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INTRODUCTION: The prevalence of preoperative paraneoplastic syndromes (PNS) in renal cell carcinoma (RCC) is poorly understood. Many laboratory abnormalities representative of PNS have demonstrated prognostic value when incorporated into predictive survival models in RCC. We sought to characterize the relationship between baseline prevalence of PNS with overall survival (OS) and cancer-specific survival (CSS) in RCC patients following nephrectomy. METHODS: Our prospectively maintained nephrectomy database was retrospectively reviewed for any stage, major histology RCC patients that underwent surgery from 2000 to 2022. Baseline laboratory values within 90 days (closest used) were required. Presence of PNS was defined according to established laboratory cutoffs. Kaplan-Meier curves estimated survival rates, and multivariable Cox proportional hazards models examined the association between PNS with OS and CSS following nephrectomy. RESULTS: 2599 patients were included with listed staging: 1494 Stage I; 180 Stage II; 616 Stage III; 306 Stage IV. Proportion of patients presenting with >1 PNS significantly increased from stage I (31.3%) to stage IV (74.2%) RCC (P < .001). Elevated C-reactive protein was the most prevalent PNS (45.4%). On multivariable analysis, the presence of >1 PNS was associated with higher risk of all-cause (HR 2.09; P < .001) and cancer-specific mortality (HR 2.55; P < .001). The 10-year OS estimates as reported: 65.2% (no PNS), 52.3% (1 PNS), 36.6% (>1 PNS); and 10-year CSS estimates: 88.3% (no PNS), 79.3% (1 PNS), 61.6% (>1 PNS). DISCUSSION: Increased prevalence of PNS in major histology RCC was associated with a significant increase in the risk of all-cause and cancer-specific mortality even when accounting for patient and disease characteristics.

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Background: Only a small percentage of patients with large hepatocellular carcinoma (HCC) can undergo surgical resection (SR) therapy while the prognosis of patients with large HCC is poor. However, innovations in surgical techniques have expanded the scope of surgical interventions accessible to patients with large HCC. Currently, most of the existing nomograms are focused on patients with large HCC, and research on patients who undergo surgery is limited. This study aimed to establish a nomogram to predict cancer-specific survival (CSS) in patients with large HCC who will undergo SR. Methods: The study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database encompassing patients with HCC between 2010 and 2015. Patients with large HCC accepting SR were eligible participants. Patients were randomly divided into the training (70%) and internal validation (30%) groups. Patients from Air Force Medical Center between 2012 and 2019 who met the inclusion and exclusion criteria were used as external datasets. Demographic information such as sex, age, race, etc. and clinical characteristics such as chemotherapy, histological grade, fibrosis score, etc. were analyzed. CSS was the primary endpoint. All-subset regression and Cox regression were used to determine the relevant variables required for constructing the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to validate the nomogram. The Kaplan-Meier curve was used to assess the CSS of patients with HCC in different risk groups. Results: In total, 1,209 eligible patients from SEER database and 21 eligible patients from Air Force Medical Center were included. Most patients were male and accepted surgery to lymph node. The independent prognostic factors included sex, histological grade, T stage, chemotherapy, α-fetoprotein (AFP) level, and vascular invasion. The CSS rate for training cohort at 12, 24, and 36 months were 0.726, 0.731, and 0.725 respectively. The CSS rate for internal validation cohort at 12, 24, and 36 months were 0.785, 0.752, and 0.734 respectively. The CSS rate for external validation cohort at 12, 24, and 36 months were 0.937, 0.929, and 0.913 respectively. The calibration curve demonstrated good consistency between the newly established nomogram and real-world observations. The Kaplan-Meier curve showed significantly unfavorable CSS in the high-risk group (P<0.001). DCA demonstrated favorable clinical applicability of the nomogram. Conclusions: The nomogram constructed based on sex, histological grade, T stage, chemotherapy and AFP levels can predict the CSS in patients with large HCC accepting SR, which may aid in clinical decision-making and treatment.

