequently, two nomograms were devised to estimate the likelihood of all-cause and cancer-specific early death among individuals with ECRC. The nomograms exhibited robust predictive accuracy for predicting early death of ECRC patients, as evidenced by both internal and external validation.

Conclusion

We developed two easy-to-use nomograms to predicting the likelihood of early death in ECRC patients, which would contribute significantly to the improvement of clinical decision-making and the formulation of personalized treatment approaches for this particular population.

Prognostic model for oversurvival and tumor-specific survival prediction in patients with advanced extrahepatic cholangiocarcinoma: a population-based analysis

BACKGROUND

The prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) must be determined with precision. However, the usual TNM staging system has the drawback of ignoring age, adjuvant therapy, and gender and lacks the ability to more correctly predict patient prognosis. Therefore, we determine the risk factors of survival for patients with advanced ECCA patients and developed brand-new nomograms to forecast patients with advanced ECCA's overall survival (OS) and cancer-specific survival (CSS).

METHOD

From the Epidemiology and End Results (SEER) database, patients with advanced ECCA were chosen and randomly assigned in a ratio of 6:4 to the training and validation subgroups. The cumulative incidence function (CIF) difference between groups was confirmed by applying Gray's and Fine test and competing risk analyses. Next, the cancer-specific survival (CSS) and overall survival (OS) nomograms for advanced ECCA were developed and validated.

RESULTS

In accordance with the selection criteria, 403 patients with advanced ECCA were acquired from the SEER database and then split at random into two groups: a training group (n = 241) and a validation group (n = 162). The 1-, 2-, and 3-year cancer-specific mortality rates were 58.7, 74.2, and 78.0%, respectively, while the matching mortality rates for the competition were 10.0, 13.8, and 15.0%. Nomograms were generated for estimating OS and CSS, and they were assessed using the ROC curve and the C-index. The calibration curves showed that there was a fair amount of agreement between the expected and actual probabilities of OS and CSS. Additionally, greater areas under the ROC curve were seen in the newly developed nomograms for OS and CSS when compared to the 7th AJCC staging system. The advanced ECCA patients were divided into groupings with an elevated risk and those with a low risk and the Kaplan-Meier method was used for the survival analysis, which showed that survival time was shorter in the high-risk group than in the low-risk group.

CONCLUSION

The proposed nomograms have good predictive ability. The nomograms may can help doctors determine the prognosis of patients with advanced ECCA as well as provide more precise treatment plans for them.

Development and validation of an online dynamic prognostic nomogram for incidental gallbladder adenocarcinoma patients without distant metastasis after surgery: a population-based study

Background

Gallbladder cancer is the most common malignant tumor of the biliary system, most of which is adenocarcinoma. Our study explored developing and validating a nomogram to predict overall and cancer-specific survival probabilities internally and externally for incidental gallbladder adenocarcinoma patients without distant metastasis after surgery.

Methods

Patients screened and filtered in the Surveillance, Epidemiology, and End Results (SEER) database, whose years of diagnosis between 2010 and 2015 were collected as a derivation cohort, while those between 2016 and 2019 were a temporal validation cohort. Overall survival (OS) and cancer-specific survival (CSS) were chosen as the primary and secondary endpoints of the retrospective study cohort. Potential clinical variables were selected for a Cox regression model analysis by performing both-direction stepwise selection to confirm the final variables. The performance of final nomograms was evaluated by Harrell's C statistic and Brier score, with a graphical receptor operating characteristic (ROC) curve and calibration curve.

Results

Seven variables of age, race, tumor size, histologic grade, T stage, regional lymph nodes removed, and positive regional lymph nodes were finally determined for the OS nomogram; sex had also been added to the CSS nomogram. Novel dynamic nomograms were established to predict the prognosis of incidental gallbladder adenocarcinoma patients without distant metastasis after surgery. The ROC curve demonstrated good accuracy in predicting 1-, 3-, and 5-year OS and CSS in both derivation and validation cohorts. Correspondingly, the calibration curve presented perfect reliability between the death or cancer-specific death probability and observed death or cancer-specific death proportion in both derivation and validation cohorts.

