End Results Database Research Articles

Long-term survival and risk factors of synchronous bone metastasis in oral tongue squamous cell carcinoma patients.

The objective of this study was to analyze the risk factors for synchronous bone metastases in patients with oral tongue squamous cell carcinomas (OTSCC). OTSCC patients were extracted from the Surveillance, Epidemiology and End Results database between 2014 and 2017. We examined the association between risk factors and synchronous bone metastases using Chi-squared tests. Predictors of survival rates were assessed using univariate and multivariate analyses. A total of 3902 patients were analyzed, which include 12 patients (0.3%) with synchronous bone metastases and 3890 patients without synchronous bone metastases (99.7%). Multivariate logistic regression analysis showed that highly differentiated disease, lower T classification, and lower N classification were associated with a significantly lower risk of bone metastases (P < 0.05, respectively). Unmarried and elderly patients who harbored with poorly differentiated disease, higher T or N classification, multiple sites of metastases, and no surgical therapy to primary tumor were more likely to influence patients' survival. By analyzing data from a large cohort, factors affecting bone metastases are primarily related to grade, T classification and N classification. Multivariable analysis showed that unmarried and elderly patients who harbored with poorly differentiated disease, higher T or N classification, multiple sites of metastases, and no surgical therapy to primary tumor were more likely to influence patients' survival. More accurate assessments of bone metastasis will be imperative for early diagnosis and treatment in poorly differentiated disease, higher T classification or N classification patients.

Prognostic value of adjuvant chemotherapy for hormone receptor-negative T1a and T1bN0M0 breast cancer patients

The benefit of adjuvant chemotherapy (CT) for hormone receptor-negative T1a and T1bN0M0 breast cancer remains uncertain. Our study was to explore prognostic value and identify candidates of adjuvant CT for these patients. The data of hormone receptor-negative T1a and T1bN0M0 breast cancer patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. All patients were divided into two groups according to the history of adjuvant CT namely the CT group and the no CT (No CT) group. Univariate and multivariate Cox regression analysis were utilized to identify factors linked with cancer specific survival (CSS) and overall survival (OS) for the patients. Kaplan-Meier method was employed to determine survival benefit of adjuvant CT. A total of 3889 patients were included. After propensity score-matching, 1217 patients were assigned to the CT group and 1217 patients were assigned to the No CT group respectively. Based on multivariate Cox regression analysis of OS, older age, single, T1b stage, triple-negative tumor and absence of adjuvant CT were identified as risk factors related to OS. Besides, multivariable Cox regression analysis of CSS showed significant association between grade III+IV, T1b stage, triple-negative tumor and absence of adjuvant CT and CSS. The results from Kaplan-Meier curves revealed that adjuvant CT could bring OS benefit for these patients with more than two risk factors and could improve CSS for the patients with more than one risk factor. Our study supports the implementation of individualized strategies for hormone receptor-negative T1a and T1bN0M0 breast cancer patients. Adjuvant CT was recommended for potential beneficial patients after undertaking a risk-benefit discussion.

Machine learning-based prognostic modeling and surgical value analysis of de novo metastatic invasive ductal carcinoma of the breast.

Whether primary lesion surgery improves survival in patients with de novo metastatic breast cancer (dnMBC) is inconclusive. We aimed to establish a prognostic prediction model for patients with de novo metastatic breast invasive ductal carcinoma (dnMBIDC) based on machine learning algorithms and to investigate the value of primary site surgery. The data used in our study were obtained from the Surveillance, Epidemiology, and End Results database (SEER, 2010-2021) and the First Affiliated Hospital of Nanchang University (1st-NCUH, June 2013-June 2023). We used COX regression analysis to identify prognostic factors. We divided patients into training and validation groups and constructed Extreme Gradient Boosting (XGBoost) prognostic prediction model. In addition, we used propensity score matching (PSM), K-M survival analysis, and COX regression analysis to explore the survival benefit of patients undergoing primary lesion surgery. A total of 13,383 patients were enrolled, with 13,326 from SEER and 57 from 1st-NCUH. The results showed that XGboost had good predictive ability (training set C-index = 0.726, 1year AUC = 0.788, 3year AUC = 0.774, 5year AUC = 0.774; validation set C-index = 0.723, 1year AUC = 0.785.1, 3year AUC = 0.770, 5year AUC = 0.764), which has better predictive power than the Coxph model. We used Shiny-Web to make our model easily available. Furthermore, we found that surgery was associated with a better prognosis in dnMBIDC patients. Based on the XGboost, we can accurately predict the survival of dnMBIDC patients, which can provide a reference for clinicians to treat patients. In addition, surgery may bring survival benefits to dnMBIDC patients.