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Background: More muscle-invasive bladder cancer (MIBC) patients are now eligible for bladder-preserving therapy (BPT), underscoring the need for precision medicine. This study aimed to identify prognostic predictors and construct a predictive model among MIBC patients who undergo BPT. Methods: Data relating to MIBC patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2016. Eleven features were included to establish multiple models. The predictive effectiveness was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curve (CIC). SHapley Additive exPlanations (SHAP) were used to explain the impact of features on the predicted targets. Results: The ROC showed that Catboost and Random Forest (RF) obtained better predictive discrimination in both 3- and 5-year models [test set area under curves (AUC) =0.80 and 0.83, respectively]. Furthermore, Catboost showed better performance in calibration plots, DCA and CIC. SHAP analysis indicated that age, M stage, tumor size, chemotherapy, T stage and gender were the most important features in the model for predicting the 3-year cancer-specific survival (CSS). In contrast, M stage, age, tumor size and gender as well as the N and T stages were the most important features for predicting the 5-year CSS. Conclusions: The Catboost model exhibits the highest predictive performance and clinical utility, potentially aiding clinicians in making optimal individualized decisions for MIBC patients with BPT.

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Background: Bone metastasis (BM) is a common site of metastasis in patients with intrahepatic cholangiocarcinoma (ICC), significantly impacting the quality of life and prognosis of affected individuals. This investigation aimed to assess the risk of BM development in ICC patients and to prognosticate for patients with ICC-associated BM (ICCBM) through the construction of two nomograms. Methods: We conducted a retrospective analysis of data from 2,651 ICC patients, including 148 cases of BM, documented in the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. Independent predictors for the occurrence of BM in ICC patients were identified via univariate and multivariate logistic regression analyses; simultaneously, independent prognostic indicators for ICCBM patients were ascertained through univariate and multivariate Cox regression analyses. The utility of the nomograms was evaluated through calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and Kaplan-Meier (KM) analysis. Results: Independent risk factors for BM in ICC included sex, tumor size, lung metastasis, brain metastasis, and intrahepatic metastasis. For ICCBM patients, independent prognostic factors comprised age, chemotherapy, and radiotherapy. The prognostic nomogram exhibited C-indexes of 0.737 [95% confidential interval (CI): 0.682-0.792] for the training cohort and 0.696 (95% CI: 0.623-0.769) for the validation cohort. Calibration curves demonstrated strong concordance between predicted outcomes and observed events. The areas under the curve (AUC) for 3-, 6-, and 12-month cancer-specific survival (CSS) were 0.853, 0.781, and 0.739, respectively, in the training cohort, and 0.794, 0.822, and 0.780 in the validation cohort. DCA illustrated significant net benefits across a broad spectrum of threshold probabilities. KM analysis revealed 1-, 2-, and 3-year CSS rates of 23.91%, 7.55%, and 2.35%, respectively, with a median CSS of 6 months, underscoring the nomograms' capacity to distinctly stratify patients according to survival risk. Conclusions: The development of these nomograms offers substantial clinical utility in forecasting BM risk among ICC patients and prognosticating for those with ICCBM, thereby facilitating the formulation of more efficacious treatment modalities.

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Background: It has been discovered that the prognosis of patients with differentiated thyroid cancer (DTC) correlates with age at initial diagnosis. However, there are disagreements over the optimal cutoff age among the numerous staging and risk stratification criteria, which make it inconsistent to predict the clinical prognosis of specific DTC patients. This study aimed to determine the optimum cutoff age for diagnosis in relation to the clinical outcomes of DTC using data from the Surveillance, Epidemiology and End Results (SEER) database. Methods: The best age cutoff value was determined by the X-tile software. The link between clinical characteristics and cancer-specific survival (CSS) was examined using univariate and multivariate Cox regression models. An additional application of the independent prognostic criteria, such as age stratifications, was applied to construct a nomogram model for predicting the chances of patient survival. Results: The most accurate diagnosis cutoff age for DTC patients was suggested to be 67 years old. The multivariate analysis, using factors determined by univariate analysis, showed that age [>67 years, hazard rate (HR) =5.049, 95% confidence interval (CI): 4.509-5.653, P<0.001], sex (female, HR =0.651, 95% CI: 0.584-0.727, P<0.001), tumor size (>20 and ≤40 mm, HR =2.296, 95% CI: 1.983-2.658, P<0.001; >40 mm, HR =4.976, 95% CI: 4.304-5.752, P<0.001), lymphadenectomy (HR =1.337, 95% CI: 1.186-1.506, P<0.001), distant metastasis (HR =12.166, 95% CI: 10.749-13.769, P<0.001) and surgical treatment (HR =0.173, 95% CI: 0.144-0.210, P<0.001) were independent factors for CSS. Patients in the high-risk group had worse survival rates, and the C-index for the CSS prediction model with age (cutoff of 67) and other independent clinicopathological variables was 0.906. Conclusions: Accordingly, the optimal cutoff age for predicting death from DTC specifically is 67 years old at the time of the initial diagnosis. It might be a more suitable factor when used in risk stratification for patients with DTC.