Conclusion

Our study established novel dynamic nomograms based on seven and eight clinical variables separately to predict OS and CSS of incidental gallbladder adenocarcinoma patients without distant metastasis after surgery, which might assist doctors in advising and guiding therapeutic strategies for postoperative gallbladder adenocarcinoma patients in the future.

Risk factors for distant metastasis of Chondrosarcoma in the middle-aged and elderly people

Chondrosarcoma is the second most common primary bone malignancy with the highest incidence in middle-aged and elderly people, where distant metastasis (DM) still leads to poor prognosis. The purpose of this study was to construct a nomogram for studying the diagnosis of DM in middle-aged and elderly patients with chondrosarcoma. Data on chondrosarcoma patients aged ≥ 40 years diagnosed from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The data were divided into a training set and an internal validation set according to a 7:3 ratio, and the training set data were screened for independent risk factors for DM in chondrosarcoma patients using univariate and multivariate logistic regression analysis. The screened independent risk factors were then used to build a nomogram. In addition, data from 144 patients with chondrosarcoma aged ≥ 40 years diagnosed in a tertiary hospital in China from 2012 to 2021 were collected as the external validation set. The results were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis in the training set, internal validation set, and external validation set. A total of 1462 middle-aged and elderly patients with chondrosarcoma were included, and 92 (6.29%) had DM at the time of diagnosis. Independent risk factors for DM in middle-aged and elderly patients with chondrosarcoma included being married (OR: 2.119, 95% CI: 1.094-4.105), histological type of dedifferentiated chondrosarcoma (OR: 1.290, 95% CI: 1.110-1.499), high-grade tumor (OR: 1.511, 95% CI: 1.079-2.115), T3 stage (OR: 4.184, 95% CI: 1.977- 8.858), and N1 staging (OR: 5.666, 95% CI: 1.964-16.342). The area under the receiver operating characteristic curve (AUC) was 0.857, 0.820, and 0.859 in the training set, internal validation set, and external validation set, respectively. The results of the calibration curve and decision curve analysis also confirmed that the established nomogram could accurately predict DM in middle-aged and elderly patients with chondrosarcoma. Married, histological type of dedifferentiated chondrosarcoma, high-grade tumor, T3 stage, and N1 stage are independent risk factors for DM in middle-aged and elderly chondrosarcoma patients, and clinicians should see more attention.

Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study

Background: Owing to an aging society, there has been an observed increase in the average age of patients diagnosed with hepatocellular carcinoma (HCC). Consequently, this study is centered on identifying the prognostic factors linked with early death among this elderly demographic diagnosed with HCC. Additionally, our focus extends to developing nomograms capable of predicting such outcomes. Methods: The Surveillance, Epidemiology and End Results (SEER) database underpinned this study, showcasing participants aged 75 and above diagnosed with HCC within the timeframe from 2010 to 2015. These participants were divided randomly, at a 7:3 ratio, into training and validation cohorts. Univariable and multivariable logistic regressions were applied to the training cohort in the identification of prognostic indicators of early death, forming the basis for nomogram development. To measure the efficacy of these nomograms within both cohorts, we resorted to Receiver Operating Characteristic (ROC) curves, along with GiViTI calibration belt and Decision Curve Analysis (DCA). Results: The study involved 1,163 elderly individuals diagnosed with HCC, having reported instances of 397 all-cause early deaths and 356 HCC-specific early deaths. The sample group was divided into two cohorts: a training group consisting of 815 individuals, and a validation cohort, comprised of 348 individuals. Multifactorial analysis identified grade, T-stage, surgery, radiation, chemotherapy, bone and lung metastasis as significant predictors of mortality from all causes. Meanwhile, race, grade, T-stage, surgery, radiation, chemotherapy, and bone metastasis were revealed to be estimative factors for cancer-specific mortality. Subsequently, these factors were used to develop nomograms for prediction. GiViTI calibration belt corroborated the acceptable coherence of the nomograms, DCA confirmed their valuable clinical applicability, and ROC curves evidenced satisfactory discriminative capacity within both training and validation cohorts. Conclusion: The nomograms utilized in this study proved instrumental in detecting early death among elderly individuals afflicted with HCC. This tool could potentially assist physicians in formulating individualized treatment strategies.

