Predicting cancer-specific mortality in patients with parotid gland carcinoma by competing risk nomogram

Prognostic effects of previous cancer history on patients with major salivary gland cancer

OBJECTIVES

To explore the prognostic effects of previous cancer history on patients with major salivary gland cancer (SGC).

SUBJECTS AND METHODS

SGC patients with (sec-SGC) and without (one-SGC) a previous cancer from the SEER database were identified. Cox proportional hazards regression (CoxPH) models were used to compare the prognosis between sec-SGC and one-SGC patients. Subgroup analyses for sec-SGC patients by gender, previous cancer types, previous cancer histology, and cancer diagnosis interval (CDI) were performed. Two CoxPH models were constructed to distinguish sec-SGC patients with different prognostic risks.

RESULTS

9098 SGC patients were enrolled. Overall, sec-SGC patients (adjusted HR [aHR] = 1.26, p < 0.001), especially those with a CDI ≤ 5 years (aHR = 1.47, p < 0.001), had worse overall survival (OS) than one-SGC patients. In subgroup analysis, only sec-SGC patients with a previous head and neck cancer who were female (aHR = 2.38, p = 0.005), with a CDI ≤ 5 years (aHR = 1.65, p = 0.007) or with a previous squamous cell carcinoma (aHR = 6.52, p < 0.001) had worse OS. Our models successfully differentiated all sec-SGC patients into high-, intermediate- and low-risk groups with different prognosis.

CONCLUSIONS

Sec-SGC patients with different previous cancer types, gender, CDI and previous cancer histology had varied prognosis. The models we constructed could help differentiate the prognosis of sec-SGC patients with different risks.

Predicting cancer-specific mortality in T1/2 hepatocellular carcinoma after radiofrequency ablation by competing risk nomogram: A population-based analysis

BACKGROUND

Radiofrequency ablation (RFA) is one of the primary treatment methods for T1/2 hepatocellular carcinoma (HCC), but the risk factors after RFA remain controversial. This study aims to identify the key factors associated with cancer-specific mortality (CSM) in patients with T1/2 HCC after RFA using competing risk analysis and to establish a prognostic nomogram for improved clinical management.

METHODS

A total of 2,135 T1/2 HCC patients treated with RFA were obtained from the Surveillance, Epidemiology, and End Results (SEER) database and randomly categorized into training and validation sets. Univariate and multivariable competing risk analyses were performed to identify risk factors associated with CSM and construct a competing risk nomogram. Receiver operating characteristic (ROC) curves, concordance indices (C-indexes), calibration plots, and decision curve analysis (DCA) were conducted to evaluate the predictive efficiency and clinical applicability of the nomogram in the training and validation sets. Patients were stratified according to their nomogram score, and the different risk groups were compared using cumulative incidence function (CIF) curves and Gray's validation .

RESULTS

The 5-year CSM rate for HCC patients treated with RFA was 30.1 %. Grade, tumor size, tumor number, cirrhosis, and AFP level were identified as independent risk factors for CSM. A prognostic nomogram was developed based on these risk factors. The time-dependent C-indexes (0.65) were greater than those of the AJCC stage model (0.55) during the 12 to 60 months of follow-up. The calibration plots of the competing risk nomograms demonstrated excellent consistency between actual survival and nomogram predictions. ROC analyses showed that the 1-, 3-, and 5-year AUC values in both the training and validation cohorts were all greater than 0.63 and exceeded those of the AJCC stage model. DCA demonstrated the clinical usefulness of the nomogram. Patients were classified into low-, moderate-, and high-risk groups based on the nomogram scores, with the high-risk group showing significantly higher CSM rates after RFA compared to the other two groups.

CONCLUSIONS

We identified Grade, AFP, cirrhosis, tumor size, and tumor number as independent risk factors associated with CSM. The competing risk nomogram exhibited high performance in predicting the probability of CSM for HCC patients undergoing RFA.

Clinical features and prognostic factors for malignant parotid tumors in children and adolescents: A population-based study

PURPOSE

We performed a population-based cohort study to investigate the clinical characteristics and survival rates of primary malignant parotid tumors (MPT) in children and adolescents.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify all pediatric and adolescent patients with MPT who were diagnosed between 2000 and 2018. Based on a number of parameters, survival curves were produced using Kaplane-Meier estimates. The log-rank test was used to compare survival curves. The influence of each component on overall survival (OS) was examined using a multivariate Cox proportional hazards model.

