Nomograms predicting survival of patients with unresectable or metastatic gastric cancer who receive combination cytotoxic chemotherapy as first-line treatment

Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery

BACKGROUND

To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery.

METHODS

A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance.

RESULTS

The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

Development of a novel staging classification for Siewert II adenocarcinoma of the esophagogastric junction after neoadjuvant chemotherapy

BACKGROUND

Stage classification for Siewert II adenocarcinoma of the esophagogastric junction (AEG) treated with neoadjuvant chemotherapy (NAC) has not been established.

AIM

To investigate the optimal stage classification for Siewert II AEG with NAC.

METHODS

A nomogram was established based on Cox regression model that analyzed variables associated with overall survival (OS) and disease-specific survival (DSS). The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test, Akaike information criterion, Harrell concordance index, time-receiver operating characteristic curve, and decision curve analysis.

RESULTS

Data from 725 patients with Siewert type II AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate analyses revealed that sex, marital status, race, ypT stage, and ypN stage were independent prognostic factors of OS, whereas sex, race, ypT stage, and ypN stage were independent prognostic factors for DSS. These factors were incorporated into the OS and DSS nomograms. Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8^th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer. Finally, a user-friendly web application was developed for clinical use.

CONCLUSION

The nomogram established specifically for patients with Siewert type II AEG receiving NAC demonstrated good prognostic performance. Validation using external data is warranted before its widespread clinical application.

Development of a novel staging classification for Siewert II adenocarcinoma of the esophagogastric junction after neoadjuvant chemotherapy

BACKGROUND

Stage classification for Siewert II adenocarcinoma of the esophagogastric junction (AEG) treated with neoadjuvant chemotherapy (NAC) has not been established.

AIM

To investigate the optimal stage classification for Siewert II AEG with NAC.

METHODS

A nomogram was established based on Cox regression model that analyzed variables associated with overall survival (OS) and disease-specific survival (DSS). The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test, Akaike information criterion, Harrell concordance index, time-receiver operating characteristic curve, and decision curve analysis.

RESULTS

Data from 725 patients with Siewert type II AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate analyses revealed that sex, marital status, race, ypT stage, and ypN stage were independent prognostic factors of OS, whereas sex, race, ypT stage, and ypN stage were independent prognostic factors for DSS. These factors were incorporated into the OS and DSS nomograms. Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer. Finally, a user-friendly web application was developed for clinical use.

CONCLUSION

The nomogram established specifically for patients with Siewert type II AEG receiving NAC demonstrated good prognostic performance. Validation using external data is warranted before its widespread clinical application.

Multiparametric MRI-Based Radiomics Signature with Machine Learning for Preoperative Prediction of Prognosis Stratification in Pediatric Medulloblastoma

RATIONALE AND OBJECTIVES

Despite advances in risk-stratified treatment strategies for children with medulloblastoma (MB), the prognosis for MB with short-term recurrence is extremely poor, and there is still a lack of evaluation of short-term recurrence risk or short-term survival. This study aimed to construct and validate a radiomics model for predicting the outcome of MB based on preoperative multiparametric magnetic resonance images (MRIs) and to provide an objective for clinical decision-making.

MATERIALS AND METHODS

The clinical and imaging data of 64 patients with MB admitted to Shenzhen Children's Hospital from December 2012 to December 2021 and confirmed by pathology were retrospectively collected. According to the 18-month progression-free survival, the cases were classified into a good prognosis group and a poor prognosis group, and all cases were divided into training group (70%) and validation group (30%) randomly. Radiomics features were extracted from MRI of each child. The consistency test, t-test, and the least absolute shrinkage and selection operator were used for feature selection. The support vector machine (SVM) and receiver operator characteristic were used to evaluate the distinguishing ability of the selected features to the prognostic groups. RAD score was calculated based on the selected features. The clinical characteristics and RAD score were included in the multivariate logistic regression, and prediction models were constructed by screening out independent influences. The radiomics nomogram was constructed, and its clinical significance was evaluated.

RESULTS

A total of 1930 radiomic features were extracted from the images of each patient, and 11 features were included in the construction of radiomics score after selected. The area under the curve (AUC) values of the SVM model in the training and validation groups were 0.946 and 0.797, respectively. The radiomics nomogram was constructed based on the training cohort, and the AUC values in the training group and the validation group were 0.926 and 0.835, respectively. The results of clinical decision curve analysis showed that a good net benefit could be obtained from the nomogram.

CONCLUSION

The radiomics nomogram established based on MRI can be used as a noninvasive predictive tool to evaluate the prognosis of children with MB, which is expected to help neurosurgeons better conduct preoperative planning and patient follow-up management.

Construction and validation of a nomogram to predict the overall survival of small cell lung cancer: a multicenter retrospective study in Shandong province, China

BACKGROUND

Patients diagnosed with small cell lung cancer (SCLC) typically experience a poor prognosis, and it is essential to predict overall survival (OS) and stratify patients based on distinct prognostic risks.

METHODS

Totally 2309 SCLC patients from the hospitals in 15 cities of Shandong from 2010 - 2014 were included in this multicenter, population-based retrospective study. The data of SCLC patients during 2010-2013 and in 2014 SCLC were used for model development and validation, respectively. OS served as the primary outcome. Univariate and multivariate Cox regression were applied to identify the independent prognostic factors of SCLC, and a prognostic model was developed based on these factors. The discrimination and calibration of this model were assessed by the time-dependent C-index, time-dependent receiver operator characteristic curves (ROC), and calibration curves. Additionally, Decision Curve Analysis (DCA) curves, Net Reclassification Improvement (NRI), and Integrated Discriminant Improvement (IDI) were used to assess the enhanced clinical utility and predictive accuracy of the model compared to TNM staging systems.

RESULTS

Multivariate analysis showed that region (Southern/Eastern, hazard ratio [HR] = 1.305 [1.046 - 1.629]; Western/Eastern, HR = 0.727 [0.617 - 0.856]; Northern/Eastern, HR = 0.927 [0.800 - 1.074]), sex (female/male, HR = 0.838 [0.737 - 0.952]), age (46-60/≤45, HR = 1.401 [1.104 - 1.778]; 61-75/≤45, HR = 1.500 [1.182 - 1.902]; >75/≤45, HR = 1.869 [1.382 - 2.523]), TNM stage (II/I, HR = 1.119[0.800 - 1.565]; III/I, HR = 1.478 [1.100 - 1.985]; IV/I, HR = 1.986 [1.477 - 2.670], surgery (yes/no, HR = 0.677 [0.521 - 0.881]), chemotherapy (yes/no, HR = 0.708 [0.616 - 0.813]), and radiotherapy (yes/no, HR = 0.802 [0.702 - 0.917]) were independent prognostic factors of SCLC patients and were included in the nomogram. The time-dependent AUCs of this model in the training set were 0.699, 0.683, and 0.683 for predicting 1-, 3-, and 5-year OS, and 0.698, 0.698, and 0.639 in the validation set, respectively. The predicted calibration curves aligned with the ideal curves, and the DCA curves, the IDI, and the NRI collectively demonstrated that the prognostic model had a superior net benefit than the TNM staging system.

CONCLUSION

The nomogram using SCLC patients in Shandong surpassed the TNM staging system in survival prediction accuracy and enabled the stratification of patients with distinct prognostic risks based on nomogram scores.

Novel predictors for livebirth delivery rate in patients with idiopathic non-obstructive azoospermia based on the clinical prediction model

Objective

To build a prediction model for live birth delivery per intracytoplasmic sperm injection (ICSI) in iNOA patients by obtaining sperm by microdissection testicular sperm extraction (mTESE).

Methods

A retrospective cohort study of 377 couples with iNOA male partners treated with 519 mTESE-ICSI cycles was conducted from September 2013 to July 2021 at the Reproductive Medical Centre of Peking University Third Hospital. Following exclusions, 377 couples with iNOA male partners treated with 482 mTESE-ICSIs were included. A prediction model for live birth delivery per ICSI cycle was built by multivariable logistic regression and selected by 10-fold cross-validation. Discrimination was evaluated by c-statistics and calibration was evaluated by the calibration slope.

Results

The live birth delivery rate per mTESE-ICSI cycle was 39.21% (189/482) in these couples. The model identified that the presence of motile sperm during mTESE, bigger testes, higher endometrial thickness on the day of human chorionic gonadotrophin (hCG) administration (ET-hCG), and higher quality embryos are associated with higher live birth delivery success rates. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the mean ROC curve was 0.71 ± 0.05 after 10-fold cross-validation, indicating that the prediction model had certain prediction precision. A calibration plot with an estimated intercept of -1.653 (95% CI: -13.403 to 10.096) and a slope of 1.043 (95% CI: 0.777 to 1.308) indicated that the model was well-calibrated.

