The influence of marital status on survival in patients with oral tongue squamous cell carcinoma

Long-term trends in the incidence of male breast cancer and nomogram for predicting survival in male breast cancer patients: a population-based epidemiologic study

Male breast cancer (MBC) is rare, and due to the absence of male-specific screening programs, many patients are diagnosed at advanced stages and older ages. This study aims to analyze the long-term trend of MBC incidence and develop a competing risk model to improve survival rates. MBC data from the Surveillance, Epidemiology, and End Results (SEER) database (1975-2019) were analyzed using the Age-Period-Cohort (APC) model to examine trends in age, period, and birth cohort effects of MBC incidence. A competing risk model was used to build a nomogram predicting breast cancer-specific survival (BCSS) for MBC patients, with model accuracy assessed using the concordance index (C-index) and calibration curves. These results were compared with the nomogram established by Cox model. Comparisons were made with traditional AJCC staging system using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). APC analysis showed MBC incidence increases with age, while period and birth cohort effects were not significant. A total of 2,057 patients were included in the competing risk analysis, which identified factors like older age, estrogen receptor (ER) negative, progesterone receptor (PR) negative, and advanced AJCC stage as being associated with shorter survival. The competing risk model demonstrated excellent predictive ability, surpassing the AJCC staging system in both accuracy and clinical utility. In contrast, the Cox regression model overestimated the risk of endpoint events and failed to provide accurate effect estimates. The high incidence and poor prognosis of MBC in the elderly population emphasize the need for improved screening and early diagnosis in high-risk groups. Our competing risk model, compared to the Cox model, more accurately reflects real-world conditions. Additionally, we have developed a competing risk nomogram to assist in identifying high-risk individuals and guiding clinical decision-making.

Survival trends and conditional survival in primary non-metastatic esophageal cancer: a SEER population-based study and external validation

Background

The dynamic survival trend of patients with primary non-metastatic esophageal cancer (nMEC) is unknown. We conducted a conditional survival (CS) analysis and developed a novel nomogram to predict it.

Methods

Patients with primary nMEC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors of cancer-specific survival (CSS) were identified. The log-rank test and Cox analysis were used to calculate probabilities of CS. We constructed nomograms to predict survival trends and CS probabilities based on the prognostic factors. Calibration curves and C-indexes were used for internal and external validation.

Results

A total of 9,008 patients were identified from the SEER database and 37 patients were recruited as an external validation cohort. The 1- and 3-year CS rates were 69.6% and 43.1% at diagnosis, rising to 95.2% and 86.2% at the fifth conditional year. CS probabilities by different variables continuously improved over time. The calibration curves of the CS nomograms fit well. The C-indexes were 0.700 (95% CI: 0.693-0.709) in the training cohort, 0.693 (95% CI: 0.669-0.717) in the internal validation cohort, and 0.683 (95% CI: 0.556-0.810) in the external validation cohort.

Conclusions

CS rates are more dynamic than traditional survival rates for patients surviving for a relatively longer period. The CS rates of patients with nMEC improved over time and became stable after surviving for a few years. We developed and validated nomograms to predict CS probabilities.

Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning

BACKGROUND

A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage.

OBJECTIVES

We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to stratify advanced-stage TSCC patients into the likelihood of overall survival (OS) for evidence-based treatment. We compared the survival outcome of patients who received either surgical treatment only (Sx) or surgery combined with postoperative radiotherapy (Sx + RT) or postoperative chemoradiotherapy (Sx + CRT).

MATERIAL AND METHODS

A total of 428 patients from Surveillance, Epidemiology, and End Results (SEER) database were reviewed. Kaplan-Meier and Cox proportional hazards models examine OS. In addition, a ML model was developed for OS likelihood stratification.

RESULTS

Age, marital status, N stage, Sx, and Sx + CRT were considered significant. Patients with Sx + RT showed better OS than Sx + CRT or Sx alone. A similar result was obtained for T3N0 subgroup. For T3N1 subgroup, Sx + CRT appeared more favorable for 5-year OS. In T3N2 and T3N3 subgroups, the numbers of patients were small to make insightful conclusions. The OS predictive ML model showed an accuracy of 86.3% for OS likelihood prediction.