RESULTS

There were 352 identified pediatric and adolescent patients with MPT. At diagnosis, the age ranged from 1.0 to 19 years, with a median of 15 years. Mucoepidermoid carcinoma (MC) (46.5 %) was the most common histological subtype, followed by acinar cell carcinoma (ACC) (36.4 %) and others (17.1 %) such as adenoid cystic carcinoma and squamous cell carcinoma. All patients had overall survival rates of 98.8 %, 95.6 %, and 94.6 % at 1-year, 3-year and 5-year, respectively. The results of the Cox proportional hazard regression showed that tumor grade, SEER stage, radiotherapy, and treatment regimens were significant independent predictors of overall survival.

CONCLUSIONS

In pediatric and adolescent MPT, tumor grade, SEER stage, adjuvant radiation, and treatment regimens were found to be important independent predictors of survival. More research is required to validate the role of adjuvant radiation.

A novel postoperative nomogram and risk classification system for individualized estimation of survival among patients with parotid gland carcinoma after surgery

BACKGROUND

Parotid gland carcinoma (PGC) is a rare but aggressive head and neck cancer, and the prognostic model associated with survival after surgical resection has not yet been established. This study aimed to construct a novel postoperative nomogram and risk classification system for the individualized prediction of overall survival (OS) among patients with resected PGC.

METHODS

Patients with PGC who underwent surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training and validation cohorts (7:3). A nomogram developed using independent prognostic factors based on the results of the multivariate Cox regression analysis. Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and calibration plots were used to validate the performance of the nomogram. Moreover, decision curve analysis (DCA) was performed to compare the clinical use of the nomogram with that of traditional TNM staging.

RESULTS

In this study, 5077 patients who underwent surgery for PGC were included. Age, sex, marital status, tumor grade, histology, TNM stage, surgery type, radiotherapy, and chemotherapy were independent prognostic factors. Based on these independent factors, a postoperative nomogram was developed. The C-index of the proposed nomogram was 0.807 (95% confidence interval 0.797-0.817). Meanwhile, the time-dependent AUC (> 0.8) indicated that the nomogram had a satisfactory discriminative ability. The calibration curves showed good concordance between the predicted and actual probabilities of OS, and DCA curves indicated that the nomogram had a better clinical application value than the traditional TNM staging. Moreover, a risk classification system was built that could perfectly classify patients with PGC into three risk groups.

CONCLUSIONS

This study constructed a novel postoperative nomogram and corresponding risk classification system to predict the OS of patients with PGC after surgery. These tools can be used to stratify patients with high or low risk of mortality and provide high-risk patients with more directed therapies and closer follow-up.

A competing risk-based nomogram to predict cancer-specific survival in patients with retroperitoneal leiomyosarcoma

Considering the rarity and aggressive nature of retroperitoneal leiomyosarcoma (RLMS), several prognostic factors might contribute to the cancer-specific mortality of these patients. This study aimed to construct a competing risk-based nomogram to predict cancer-specific survival (CSS) for patients with RLMS. In total, 788 cases from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2015) were included. Based on the Fine & Gray's method, independent predictors were screened to develop a nomogram for predicting 1-, 3-, and 5-year CSS. After multivariate analysis, CSS was found significantly associated with tumor characteristics (tumor grade, size, range), as well as surgery status. The nomogram showed solid prediction power and was well calibrated. Through decision curve analysis (DCA), a favorable clinical utility of the nomogram was demonstrated. Additionally, a risk stratification system was developed and distinctive survival between risk groups was observed. In summary, this nomogram showed a better performance than the AJCC 8th staging system and can assist in the clinical management of RLMS.

Competing risk nomogram for predicting cancer-specific mortality in patients with non-melanoma skin cancer

PURPOSE

This study aimed to assess the cumulative incidences of Non-melanoma skin cancer (NMSC)-specific mortality (NMSC-SM) and develop a competing risk nomogram for NMSC-SM.

METHODS

Data on patients diagnosed with NMSC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. To identify the independent prognostic factors, univariate and multivariate competing risk models were used, and a competing risk model was constructed. Based on the model, we developed a competing risk nomogram to predict the 1-, 3-, 5-, and 8-year cumulative probabilities of NMSC-SM. The precision and ability to discriminate of the nomogram were evaluated through the utilization of metrics, such as receiver-operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve. Decision curve analysis (DCA) was employed to assess the clinical usefulness of the nomogram.

RESULTS

Race, age, the primary site of the tumor, tumor grade, size, histological type, summary stage, stage group, order of radiation and surgery, and bone metastases were identified as independent risk factors. The prediction nomogram was constructed using the variables mentioned above. The ROC curves implied the good discrimination ability of the predictive model. The nomogram's C-index was 0.840 and 0.843 in the training and validation sets, respectively, and the calibration plots were well fitted. In addition, the competing risk nomogram demonstrated good clinical usefulness.

CONCLUSION

The competing risk nomogram displayed excellent discrimination and calibration for predicting NMSC-SM, which can be used in clinical contexts to help guide treatment decisions.