Conclusion

Our prediction model will provide valuable information about the chances of live birth delivery in couples with iNOA male partners who have a plan for mTESE-ICSI treatment. Therefore, it can improve and personalize counseling for the medical treatment of these patients.

A nomogram for predicting recurrence-free survival of intermediate and high-risk neuroblastoma

This study aimed to confirm the independent risk factors for recurrence-free survival (RFS) in intermediate and high-risk neuroblastoma (NB) patients and set up an effective nomogram model for predicting the recurrence of NB. A total of 212 children with intermediate- and high-risk neuroblastoma, who had ever achieved complete remission (CR) or very good partial remission (VGPR) after standardized treatment in this hospital, were chosen as study objects. After retrospective analysis of the clinical data, Cox regression model was used to explore the factors related to the recurrence of neuroblastoma, to determine the variables to construct the Nomogram. The consistency index would predict the accuracy of this nomogram. RFS rate in 1-year, 3-year, 5-year, and 10-year was 0.811, 0.662, 0.639, and 0.604, respectively. Children with MYCN amplification had a higher neuron-specific enolase (NSE) value (P = 0.031) at the initial diagnosis than MYCN non-amplification. The univariate analysis predicted that increased vanillylmandelic acid (VMA) and NSE value and dehydrogenase (LDH) > 1000 U/L were important adverse factors for the recurrence of NB. Multivariate analysis demonstrated that age at diagnosis, tumor localization, MYCN state, histologic subtype, and tumor capsule were significantly associated with RFS (all P values < 0.05). Nomograms were established for predicting the recurrence of NB according to the Cox regression analysis. Internal verification by the Bootstrap method showed that the prediction of the nomogram's consistency index (C-index) was 0.824 (P = 0.023).   Conclusion: Age at diagnosis, tumor localization, MYCN state, histologic category, and tumor capsule were independent risk factors for the recurrence of NB. The nomogram model could accurately predict the recurrence of children with neuroblastoma. What is Known: • The prognoses of neuroblastoma (NB) could vary greatly due to the high heterogeneity, the 5-year survival rate of low-risk NB exceeded 90%, while the 5-year survival rate of children in the intermediate and high-risk groups was not satisfactory.. What is New: • Increased vanillylmandelic acid (VMA) and neuron-specific enolase (NSE) value, and lactate dehydrogenase (LDH)>1000U/L were important adverse factors for the recurrence of NB. • NSE value was more valuable for predicting NB recurrence.

Comparison of nomogram with random survival forest for prediction of survival in patients with spindle cell carcinoma

Purpose

Spindle cell carcinoma (SpCC) is a relatively rare tumor with an unfavorable prognosis. This study aimed to develop and validate a prediction model for the individual survival of patients with SpCC using Cox regression and the random survival forest (RSF) model.

Methods

Patients diagnosed with SpCC between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided into training and validating cohorts. Cox regression and RSF were used to identify prognostic predictors and build prediction models. A nomogram based on Cox regression was constructed to predict the 1-, 3-, and 5-year survival of patients with SpCC. Internal validation was conducted using the bootstrapping method. We evaluated the discrimination accuracy and calibration of the model using Harrell's C-index and calibration plot, respectively.

Results

Two hundred and fifty patients diagnosed with SpCC with required information were enrolled in this study. Multivariate Cox regression and RSF identified age, primary site, grade, SEER stage, tumor size, and treatment as significant prognostic predictors of SpCC. The bootstrapped and validated C-indices were 0.812 and 0.783 for nomogram, and 0.790 and 0.768 for RSF, respectively. Calibration plot of the nomogram showed an agreement between the prediction and actual observation.

Conclusions

The nomogram developed in this study is a promising tool with a simplified presentation that can easily be used and interpreted by clinicians for evaluating the survival of each patient with SpCC; its performance was comparable to that of RSF. Application of such models are needed to help oncologists identify the high-risk patients and improve clinical decision making of SpCC treatment.

A nomogram model of postoperative prognosis for metastatic lung adenocarcinoma: A study based on the SEER database

We have observed that patients with metastatic lung adenocarcinoma can obtain survival benefits from surgical resection of the primary tumor. A model was developed to evaluate the prognosis of patients. The patients with metastatic lung adenocarcinoma were identified in the Surveillance, Epidemiology, and End Results database and divided into surgery group and non-surgical group. Through Kaplan-Meier analysis, the survival rate of the non-surgical group was found to be significantly lower no matter before or after propensity score matching. One thousand one hundred and seventy surgical patients were divided into a training group and a verification group. In the training group, univariate and multivariate Cox models were used to explore the prognostic factors, and logistic regression was used to establish a nomogram based on significant predictors. In total, 12,228 patients with metastatic lung adenocarcinoma were recognized; primary tumor surgery accounted for 9.5%. After propensity score matching, the median survival time of 2 groups was significantly different. For the training group, univariate and multivariate COX analysis was conducted, and a nomogram was constructed. Acceptable agreement has been achieved between the predicted and observed survival rates, and the nomogram can divide patients with metastatic lung adenocarcinoma into different risk groups and predict their prognostic survival rate.

Establishment and validation of a novel prognostic model for non-virus-related hepatocellular carcinoma

Objective

The incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients.

Methods

Overall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan–Meier (K–M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment.

Results

According to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723–0.797) in the development cohort and 0.796 (95% CI 0.737–0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K–M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively.

Conclusion

For NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies.

Construction of a nomogram to predict the survival of metastatic gastric cancer patients that received immunotherapy

Background

Immunotherapy has shown promising results for metastatic gastric cancer (MGC) patients. Nevertheless, not all patients can benefit from anti-PD-1 treatment. Thus, this study aimed to develop and validate a prognostic nomogram for MGC patients that received immunotherapy.

Methods

Herein, MGC patients treated with anti-PD-1 between 1 October 2016 and 1 June 2022 at two separate Chinese PLA General Hospital centers were enrolled and randomly divided into training and validation sets (186 and 80 patients, respectively). The nomogram was constructed based on a multivariable Cox model using baseline variables from the training cohort. Its predictive accuracy was validated by the validation set. The consistency index (C-index) and calibration plots were used to evaluate the discriminative ability and accuracy of the nomogram. The net benefit of the nomogram was evaluated using decision curve analysis (DCA). Finally, we stratified patients by median total nomogram scores and performed Kaplan–Meier survival analyses.

Results

We developed the nomogram based on the multivariate analysis of the training cohort, including four parameters: surgery history, treatment line, lung immune prognostic index (LIPI), and platelet-to-lymphocyte ratio (PLR). The C-index of the nomogram was 0.745 in the training set. The calibration curve for 1- and 2-year survival showed good agreement between nomogram predictions and actual observations. In the validation group, the calibration curves demonstrated good performance of the nomogram, with a C-index for overall survival (OS) prediction of 0.713. The OS of patients with a score greater than the median nomogram score was significantly longer than patients with a score lower or equal to the median (p < 0.001).

Conclusion

We constructed a nomogram to predict the outcomes of MGC patients that received immunotherapy. This nomogram might facilitate individualized survival predictions and be helpful during clinical decision-making for MGC patients under anti-PD-1 therapy.

Incorporation of liver chemistry score in predicting survival of liver-involved advanced gastric cancer patients who received palliative chemotherapy

BACKGROUND

Gastric cancer liver metastasis (GCLM) patients usually accompany by abnormal serum liver function tests (LFTs) more or less; however, the prognostic value of LFTs is not fully understood. This study aimed to develop a liver chemistry score (LCS) based on LFTs and incorporate it into prognosis determination for GCLM patients who received palliative chemotherapy.

METHODS

Data were derived from hospitalized GCLM patients in two general hospitals in China. LCS was generated based on the results of LFTs by LASSO regression. Cutoff value of the score was determined by restricted cubic spline. The score was then incorporated into Cox regression analysis to construct a predictive nomogram; the model was then evaluated internally and externally by AUC of time-dependent receiver operating characteristic curves (ROC) and calibration curves.

RESULTS

Three hundred and thirty-six and 72 patients were included in development and validation cohort, respectively. LASSO regression analysis in development cohort finally reached a two-parametric LCS calculated on AST and ALP levels as 0.03343515 × ln (AST, U/L) + 0.02687997 × ln (ALP, U/L), and 0.232 was set as optimal cutoff value. Patients in low (LCS < 0.232) or high (LCS ≥ 0.232) score group experienced different survival times; median OS was 13.54 (95% CI: 11.1-15.6) months in the low LCS group and 7.3 (6.6-9.3) months in the high LCS group (p < 0.001). A nomogram including LCS and other clinical parameters was constructed and showed superior performance than model not including LCS. AUC of 6-month ROC improved from 0.647 (95% CI: 0.584-0.711) to 0.699 (0.638-0.759) in internal validation, and 0.837 (0.734-0.940) to 0.875 (0.784-0.966) in external validation.