Prognostic nomogram for colorectal cancer patients with multi-organ metastases: a Surveillance, Epidemiology, and End Results program database analysis

BACKGROUND

A nomogram that integrates risk models and clinical characteristics can accurately predict the prognosis of individual patients. We aimed to identify the prognostic factors and establish nomograms for predicting overall survival (OS) and cause-specific survival (CSS) in patients with multi-organ metastatic colorectal cancer (CRC).

METHODS

Demographic and clinical information on multi-organ metastases from 2010 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program. Univariate and multivariate Cox analyses were used to identify independent prognostic factors that were used to develop nomograms to predict CSS and OS, and to assess the concordance index (C-index), area under the curve (AUC), and calibration curve.

RESULTS

The patients were randomly assigned to the training and validation groups at a 7:3 ratio. A Cox proportional hazards model was conducted for CRC patients to identify independent prognostic factors, including age, sex, tumor size, metastases, degree of differentiation, stage T, stage N, primary and metastasis surgery. The competing risk models employed by Fine and Gray were used to identify the risk factors for CRC. Death from other causes was treated as a competing event, and Cox models were used to identify the factors for death to identify the independent factors of CSS. By incorporating the corresponding independent prognostic factors, we established prognostic nomograms for OS and CSS. Finally, we used the C-index, ROC curve, and calibration plots to assess the utility of the nomogram.

CONCLUSIONS

Using the SEER database, we constructed a predictive model for CRC patients with multi-organ metastases. Nomograms provide clinicians with 1-, 3-, and 5-year OS and CSS predictions for CRC, allowing them to formulate appropriate treatment plans.

Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients

BACKGROUND

Colorectal cancer (CRC) is a frequent cancer worldwide with varied survival outcomes.

OBJECTIVE

We aimed to develop a nomogram model to predict the overall survival (OS) of CRC patients after surgery.

DESIGN

This is a retrospective study.

SETTING

This study was conducted from 2015 to 2016 in a single tertiary center for CRC.

PATIENTS

CRC patients who underwent surgery between 2015 and 2016 were enrolled and randomly assigned into the training (n = 480) and validation (n = 206) groups. The risk score of each subject was calculated based on the nomogram. All participants were categorized into two subgroups according to the median value of the score.

MAIN OUTCOME MEASURES

The clinical characteristics of all patients were collected, significant prognostic variables were determined by univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. The tuning parameter (λ) for LASSO regression was determined by cross-validation. Independent prognostic variables determined by multivariable analysis were used to establish the nomogram. The predictive capacity of the model was assessed by risk group stratification.

RESULTS

Infiltration depth, macroscopic classification, BRAF, carbohydrate antigen 19 - 9 (CA-199) levels, N stage, M stage, TNM stage, carcinoembryonic antigen levels, number of positive lymph nodes, vascular tumor thrombus, and lymph node metastasis were independent prognostic factors. The nomogram established based on these factors exhibited good discriminatory capacity. The concordance indices for the training and validation groups were 0.796 and 0.786, respectively. The calibration curve suggested favorable agreement between predictions and observations. Moreover, the OS of different risk subgroups was significantly different.

LIMITATIONS

The limitations of this work included small sample size and single-center design. Also, some prognostic factors could not be included due to the retrospective design.

CONCLUSIONS

A prognostic nomogram for predicting the OS of CRC patients after surgery was developed, which might be helpful for evaluating the prognosis of CRC patients.