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Introduction  Gastric cancer, a significant public health concern, remains one of the most challenging malignancies to treat effectively. In the United States, survival rates for gastric cancer have historically been low, partly due to late-stage diagnosis and disparities in access to care. The Affordable Care Act (ACA) sought to address such disparities by expanding healthcare coverage and improving access to preventive and early treatment services.  Objective This study aims to determine the causal effects of the ACA's implementation on gastric cancer survival rates, focusing on a comparative analysis between two distinct U.S. states: New Jersey, which fully embraced ACA provisions, and Georgia, which has not adopted the policy, as of 2023.  Methods In this retrospective analysis, we utilized data from the Surveillance, Epidemiology, and End Results Program (SEER) registry to assess the impact of the ACA on cancer-specific survival (CSS) among gastric cancer patients. The study spanned the period from 2000 to 2020, divided into pre-ACA (2000-2013) and post-ACA (2016-2020) periods, with a two-year washout (2013-2015). We compared Georgia (a non-expansion state) to New Jersey (an expansion state since 2014) using a Difference-in-Differences (DiD) approach. We adjusted for patient demographics, income, metropolitan status, disease stage, and treatment modalities.  Results Among 25,061 patients, 58.7% were in New Jersey (14,711), while 41.3% were in Georgia (10,350). The pre-ACA period included 18,878 patients (40.0% in Georgia and 60.0% in New Jersey), and 6,183 patients were in the post-ACA period (45.2% in Georgia and 54.8% in New Jersey). The post-ACA period was associated with a 20% reduction in mortality hazard among gastric cancer patients, irrespective of the state of residence (HR = 0.80, 95% CI: 0.73-0.88). Patients who were residents of New Jersey experienced a 12% reduction in mortality hazard compared to those who resided in Georgia in the post-ACA period (HR = 0.88, 95% CI: 0.78-0.99). Other factors linked to improved survival outcomes included surgery (OR = 0.30, 95% CI: 0.28-0.34) and female gender (OR=0.83, 95% CI: 0.76-0.91).  Conclusion The study underscores the ACA's potential positive impact on CSS among gastric cancer patients, emphasizing the importance of healthcare policy interventions in improving patient outcomes.

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Background: Cisplatin-based combination chemotherapy alone is currently considered the standard of care for patients with metastatic upper tract urothelial carcinoma (mUTUC). However, less research has been done on the efficacy of other combinations. In this study, we explored the role of cytoreductive surgery in patients with mUTUC receiving different types of systemic therapy. Methods: Data from 9,436 anonymized records were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2008-2018. Of these, 508 individuals received systemic therapy subsequent to being diagnosed with mUTUC. These patients had all been treated with systemic therapies such as chemotherapy and/or radiotherapy. Patients were stratified into either a non-surgical or surgical group based on cytoreductive surgery status before systemic therapeutics commenced. Kaplan-Meier curves were used to compare overall survival (OS) and cancer-specific survival (CSS). Cox's proportional hazard models were then used to analyze prognostic factors related to OS and CSS. Results: Of the 508 cases, 36.8% (n=187) had received cytoreductive surgery with systemic treatments. The remaining 63.2% (n=321) received either chemotherapy and/or radiotherapy alone. Kaplan-Meier curves showed that 11.6% had 3-year OS [95% confidential interval (CI): 7.1-17.3] for cytoreductive surgery with systemic treatment and 4.9% (95% CI: 2.7-8.0) for systemic treatment alone (P=0.001). The 3-year CSS was 14.9% for cytoreductive surgery plus systemic treatment (95% CI: 9.4-21.7%) and 6.0% (95% CI: 3.4-9.8%) for systemic treatments alone (P=0.003). Under multivariate regression analysis, primary ureter site OS had a hazard ratio (HR) of 0.74 (95% CI: 0.58-0.95, P=0.02) and a CSS HR of 0.72 (95% CI: 0.56-0.94, P=0.01). The cytoreductive surgery OS HR was 0.79 (95% CI: 0.65-0.95, P=0.02) and the CSS HR was 0.75 (95% CI: 0.61-0.92, P=0.006). Additionally, chemotherapy had an OS HR of 0.46 (95% CI: 0.33-0.0.65, P<0.001) and a CSS HR of 0.44 (95% CI: 0.31-0.63, P<0.001). Bones and liver metastases were also indicative of poorer prognosis. Validation was conducted through subgroup analysis which suggested cytoreductive surgery was effective only for patients who received chemotherapy or combined chemo-radiotherapy but not for radiotherapy alone. Conclusions: Cytoreductive surgery provided significantly increased OS and CSS for mUTUC patients who received chemotherapy or combined chemo-radiotherapy in this study. In addition, the primary tumor and metastatic sites were shown to be related to improved patient survival although this was a small and relatively homogeneous cohort of study, sample therefore, further research is required.