CONCLUSIONS

Liver chemistry score is useful in determining the prognosis of gastric cancer patients with liver metastasis and may be helpful to clinicians in decision-making.

A novel prognostic model for malignant patients with Gram-negative bacteremia based on real-world research

Gram-negative bacteremia (GNB) is a common complication in malignant patients. Identifying risk factors and developing a prognostic model for GNB might improve the survival rate. In this observational and real-world study, we retrospectively analyzed the risk factors and outcomes of GNB in malignant patients. Multivariable regression was used to identify risk factors for the incidence of GNB, while Cox regression analysis was performed to identify significant prognostic factors. A prognostic model was constructed based on Cox regression analysis and presented on a nomogram. ROC curves, calibration plots, and Kaplan–Meier analysis were used to estimate the model. It comprised 1004 malignant patients with Bloodstream infection (BSI) in the study cohort, 65.7% (N = 660) acquired GNB. Multivariate analysis showed gynecologic cancer, hepatobiliary cancer, and genitourinary cancer were independent risk factors related to the incidence of GNB. Cox regression analysis raised that shock, admission to ICU before infection, pulmonary infection, higher lymphocyte counts, and lower platelet counts were independent risk factors for overall survival (OS). The OS was significantly different between the two groups classified by optimal cut-off value (log-rank, p < 0.001). Above all, a nomogram was created based on the prognostic model, which was presented on a website freely. This real-world study was concentrated on the malignant patients with GNB and proved that shock, admission to ICU before infection, pulmonary infection, higher lymphocyte counts, and lower platelet counts were related to the death of these patients. And a prognostic model was constructed to estimate the risk score of mortality, further to reduce the risk of death.

Construction and validation of prognostic nomograms for elderly patients with metastatic non-small cell lung cancer

BACKGROUND

Metastatic non-small cell lung cancer (NSCLC) is mostly seen in older patients and is associated with poor prognosis. There is no reliable method to predict the prognosis of elderly patients (≥60 years old) with metastatic NSCLC. The aim of our study was to develop and validate nomograms which accurately predict survival in this group of patients.

METHODS

NSCLC patients diagnosed between 2010 and 2015 were all identified from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed by significant clinicopathological variables (p < 0.05) selected in multivariate Cox analysis regression.

RESULTS

A total of 9584 patients met the inclusion criteria and were randomly allocated in the training (n = 6712) and validation (n = 2872) cohorts. In training cohort, independent prognostic factors included age, gender, race, grade, tumor site, pathology, T stage, N stage, radiotherapy, surgery, chemotherapy, and metastatic site (p < 0.05) for lung cancer-specific survival (LCSS) and overall survival (OS) were identified by the Cox regression. Nomograms for predicting 1-, 2-, and 3-years LCSS and OS were established and showed excellent predictive performance with a higher C-index than that of the 7th TNM staging system (LCSS: training cohort: 0.712 vs. 0.534; p < 0.001; validation cohort: 0.707 vs. 0.528; p < 0.001; OS: training cohort: 0.713 vs. 0.531; p < 0.001; validation cohort: 0.710 vs. 0.528; p < 0.001). The calibration plots showed good consistency from the predicted to actual survival probabilities both in training cohort and validation cohort. Moreover, the decision curve analysis (DCA) achieved better net clinical benefit compared with TNM staging models.

CONCLUSIONS

We established and validated novel nomograms for predicting LCSS and OS in elderly patients with metastatic NSCLC with desirable discrimination and calibration ability. These nomograms could provide personalized risk assessment for these patients and assist in clinical decision.

Occult Non-Small Cell Lung Cancer: An Underappreciated Disease

Background: The number of researches on occult non-small cell lung cancer (NSCLC) is modest. Herein, we defined the clinicopathological features, prognosis and survival outcome of this underappreciated tumor, with purpose of obtaining a clearer picture on this disease. Methods: The entire cohort was categorized into two groups (occult NSCLC and other NSCLC) and further into five groups (occult, T1, T2, T3 and T4). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was performed to identify the prognostic indicators. A nomogram and a risk-classifying system were formulated. Kaplan–Meier with Log-rank method was carried out to compare overall survival (OS) and cancer specific survival (CSS) differences between groups. Results: 59,046 eligible NSCLC cases (occult NSCLC: 1158 cases; other NSCLC: 57,888 cases) were included. Occult NSCLC accounted for 2.0% of the included cases. Multivariate analysis revealed that age, sex, tumor location, histology, grade and surgery were prognostic factors for OS. The corresponding prognostic nomogram classified occult NSCLC patients into low-risk and high-risk group, and its performance was acceptable. Survival curves demonstrated that occult NSCLC patients exhibited worse survivals than other NSCLC. In further analyses, the survival of low-risk occult NSCLC and stage T3 NSCLC were comparable, and the high-risk occult NSCLC patients still owned the worst survival rate. Conclusions: Occult NSCLC was an aggressive tumor with poor prognosis, and surgery was the preferred treatment. More attention should be paid to this overlooked disease due to no evidence of tumor imaging.

Comprehensive analysis of N6-methyladenosine regulators with the tumor immune landscape and correlation between the insulin-like growth factor 2 mRNA-binding protein 3 and programmed death ligand 1 in bladder cancer

Background

N⁶-methyladenosine (m⁶A) is one of the most abundant post-transcriptional modifications of RNA. However, there is limited information about the potential roles of m⁶A regulators in tumor immunity. Therefore, in this study, we aimed to testify the functions of m⁶A regulators in bladder cancer as well as their association with the tumor immune landscape.

Methods

We reported the variation and expression levels of m6A regulators in the TCGA database and GTEx database of bladder cancer. Clusters, risk score patterns, and nomograms were constructed to evaluate the function and prognostic value of m⁶A regulators. Furthermore, we constructed nomogram to evaluate the prognosis of the individual patients. The correlation between insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) and programmed cell death ligand 1 (PD-L1) was evaluated both in vitro and in vivo.

Results

We found that the tumor grade and DNA damage pathways were strongly correlated with distinct clusters. Furthermore, two risk score groups with six m⁶A regulators were identified using the least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analysis, which could be regarded as independent prognostic markers in patients with bladder cancer. The risk score pattern was linked to the tumor immune landscape, indicating a correlation between immune checkpoints and m⁶A regulators. Moreover, an m⁶A regulator, IGF2BP3, was found to be highly expressed in the tumor samples, regulating both the total and membrane-bound PD-L1 expression levels.

Conclusions

The results of this study revealed that the m⁶A clusters and patterns play crucial roles in the regulation of tumor immunity, which may be used to develop comprehensive treatment strategies for the management of bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02456-7.

A Nomogram for Predicting the Cancer-Specific Survival of Patients with Initially Diagnosed Metastatic Gastric Cancer

Background:

There are few models to predict the survival of patients of different ethnicities initially diagnosed with metastatic gastric cancer (mGC). Therefore, the aim of this study was to construct a nomogram to predict the cancer-specific survival (CSS) of these patients.

Methods:

Data for 994 patients initially diagnosed with mGC between 2000 and 2013 were extracted from the Surveillance, Epidemiology, and End Results database. Patients were randomly classified into a training (n = 696) or internal validation (n = 298) cohort, and a cohort of 133 patients from Fudan cohort was used for external validation. A nomogram to predict the CSS of mGC patients was derived and validated using a concordance index (C-index), calibration curves, and decision-curve analysis (DCA).

Results:

Multivariate Cox regression indicated that five factors were independent predictors of CSS: differentiation grade, T stage, N stage, metastatic site at diagnosis, and with or without chemotherapy. Thus, these factors were integrated into the nomogram model. The C-index value of the nomogram model was 0.63 (95% CI: 0.60–0.65), and those of the internal and external validation cohorts were 0.60 (95%: CI 0.55–0.64) and 0.63 (95%: CI 0.57–0.69), respectively. The calibration curves showed good consistency between the actual and predicted survival rates in both the internal and external validation cohorts. The DCA also showed the clinical utility of the nomogram model.

Conclusions:

We established a practical nomogram to predict the CSS of patients initially diagnosed with mGC. The nomogram can be used for individualized prediction of survival and to guide clinicians in making treatment decisions.

Development and validation of a prognostic scoring model for mortality risk stratification in patients with recurrent or metastatic gastric carcinoma

Background

Survival times differ among patients with advanced gastric carcinoma. A precise and universal prognostic evaluation strategy has not yet been established. The current study aimed to construct a prognostic scoring model for mortality risk stratification in patients with advanced gastric carcinoma.