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.

Optimized Deep Learning Model for Predicting Liver Metastasis in Colorectal Cancer Patients

Colorectal cancer is a leading type of cancer worldwide and a major contributor to cancer fatalities, and liver metastasis is the most likely distant metastasis in colorectal cancer patients. Classifying and predicting whether liver metastasis occurs in colorectal cancer patients can help doctors timely determine the progress of the disease and form a more reasonable treatment plan, which results in a better prognosis for patients. In this paper, using the Surveillance, Epidemiology, and End Results database, selecting both symmetric and asymmetric features, we extracted the disease-related data of 40,870 patients who were pathologically diagnosed with colorectal cancer from 2010 to 2015 and classified and modeled whether the patients developed liver metastasis to show the symmetry of this study. A total of six deep learning models were utilized, and hyperparameter optimization was performed on the models using the Crested Porcupine Optimizer. The best-performing model was selected and model interpretation was performed to explore the features that affect whether patients develop liver metastasis. Among the six deep learning models selected, the FT-Transformer model, which was hyperparameter optimized by the Crested Porcupine Optimizer, performed the best, with an accuracy of 0.945, with a 95% confidence interval (CI) of [0.942, 0.952], and an AUC of 0.949, with a 95% CI of [0.942, 0.957]. This study can help doctors make medical decisions, detect patients with liver metastases of colorectal cancer earlier, monitor the indicators that have a significant impact on the occurrence of liver metastasis in patients, and use timely surgical treatment, radiotherapy, chemotherapy, and other corresponding therapeutic interventions to improve the survival rate of patients.

Prognostic significance of three lymph node staging systems in pancreatic cancer with ≤ 12 and > 12 retrieved lymph nodes.

This study aimed to evaluate and compare the predictive performance of negative lymph nodes (NLN), lymph node ratio (LNR), and N stage in pancreatic ductal adenocarcinoma (PDAC) among patients with ≤ 12 retrieved lymph nodes and those with > 12 retrieved lymph nodes. Moreover, the association between the three nodal staging systems and survival was also explored. Clinical data on patients diagnosed with PDAC between 2004 and 2020 were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression was performed to identify independent predictors of cancer specific survival (CSS) and overall survival (OS). Survival probability was calculated and compared by the Kaplan-Meier method and log rank test. Akaike information criterion (AIC) and Harrell's C-index were used to evaluate the prognostic ability of each nodal staging system. All three lymph node staging systems were independent predictors of CSS and OS. A higher NLN, a lower N stage, and a lower LNR were associated with improved survival. Compared with N stage, LNR staging performed better with a lower AIC and higher C-index for predicting the prognosis regardless of the sufficiency of retrieved lymph nodes, while NLN staging performed poorly in both the training and validation set. Subgroup analyses showed that the NLN successfully predicted survival outcomes in both lymph node-positive and node-negative patients. LNR demonstrated better predictive performance in PDAC patients regardless of the sufficiency of retrieved lymph nodes. Notably, for stage N0 disease, NLN was a more important prognostic predictor. The combination of LNR and NLN may offer more precise information on lymph node staging than the current staging system.

Non-Caucasian Race/Ethnicity Predisposes to Unfavorable Stage and Grade at Upper Tract Urothelial Carcinoma Diagnosis.