CONCLUSIONS AND SIGNIFICANCE

Patients stratified as having high likelihood of OS may be managed with Sx + RT. Further external validation studies are needed to confirm these results.

The Importance of Marital Status in the Morbidity and Prognosis of Lung Metastasis in Newly Diagnosed Ovarian Cancer

Objective: The study aimed to evaluate the risk factors for the morbidity and prognosis of lung metastases (LM) in patients with newly diagnosed ovarian cancer (OC), and further explore the important role of marital status. Materials and methods: Based on the Surveillance, Epidemiology, and End Results (SEER) dataset, OC patients from 2010 and 2019 were retrospectively analyzed. Logistic regression analysis and Kaplan-Meier method were applied to evaluate the vital factors of incidence and survival outcome in LM population. Cox regression analysis was performed to identify risk factors for the prognosis of OC patients with LM. The predictive potential was showed by two established nomograms and examined by the concordance index (C-index), calibration curves, the area under the curve (AUC), decision curve analyses (DCAs) and clinical impact curves (CICs). Results: There are 25,202 eligible OC patients were enrolled in the study, the morbidity of LM at 5.61%. Multivariable logistic regression models illustrated that chemotherapy (P<0.01), surgical treatment of bilateral or more areas (P<0.01), T stage (P<0.01), N1 stage (P<0.01), bone metastasis (P<0.01), brain metastasis (P<0.01) and liver metastasis (P<0.01) were all significantly connected with LM in OC. Multivariable Cox regression analyses illustrated that unmarried, radiotherapy, elder people and positive cancer antigen 125 (CA-125) were significantly associated with shorter survival time, while chemotherapy made contributions to improve survival. Our study found that marital relationships promoted LM and was associated with the better prognosis, while unmarried patients had the opposite results. With the further development of our research, the cross-action of social, economic and psychological factors together determined the great impact of marital status on the morbidity and prognosis of OC patients combined with LM. Finally, the stability of the models was proved by internal verification. Conclusion: The population-based cohort study provides references for guiding clinical screening and individualized treatment of OC patients with LM. Under the influence of society and economy, marital status is closely related to the morbidity and prognosis of OC, which can be an important direction to explore the risk of OC lung metastasis in the future.

Development and validation of novel nomograms to predict survival of patients with tongue squamous cell carcinoma

BACKGROUND

There is no unified standard to predict postoperative survival in patients with tongue squamous cell carcinoma (TSCC), hence the urgency to develop a model to accurately predict the prognosis of these patients.

AIM

To develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) of patients with TSCC.

METHODS

A cohort of 3454 patients with TSCC from the Surveillance, Epidemiology, and End Results (SEER) database was used to develop nomograms; another independent cohort of 203 patients with TSCC from the Department of Oral and Maxillofacial Surgery, First Affiliated Hospital of Zhejiang University School of Medicine, was used for external validation. Univariate and multivariate analyses were performed to identify useful variables for the development of nomograms. The calibration curve, area under the receiver operating characteristic curve (AUC) analysis, concordance index (C-index), net reclassification index (NRI), and decision curve analysis (DCA) were used to assess the calibration, discrimination ability, and clinical utility of the nomograms.

RESULTS

Eight variables were selected and used to develop nomograms for patients with TSCC. The C-index (0.741 and 0.757 for OS and CSS in the training cohort and 0.800 and 0.830 in the validation cohort, respectively) and AUC indicated that the discrimination abilities of these nomograms were acceptable. The calibration curves of OS and CSS indicated that the predicted and actual values were consistent in both the training and validation cohorts. The NRI values (training cohort: 0.493 and 0.482 for 3- and 5-year OS and 0.424 and 0.402 for 3- and 5-year CSS; validation cohort: 0.635 and 0.750 for 3- and 5-year OS and 0.354 and 0.608 for 3- and 5-year CSS, respectively) and DCA results indicated that the nomograms were significantly better than the tumor-node-metastasis staging system in predicting the prognosis of patients with TSCC.

CONCLUSION

Our nomograms can accurately predict patient prognoses and assist clinicians in improving decision-making concerning patients with TSCC in clinical practice.