Methods

Patients with advanced gastric carcinoma from two hospitals (development and validation cohort) were included. Cox proportional hazards regression analysis was conducted to identify independent risk factors for survival. A prognostic nomogram model was developed using R statistics and validated both in bootstrap and external cohort. The concordance index and calibration curves were plotted to determine the discrimination and calibration of the model, respectively. The nomogram score and a simplified scoring system were developed to stratify patients in the two cohorts.

Results

Development and validation cohort was comprised of 401 and 214 gastric cancer patients, respectively. Mucinous or non-mucinous histology, ECOG score, bone metastasis, ascites, hemoglobin concentration, serum albumin level, lactate dehydrogenase level, carcinoembryonic antigen level, and chemotherapy were finally incorporated into prognostic nomogram. The concordance indices were 0.689 (95% CI: 0.664 ~ 0.714) and 0.673 (95% CI: 0.632 ~ 0.714) for bootstrap and external validation. 100 and 200 were set as the cut-off values of nomogram score, patients in development cohort were stratified into low-, intermediate- and high-risk groups with median overall survival time 15.8 (95% CI: 12.2 ~ 19.5), 8.4 (95% CI: 6.7 ~ 10.2), and 3.9 (95% CI: 2.7 ~ 5.2) months, respectively; the cut-off values also worked well in validation cohort with different survival time in subgroups. A simplified model was also established and showed good consistency with the nomogram scoring model in both of development and validation cohorts.

Conclusion

The prognostic scoring model and its simplified surrogate can be used as tools for mortality risk stratification in patients with advanced gastric carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09079-7.

Nomogram to predict survival of patients with advanced and metastatic pancreatic Cancer

BACKGROUND

Nomograms are rarely employed to estimate the survival of patients with advanced and metastatic pancreatic cancer (PC). Herein, we developed a comprehensive approach to using a nomogram to predict survival probability in patients with advanced and metastatic PC.

METHODS

A total of 323 patients with advanced and metastatic PC were identified from the Chinese People's Liberation Army (PLA) General Hospital. A baseline nomogram was constructed using baseline variables of 323 patients. Additionally, 233 patients, whose tumors showed initial responses to first-line chemotherapy, were enrolled in the chemotherapy response-based model. 128 patients and 108 patients with advanced and metastatic PC from January 2019 to April 2021 were selected for external validating baseline model and chemotherapy response-based model. The 1-year and 2-year survival probability was evaluated using multivariate COX regression models. The discrimination and calibration capacity of the nomograms were assessed using C-statistic and calibration plots. The predictive accuracy and net benefit of the nomograms were evaluated using ROC curve and DCA, respectively.

RESULTS

In the baseline model, six variables (gender, KPS, baseline TB, baseline N, baseline WBC and baseline CA19-9) were used in the final model. In the chemotherapy response-based model, nine variables (KPS, gender, ascites, baseline N, baseline CA 19-9, baseline CEA, change in CA 19-9 level at week, change in CEA level at week and initial response to chemotherapy) were included in the final model. The C-statistics of the baseline nomogram and the chemotherapy response-based nomogram were 0.67 (95% CI, 0.62-0.71) and 0.74 (95% CI, 0.69-0.77), respectively.

CONCLUSION

These nomograms were constructed to predict the survival probability of patients of advanced and metastatic PC. The baseline model and chemotherapy response-based model performed well in survival prediction.

Development and validation of a prognostic nomogram model for Chinese patients with primary small cell carcinoma of the esophagus

BACKGROUND

Primary small cell carcinoma of the esophagus (PSCE) is a highly invasive malignant tumor with a poor prognosis compared with esophageal squamous cell carcinoma. Due to the limited samples size and the short follow-up time, there are few reports on elucidating the prognosis of PSCE, especially on the establishment and validation of a survival prediction nomogram model covering general information, pathological factors and specific biological proteins of PSCE patients.

AIM

To establish an effective nomogram to predict the overall survival (OS) probability for PSCE patients in China.

METHODS

The nomogram was based on a retrospective study of 256 PSCE patients. Univariate analysis and multivariate Cox proportional hazards regression analysis were used to examine the prognostic factors associated with PSCE, and establish the model for predicting 1-, 3-, and 5-year OS based on the Akaike information criterion. Discrimination and validation were assessed by the concordance index (C-index) and calibration curve and decision curve analysis (DCA). Histology type, age, tumor invasion depth, lymph node invasion, detectable metastasis, chromogranin A, and neuronal cell adhesion molecule 56 were integrated into the model.

RESULTS

The C-index was prognostically superior to the 7^th tumor node metastasis (TNM) staging in the primary cohort [0.659 (95%CI: 0.607-0.712) vs 0.591 (95%CI: 0.517-0.666), P = 0.033] and in the validation cohort [0.700 (95%CI: 0.622-0.778) vs 0.605 (95%CI: 0.490-0.721), P = 0.041]. Good calibration curves were observed for the prediction probabilities of 1-, 3-, and 5-year OS in both cohorts. DCA analysis showed that our nomogram model had a higher overall net benefit compared to the 7^th TNM staging .

CONCLUSION

Our nomogram can be used to predict the survival probability of PSCE patients, which can help clinicians to make individualized survival predictions.

Development and External Validation of a Nomogram for Predicting Overall Survival in Stomach Cancer: A Population-Based Study

Objective

The study was to develop and externally validate a prognostic nomogram to effectively predict the overall survival of patients with stomach cancer.

Methods

Demographic and clinical variables of patients with stomach cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2007–2016 were retrospectively collected. Patients were then divided into the Training Group (n = 4,456) for model development and the Testing Group (n = 4,541) for external validation. Univariate and multivariate Cox regressions were used to explore prognostic factors. The concordance index (C-index) and the Kolmogorov–Smirnov (KS) value were used to measure the discrimination, and the calibration curve was used to assess the calibration of the nomogram.

Results

Prognostic factors including age, race, marital status, TNM stage, surgery, chemotherapy, grade, and the number of regional nodes positive were used to construct a nomogram. The C-index was 0.790 and the KS value was 0.45 for the Training Group, and the C-index was 0.789 for the Testing Group, all suggesting the good performance of the nomogram.

Conclusion

We have developed an effective nomogram with ten easily acquired prognostic factors. The nomogram could accurately predict the overall survival of patients with stomach cancer and performed well on external validation, which would help improve the individualized survival prediction and decision-making, thereby improving the outcome and survival of stomach cancer.

Nomogram predicting the risk of three-year chronic kidney disease adverse outcomes among East Asian patients with CKD

Background

Chronic kidney disease (CKD) is a common health challenge. There are some risk models predicting CKD adverse outcomes, but seldom focus on the Mongoloid population in East Asian. So, we developed a simple but intuitive nomogram model to predict 3-year CKD adverse outcomes for East Asian patients with CKD.

Methods

The development and internal validation of prediction models used data from the CKD-ROUTE study in Japan, while the external validation set used data collected at the First People’s Hospital of Foshan in southern China from January 2013 to December 2018. Models were developed using the cox proportional hazards model and nomogram with SPSS and R software. Finally, the model discrimination, calibration and clinical value were tested by R software.

Results

The development and internal validation data-sets included 797 patients (191 with progression [23.96%]) and 341 patients (89 with progression [26.10%]), respectively, while 297 patients (108 with progression [36.36%]) were included in the external validation data set. The nomogram model was developed with age, eGFR, haemoglobin, blood albumin and dipstick proteinuria to predict three-year adverse-outcome-free probability. The C-statistics of this nomogram were 0.90(95% CI, 0.89–0.92) for the development data set, 0.91(95% CI, 0.89–0.94) for the internal validation data set and 0.83(95% CI, 0.78–0.88) for the external validation data-set. The calibration and decision curve analyses were good in this model.

Conclusion

This visualized predictive nomogram model could accurately predict CKD three-year adverse outcomes for East Asian patients with CKD, providing an easy-to-use and widely applicable tool for clinical practitioners.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02496-7.

Low prevalence and independent prognostic role of del(11q) in Chinese patients with chronic lymphocytic leukemia

The 11q deletion (del(11q)) is a conventional cytogenetic aberration observed in chronic lymphocytic leukemia (CLL) patients. However, the prevalence and the prognostic value of del(11q) are still controversial. In this research, we retrospectively explored the prevalence, association, and prognostic significance of del(11q) in 352 untreated and 99 relapsed/refractory Chinese CLL patients. Totally 11.4% of untreated and 19.2% of relapsed/refractory patients harbored del(11q). Del(11q) was more common in patients with β2-microglobulin > 3.5 mg/L, positive CD38, positive zeta-chain associated protein kinase 70, unmutated immunoglobulin heavy variable-region gene and ataxia telangiectasia mutated mutation. Kaplan-Meier method and univariate Cox regression indicated that del(11q) was an independent prognostic factor for overall survival (OS). Based on the results of univariate Cox regression analysis, two nomograms that included del(11q) were established to predict survival. Desirable area under curve of receiver operating characteristic curves was obtained in the training and validation cohorts. In addition, the calibration curves for the probability of survival showed good agreement between the prediction by nomogram and actual observation. In summary, the prevalence of del(11q) is relatively low in our cohort and del(11q) is an unfavorable prognostic factor for untreated CLL patients. Besides, these two nomograms could be used to accurately predict the prognosis of untreated CLL patients.