Prognostic modeling of overall survival and analysis of K-M survival curves in patients with primary colon cancer: A SEER-based study

This study aimed to establish a validated prognostic survival column line chart by analyzing data from patients with colon cancer (CC) in the SEER database. The nomogram proposed in this study was based on the retrospective data of patients diagnosed with CC in the SEER database from 1975 to 2015. Randomly divided into training and validation sets, the nomogram was constructed using the Cox model, and the discriminatory power of the nomogram and its predictive accuracy were determined using the consistency index and associated calibration curves. In a multifactorial analysis of the main cohort, the independent factors for survival were age, sex, race, tumor stage, and tumor grade, all of which were included in the nomogram and were prognostic factors for patients with CC (P < .05). The calibration curve of the survival probability showed good agreement between the prediction of the nomogram and the actual observation. The validation calibration curve showed good correlation and agreement between predicted and observed values. Multifactorial analysis showed that the factors affecting the prognosis of patients with CC included age, sex, race, tumor-node-metastasis stage, and tumor pathological stage. The nomogram prediction model proposed in this study has high accuracy and can provide more accurate prognostic prediction and relevant reference values for assessing the postoperative survival of CC patients and guiding clinical decision-making.

Elderly patients over 80 years undergoing colorectal cancer resection: Development and validation of a predictive nomogram for survival

BACKGROUND

Surgery remains the primary treatment for localized colorectal cancer (CRC). Improving surgical decision-making for elderly CRC patients necessitates an accurate predictive tool.

AIM

To build a nomogram to predict the overall survival of elderly patients over 80 years undergoing CRC resection.

METHODS

Two hundred and ninety-five elderly CRC patients over 80 years undergoing surgery at Singapore General Hospital between 2018 and 2021 were identified from the American College of Surgeons – National Surgical Quality Improvement Program (ACS-NSQIP) database. Prognostic variables were selected using univariate Cox regression, and clinical feature selection was performed by the least absolute shrinkage and selection operator regression. A nomogram for 1- and 3-year overall survival was constructed based on 60% of the study cohort and tested on the remaining 40%. The performance of the nomogram was evaluated using the concordance index (C-index), area under the receiver operating characteristic curve (AUC), and calibration plots. Risk groups were stratified using the total risk points derived from the nomogram and the optimal cut-off point. Survival curves were compared between the high- and low-risk groups.

RESULTS

Eight predictors: Age, Charlson comorbidity index, body mass index, serum albumin level, distant metastasis, emergency surgery, postoperative pneumonia, and postoperative myocardial infarction, were included in the nomogram. The AUC values for the 1-year survival were 0.843 and 0.826 for the training and validation cohorts, respectively. The AUC values for the 3-year survival were 0.788 and 0.750 for the training and validation cohorts, respectively. C-index values of the training cohort (0.845) and validation cohort (0.793) suggested the excellent discriminative ability of the nomogram. Calibration curves demonstrated a good consistency between the predictions and actual observations of overall survival in both training and validation cohorts. A significant difference in overall survival was seen between elderly patients stratified into low- and high-risk groups (P < 0.001).

CONCLUSION

We constructed and validated a nomogram predicting 1- and 3-year survival probability in elderly patients over 80 years undergoing CRC resection, thereby facilitating holistic and informed decision-making among these patients.

A nomogram for individually predicting the overall survival in colonic adenocarcinoma patients presenting with perineural invasion: a population study based on SEER database

Background

Colonic adenocarcinoma, representing the predominant histological subtype of neoplasms in the colon, is commonly denoted as colon cancer. This study endeavors to develop and validate a nomogram model designed for predicting overall survival (OS) in patients with colon cancer, specifically those presenting with perineural invasion (PNI).

Methods

The Surveillance, Epidemiology, and End Results (SEER) database supplied pertinent data spanning from 2010 to 2015, which facilitated the randomization of patients into distinct training and validation cohorts at a 7:3 ratio. Both univariate and multivariate analyses were employed to construct a prognostic nomogram based on the training cohort. Subsequently, the nomogram's accuracy and efficacy were rigorously evaluated through the application of a concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic (ROC) curves.