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Background: Hepatocellular carcinoma (HCC) poses a global threat to life; however, numerical tools to predict the clinical prognosis of these patients remain scarce. The primary objective of this study is to establish a clinical scoring system for evaluating the overall survival (OS) rate and cancer-specific survival (CSS) rate in HCC patients. Methods: From the Surveillance, Epidemiology, and End Results (SEER) Program, we identified 45,827 primary HCC patients. These cases were randomly allocated to a training cohort (22,914 patients) and a validation cohort (22,913 patients). Univariate and multivariate Cox regression analyses, coupled with Kaplan-Meier methods, were employed to evaluate prognosis-related clinical and demographic features. Factors demonstrating prognostic significance were used to construct the model. The model's stability and accuracy were assessed through C-index, receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curve analysis (DCA), while comparisons were made with the American Joint Committee on Cancer (AJCC) staging. Ultimately, machine learning (ML) quantified the variables in the model to establish a clinical scoring system. Results: Univariate and multivariate Cox regression analyses identified 11 demographic and clinical-pathological features as independent prognostic indicators for both CSS and OS using. Two models, each incorporating the 11 features, were developed, both of which demonstrated significant prognostic relevance. The C-index for predicting CSS and OS surpassed that of the AJCC staging system. The area under the curve (AUC) in time-dependent ROC consistently exceeded 0.74 in both the training and validation sets. Furthermore, internal and external calibration plots indicated that the model predictions aligned closely with observed outcomes. Additionally, DCA demonstrated the superiority of the model over the AJCC staging system, yielding greater clinical net benefit. Ultimately, the quantified clinical scoring system could efficiently discriminate between high and low-risk patients. Conclusions: A ML clinical scoring system trained on a large-scale dataset exhibits good predictive and risk stratification performance in the cohorts. Such a clinical scoring system is readily integrable into clinical practice and will be valuable in enhancing the accuracy and efficiency of HCC management.

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Background: Surgery is the only curative treatment strategy for parathyroid carcinoma (PC). However, the optimal extent of surgery remains uncertain, particularly regarding whether routine central lymph node dissection (LND) confers a survival advantage to patients with PC. This study aimed to evaluate the prognostic value of LND in PC patients. Methods: Patients diagnosed with PC between 2004 and 2018 were identified in the Surveillance, Epidemiology, and End Results (SEER)-18 registries. With inclusion and exclusion criteria, a total of 338 patients were included as cohort 1 to describe the characteristics of PC, while 215 patients were selected as cohort 2 to assess the effect of LND on cancer-specific survival (CSS). Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors associated with CSS. Propensity score matching (PSM) was performed to adjust for potential confounding variables. The prognostic value of LND was further analyzed in subgroups stratified by predictors associated with CSS. Results: The 5- and 10-year CSS were 94.4% and 87.9% respectively in cohort 1. LND failed to significantly improve CSS in the entire cohort 2 and the PSM cohort 2. Large tumor size (>40 mm) and distant metastasis were independently associated with poor CSS. Subgroup analyses revealed that LND was not significantly associated with improved CSS in patients with aggressive PC, such as those with a tumor size greater than 40 mm. Unexpectedly, LND may compromise CSS in patients with distant disease (P=0.03). Conclusions: PC is a rare and indolent endocrine malignancy. The presence of large tumors and distant metastases are independent predictors of poor CSS. Routine central LND as part of initial surgery does not significantly improve CSS in PC patients, even for those with large tumors, lymph node metastasis, or distant disease.