A stable gene set for prediction of prognosis and efficacy of chemotherapy in gastric cancer

Background

Gastric cancer (GC) is a primary reason for cancer death in the world. At present, GC has become a public health issue urgently to be solved to. Prediction of prognosis is critical to the development of clinical treatment regimens. This work aimed to construct the stable gene set for guiding GC diagnosis and treatment in clinic.

Methods

A public microarray dataset of TCGA providing clinical information was obtained. Dimensionality reduction was carried out by selection operator regression on the stable prognostic genes discovered through the bootstrap approach as well as survival analysis.

Findings

A total of 2 prognostic models were built, respectively designated as stable gene risk scores of OS (SGRS-OS) and stable gene risk scores of PFI (SGRS-PFI) consisting of 18 and 21 genes. The SGRS set potently predicted the overall survival (OS) along with progression-free interval (PFI) by means of univariate as well as multivariate analysis, using the specific risk scores formula. Relative to the TNM classification system, the SGRS set exhibited apparently higher predicting ability. Moreover, it was suggested that, patients who had increased SGRS were associated with poor chemotherapeutic outcomes.

Interpretation

The SGRS set constructed in this study potentially serves as the efficient approach for predicting GC patient survival and guiding their treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08444-w.

Bone Metastasis in Renal Cell Carcinoma Patients: Risk and Prognostic Factors and Nomograms

Background

Bone metastasis (BM) is one of the common sites of renal cell carcinoma (RCC), and patients with BM have a poorer prognosis. We aimed to develop two nomograms to quantify the risk of BM and predict the prognosis of RCC patients with BM.

Methods

We reviewed patients with diagnosed RCC with BM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Multivariate logistic regression analysis was used to determine independent factors to predict BM in RCC patients. Univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors for BM in RCC patients. Two nomograms were established and evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

The study included 37,554 patients diagnosed with RCC in the SEER database, 537 of whom were BM patients. BM's risk factors included sex, tumor size, liver metastasis, lung metastasis, brain metastasis, N stage, T stage, histologic type, and grade in RCC patients. Currently, independent prognostic factors for RCC with BM included grade, histologic type, N stage, surgery, brain metastasis, and lung metastasis. The calibration curve, ROC curve, and DCA showed good performance for diagnostic and prognostic nomograms.

Conclusions

Nomograms were established to predict the risk of BM in RCC and the prognosis of RCC with BM, separately. These nomograms strengthen each patient's prognosis-based decision making, which is critical in improving the prognosis of patients.

Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency.

Methods: Lasso regression analysis followed by univariate Cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package ‘clusterProfiler’ was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by single-sample GSEA (ssGSEA) of R package ‘GSVA’.

Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective by combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition.

Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.

Computerized Assessment of the Tumor-stromal Ratio and Proposal of a Novel Nomogram for Predicting Survival in Invasive Breast Cancer

Background: Various studies have verified the prognostic significance of the tumor-stromal ratio (TSR) in several types of carcinomas using manually assessed H&E stained histologic sections. This study aimed to establish a computerized method to assess the TSR in invasive breast cancer (BC) using immunohistochemistry (IHC)-stained tissue microarrays (TMAs), and integrate the TSR into a novel nomogram for predicting survival.

Methods: IHC-staining of cytokeratin (CK) was performed in 7 prepared TMAs containing 240 patients with 480 invasive BC specimens. The ratio of tumor areas and stromal areas was determined by the computerized method, and categorized as stroma-low and stroma-high groups using the X-tile software. The prognostic value of the TSR at 5-year disease free survival (5-DFS) in each subgroup was analyzed. Univariate and multivariate analyses were performed and a novel nomogram for predicting survival in invasive breast cancer was established and assessed.

Results: The newly developed computerized method could accurately recognize CK-labeled tumor areas and non-labeled stromal areas, and automatically calculate the TSR. Stroma-low and stroma-high accounted for 38.8% (n = 93) and 61.2% (n = 147) of the cases, according to the cut-off value of 55.5% for stroma ratio. The Kaplan-Meier analysis showed that patients in the stroma-high group had a worse 5-DFS compared to patients in the stroma-low group (P = 0.031). Multivariable analysis indicated that the T stage, N status, histological grade, ER status, HER-2 gene, and the TSR were potential risk factors of invasive BC patients, which were included into the nomogram (P < 0.10 for all). The nomogram was well calibrated to predict the probability of 5-DFS and the C-index was 0.817, which was higher than any single predictor. A dynamic nomogram was built for convenient use. The area under the curve (AUC) of the nomogram was 0.870, while that of the TNM staging system was 0.723. The Kaplan-Meier analysis showed that the nomogram had a better risk stratification for invasive BC patients than the TNM staging system.

Conclusions: Based on IHC staining of CK on TMAs, this study successfully developed a computerized method for TSR assessment and established a novel nomogram for predicting survival in invasive BC patients.

Prognostic nomograms for predicting overall survival and cause-specific survival of signet ring cell carcinoma in colorectal cancer patients

BACKGROUND

Signet ring cell carcinoma (SRCC) is an uncommon subtype in colorectal cancer (CRC), with a short survival time. Therefore, it is imperative to establish a useful prognostic model. As a simple visual predictive tool, nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years. Until now, there has been no nomogram to predict the outcome of CRC patients with SRCC.

AIM

To build effective nomograms for predicting overall survival (OS) and cause-specific survival (CSS) of CRC patients with SRCC.

METHODS

Data were extracted from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms. Performance of the nomograms was assessed by concordance index, calibration curves, and receiver operating characteristic (ROC) curves. ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis (TNM) staging system. Patients were classified as high-risk, moderate-risk, and low-risk groups using the novel nomograms. Kaplan-Meier curves were plotted to compare survival differences.

RESULTS

In total, 1230 patients were included. The concordance index of the nomograms for OS and CSS were 0.737 (95% confidence interval: 0.728-0.747) and 0.758 (95% confidence interval: 0.738-0.778), respectively. The calibration curves and ROC curves demonstrated good predictive accuracy. The 1-, 3-, and 5-year area under the curve values of the nomogram for predicting OS were 0.796, 0.825 and 0.819, in comparison to 0.743, 0.798, and 0.803 for the TNM staging system. In addition, the 1-, 3-, and 5-year area under the curve values of the nomogram for predicting CSS were 0.805, 0.847 and 0.863, in comparison to 0.740, 0.794, and 0.800 for the TNM staging system. Based on the novel nomograms, stratified analysis showed that the 5-year probability of survival in the high-risk, moderate-risk, and low-risk groups was 6.8%, 37.7%, and 67.0% for OS (P < 0.001), as well as 9.6%, 38.5%, and 67.6% for CSS (P < 0.001), respectively.

CONCLUSION

Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC, which are superior to the conventional TNM staging system.

miR-299-3p suppresses cell progression and induces apoptosis by downregulating PAX3 in gastric cancer

Gastric cancer (GC) is ranked the fourth leading cause of cancer-related death, with an over 75% mortality rate worldwide. In recent years, miR-299-3p has been identified as a biomarker in multiple cancers, such as acute promyelocytic leukemia, thyroid cancer, and lung cancer. However, the regulatory mechanism of miR-299-3p in GC cell progression is still largely unclear. Cell viability and apoptosis tests were performed by CCK8 and flow cytometry assay, respectively. Transwell assay was recruited to examine cell invasion ability. The interaction between miR-299-3p and PAX3 was determined by the luciferase reporter system. PAX3 protein level was evaluated by western blot assay. The expression of miR-299-3p was downregulated in GC tissues and cell lines (MKN-45, AGS, and MGC-803) compared with the normal tissues and cells. Besides, overexpression of miR-299-3p significantly suppressed proliferation and invasion and promoted apoptosis in GC. Next, we clarified that PAX3 expression was regulated by miR-299-3p using a luciferase reporter system, qRT-PCR, and western blot assay. Additionally, downregulation of PAX3 repressed GC cell progression. The rescue experiments indicated that restoration of PAX3 inversed miR-299-3p-mediated inhibition on cell proliferation and invasion. miR-299-3p suppresses cell proliferation and invasion as well as induces apoptosis by regulating PAX3 expression in GC, representing desirable biomarkers for GC diagnosis and therapy.