Oral squamous cell carcinoma outcome in adolescent/young adult: Systematic review and meta-analysis

To perform a systematic review focusing on the prognosis of oral cavity squamous cell carcinoma (OSCC) in young patients (≤40 years old) compared to older (>40 years old). Four databases were used in our search strategy. First, all titles were systematically organized using the Covidence platform online. In the second phase, 118 full texts of potentially eligible studies were analyzed by reviewers independently and in pairs. Twelve studies were considered eligible for data extraction. The relapse was higher in the young than in controls (pooled relative risk (RR) = 1.31; 95% CI [1.10-1.56]). The 5-year disease-free survival (DFS) was worse in young group (pooled hazard ratio (HR) = 0.73; 95% CI [0.63-0.85]) but the 5-year overall survival (OS) estimate was similar between the groups (pooled HR = 0.84; 95% CI [0.70-1.00]). While the 5-year OS was similar between groups, the number of relapses and 5-year DFS were worse in patients with OSCC ≤40 years old.

Research on Effectiveness of Prior Cancer on Survival Outcomes for Patients with Nonmetastatic Triple-Negative Breast Cancer: A Competing Risk Analysis and Propensity Score Matching Analysis of the SEER Database

Introduction

Knowledge of the effect of prior cancer on long-term survival outcomes for patients with nonmetastatic triple-negative breast cancer (TNBC) remained unclear. The aim of this study was to explore and identify the effectiveness of prior cancer on breast cancer-specific death (BCSD) and other cause-specific death (OCSD) in patients with nonmetastatic TNBC.

Materials and Methods

Data of 29,594 participants with nonmetastatic TNBC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Prognostic predictors were identified by propensity score matching (PSM) analysis combined with univariate cumulative incidence function (CIF) and multivariate Fine and Gray competitive risk analyses.

Results

Among the women with nonmetastatic TNBC included in the unmatched cohort, a total of 5,375 (18.2%) subjects had prior cancers (P-TNBC) and 24,219 (81.8%) had no prior cancer (NP-TNBC). Patients with P-TNBC tended to have poorer BCSD (Gray's test, p=0.0131) and OCSD (Gray's test, p=0.0009) in comparison with those with NP-TNBC after PSM. However, the risk of BCSD (p=0.291) and OCSD (p=0.084) found no difference among P-TNBC patients with one prior cancer and two or more prior cancers after PSM. Additionally, subjects with younger age, advanced T stage, advanced N stage, and advanced differentiation grade tumors were likely to develop BCSD, whereas those with breast-conserving surgery (BCS), radiotherapy, or chemotherapy tended to have a lower incidence of BCSD.

Conclusion

Our study demonstrated that prior cancer was related to the worse BCSD and OCSD rate and could be identified as a reliable survival predictor for patients with nonmetastatic TNBC. This study may provide some reference value for the treatment mode of TNBC patients with prior cancer in the future.

Short-Term Quality of Life, Functional Status, and Their Predictors in Tongue Cancer Patients After Anterolateral Thigh Free Flap Reconstruction: A Single-Center, Prospective, Comparative Study

Purpose

To assess the quality of life, functional status, and their predictors in tongue cancer patients up to three months after anterolateral thigh free flap (ALTFF) reconstruction.

Patients and Methods

Tongue cancer patients were examined before and one and three months after ALTFF reconstruction using three validated questionnaires: the MD Anderson Dysphagia Inventory (MDADI), Eating Assessment Tool (EAT-10), and Functional Assessment of Cancer Therapy-Head and Neck (FACT-H&N). Mean scores were compared using the Friedman test. Forward selection and backward elimination methods of multiple linear regression analysis were used to identify the predictors of quality of life and functional status using SPSS at a 95% significance level.

Results

The present study included 265 participants (88.68% males, mean age 46.7 ± 11.05 years). The mean MDADI and EAT-10 scores were highest at one month after the surgery and lowest at three months after the surgery (p < 0.05). The mean FACT-H&N score was lowest at one month after the surgery (p < 0.05); however, post hoc analysis revealed that the difference in the scores before and three months after the surgery was non significant (p > 0.05). Denture use, occupation, age, tumor site, illness duration, drinking habit, and diet predicted the patients' quality of life, while denture use, betel nut consumption, age, marital status, and diet predicted their functional status.