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Background: Significant advancements in systemic treatment for hepatocellular carcinoma have been made in recent years. However, the optimal timing of systemic treatment before or after surgery remains unknown. This study aims to evaluate the impact of sequencing systemic treatment and surgical intervention on the long-term prognosis of hepatocellular carcinoma patients. Methods: In our study, we analyzed data from patients diagnosed with primary liver cancer (2004-2015) extracted from the SEER database. Patients who underwent both systemic treatment and surgical intervention were selected, divided into preoperative and postoperative systemic therapy groups. The primary endpoint of the study is overall survival(OS), and the secondary endpoint is cancer-specific survival (CSS). Propensity score matching (PSM) reduced the influence of confounding factors, while Kaplan-Meier curves and a multivariable Cox proportional hazards model accounted for variables during survival analysis. Results: A total of 1918 eligible HCC patients were included, with 1406 cases in the preoperative systemic treatment group and 512 cases in the postoperative systemic treatment group. Survival analysis showed that both the preoperative group demonstrated longer median overall survival (OS) and median cancer-specific survival (CSS) before and after PSM. After conducting multivariate COX regression analysis with stepwise adjustment of input variables, the postoperative systemic treatment group continued to exhibit a higher risk of all-cause mortality (HR: 1.84, 95% CI: 1.55-2.1) and cancer-specific mortality (HR: 2.10, 95% CI: 1.73-2.54). Subgroup analysis indicated consistent results for overall survival (OS) across different subgroups. Conclusions: Hepatocellular carcinoma patients from the SEER database who received preoperative systemic therapy had superior OS and CSS compared to those who received postoperative systemic therapy.

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Background: Hepatoblastoma (HB) is a prevalent form of liver cancer in pediatric patients, characterized by an embryonal malignant tumor. In the current study, a clinical prediction model was developed; that can effectively assess the likelihood of a patient's survival with HB. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database for cases of HB between 2010 and 2019 were used in this retrospective research. Information on clinicopathologic characteristics, therapeutic interventions, and survival outcomes were included in the data. The HB patients were randomly assigned to the training or validation cohort in a 7:3 ratio. Using univariate and multivariate Cox proportional hazards regression models, the prognostic indicators for overall survival (OS) and cancer-specific survival (CSS) were identified. The area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and concordance index (C-index) were used to evaluate the accuracy and calibration of these models. The clinical utility of the models was examined using decision curve analysis (DCA). Results: The multivariate Cox regression analysis revealed multiple autonomous prognostic determinants for the OS and CSS, including age, surgical interventions, and chemotherapy administration. Significantly, tumor size was found to be a strong predictor of OS. AUC values of 0.915, 0.846, and 0.847 for 1-, 3-, and 5-year OS, respectively, indicated that the nomogram-based models were highly accurate at predicting outcomes. Similarly, the AUC values for CSS were 0.871, 0.814, and 0.825. The C-index measurements, which quantify the discriminatory performance of the models, produced CSS values of 0.836 and OS values of 0.864. Furthermore, the calibration plots accurately represented the actual survival rates. Concurrently, the DCA had validated the clinical relevance of the nomogram-based models. Conclusions: The present study successfully developed and validated user-friendly nomogram-based models, allowing for accurate assessment of OS and CSS in pediatric HB patients. These tools enable personalized survival predictions, enhance risk stratification, and strengthen clinical decision-making for managing HB.

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Background and objectives: The purpose of this research was to develop and validate the first prognostic nomograms for 3-, 5-, and 10-year cancer-specific survival (CSS) and overall survival (OS) in patients diagnosed with locally advanced thyroid cancer (LATC) by evaluating independent predictors of prognosis in a population of LATC patients. Methods: Demographics, clinicopathologic characteristics, treatment, and follow-up of 2396 LATC patients in the surveillance, epidemiology, and end results database from 2004 to 2015 were retrospectively analyzed and compared with patients with LATC according to staging. We randomized all LATC patients into training and validation groups in a 7:3 ratio. Cox regression analyses helped us to derive independent prognostic factors for LATC patients. According to these results, we established and validated the first prognostic nomograms and risk stratification. Results: In our research, the clinical information of LATC patients was compared and significant differences were found in the relevant variables including CSS and OS (P < 0.05), with CSS of 82.0 % and 49.0 %, and OS of 70.6 % and 40.0 %, respectively. Cox regression analyses showed that age at diagnosis, tumor diameter, presence of DM, extrathyroidal extension sites, histological type, thyroidectomy scope, radiotherapy status, and chronological sequence of radiotherapy and surgery were observably correlated with CSS in LATC patients, and in addition to the above factors, gender, marital status, and chemotherapy status were also observably correlated with OS in LATC patients. The prognostic predictive power of the above factors is visualized by the Kaplan-Meier survival curve. The concordance index of nomograms for CSS and OS were 0.933, 0.925, and 0.926 (CSS), 0.918, 0.909, and 0.906 (OS), respectively, and the time-dependent receiver operating characteristic curve, area under curve, calibration curve and decision curve analysis curve indicate that the nomograms have good discriminatory ability, accuracy and clinical applicability in both the training and validation groups. Conclusions: In these findings, we drawed a conclusion that there were significant differences in clinical information between patients with T4a and T4b LATC, and we established and validated the first prognostic nomograms and risk stratification of CSS and OS for LATC patients at 3, 5, and 10 years, which will help clinicians to individualize their postoperative treatment and individualized follow-up.