Results

In the training cohorts, multivariable analysis identified age, grade, T-stage, N-stage, M-stage, chemotherapy, tumor size, carcinoembryonic antigen (CEA), marital status, and insurance as independent risk factors for OS, all with P-values less than 0.05. Subsequently, a new nomogram was constructed. The C-index of this nomogram was 0.765 (95% CI: 0.755-0.775), outperforming the American Joint Committee on Cancer (AJCC) TNM staging system's C-index of 0.686 (95% CI: 0.674-0.698). Calibration plots for 3- and 5-year OS demonstrated good consistency, while DCA for 3- and 5-year OS revealed excellent clinical utility in the training cohorts. Comparable outcomes were observed in the validation cohorts. Furthermore, we developed a risk stratification system, which facilitated better differentiation among three risk groups (low, intermediate, and high) in terms of OS for all patients.

Conclusion

In this study, we have devised a robust nomogram and risk stratification system to accurately predict OS in colon cancer patients exhibiting PNI. This innovative tool offers valuable guidance for informed clinical decision-making, thereby enhancing patient care and management in oncology practice.

A postsurgical prognostic nomogram for patients with lymph node positive rectosigmoid junction adenocarcinoma

OBJECTIVE

The definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery.

METHODS

Based on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n = 2344) and validation (n = 1004) cohorts at a ratio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan-Meier method together with the log-rank test.

RESULTS

Age, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737-0.765) and validation cohorts (0.750;0.764-0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665-0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan-Meier curves in the low and high groups showed significant difference in patient OS.

CONCLUSIONS

We established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.

Nomogram for predicting the overall survival of underweight patients with colorectal cancer: a clinical study

BACKGROUND

An underweight individual is defined as one whose Body Mass Index (BMI) is < 18.5 kg/m². Currently, the prognosis in patients with colorectal cancer (CRC) who are also underweight is unclear.

METHODS

Information on South Korean patients who underwent curative resection for CRC without distant metastasis was collected from health insurance registry data between January 2014 and December 2016. We compared the overall survival (OS) of underweight and non-underweight (BMI ≥ 18.5 kg/m²) patients after adjusting for confounders using propensity score matching. A nomogram to predict OS in the underweight group was constructed using the significant risk factors identified in multivariate analysis. The predictive and discriminative capabilities of the nomogram for predicting 3- and 5-year OS in the underweight group were validated and compared with those of the tumor, node, and metastasis (TNM) staging system in the training and validation sets.

RESULTS

A total of 23,803 (93.6%) and 1,644 (6.4%) patients were assigned to the non-underweight and underweight groups, respectively. OS was significantly worse in the underweight group than in the non-underweight group for each pathological stage (non-underweight vs. underweight: stage I, 90.1% vs. 77.1%; stage IIA, 85.3% vs. 67.3%; stage IIB/C, 74.9% vs. 52.1%; and stage III, 73.2% vs. 59.4%, P < 0.001). The calibration plots demonstrated that the nomogram exhibited satisfactory consistency with the actual results. The concordance index (C-index) and area under the receiver operating characteristic curve (AUC) of the nomogram exhibited better discriminatory capability than those of the TNM staging system (C-index, nomogram versus TNM staging system: training set, 0.713 versus 0.564, P < 0.001; validation set, 0.691 versus 0.548, P < 0.001; AUC for 3- and 5- year OS, nomogram versus TNM staging system: training set, 0.748 and 0.741 versus 0.610 and 0.601; validation set, 0.715 and 0.753 versus 0.586 and 0.579, respectively).

CONCLUSIONS

Underweight patients had worse OS than non-underweight patients for all stages of CRC. Our nomogram can guide prognostic predictions and the treatment plan for underweight patients with CRC.

Bioinformatic Exploration of Hub Genes and Potential Therapeutic Drugs for Endothelial Dysfunction in Hypoxic Pulmonary Hypertension