To test whether race/ethnicity affects stage or grade distribution at upper tract urothelial carcinoma (UTUC) diagnosis. Within the Surveillance, Epidemiology, and End Results (SEER) database 2004-2020, UTUC patients were identified. Multivariable logistic regression models tested for the association between race/ethnicity and stage as well as grade at diagnosis according to renal pelvis vs. ureteral origin. Stage at presentation was defined as (i) metastatic stage (TanyNanyM1) and (ii) advanced stage (locoregional or metastatic (T3-4N0-2M0-1)). Of 14,384 UTUC patients, 8926 (62%) were renal pelvis. Of renal pelvis UTUC patients, 7064 (79%) were Caucasian, 797 (9%) Hispanic, 623 (7%) Asian Pacific Islander (API), and 442 (5%) African American (AA). Relative to Caucasians, API (odd ratio (OR) 1.85, 95% confidence interval (CI) 1.44-2.37, p < 0.001) and AA (OR 1.37, 95% CI 1.02-1.83, p = 0.03) patients more frequently harbored metastatic stage. APIs also more frequently harbored advanced (OR 1.33, 95% CI 1.11-1.60, p = 0.002) stage and high (OR 1.33, 95% CI 1.07-1.67, p = 0.01) grade. Of 5458 (38%) ureteral UTUC patients, 4360 (80%) were Caucasian, 362 (7%) Hispanic, 509 (9%) API, and 227 (4%) AA. Relative to Caucasians, Hispanic (OR 1.45, 95% CI 1.01-2.05, p = 0.03) patients more frequently harbored metastatic stage. APIs also more frequently harbored advanced stage (OR 1.22, 95% CI 1.02-1.47, p = 0.03) and high (OR 1.94, 95% CI 1.49-2.55, p < 0.001) grade. Race/ethnicity other than Caucasian, such as API and Hispanic, may predispose to higher odds of metastatic (TanyNanyM1) or advanced (T3-4N0-2M0-1) stages as well as to higher grade at initial diagnosis.

Location of Primary Tumor Impacts Survival After Pulmonary Metastasectomy for Colorectal Cancer.

The benefit of pulmonary metastasectomy (PM) in patients with colorectal cancer (CRC) with isolated lung metastases remains unclear and failure to separate colon from rectal cancer may contribute. Utilizing a large national database, we investigate whether PM is associated with survival benefits in patients presenting with CRC with synchronous lung metastases based upon primary tumor location. The Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 was queried to identify patients with stage IV CRC with isolated synchronous lung metastases at initial diagnosis. These patients were restricted to include only those where the location of the primary tumor being either colon or rectum could be conclusively identified, and the primary site was resected. SEER-linked Medicare claims were also queried to identify cases of PM that were not adequately recorded in SEER alone. Patients were then analyzed using Kaplan-Meier (K-M) methods and multivariate analysis was performed to identify variables associated with overall survival (OS). From the SEER database 185,871 metastatic CRC patients were identified. Only 588 had isolated synchronous lung metastases, with 441 with colon cancer and 147 with rectal cancer. PM was performed in 15.3% (n=90) with two-thirds being colon cancer. Univariate K-M demonstrated worse OS for rectal cancer compared to colon cancer, which remained significant on multivariate analysis. OS is associated with site of primary CRC in patients undergoing PM. Distinct mutational and molecular characteristics differences between colon and rectal cancer may explain these findings and is an area for future research.

Dynamic assessment of long-term survival in survivors with stage III non-small cell lung cancer: a novel conditional survival model with a web-based calculator

BackgroundConditional survival (CS) analysis can estimate further survival probabilities based on the time already survived, providing dynamic updates for prognostic information. This study aimed to develop a CS-nomogram to promote individualized disease management for stage III non-small cell lung cancer (NSCLC).MethodsThis study included patients diagnosed with stage III NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2017 (N = 3,512). The CS was calculated as CS(y|x) = OS(y + x)/OS(x), where OS(y + x) and OS(x) were the overall survival (OS) in the year (y + x) and year x, respectively, calculated by the Kaplan–Meier method. We used the least absolute shrinkage and selection operator (LASSO) regression to identify predictors and developed the CS-nomogram based on these predictors and the CS formula.ResultsThe CS analysis provided real-time updates on survival, with 5-year OS improving dynamically from 14.4 to 29.9%, 47.9, 66.0, and 80.8% (after 1–4 years of survival). Six independent predictors (age, tumor size, N status, surgery, radiotherapy and chemotherapy) were identified for the development of the CS-nomogram and its web version (https://dynapp.shinyapps.io/NSCLC/). The model performed with an excellent concordance index (C-index) of 0.71 (95% CI: 0.70–0.72), and a median time-dependent AUC of 0.71–0.73 from 200 iterations 5-fold cross-validation.ConclusionThe study demonstrated the improvement in real-time OS over time in stage III NSCLC survivors and developed the novel CS-nomogram to provide patients with updated survival data. It provided novel insights into clinical decisions in follow-up and treatment for survivors, offering a convenient tool for optimize resource allocation.

Comparative evaluation of TNM staging systems (eighth vs. ninth edition) for the non-surgical treatment of localized and locally advanced anal squamous cell carcinoma: Prognostic significance of T classification and lymph node status.