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Background: Medullary breast carcinoma (MBC) is a rare type of breast cancer. Our study aimed to compare the differences in clinical characteristics and prognosis between MBC and invasive ductal carcinoma (IDC), and to further develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in MBC patients. Methods: A total of 179,613 patients from the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2015, including 596 MBC patients, were analyzed using the Kaplan-Meier method and propensity score matching (PSM) to compare patients' OS and CSS. Cox proportional hazard regression model was used to determine independent prognostic factors for OS and CSS in MBC patients. Nomograms were constructed based on Cox regression analysis whereas receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy. Results: There were significant differences in the clinical characteristics between MBC and IDC. According to the logrank test, MBC had better OS and CSS than IDC before and after PSM. Cox multivariate analysis showed that age, race, tumor size, lymph node (LN), and radiation therapy were independent prognostic factors for OS, whereas age, tumor size, American Joint Committee on Cancer (AJCC) stage, laterality, type of surgery, and chemotherapy were independent prognostic factors for CSS. Nomograms of OS and CSS were constructed based on independent prognostic factors. Conclusions: MBC had better OS and CSS than IDC. Nomograms based on clinicopathological features were sufficiently accurate in predicting the OS and CSS for MBC patients, which can effectively predict the survival risk of MBC patients and guide clinicians to provide more effective treatment measures.

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OBJECTIVES: This study aimed to gather evidence for the survival benefit of cancer-directed surgery (CDS) in metastatic head and neck cancer (M1 HNC) and identify which patients will benefit most from CDS. METHODS: Patients with M1 HNC were identified within the SEER database. According to whether received CDS, patients were divided into the CDS and non-CDS groups. The bias between the two groups was minimized using Propensity Score Matching (PSM), and the prognostic role of CDS was investigated using Kaplan-Meier analysis, log-rank test, and Cox proportional hazard models. The primary endpoint was overall survival (OS), and the secondary endpoint was cancer-specific survival (CSS). RESULTS: A total of 3215 patients with M1 HNC were extracted, including 566 patients who received CDS that were 1:1 propensity score-matched with patients who did not receive CDS. In the matched dataset, the median OS and CSS in CDS groups were significantly higher than in non-CDS groups (OS: 19.0 vs. 9.0 months, p < 0.001; CSS: 21.0 vs. 9.0 months, p < 0.001). Meanwhile, multivariable Cox regression analysis also revealed that CDS was a favorable prognostic factor for both OS and CSS. Furthermore, subgroups of patients with M1 HNC (younger age, being married, grade I-II, oropharynx site, earlier T/N stage, radiotherapy) were inclined to benefit from CDS, while those patients who received chemotherapy failed to benefit from CDS. CONCLUSIONS: This study indicated that CDS was associated with improved survival in M1 HNC, especially for those subpopulations that benefit more from CDS treatment. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1288-1298, 2024.

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Background: This study aimed to construct and verify nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for locally advanced gastric cancer (LAGC) based on a therapeutic selection, demographic factors, and pathological features. Methods: The data used for the analysis were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed based on the Cox regression model. Results: The entire cohort comprised 21,757 patients with histologically confirmed LAGC, and was randomly distributed into training and verification groups at a ratio of 2:1 for building the prognostic predictive model. According to the multivariate analysis, 13 variables [i.e., age, marital status, race, tumor location, pathological grade, histological type, T and N stage, surgery, radiotherapy, chemotherapy, tumor size, and regional nodes examined (RNE)] were confirmed as independent predictors for both OS and CSS. All of the significant variables were used to create the nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), the C-index, and calibration curves were applied to identify the discriminating superiority of the nomograms. Conclusions: The nomograms for OS and CSS in LAGC were built and validated based on the therapeutic selection and pathological and demographic variables using a national database. This study aims at helping clinicians make better clinical decisions and encouraging patients receive treatment actively.