Hypoxic pulmonary hypertension (HPH) is a fatal chronic pulmonary circulatory disease, characterized by hypoxic pulmonary vascular constriction and remodeling. Studies performed to date have confirmed that endothelial dysfunction plays crucial roles in HPH, while the underlying mechanisms have not been fully revealed. The microarray dataset GSE11341 was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between hypoxic and normoxic microvascular endothelial cell, followed by Gene Ontology (GO) annotation/Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis, and protein-protein interaction (PPI) network construction. Next, GSE160255 and RT-qPCR were used to validate hub genes. Meanwhile, GO/KEGG and GSEA were performed for each hub gene to uncover the potential mechanism. A nomogram based on hub genes was established. Furthermore, mRNA-miRNA network was predicted by miRNet, and the Connectivity Map (CMAP) database was in use to identify similarly acting therapeutic candidates. A total of 148 DEGs were screened in GSE11341, and three hub genes (VEGFA, CDC25A, and LOX) were determined and validated via GSE160255 and RT-qPCR. Abnormalities in the pathway of vascular smooth muscle contraction, lysosome, and glycolysis might play important roles in HPH pathogenesis. The hub gene-miRNA network showed that hsa-mir-24-3p, hsa-mir-124-3p, hsa-mir-195-5p, hsa-mir-146a-5p, hsa-mir-155-5p, and hsa-mir-23b-3p were associated with HPH. And on the basis of the identified hub genes, a practical nomogram is developed. To repurpose known and therapeutic drugs, three candidate compounds (procaterol, avanafil, and lestaurtinib) with a high level of confidence were obtained from the CMAP database. Taken together, the identification of these three hub genes, enrichment pathways, and potential therapeutic drugs might have important clinical implications for HPH diagnosis and treatment.

Construction of survival prediction model for elderly esophageal cancer

Background

The purpose of this study was to analyze the clinical characteristics and prognosis of EPEC and to construct a prediction model based on the SEER database.

Methods

All EPECs from the SEER database were retrospectively analyzed. A comprehensive and practical nomogram that predicts the overall survival (OS) of EPEC was constructed. Univariate and multivariate Cox regression analysis was performed to explore the clinical factors influencing the prognosis of EPEC, and finally, the 1 -, 3 - and 5-year OS were predicted by establishing the nomogram. The discriminant and predictive ability of the nomogram was evaluated by consistency index (C-index), calibration plot, area under the curve (AUC), and receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was used to evaluate the clinical value of the nomogram.

Results

A total of 3478 patients diagnosed with EPEC were extracted from the SEER database, and the data were randomly divided into the training group (n=2436) and the validation group (n=1402). T stage, N stage, M stage, surgery, chemotherapy, radiotherapy, age, grade, and tumor size were independent risk factors for 1 -, 3 - and 5-year OS of EPEC (P< 0.05), and these factors were used to construct the nomogram prediction mode. The C-index of the validation and training cohorts was 0.718 and 0.739, respectively, which were higher than those of the TNM stage system. The AUC values of the nomogram used to predict 1-, 2-, and 3-year OS were 0.751, 0.744, and 0.786 in the validation cohorts (0.761, 0.777, 0.787 in the training cohorts), respectively. The calibration curve of 1-, 2-, and 3-year OS showed that the prediction of the nomogram was in good agreement with the actual observation. The nomogram exhibited higher clinical utility after evaluation with the 1-, 2-, and 3-year DCA compared with the AJCC stage system.

Conclusions

This study shows that the nomogram prediction model for EPEC based on the SEER database has high accuracy and its prediction performance is significantly better than the TNM staging system, which can accurately and individually predict the OS of patients and help clinicians to formulate more accurate and personalized treatment plans.

Establishment and validation of a clinicopathological prognosis model of gastroenteropancreatic neuroendocrine carcinomas

Background

Gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs) are a rare, highly malignant subset of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). However, how to predict the prognosis of GEP-NECs by clinical features is still under study. This study aims to establish and validate a nomogram model of overall survival (OS) in patients with GEP-NECs for predicting their prognosis.

Methods

We selected patients diagnosed with GEP-NECs from the Surveillance, Epidemiology, and End Results (SEER) database and two Chinese hospitals. After randomization, we divided the data in the SEER database into the train cohort and the test cohort at a ratio of 7:3 and used the Chinese cohort as the validation cohort. The Cox univariate and multivariate analyses were performed to incorporate statistically significant variables into the nomogram model. We then established a nomogram and validated it by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and the decision curve analysis (DCA) curve.