Survival benefit of conversion surgery after intensive chemotherapy for unresectable metastatic gastric cancer: a propensity score-matching analysis

PURPOSE

The clinical benefit of conversion surgery (CS) for unresectable gastric cancer (GC), whereby unresectable GC responds to chemotherapy and subsequently receives curative-intent surgery, remains unclear. Here, we aimed to clarify the clinical value of CS.

METHODS

In this retrospective cohort study, we analyzed 175 unresectable GC, who received triple combined chemotherapy between 2004 and 2019. We divided patients into two groups: those who underwent CS and those receiving chemotherapy only (CS and C groups, respectively). Propensity score matching was used to minimize confounding bias.

RESULTS

Of 175 cases, 61 (34.9%) underwent CS. R0 resection was obtained in 85.2%. After matching, 44 pairs were selected; there were no significant differences in baseline covariants. Group CS had a significantly better median overall survival (OS) (18.8 vs. 46.0 months, p < 0.001), and prolonged progression-free survival (7.4 vs. 25.8 months, p < 0.001). Subgroup analysis of OS showed a favorable trend for CS for almost all subgroups. Multivariate analysis revealed that good ECOG performance status and CS were associated with a longer OS.

CONCLUSION

The survival benefit of CS was consistently demonstrated in the univariate and multivariate analysis, even in the matched cohort. Additional large-scale trials are needed for further validation.

A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database

Background

Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC.

Methods

LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a testing dataset for external validation. The nomogram model was also compared with the currently used American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (8^th edition) by using C-index and a decision curve analysis.

Results

Eight variables-age, sex, race, marital status, T stage, N stage, M stage, and treatment strategy-were statistically significant in the multivariate Cox model and were selected to develop the nomogram model. This model exhibited excellent predictive performance. The C-index and AUC value were 0.761 [95% confidence interval (CI), 0.754 to 0.768] and 0.886 for the training dataset and 0.773 (95% CI, 0.765 to 0.781) and 0.876 for the testing dataset, respectively. This model also predicted three-year and five-year lung cancer-specific survival (LCSS) in both datasets with good fidelity. This nomogram model performs significantly better than the 8th edition AJCC TNM staging system, with a higher C-index (P<0.001) and better net benefits in predicting LCSS in LCLC patients.

Conclusions

We developed and validated a prognostic nomogram model for predicting 3- and 5-year LCSS in LCLC patients with good discrimination and calibration abilities. The nomogram may be useful in assisting clinicians to make individualized decisions for appropriate treatment in LCLC.

Construction of an RNA-Binding Protein-Related Prognostic Model for Pancreatic Adenocarcinoma Based on TCGA and GTEx Databases

Background: Recently, RNA-binding proteins (RBPs) were reported to interact with target mRNA to regulate gene posttranscriptional expression, and RBP-mediated RNA modification can regulate the expression and function of proto-oncogenes and tumor suppressor genes. We systematically analyzed the expression of RBPs in pancreatic adenocarcinoma (PAAD) and constructed an RBP-associated prognostic risk model. Methods: Gene expression data of normal pancreatic samples as well as PAAD samples were downloaded from TCGA-PAAD and GTEx databases. Wilcoxon test and univariate Cox analysis were, respectively, applied to screen differential expression RBPs (DE-RBPs) and prognostic-associated RBPs (pRBPs). Functional enrichment was analyzed by GO, KEGG, and GSEA. Protein-protein interaction (PPI) network was constructed by STRING online database. Modeling RBPs were selected by multivariate Cox analysis. Kaplan-Meier survival and Cox analysis were applied to evaluate the effects of risk score on the overall survival of PAAD patients. ROC curves and validation cohort were applied to verify the accuracy of the model. Nomogram was applied for predicting 1-, 3-, and 5-year overall survival (OS) of PAAD patients. At last, modeling RBPs were further analyzed to explore their differential expression, prognostic value, as well as enrichment pathways in PAAD. Results: RBPs (453) were differentially expressed in normal and tumor samples, besides, 28 of which were prognostic associated. DE-RBPs (453) are functionally associated with ribosome, ribonuclease, spliceosome, etc. Eight RBPs (PABPC1, PRPF6, OAS1, RBM5, LSM12, IPO7, FXR1, and RBM6) were identified to construct a prognostic risk model. Higher risk score not only predicted poor prognosis but also was an independent poor prognostic indicator, which was verified by ROC curves and validation cohort. Eight modeling RBPs were confirmed to be significantly differentially expressed between normal and tumor samples from RNA and protein level. Besides, all of eight RBPs were related with overall survival of PAAD patients. Conclusions: We successfully constructed an RBP-associated prognostic risk model in PAAD, which has a potential clinical application prospect.

Nomograms Predict Overall Survival and Cancer-Specific Survival in Patients with Fibrosarcoma: A SEER-Based Study

Purpose

Due to the rarity, it is difficult to predict the survival of patients with fibrosarcoma. This study aimed to apply a nomogram to predict survival outcomes in patients with fibrosarcoma.

Methods

A total of 2235 patients with diagnoses of fibrosarcoma were registered in the Surveillance, Epidemiology, and End Results database, of whom 663 patients were eventually enrolled. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. Nomograms were constructed to predict 3-year and 5‐year overall survival and cancer‐specific survival of patients with fibrosarcoma.

Results

In univariate and multivariate analyses of OS, age, sex, race, tumor stage, pathologic grade, use of surgery, and tumor size were identified as independent prognostic factors. Age, sex, tumor stage, pathologic grade, use of surgery, and tumor size were significantly associated with CSS. These characteristics were further included to establish the nomogram for predicting 3-year and 5-year OS and CSS. For the internal validation of the nomogram predictions of OS and CSS, the C-indices were 0.784 and 0.801.

Conclusion

We developed the nomograms that estimated 3-year and 5-year OS and CSS. These nomograms not only have good discrimination performance and calibration but also provide patients with better clinical benefits.

The incidence, risk factors and predictive nomograms for early death among patients with stage IV gastric cancer: a population-based study

Background

Although advances in the treatment of stage IV gastric cancer (GC) patients, some patients were observed to die within 3 months of initial diagnosis. The present study aimed to explore the early mortality and risk factors for stage IV GC and further develop nomograms.

Methods

A total of 2,174 eligible stage IV GC patients were selected from the Surveillance, Epidemiology, and End Results database. Logistic regression analyses were used to determine the risk factors and develop the nomograms to predict all-cause early death and cancer-specific early death. The predictive performance of the nomograms was assessed by receiver operating characteristic curves (ROC), calibration plots and decision curve analyses (DCA) in both training and validation cohorts.

Results

Of 2,174 patients enrolled, 708 died within 3 months of initial diagnosis (n=668 for cancer-specific early death). Early mortality remained stable from 2010-2015. Non-Asian or Pacific Islander (API) race, poorer differentiation, middle sites of the stomach, no surgery, no radiotherapy, no chemotherapy, lung metastases and liver metastases were associated with high risk of both all-causes early death and cancer-specific early death. The nomograms constructed based on these factors showed favorable sensitivity, with the area under the ROC range of 0.816-0.847. The calibration curves and DCAs also exhibited adequate fit and ideal net benefit in prediction and clinical application.

Conclusions

Approximately one-third of stage IV GC patients experienced early death. These associated risk factors and predictive nomograms may help clinicians identify the patients at high risk of early death and be the reference for treatment choices.

The role of CD28 in the prognosis of young lung adenocarcinoma patients

Background

The prognosis of lung cancer was found to be associated with a series of biomarkers related to the tumor immune microenvironment (TIME), which can modulate the biological behaviors and consequent outcomes of lung cancer. Therefore, establishing a prognostic model based on the TIME for lung cancer patients, especially young patients with lung adenocarcinoma (LUAD), is urgently needed.

Methods

In all, 809 lung cancer patients from the TCGA database and 71 young patients with LUAD in our center were involved in this study. Univariate and multivariate analysis based on clinical characteristics and TIME-related expression patterns (as evaluated by IHC) were performed to estimate prognosis and were verified by prognostic nomograms.

Results

Both LUAD and lung cancer patients with high CD28 expression had shorter disease-free survival (DFS) (P = 0.0011; P = 0.0001) but longer overall survival (OS) (P = 0.0001; P = 0.0282). TIME-related molecules combined with clinical information and genomic signatures could predict the prognosis of young patients with LUAD with robust efficiency and could be verified by the established nomogram based on the Cox regression model. In addition, CD28 expression was correlated with an abundance of lymphocytes and could modulate the TIME. Higher CD28 levels were observed in primary tumors than in metastatic tissues.