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BACKGROUND: The aim of this study was to evaluate independent predictors of prognosis in patients with mixed medullary and follicular cell carcinoma (MMFCC) and to establish the novel prognostic nomograms in the academic community for 3-, 5-, and 10 year CSS and OS in patients with MMFCC. METHODS: Demographic information, clinicopathological characteristics, treatment information, and survival status information of 200 patients with MMFCC and 6615 patients with medullary thyroid carcinoma (MTC) from 2000 to 2020 in the SEER database were retrospectively analyzed. Independent predictors of prognosis in MMFCC patients were derived using univariate and multivariate Cox regression analyses after relevant comparisons based on pathologic typing. On this basis, we developed and validated clinical prognostic nomograms and risk-stratified the patient population. RESULTS: In this study, the clinical information of 200 patients with MMFCC was compared with that of 5947 patients with MTC (NOS) and 668 patients with MTC with amyloid stroma, and there was a significant difference in the relevant variables among the three, with the CSS being 88.5%, 87.5%, and 90.9%, and the OS being 76.5%, 75.4%, and 83.8%. Univariate and multivariate Cox regression analyses yielded that age at diagnosis, presence of distant metastases, thyroidectomy scope, and lymph node dissection status were significantly correlated with the prognosis of patients (P < 0.05), and were independent predictors of CSS and OS for patients with MMFCC, and the Kaplan-Meier survival curves plotted by these factors demonstrated their predictive power for the prognosis of patients with MMFCC. The concordance index of the prognostic nomograms of CSS and OS established on this basis was 0.838 and 0.794, respectively, and the time-dependent area under curve, calibration curve, and decision curve analysis curve showed that the model had good discriminative ability, accuracy, and clinical applicability. CONCLUSIONS: In this study, we concluded that there are large differences between MMFCC and MTC in terms of demographic information, clinicopathological characteristics, treatment information, and survival status information, and we constructed the novel prognostic nomograms for 3-, 5-, and 10 year CSS and OS for patients with MMFCC with risk stratification, which will help clinicians to develop individualized protocols for their postoperative treatments and follow-ups.

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Background: Current staging systems for hepatocellular carcinoma (HCC) still have limitations in clinical practice. Our study aimed to explore the prognostic factors and develop a new nomogram to predict the cancer-specific survival (CSS) for patients with HCC. Methods: A total of 6,166 HCC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly grouped into the training cohort (70%) and validation cohort (30%). Multivariate Cox analysis was used to identify prognostics factors for CSS of patients, then we incorporated these variables and presented a new nomogram to predict 2- and 5-year CSS. The performance of the nomogram was assessed with respect to its calibration, concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), and decision curve analysis (DCA). Results: Multivariate Cox analysis revealed that American Joint Committee on Cancer (AJCC) stage, race, grade, surgery, chemotherapy, radiation, tumor size, bone metastasis (BM), and alpha-fetoprotein (AFP) were independently associated with CSS. The prediction nomogram which contained these predictors showed good performance, with a C-index of 0.802 [95% confidence interval (CI), 0.792-0.812] in the training cohort and 0.801 (95% CI, 0.787-0.815) in the validation cohort. The calibration curves demonstrated good agreement between the actual observation and the nomogram prediction. Furthermore, the nomogram showed improved discriminative capacity (AUC, 0.873 and 0.875 for 2- and 5-year CSS in validation set) compared to the 7th tumor-node-metastasis (TNM) staging system (AUC, 0.735 and 0.717). The DCA also indicated good application of the nomogram. Conclusions: This study presents a novel nomogram that incorporates the important prognostic factors of HCC, which can be conveniently used to accurately predict the 2- and 5-year CSS of patients with HCC, thus assisting individualized clinical decision making.