Conclusion

Although tongue cancer patients have a poor quality of life and functional status in the first month after ALTFF reconstruction, their quality of life and functional status improve thereafter. We recommend the implementation of swallowing training programs and case-oriented psychological interventions to assist patients in coping with temporary deterioration during the first month after the surgery.

Prognostic Factors in Patients with Rhabdomyosarcoma Using Competing-Risks Analysis: A Study of Cases in the SEER Database

BACKGROUND

Rhabdomyosarcoma (RMS) is a rare malignant soft-tissue sarcoma characterized by a poor outcome and unclear prognostic factors. This study applied a competing-risks analysis using data from the Surveillance, Epidemiology, and End Results (SEER) database to RMS patients, with the aim of identifying more accurate prognostic factors.

METHODS

Data of all patients with RMS during 1986-2015 were extracted from the SEER database. We used the competing-risks approach to calculate the cumulative incidence function (CIF) for death due to rhabdomyosarcoma (DTR) and death from other causes (DOC) at each time point. The Fine-Gray subdistribution proportional-hazards model was then applied in univariate and multivariate analyses to determine how the CIF differs between groups and to identify independent prognostic factors. The potential prognostic factors were analyzed using the competing-risks analysis methods in SAS and R statistical software.

RESULTS

This study included 3399 patients with RMS. The 5-year cumulative incidence rates of DTR and DOC after an RMS diagnosis were 39.9% and 8.7%, respectively. The multivariate analysis indicated that age, year of diagnosis, race, primary site, historic stage, tumor size, histology subtype, and surgery status significantly affected the probability of DTR and were independent prognostic factors in patients with RMS. A nomogram model was constructed based on multivariate models for DTR and DOC. The performances of the two models were validated by calibration and discrimination, with C-index values of 0.758 and 0.670, respectively.

CONCLUSIONS

A prognostic nomogram model based on the competing-risks model has been established for predicting the probability of death in patients with RMS. This validated prognostic model may be useful when choosing treatment strategies and for predicting survival.

Prognostic factors in patients with gallbladder adenocarcinoma identified using competing-risks analysis: A study of cases in the SEER database

A competing-risks model was developed in this study to identify the significant prognostic factors and evaluate the cumulative incidence of cause-specific death in gallbladder adenocarcinoma (GBAC), with the aim of providing guidance on effective clinical treatments.All patients with GBAC in the Surveillance, Epidemiology, and End Results (SEER) database during 1973 to 2015 were identified. The potential prognostic factors were identified using competing-risks analyses implemented using the R and SAS statistical software packages. We calculated the cumulative incidence function (CIF) for cause-specific death and death from other causes at each time point. The Fine-Gray proportional-subdistribution-hazards model was then applied in univariate and multivariate analyses to test the differences in CIF between different groups and identify independent prognostic factors.This study included 3836 eligible patients who had been enrolled from 2004 to 2015 in the SEER database. The univariate analysis indicated that age, race, AJCC stage, RS, tumor size, SEER historic stage, grade, surgery, radiotherapy, chemotherapy and adjuvant therapy (RCT, SRT, SCT and SRCT) were significant factors affecting the probability of death due to GBAC. The multivariate analysis indicated that age, race, AJCC stage, RS status, tumor size, grade and SRT were independent prognostic factors affecting GBAC cancer-specific death. A nomogram model was constructed based on multivariate models for death related to GBAC.We have constructed the first competing-risks nomogram for GBAC. The model was found to perform well. This novel validated prognostic model may facilitate the choosing of beneficial treatment strategies and help when predicting survival.

Nomograms to predict survival of stage IV tongue squamous cell carcinoma after surgery