This study aims to compare the survival discrimination of the Tumor-Node-Metastasis (TNM) eighth and ninth editions for patients with localized and locally advanced (LLA) anal squamous cell carcinoma (ASCC) treated non-surgically and to evaluate the prognostic impact of T classification and lymph node (LN) status with data from the Surveillance, Epidemiology, and End Results database. We retrospectively included 6,876 patients in the comparison. We observed the inversion of survival outcomes for stages IIB and IIIA diseases in the TNM eighth edition [median overall survival (OS): 112 months for stage IIB vs. not reached for stage IIIA]. By contrast, it demonstrated improvement in the TNM ninth edition (median OS: not reached for IIB disease vs. 120 months for IIIA disease, P<0.001). In the correlation analysis, we observed an increased correlation between T classification and TNM staging systems (r value increased from 0.78 to 0.93) and a decreased correlation for the LN status (r value decreased from 0.83 to 0.59). For OS, variable importance analysis demonstrated more weight of importance for the T classification than the LN status (0.0871 vs. 0.0048). Additionally, decision curve analysis and time-dependent receiver operating characteristic analysis confirmed the prognostic accuracy of T classification rather than the LN status. In conclusion, TNM ninth edition is a better prognostic indicator than the eighth edition for patients with LLA ASCC treated non-surgically. T classification plays a more important prognostic role than the LN status and warrants further validation.

Construction and evaluation of the prediction model for advanced disease in well-differentiated colorectal neuroendocrine neoplasms less than 2cm in diameter.

Advanced lesions are often ignored in well-differentiated colorectal neuroendocrine neoplasms (NENs) smaller than 2cm, and we aimed to develop an effective nomogram for these lesions. We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database and used a logistic regression model to identify independent risk factors for advanced disease. All these identified factors were included to construct the prediction model, and the receiver operating characteristic (ROC) curve, calibration plot and DCA curve were utilized to assess the predictive value. The data obtained from the National Cancer Center were utilized for external validation. In total, 3223 patients were enrolled in the training set, including 2947 (91.4%) with early disease and 276 (8.6%) with advanced disease. The logistic analysis showed that age (odds ratio (OR)=1.486, 95% confidence interval (CI): 1.102-2.003, P=0.009), tumor size (OR=11.071, 95% CI: 8.229-14.893, P<0.001), tumor location (OR=7.882, 95% CI: 5.784-10.743, P<0.001) and tumor grade (OR=1.768, 95% CI: 1.206-2.593, P=0.004) were independent variables for advanced disease. All of them were included in the final prediction model. The area under the ROC curve (AUC) was 0.838 (95% CI: 0.807-0.868). The calibration plot and Hosmer‒Lemeshow test (P=0.108) indicated favorable consistency between the predicted probabilities and actual probabilities of advanced disease. The Brier score was 0.108, indicating acceptable overall performance. The DCA curve presented a significant clinical net benefit. In the validation set, both the ROC curve and calibration plot exhibited an acceptable discrimination ability (AUC=0.807 (95% CI 0.702-0.913) and calibration (Hosmer Lemeshow P=0.997), respectively. The prediction model had good value for identifying advanced disease from well-differentiated colorectal NENs smaller than 2cm.

Radiation Therapy Improves Survival in Patients with Distant Metastatic Head and Neck Squamous Cell Carcinoma: A Retrospective Study.

The role of radiation therapy in patients with distant metastatic squamous cell carcinoma of the head and neck (mHNSCC) is unclear. In this study, we compare the differences in survival among mHNSCC patients treated with chemotherapy plus radiotherapy (RT) vs. chemotherapy alone. This study included patients with distant mHNSCC recruited from 2 cohorts: The Surveillance, Epidemiology, and End Results (SEER [N=885]) database and a Chinese single-institution registry in Sun Yat-sen University Cancer Center (SYSUCC [N=60]). The SEER cohort included 600 patients received RT plus chemotherapy and 285 patients received chemotherapy alone; in the SYSUCC cohort, 40 patients received RT plus chemotherapy and 20 patients received chemotherapy alone were recruited. The period of data collection for the SEER study was from January 2010 to December 2015, and that for SYSUCC was from January 2010 to December 2020. The study's primary outcome was overall survival (OS), with disease-specific survival (DSS) as a secondary outcome. Of the 885 patients in the SEER cohort, the addition of RT to chemotherapy increased one-year OS from 46.6% to 56.0% compared with chemotherapy alone (P =0.009) and from 10.4% to 25.1% for three-year OS (P <0.001). Patients who received RT in addition to chemotherapy were also more likely to have better three-year DSS than those who received chemotherapy alone (28.9% vs. 14.0%, P<0.001). Similarly, in the SYSUCC cohort, patients who received chemotherapy plus RT had better three-year OS than chemotherapy alone (62.8% vs. 21.0%, P=0.003). The addition of RT to chemotherapy increased median OS among patients with mHNSCC from 10 months to 13 months in the SEER cohort and from 14 months to 29 months in the SYSUCC cohort. Radiotherapy in addition to chemotherapy significantly improved OS and DSS in patients with mHNSCC.