Results

We calculated the nomogram C-index as 0.797 with a 95% confidence interval (95% CI) of 0.783–0.815 in the train cohort, 0.816 (95% CI: 0.794–0.833) in the test cohort and 0.801 (95% CI: 0.784–0.827) in the validation cohort. Then, we plotted the calibration curves and ROC curves, and AUCs were obtained to verify the specificity and sensitivity of the model, with 1-, 3- and 5-year AUCs of 0.776, 0.768, and 0.770, respectively, in the train cohort; 0.794, 0.808, and 0.799 in the test cohort; 0.922, 0.925, and 0.947 in the validation cohort. The calibration curve and DCA curves also indicated that this nomogram model had good clinical benefits.

Conclusions

We established the OS nomogram model of GEP-NEC patients, including variables of age, race, sex, tumor site, tumor grade, and TNM stage. This model has good fitting, high sensitivity and specificity, and good clinical benefits.

Prognostic Nomogram of Osteocarcinoma after Surgical Treatment

Purpose

This study aimed to establish a valid prognostic nomogram for osteocarcinoma after surgical management.

Methods

Based on the SEER database, we retrieved the clinical variables of patients confirmed to have osteocarcinoma between 1975 and 2016. Then, we performed univariate and multivariate analyses and constructed a nomogram of overall survival.

Results

Multivariate analysis of the primary cohort revealed that the independent factors for survival were age, grade, pathologic stage, T stage, and surgery performed. All these factors were showed by the nomogram. The correction curve of survival probability showed that the prediction results of nomogram well agreed with the actual observation results. The C index of the nomogram used to predict survival was 0.82; the AUC of 1-year, 3-year, and 5-year survival rates in the training cohort were 0.9, 0.819, and 0.80631, respectively, indicating that the model was accurate and reliable; whether the operation was performed or not; T stage; grade; and age were the main factors affecting the survival of patients. The AUC of the validation cohort for 1 year, 3 years, and 5 years were 0.8, 0.831, and 0.80023, respectively.

Conclusion

The proposed nomogram can more accurately predict the prognosis of patients with osteocarcinoma after surgical management. This could be a potential method that services clinical work.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Childhood with Clear Cell Sarcoma of the Kidney Based on the SEER Database

Objective. Clear cell sarcoma of the kidney (CCSK) is a lethal pediatric renal malignancy with poor prognosis. A prognostic nomogram needs to be established for overall survival (OS) prediction of patients with CCSK. Methods. Eligible 2588 CCSK patients (age 0–19) diagnosed between 2000 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomized into training and validation cohorts (7 : 3). Independent prognostic factors were identified by univariate and multifactorial Cox regression analyses and used to construct a nomogram. Receiver operating characteristics (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Moreover, a risk classification system was established based on the risk scores of the nomogram. Results. Cox analyses revealed that age, combined stage, and origin were most significant prognostic factors. Based on these prognostic factors, a nomogram was established for predicting 3- and 5-year OS of patients with CCSK. The area under the ROC curve (AUC) of 3- and 5-year OS was 0.733 and 0.728 in the training cohort, corresponding to 0.69 and 0.674 in the validation cohort. The C-index of calibration curves in the training and validation cohorts was 0.724 and 0.686. DCAs indicated the clinical utility of this nomogram. A risk classification system stratified CCSK patients into three different risk cohorts. The OS time of low-, intermediate-, and high-risk patients was 76, 68, and 65 months in the training cohort, corresponding to 69.5, 66, and 72 months in the validation cohort. Conclusion. A nomogram-based risk classification system has high accuracy for the prognostic prediction of CCSK.

Constructing and Validating a Prognosis Predictive Nomogram for Cancer-Specific Survival in Rectal Cancer Patients Receiving Preoperative Radiotherapy