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PURPOSES: Invasive pancreatic cystic neoplasms (iPCNs) are an uncommon and biologically heterogeneous group of malignant neoplasms. We aimed to investigate the clinicopathological characteristics of iPCN patients and to develop nomograms for individual survival prediction after radical surgery. METHODS: Data of patients diagnosed with iPCN and pancreatic ductal adenocarcinoma (PDAC) between 2000 and 2018 from the SEER database were retrieved. The differences in clinical outcomes were evaluated using the Kaplan-Meier analysis. Nomograms were proposed based on the Cox regression model and internally validated by C-index, area under the curve (AUC) value, and calibration plot. RESULTS: A total of 7777 iPCN patients and 154,336 PDAC patients were enrolled. Most neoplasms were advanced, with 63.1% at stage IV. The 3-year overall survival (OS) and cancer-specific survival (CSS) rates in surgical patients were as follows: 45.7% and 50.1% for invasive intraductal papillary mucinous neoplasm (IPMN), 54.8% and 59.3% for invasive mucinous cystic neoplasm (MCN), 97.8% and 98.2% for invasive solid pseudopapillary neoplasm (SPN), 88.9% and 88.9% for invasive serous cystic neoplasm (SCN), and 27.3% and 30.5% for PDAC. Subgroup analyses showed no clinical benefit from chemotherapy or radiotherapy in lymph node-negative iPCN patients who underwent surgery. The following variables associated with OS and CSS were identified: age, race, chemotherapy, radiotherapy, histologic type, pathological grade, regional nodes examined, and T, N, and M stage. The nomograms had good discrimination and calibration by internal validation, with an AUC value of 0.800 for 3-year OS and 0.814 for 3-year CSS. CONCLUSION: Our study showed that the prognosis of iPCN patients was significantly better than PDAC patients. The proposed nomograms demonstrated substantially better discrimination and calibration.

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Cutaneous squamous cell carcinoma (cSCC) is one of the most common skin malignancies. Patients with metastatic cSCC (mcSCC) tended to have unfavorable prognosis. However, there is no available models to evaluate the survival outcomes for these patients. This study retrospectively collected mcSCC cases identified from The Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2018. All patients were randomly divided into the training and validation cohorts. The independent factors which predicted the cancer-specific survival (CSS) were identified by Cox proportional hazard regression analysis, and were used to build nomogram. he discrimination ability of nomograms was evaluated using the concordance index (C-index). The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) at 1, 3, and 5 years were calculated to estimate the predictive accuracy. The performance of this nomogram was evaluated by the calibration curves. The decision curve analyses (DCAs) were conducted to assess the clinical usefulness of the models. Based on the novel nomograms, all patients were classified into low- and high-risk groups. Kaplan-Meier curves were plotted to compare the survival outcomes of two groups. All analyses were conducted by R software (Version 4.1.3). A total of 916 patients were included. Age, marital status, location of the primary sites, number of metastases, T stage, N stage, surgical resection of the primary sites, radiotherapy of the primary sites, and chemotherapy were significantly associated with CSS. A nomogram predicting CSS was built based on these parameters. C-index and ROC curves of nomogram showed promising discriminating and predicting ability for CSS. Survival analysis showed that patients in the low-risk group had significant superior CSS time than those in the high-risk group (median CSS time: 15.0 vs. 5.0 months, p < 0.001). In conclusion, we developed and validated a novel nomogram predicting the CSS in metastatic cSCC patients. This nomogram could be used to assess the prognosis of metastatic cSCC patients, and help clinicians evaluating optimal treatment options for individual patient.

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Background: Adenosquamous carcinoma of the gallbladder (GBASC) is a rare histological variant without defined appropriate clinical measures. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database, data on a cohort of patients with GBASC were collected from 21 cancer registries between 1975 and 2018. We used Kaplan-Meier analysis to evaluate the effectiveness of different treatment regimens on patients with GBASC. Then we used Cox proportional hazards regression method to determine the prognostic factors for cancer-specific survival (CSS) of GBASC patients. Results: A total of 388 patients with GBASC were identified: 80 patients diagnosed as early stage and 308 patients diagnosed as advanced stage. For early-stage GBASC, radical lymph node dissection improved the CSS significantly; for advanced-stage GBASC, radical surgery, nonradical surgery, lymph node dissection, chemotherapy, and radiotherapy improved the CSS significantly. Surgery, lymph node dissection, radiation, chemotherapy, age, race, and the American Joint Committee on Cancer (AJCC) stage were the independent risk factors for the CSS of GBASC patients. Conclusions: Radical intraoperative lymph node dissection provided a survival benefit for patients with early-stage GBASC, whereas chemotherapy and radiotherapy provided no significant benefit; surgical treatment, more complete lymph node dissection, radiotherapy, and chemotherapy provided survival benefits for patients with advanced GBASC. The prognosis for GBASC patients is affected by the factors of surgery, lymph node dissection, radiation, chemotherapy, age, race, and the AJCC stage.