Development and verification of prognostic nomogram for extraskeletal Ewing's sarcoma based on the SEER database

BackgroundExtraskeletal Ewing's sarcoma (EES) is a rare tumor, and there is currently no predictive model for overall survival of EES patients. This study sought to use data from the Surveillance, Epidemiology, and End Results (SEER) database to develop a clinical predictive model that could be used to assess the prognosis of EES patients. MethodsWe selected and downloaded prognostic data on 356 patients diagnosed with extraskeletal Ewing’s sarcoma based on screening criteria,These patients were distributed between 2004 and 2015. 356 patients were randomly divided into a training cohort (70%) and an internal validation cohort (30%). After univariate or multifactor Cox regression analysis, the relevant variables were screened and a nomogram was constructed. The consistency index (C-index), calibration chart and receiver operating characteristic (ROC) curve were used to evaluate the established nomogram. The clinical utility of the model was verified by clinical decision curve.Study conducted and outcomes reported according to STROBE statement. ResultsAfter multifactor regression analysis, we identified five factors that were significantly associated with EES prognosis, and subsequently established a nomogram. Verification data showed that the C-index of this nomogram was 0.829 (95% CI 0.774 -- 0.884). the AUCs of the nomogram for predicting the 3- and 5-year OS were 0.91 and 0.863. the calibration curves and Decision curve analysis showed that nomogram could predict the prognosis of EES patients. ConclusionStage, age, tumour size, chemotherapy, and surgery may be independent prognostic factors for EES. our study produced a survival nomogram that can be used to predict the prognosis of patients with EES and validated its performance, which may help clinicians evaluate the condition of patients with EES and choose personalized treatment.

Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma

Background There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC). Objectives We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined. Methodology Altogether, 9439 OSCC patients were extracted from the Surveillance, Epidemiology, and End Results database (US). Five ML models – voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression were used to predict OS. Three of these ML algorithms were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm following model training. Results The performance accuracy of the voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression models was 70.2%, 69.9%, 69.1%, 69.4%, and 69.5% respectively, following model training. When the voting ensemble model was compared with cML using temporal validation, the cML showed a comparable performance accuracy. The most significant prognostic factors were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiotherapy, ethnicity, chemotherapy, and M stage. Conclusions cML appears to give reliability to the final prediction and thereby may mark a paradigm shift from model individualism to a more cooperative paradigm. This approach may aid in determining an enhanced individualized treatment for OSCC patients.

Impact of Treatment Strategies on Survival and Within Multivariate Predictive Model for Renal Cell Carcinoma Based on the SEER Database: A Retrospective Cohort Study

Background This project aims to shed light on how various treatment approaches affect RCC patients’ chances of survival and create a prediction model for them. Methods Data from the Surveillance, Epidemiology, and End Results database were used in this investigation. OS and RCSS after radiation, chemotherapy, and surgery were investigated using the Kaplan-Meier approach. Fourteen factors, including gender, age, race, and others, were subjected to univariate and multivariate COX analyses. Predicting RCSS at three, five, or ten years is the main goal. Predicting OS at three, five, or ten years is the secondary endpoint. Cox analyses, both univariate and multivariate, were used to identify prognostic factors. Furthermore, a nomogram was developed to precisely forecast patient survival rates at 3-, 5-, and 10-year intervals. DCA, calibration curves, and ROC were used to assess the nomogram’s efficacy. Results Kaplan-Meier analysis revealed that PN was associated with better survival compared to RN for tumors ≤10 cm. Cox analysis identified 10 independent prognostic factors. These variables included gender, age, race, histological type, histological grade, AJCC stage, N stage, T stage, M stage, and surgical type. Based on these variables, a nomogram for OS and RCSS prediction was created. Conclusion PN is advised over RN for RCC patients whose tumors are less than 10 cm in diameter since it offers more advantages. The combined nomogram model, which is based on clinicopathological characteristics, therapy data, and demographic variables, may be used to predict the survival of RCC patients and perform prognostic and survival analysis with accuracy.