To develop clinical nomograms for prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with stage IV tongue squamous cell carcinoma (TSCC) after surgery based on the Surveillance, Epidemiology, and End Results (SEER) program database.We collected data of resected stage IV TSCC patients from the SEER database, and divided them into the training set and validation set by 7:3 randomly. Kaplan-Meier analysis and Cox regression analysis were adopted to distinguish independent risk factors for OS and CSS. Clinical nomograms were constructed to predict the 3-year and 5-year probabilities of OS and CSS for individual patients. Calibration curves and Harrell C-indices were used for internal and external validation.A total of 1550 patients with resected stage IV TSCC were identified. No statistical differences were detected between the training and validation sets. Age, race, marital status, tumor site, AJCC T/N/M status, and radiotherapy were recognized as independent prognostic factors associated with OS as well as CSS. Then nomograms were developed based on these variables. The calibration curves displayed a good agreement between the predicted and actual values of 3-year and 5-year probabilities for OS and CSS. The C-indices predicting OS were corrected as 0.705 in the training set, and 0.664 in the validation set. As for CSS, corrected C-indices were 0.708 in the training set and 0.663 in the validation set.The established nomograms in this study exhibited good accuracy and effectiveness to predict 3-year and 5-year probabilities of OS and CSS in resected stage IV TSCC patients. They are useful tools to evaluate survival outcomes and helped choose appropriate treatment strategies.

Impact of Insurance Status on Stage, Treatment, and Survival in Patients with Colorectal Cancer: A Population-Based Analysis

BACKGROUND This study aimed to analyze data from the Surveillance, Epidemiology, and End Results (SEER) program to identify patients with colorectal cancer (CRC) who had specific insurance details and the effects of stage at diagnosis, definitive treatment, and survival outcome with insurance status. MATERIAL AND METHODS Between 2007 and 2009, SEER database analysis identified 54,232 patients with CRC. Logistic models examined the associations between insurance status and disease stage and definitive treatment. Kaplan-Meier analysis, the Cox model, and the Fine and Gray model were used to compare the tumor cause-specific survival (TCSS) for patients with different insurance status. RESULTS Insured patients were more likely to have earlier tumor stage at diagnosis when compared with patients receiving Medicaid (adjusted OR, 1.318; 95% CI, 1.249-1.391; P<0.001) and when compared with uninsured patients (adjusted OR, 1.479; 95% CI, 1.352-1.618; P<0.001). Insured patients were significantly more likely to undergo definitive treatment when compared with patients receiving Medicaid (adjusted OR, 0.591; 95% CI, 0.470-0.742; P<0.001) and compared with patients who were uninsured (adjusted OR, 0.404; 95% CI, 0.282-0.579; P<0.001). Insured patients had a significantly increased TCSS when compared with patients receiving Medicaid (HR, 1.298; 95% CI, 1.236-1.363; P<0.001) and compared with patients who were uninsured (HR 1.195, 95% CI, 1.100-1.297; P<0.001). CONCLUSIONS Insurance status was a significant factor that determined early diagnosis, definitive treatment, and clinical outcome and was an independent factor for TCSS in patients with CRC.

Nomogram Predicting Cause-Specific Mortality in Nonmetastatic Male Breast Cancer: A Competing Risk Analysis

Introduction: Male breast cancer (MBC) is a rare tumor with few cases for research. Using the Surveillance, Epidemiology, and End Results program database, we carried out a competing risk analysis in patients with primary nonmetastatic MBC and built a predictive nomogram. Materials and Methods: We extracted primary nonmetastatic MBC patients according to the inclusion and exclusion criteria. Cumulative incidence function (CIF) and proportional subdistribution hazard model were adopted to explore risk factors for breast cancer-specific death (BCSD) and other cause-specific death (OCSD). Then we built a nomogram to predict the 3-year, 5-year and 8-year probabilities of BCSD and OCSD. C-indexes, Brier scores and calibration curves were chosen for validation. Results: We identified 1,978 nonmetastatic MBC patients finally. CIF analysis showed that the 3-year, 5-year and 8-year mortalities were 5.2%, 10.6% and 16.5% for BCSD, and 6.1%, 9.6% and 14.4% for OCSD. After adjustment of Fine and Gray models, black race, PR (-), advanced T/N/grade and no surgery were independently associated with BCSD. Meanwhile, elderly, unmarried status, advanced AJCC stage and no chemotherapy resulted in OCSD more possibly. A graphic nomogram was developed according to the coefficients from the Fine and Gray models. The calibration curves displayed exceptionally, with C-indexes nearly larger than 0.700 and Brier scores nearly smaller than 0.100. Conclusion: The competing risk nomogram showed good accuracy for predictive prognosis in nonmetastatic MBC patients. It was a useful implement to evaluate crude mortalities of BCSD and OCSD, and help clinicians to choose appropriate therapeutic plans.