Conclusion

TIME-related molecules were identified as compelling biomarkers for predicting the prognosis of lung cancer, especially in a cohort of young patients. Furthermore, CD28, which is associated with poor DFS but long OS, might participate in the modulation of the TIME and has a different role in the prognosis of young patients with LUAD.

The impact of previous cancer on overall survival of bladder cancer patients and the establishment of nomogram for overall survival prediction

To investigate the role of previous cancer on overall survival in patients with bladder cancer (BCa) and to establish an effective prognostic tool for individualized overall survival prediction.A total of 78,660 patients diagnosed with BCa between 2000 and 2013 were selected from the Surveillance, Epidemiology, and End Results (SEER) database, among which 8915 patients had a history of other cancers. We compared the overall survival between patients with and without previous cancer after propensity score matching and we further established a nomogram for overall survival prediction.Univariate and multivariate Cox analyses were used to determine independent prognostic factors. The calibration curve and concordance index (C-index) were used to assess the accuracy of the nomogram. Cox proportional hazards models and Kaplan-Meier analysis were used to compare survival outcomes.BCa patients with previous cancer had worse overall survival compared with those without previous cancer (HR = 1.37; 95%CI = 1.32-1.42, P < .001). Cancers in lung prior to BCa had the most adverse impact on overall survival (HR = 2.35; 95%CI = 2.10-2.63; P < .001), and the minimal impact was located in prostate (HR = 1.16; 95%CI = 1.10-1.22; P < .001) for male and in gynecological (HR = 1.15; 95%CI = 1.02-1.30; P = .027) for female. The shorter interval time between 2 cancers and the higher stage of the previous cancer development, the higher risk of death. Age, race, sex, marital status, surgery, radiation, grade, stage, type of previous cancer as the independent prognostic factors were selected into the nomogram. The favorable calibration curve and C-index value (0.784, 95%CI = 0.782-0.786) indicated the nomogram could accurately predict the 1-, 3-, and 5-year overall survival rate of BCa patients.Previous cancer has a negative impact on the overall survival of BCa patients and requires more effective clinical management. The nomogram provides accurate survival prediction for BCa patients and might be helpful for clinical treatment selection and follow-up strategy adjustment.

Prediction model for clinical pregnancy for ICSI after surgical sperm retrieval in different types of azoospermia

STUDY QUESTION

Can a counselling tool be developed for couples with different types of azoospermia to predict the probability of clinical pregnancy in ICSI after surgical sperm retrieval?

SUMMARY ANSWER

A prediction model for clinical pregnancy in ICSI after surgical sperm retrieval in different types of azoospermia was created and clinical type of azoospermia, testicular size, male FSH, male LH, male testosterone, female age, female antral follicle count (AFC) and female anti-Müllerian hormone (AMH) were used as predictors.

WHAT IS KNOWN ALREADY

Prediction models are used frequently to predict treatment success in reproductive medicine; however, there are few prediction models only for azoospermia couples who intend to conceive through surgical sperm retrieval and ICSI. Furthermore, no specific clinical types of azoospermia have been reported as predictors.

STUDY DESIGN, SIZE, DURATION

A cohort study of 453 couples undergoing ICSI was conducted between 2016 and 2019 in an academic teaching hospital.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Couples undergoing ICSI with surgically retrieved sperm were included, with 302 couples included in the development set and 151 couples included in the validation set. We constructed a prediction model using multivariable logistic regression analysis. The internal validation was based on discrimination and calibration.

MAIN RESULTS AND THE ROLE OF CHANCE

We found that for male patients involved in our model, different clinical types of azoospermia are associated with different clinical pregnancy outcomes after ICSI. Considering the clinical type of azoospermia, larger testicular volume and higher levels of FSH, LH and testosterone in the body are associated with higher clinical pregnancy success rates. For women involved in our model, younger age and higher AFC and AMH levels are associated with higher clinical pregnancy success rates. In the development set, the AUC was 0.891 (95% CI 0.849–0.934), indicating that the model had good discrimination. The slope of the calibration plot was 1.020 (95% CI 0.899–1.142) and the intercept of the calibration plot was −0.015 (95% CI −0.112 to 0.082), indicating that the model was well-calibrated. From the validation set, the model had good discriminative capacity (AUC 0.866, 95% CI 0.808–0.924) and calibrated well, with a slope of 1.015 (95% CI 0.790–1.239) and an intercept of −0.014 (95% CI −0.180 to 0.152) in the calibration plot.

LIMITATIONS, REASONS FOR CAUTION

We found that BMI was not an effective indicator for predicting clinical pregnancy, which was inconsistent with some other studies. We lacked data about the predictors that reflected sperm characteristics, therefore, we included the clinical type of azoospermia instead as a predictor because it is related to sperm quality. We found that almost all patients did not have regular alcohol consumption, so we did not use alcohol consumption as a possible predictor, because of insufficient data on drinking habits. We acknowledge that our development set might not be a perfect representation of the population, although this is a common limitation that researchers often encounter when developing prediction models. The number of non-obstructive azoospermia patients that we could include in the analysis was limited due to the success rate of surgical sperm retrieval, although this did not affect the establishment and validation of our model. Finally, this prediction model was developed in a single centre. Although our model was validated in an independent dataset from our centre, validation for different clinical populations belonging to other centres is required before it can be exported.

WIDER IMPLICATIONS OF THE FINDINGS

This model enables the differentiation between couples with a low or high chance of reaching a clinical pregnancy through ICSI after surgical sperm retrieval. As such it can provide couples dealing with azoospermia a new approach to help them choose between surgical sperm retrieval with ICSI and the use of donor sperm.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by a grant from the National Natural Science Foundations of China (81501246 and 81501020 and 81671443). The authors declare no competing interest.

TRIAL REGISTRATION NUMBER

N/A.

Nomogram-based prediction of survival in unresectable or metastatic gastric cancer patients with good performance status who received first-line chemotherapy

Background

Good performance status (PS) is widely acknowledged to have a high prognostic ability, although the prognostic parameters of cancer patients with good PS are still uncertain. This study was conducted to establish and validate a point-based nomogram to assist with predicting prognosis in unresectable or metastatic gastric cancer (GC) patients who had good PS and underwent first-line chemotherapy.

Methods

At random, a total of 309 patients with GC were split into 2 cohorts: a training cohort (n=259) and an internal validation cohort (n=50). An independent external validation cohort comprising 147 patients was also recruited. Both univariate and multivariate Cox regression analyses were used to evaluate patients based on the overall survival (OS) to develop the nomogram, which was subsequently validated using the concordance index (c-index), calibration curve, and decision curve analysis (DCA).

Results

The nomogram contained 3 independent prognostic variables in the training cohort: the number of distant metastatic sites (P<0.001), carbohydrate antigen 199 (CA199) level (P=0.002), and fibrinogen (P=0.020). The nomogram predicted an OS with a c-index of 0.623 (95% CI, 0.58–0.67) in the training cohort. The internal validation showed that the nomogram had a c-index of 0.614 (95% CI, 0.51–0.72). For external validation, the c-index was 0.638 (95% CI, 0.58–0.70).

Conclusions

A reliable point-based nomogram for predicting the prognosis of patients who had unresectable or metastatic GC and good PS who underwent first-line chemotherapy was developed and validated.

Keywords

Nomogram-based prediction; overall survival; unresectable gastric cancer; metastatic gastric cancer; good performance status; first-line chemotherapy

Nomograms for predicting overall survival and cancer-specific survival in young patients with pancreatic cancer in the US based on the SEER database

Background

The incidence of young patients with pancreatic cancer (PC) is on the rise, and there is a lack of models that could effectively predict their prognosis. The purpose of this study was to construct nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of young patients with PC.

Methods

PC patients younger than 50 years old from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were selected and randomly divided into training set and validation set. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent factors affecting OS. The Fine and Gray competing risk regression model was used to determine the independent factors affecting CSS. We used significant variables in the training set to construct nomograms predicting prognosis. The discrimination and calibration power of models were evaluated by concordance index (C-index), calibration curve and 10-flod cross-validation.

Results

A total of 4,146 patients were selected. Multivariable Cox analysis showed that gender, race, grade, pathological types, AJCC stage and surgery were independent factors affecting OS. The C-index of the nomogram predicting OS in training and validation was 0.733 (average = 0.731, 95% CI [0.724–0.738]) and 0.742 (95% CI [0.725–0.759]), respectively. Competing risk analysis showed that primary site, pathological types, AJCC stage and surgery were independent factors affecting CSS. The C-index of the nomogram predicting CSS in training and validation set was 0.792 (average = 0.765, 95% CI [0.742–0.788]) and 0.776 (95% CI [0.773–0.779]), respectively. C-index based on nomogram was better in training and validation set than that based on AJCC stage. Calibration curves showed that these nomograms could accurately predict the 1-, 3- and 5-year OS and CSS both in training set and validation set.

