BACKGROUND: 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).
CONCLUSIONS: 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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: Few models have been developed to predict survival outcomes for lung adenocarcinoma (LUAD). In this study, we aimed to establish a nomogram for the prediction of cancer-specific survival (CSS) in LUAD patients which can be further developed as a convenient web-based calculator.
UNASSIGNED: We performed a retrospective analysis of 50,007 LUAD patients selected from the Surveillance, Epidemiology, and End Result (SEER) 18 registry database. To enhance the reliability of the analysis, the patients\' data were further randomly divided into the training cohort (70%) and validation cohort (30%). The optimal age cut-off points were determined using X-tile software, and patients were divided into three age groups: 10-72, 73-79, and 80-99 years. We selected independent prognostic factors from 17 variables by Cox regression, and plotted a visual nomogram to predict the 1-, 3-, and 5-year CSS. The predictive performance of the nomogram was evaluated through the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve. To facilitate CSS forecast, a web-based calculator has subsequently been developed.
UNASSIGNED: We selected sex, age, race, marital status, N stage, tumor size, surgery, radiotherapy, chemotherapy, and metastasis (bone, brain, liver, and lung) as independent prognostic factors. The C-index was 0.779 [95% confidence interval (CI): 0.775-0.783] in the training set prediction model, and 0.782 (95% CI: 0.778-0.786) in the validation set. ROC analysis showed that area under the curve (AUC) values were 0.700, 0.733 and 0.669 for the 1-, 3- and 5-year CSS in the training set and 0.700, 0.744 and 0.669 in the validation set, respectively. In the nomogram calibration curve, there was strong correlation between the observed and predictive values. A web-based calculator can be accessed at: https://hjhlovelfb.shinyapps.io/DynNomapp/.
UNASSIGNED: This nomogram model has good predictive power and can help clinicians identify LUAD patients at high risk of cancer-related death. This nomogram is expected to be a precise and personalized tool for predicting the prognosis of patients with LUAD.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.