Background A predictive tool is required to identify the cancer-specific survival in rectal cancer (RC) patients who have opted to receive preoperative radiotherapy.Methods A database containing the data on RC patients' records of Surveillance, Epidemiology, and End Results (SEER) receiving surgery during 2000-2014 was selected. All patients received neoadjuvant radiotherapy (NR). The correlation of clinicopathological parameters was analyzed using the Chi-square test and the survival risk factors were analyzed using the Cox proportional hazards analysis (univariate and multivariate). Finally, the nomogram was developed and validated to visually represent an accurate prediction of the probability of 3- and 5-year cancer-specific survival (CSS) based on the screened variables of the cohort.Results 11,499 rectal cancer patients were included in our cohort. Patients' records were randomly allocated to either the development or validation cohorts based on an equal ratio (1:1). Performing the multivariate Cox regression analysis incorporating these variables in the development cohort determined 11 independent prognostic factors. Statistically significant differences were recorded among subgroups using log-rank tests, which confirmed the appropriateness and acceptability of factor stratifications. Then, the nomogram was constructed and its concordance index (C-index) values in the development cohort (0.720) and validation cohort (0.717) were evaluated to be higher (P<0.05) than those of the AJCC stage (0.631 and 0.633 respectively). Also, the 3-year AUC values of this nomogram were higher than those of the AJCC stage in both the development cohort (0.746 vs. 0.631) and the validation cohort (0.745 vs. 0.640). Using DCA curves, the predictive potential of the currently developed nomogram outperformed the conventional AJCC staging system.Conclusion The nomogram model might be a more reliable tool to predict prognosis accurately in rectal cancer patients receiving preoperative radiotherapy.

Development and validation of a nomogram to predict overall survival in young non-metastatic rectal cancer patients after curative resection: a population-based analysis

PURPOSE

This study aimed to establish and validate a nomogram for predicting overall survival (OS) in young non-metastatic rectal cancer (RC) patients after curative resection.

METHODS

Young RC patients (under 50 years of age) from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Those patients randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. The independent prognostic factors for OS were identified by univariate and multivariate Cox regression analysis. A nomogram model was built based on the independent prognostic variables and was evaluated by concordance index (C-index), receiver operating characteristics (ROC) curves, calibration plot, and decision curve analysis (DCA).

RESULTS

A total number of 3026 young RC patients were extracted from SEER database. OS nomogram was constructed based on race, histological type, tumor grade, T stage, N stage, carcinoembryonic antigen (CEA) level, and number of lymph nodes (LN) examined. C-index, ROC curves, calibration plot, and DCA curves presented satisfactory performance of the above nomogram in predicting the prognosis of young non-metastatic RC patients after curative resection. The nomogram can identify three subgroups of patients at different risks, which showed different prognostic outcomes both in the training cohort and validation cohort.

CONCLUSION

We successfully established a reliable and insightful nomogram to predict OS for young non-metastatic RC patients after curative resection. The nomogram may provide accurate prognosis prediction to guide individualized follow-up and treatment plans.

Prognostic factors and survival outcome of primary pulmonary acinar cell carcinoma

Purpose

The objective of our study was to investigate the epidemiologic characteristics and prognostic factors in patients with pulmonary acinar cell carcinoma (PACC).

Methods

PACC patients diagnosed between 1975 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The trend in PACC incidence was assessed using joinpoint regression software. Overall survival (OS) and disease‐specific survival (DSS) were evaluated using the Kaplan–Meier method and log‐rank test. Univariate and multivariate Cox regression analysis was performed to identify the independent prognostic factors for OS and DSS. Nomograms to predict survival possibilities were constructed based on the identified independent prognostic factors.

Results

A total of 2918 patients were identified with PACC. The mean age was 65.2 ± 8.95 years with a female to male of 1.6:1. The incidence of PACC steadily increased by an annual percentage change (APC) of 3.2% (95% CI 2.1–4.4, p < 0.05). Multivariate Cox regression analysis revealed that age, gender, race, stage, grade, tumor size, number of positive lymph nodes, surgery, and chemotherapy were independent prognostic factors for survival. Nomograms specifically for PACC were constructed to predict 1‐ and 5‐year OS and DSS possibility, respectively. The concordance index (C‐index) and calibration plots showed the established nomograms had robust and accurate performance.

Conclusion

PACC was rare but the incidence has been steadily increasing over the past four decades. Survival has improved in recent years. Surgery or chemotherapy could provide better OS and DSS. The established nomograms specifically for PACC were robust and accurate in predicting 1‐ and 5‐year OS and DSS.