Squamous cell carcinoma predicts worse prognosis in stage IA (≤ 2 cm) non-small cell lung cancer patients following sublobectomy: a population-based study

Recent studies recommend sublobectomy as a surgical approach for non-small cell lung cancer (NSCLC) tumors that are 2 cm or smaller. However, it remains unclear whether NSCLC patients with squamous cell carcinoma (SCC) have comparable outcomes to those with adenocarcinoma (ADC) following sublobectomy. To that end, this study aims to compare the survival outcomes between SCC and ADC in patients with stage IA NSCLC (≤ 2 cm) who have undergone sublobectomy. We identified stage IA (≤ 2 cm) NSCLC patients diagnosed with lung squamous cell carcinoma or adenocarcinoma pathology and underwent sublobectomy from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2020. Overall survival (OS) was determined using the Kaplan–Meier method, and Cox proportional hazards regression was employed to identify risk factors for OS. A total of 9,831 patients diagnosed with stage IA NSCLC (≤ 2 cm) were evaluated. Of these, 2,078 patients met the inclusion criteria, including 1,565 with adenocarcinoma (ADC) and 513 with squamous cell carcinoma (SCC). Notably, SCC was associated with worse overall survival compared to ADC (HR: 2.02, 95% CI: 1.34–3.05, P = 0.03). Subgroup analyses revealed that SCC was comparable to ADC in terms of OS for tumors ≤ 1 cm (HR: 1.22, 95% CI: 0.47–3.18, P = 0.83), while patients with SCC displayed worse OS compared to ADC for tumors > 1 to 2 cm (HR: 2.05, 95% CI: 1.31–3.23, P = 0.002). Cox proportional hazards regression analysis identified female sex (HR: 1.53, 95% CI: 1.08–2.19, P = 0.017), high tumor grade (HR: 1.76, 95% CI: 1.02–3.03, P = 0.011), and SCC (HR: 1.58, 95% CI: 1.08–2.30, P = 0.017) as independent risk factors for OS. In patients with stage IA (≤ 2 cm) NSCLC who underwent sublobectomy, SCC is associated with worse overall survival compared to ADC. Furthermore, being female, having a high tumor grade, and SCC pathology are independent risk factors for OS in these patients.

Prognostic value of lymph node metrics in lung squamous cell carcinoma: an analysis of the SEER database

IntroductionAlthough the Tumor-Node-Metastasis (TNM) staging system is widely used for staging lung squamous cell carcinoma (LSCC), the TNM system primarily emphasizes tumor size and metastasis, without adequately considering lymph node involvement. Consequently, incorporating lymph node metastasis as an additional prognostic factor is essential for predicting outcomes in LSCC patients.MethodsThis retrospective study included patients diagnosed with LSCC between 2004 and 2018 and was based on data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. The primary endpoint of the study was cancer-specific survival (CSS), and demographic characteristics, tumor characteristics, and treatment regimens were incorporated into the predictive model. The study focused on the value of indicators related to pathological lymph node testing, including the lymph node ratio (LNR), regional node positivity (RNP), and lymph node examination count (RNE), in the prediction of cancer-specific survival in LSCC. A prognostic model was established using a multivariate Cox regression model, and the model was evaluated using the C index, Kaplan–Meier, the Akaike information criterion (AIC), decision curve analysis (DCA), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and the predictive efficacy of different models was compared.ResultsA total of 14,200 LSCC patients (2004–2018) were divided into training and validation cohorts. The 10-year CSS rate was approximately 50%, with no significant survival differences between cohorts (p = 0.8). The prognostic analysis revealed that models incorporating LNR, RNP, and RNE demonstrated superior performance over the TNM model. The LNR and RNP models demonstrated better model fit, discrimination, and reclassification, with AUC values of 0.695 (training) and 0.665 (validation). The RNP and LNR models showed similar predictive performance, significantly outperforming the TNM and RNE models. Calibration curves and decision curve analysis confirmed the clinical utility and net benefit of the LNR and RNP models in predicting long-term CSS for LSCC patients, highlighting their value in clinical decision-making.ConclusionThis study confirms that RNP status is an independent prognostic factor for CSS in LSCC, with predictive efficacy comparable to LNR, with both models enhancing survival prediction beyond TNM staging.