Conclusions

The nomograms could effectively predict OS and CSS in young patients with PC, which help clinicians more accurately and quantitatively judge the prognosis of individual patients.

Prognostic nomograms and Aggtrmmns scoring system for predicting overall survival and cancer-specific survival of patients with kidney cancer

Background

Currently, the prognosis of kidney cancer depends mainly on the pathological grade or tumor stage. Clinicians have few effective tools that can personalize and adequately evaluate the prognosis of kidney cancer patients.

Methods

A total of 70 481 kidney cancer patients were selected from the Surveillance, Epidemiology, and End Results database, among which patients diagnosed in 2005‐2011 (n = 42 890) were used to establish nomograms for overall survival (OS) and cancer‐specific survival (CSS), and those diagnosed in 2012‐2015 (n = 24 591) were used for external validation. Univariate and multivariate Cox analyses were used to determine independent prognostic factors. Concordance index (C‐index), receiver operating characteristic curve, and calibration curve were used to evaluate the predictive capacity of the nomograms. We further reduced subgroup classification and used propensity score matching to balance clinical informations, and analyzed the effect of other variables on survival. We established a new kidney cancer prognostic score system based on the effect of all available variables on survival. Cox proportional hazard model and Kaplan‐Meier curves were used for survival comparison.

Results

Age, gender, marital status, surgery, grade, T stage, and M stage were included as independent risk factors in the nomograms. The favorable area under the curve (AUC) value (for OS, AUC = 0.812‐0.858; and for CSS, AUC = 0.890‐0.921), internal (for OS, C‐index = 0.776; and for CSS, C‐index = 0.856), and external (for OS, C‐index = 0.814‐0.841; and for CSS, C‐index = 0.894‐0.904) validation indicated that the proposed nomograms could accurately predict 1‐, 3‐, and 5‐year OS and CSS of kidney cancer patients. The Aggtrmmns prognostic scoring system based on age, gender, race, marital status, grade, TNM stage, and surgery of kidney cancer patients could stage patients more explicitly than the AJCC staging system.

Conclusion

The nomogram and Aggtrmmns scoring system can predict OS and CSS in kidney cancer patients effectively, which may help clinicians personalize prognostic assessments and clinical decisions.

A prognostic nomogram for distal bile duct cancer from Surveillance, Epidemiology, and End Results (SEER) database based on the STROBE compliant

In this study, we aimed to develop a reliable nomogram to estimate individualized prognosis for patients with distal bile duct cancer (DBDC) and compare the predictive value with the American Joint Committee on Cancer staging system.Data of 1110 patients diagnosed with DBDC were recruited from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. All patients were randomly divided into the training (n = 777) and validation (n = 333) cohorts, respectively. Multivariate Cox regression was performed to identify the independent risk factors. The Akaike information criterion was used to select covariates for constructing a nomogram. The predictive ability of the nomogram was assessed by concordance index (C-index) and area under receiver operating characteristic curve (AUROC) compared to tumor-node-metastasis (TNM) staging system.A nomogram integrating 8 risk factors was developed with a higher C-index than that of the TNM staging system (training data set, 0.70 vs 0.61; validation data set, 0.71 vs 0.57). The AUROCs of the nomogram for 1-year and 3-year overall survival (OS) predication were 0.76 and 0.78 in the training cohort, 0.78 and 0.77 in the validation cohort. However, AUROCs of the TNM stage for predicting 1-year and 3-year OS were all below 0.60. Calibration curves showed the optimal agreement in predicating OS between nomogram and actual observation. In addition, this nomogram can effectively distinguish the OS between low and high-risk groups divided by the median score (P < .01).Present study was the first one to construct a prognostic nomogram of DBDC patients, which has the potential to provide individual prediction of OS.

Nomogram to predict prolonged postoperative ileus after gastrectomy in gastric cancer

BACKGROUND

Prolonged postoperative ileus (PPOI) is one of the common complications in gastric cancer patients who underwent gastrectomy. Evidence on the predictors of PPOI after gastrectomy is limited and few prediction models of nomogram are used to estimate the risk of PPOI. We hypothesized that a predictive nomogram can be used for clinical risk estimation of PPOI in gastric cancer patients.

AIM

To investigate the risk factors for PPOI and establish a nomogram for clinical risk estimation.

METHODS

Between June 2016 and March 2017, the data of 162 patients with gastrectomy were obtained from a prospective and observational registry database. Clinical data of patients who fulfilled the criteria were obtained. Univariate and multivariable logistic regression models were performed to detect the relationship between variables and PPOI. A nomogram for PPOI was developed and verified by bootstrap resampling. The calibration curve was employed to detect the concentricity between the model probability curve and ideal curve. The clinical usefulness of our model was evaluated using the net benefit curve.

RESULTS

This study analyzed 14 potential variables of PPOI in 162 gastric cancer patients who underwent gastrectomy. The incidence of PPOI was 19.75% in patients with gastrectomy. Age older than 60 years, open surgery, advanced stage (III–IV), and postoperative use of opioid analgesic were independent risk factors for PPOI. We developed a simple and easy-to-use prediction nomogram of PPOI after gastrectomy. This nomogram had an excellent diagnostic performance [area under the curve (AUC) = 0.836, sensitivity = 84.4%, and specificity = 75.4%]. This nomogram was further validated by bootstrapping for 500 repetitions. The AUC of the bootstrap model was 0.832 (95%CI: 0.741–0.924). This model showed a good fitting and calibration and positive net benefits in decision curve analysis.

CONCLUSION

We have developed a prediction nomogram of PPOI for gastric cancer. This novel nomogram might serve as an essential early warning sign of PPOI in gastric cancer patients.

Circulating serum exosomal aHIF is a novel prognostic predictor for epithelial ovarian cancer

Purpose

Exosomes are key mediators of cellular communication by transporting molecules, including long noncoding RNAs (lncRNAs), and have been regarded as promising non-invasive biomarkers. This study aimed to evaluate the expression pattern and clinical significance of serum exosomal lncRNA antisense hypoxia inducible factor (aHIF) in epithelial ovarian cancer (EOC).

Patients and methods

Sixty-two EOC patients in Obstetrics and Gynecology Hospital of Fudan University were enrolled. The expression levels of aHIF in tissues and serum exosomes were examined by RT-qPCR. The origin of serum exosomal aHIF was explored in vitro and in vivo. Univariate and multivariate Cox regression analyses were used to evaluate the prognostic factors of EOC. A prognostic predictive nomogram was formulated in R software.

Results

We isolated exosomes, identified exosomal aHIF in the serum of EOC patients. The expression of serum exosomal aHIF was higher in EOC patients and was correlated with the aHIF level in EOC tissues. In vitro and in vivo, the results indicated that serum exosomal aHIF was derived from tumor cells. Kaplan-Meier survival analysis demonstrated that EOC patients with higher serum exosomal aHIF expression had poorer overall survival. Cox multivariate regression model revealed that FIGO stage, residual tumor size, and serum exosomal aHIF level were independent prognostic factors of EOC. Based on the prognostic value of serum exosomal aHIF, we established a nomogram model that showed a good predictive ability for EOC patients.

Conclusion

Serum exosomal aHIF is overexpressed in EOC and can serve as a noninvasive predictive biomarker for unfavorable prognosis.

Prognostic nomograms to predict overall survival and cancer-specific survival in patients with pelvic chondrosarcoma

Background

The pelvis is the most common site of chondrosarcoma (CS), and the prognosis for patients with pelvic CS is worse than that for patients with CS in the extremities. However, clinicians have had few tools for estimating the likelihood of survival in patients with pelvic CS. Our aim was to develop nomograms to predict survival of patients with pelvic CS.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with pelvic CS between 2004 and 2016 were retrieved for retrospective analysis. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. On the basis of the results of the multivariate analyses, nomograms were constructed to predict the likelihood of 3‐ and 5‐year overall survival (OS) and cancer‐specific survival (CSS) of patients with pelvic CS. The concordance index (C‐index) and calibration curves were used to test the models.

Results

In univariate and multivariate analyses of OS, sex, pathologic grade, tumor size, tumor stage, and surgery were identified as the independent risk factors. In univariate and multivariate analyses of CSS, pathologic grade, tumor size, tumor stage, and surgery were identified as the independent risk factors. These characteristics except surgery were integrated in the nomograms for predicting 3‐ and 5‐year OS and CSS, and the C‐indexes were 0.758 and 0.786, respectively.

Conclusion

The nomograms precisely and individually predict OS and CSS of patients with pelvic CS and could aid in personalized prognostic evaluation and individualized clinical decision‐making.