Identifying subgroups of ypN1 breast cancer patients who may exempt from axillary lymph node dissection after neoadjuvant chemotherapy: insights from a large cohort study.

In patients with breast cancer staged ypN1 after neoadjuvant chemotherapy (NAC), there is limited evidence-based guidance regarding exemption from axillary lymph node dissection (ALND). This study analyzed ypN1 breast cancer patients post-NAC from the Surveillance, Epidemiology, and End Results databases. Patients were categorized into the breast-conserving surgery (BCS) group and the total mastectomy (TM) group, and further divided by the number of positive lymph nodes (LNs). The effects of three axillary management strategies, ALND, sentinel lymph node biopsy combined with radiotherapy (SLNB + RT), and ALND + RT were compared. The overall survival (OS) and breast cancer-specific survival (BCSS) of all subgroups and their independent risk factors were analyzed. Independent prognostic factors selected from multivariate Cox analysis were utilized to create nomograms for predicting OS and BCSS. A total of 3641 patients were involved, with 1331 in the BCS group and 2310 in the TM group. In the TM group, patients with 3 residual positive LNs exhibited significant improvements in OS and BCSS when treated with ALND + RT. For patients with 1 or 2 residual positive LNs in the TM group and all BCS patients, no significant survival differences in survival outcomes were observed among the three axillary management methods. The accuracy of the nomograms was validated via calibration curves, receiver operating characteristic curves, and decision curve analysis curves. For TM group patients with 3 residual positive LNs after NAC, ALND + RT is recommended. For other subgroups of ypN1 patients, SLNB + RT can be considered an alternative to ALND. The nomogram developed to predict OS and BCSS in ypN1 breast cancer patients demonstrated excellent predictive ability.

Stomach and duodenal ulcer as a cause of death in patients with cancer: a cohort study

IntroductionNon-cancer deaths are now becoming a significant threat to the health of cancer patients. Death from stomach and duodenal ulcer is linked to cancer due to the side effects of treatment and its pathogenesis. However, guidelines for identifying cancer patients at the highest risk of death from stomach and duodenal ulcer remain unclear.MethodsData of all patients diagnosed with cancer between 2000 and 2021 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Data regarding the causes of death and clinicopathological features such as sex, age, race, marital status, SEER stage, and treatment procedures were extracted. We calculated standardized mortality ratios (SMRs) using the SEER*Stat software V8.4.3.ResultsOf the 6,891,191 cancer patients, 2,318 died of stomach and duodenal ulcer, a rate higher than that in the general population (SMR = 1.58, 95% CI [1.52–1.65]). Stomach and duodenal ulcer-related deaths decreased over time from 870 deaths between 2000 and 2004 to 294 deaths between 2015 and 2019. Among the 2,318 stomach and duodenal ulcer deaths, the highest numbers were observed in patients with prostate cancer (n = 389, 16.8%), and lung and bronchus cancer (n = 255, 11%). Patients with liver and intrahepatic bile duct cancers (SMR = 10.53, 95% CI [8.3-13.18]), and pancreatic cancer (SMR = 6.84, 95% CI [5.11–8.97]) had a significantly higher rate of death from stomach and duodenal ulcer than the general population.ConclusionOur study revealed a significantly higher risk of stomach and duodenal ulcer mortality among patients with cancer in the United States, underscoring the critical need for integrated care strategies that address both cancer and ulcer-related complications. To reduce ulcer-related mortality, we recommend the implementation of targeted prevention protocols, including routine gastrointestinal screenings for high-risk cancer patients, proactive management of ulcer risk factors, and collaboration between oncology, gastroenterology, and surgical teams.Graphical abstract

Impact of American Thyroid Association's Revised Cancer Management Guidelines on Thyroid Cancer Incidence Trends: A Retrospective Cohort Study, 2000-2020.

Background: The past four decades have seen a steady increase in thyroid cancer in the United States (US). This study investigated the impact of the American Thyroid Association (ATA)'s revised cancer management guidelines on thyroid cancer incidence trends and how the trends varied by socioeconomic, histologic, geographic, and racial and ethnic characteristics from 2000 to 2020. Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER) database to identify thyroid cancer cases diagnosed among US patients between 2000 and 2020. We employed joinpoint regression software to fit, assess, and compare thyroid cancer incidence trends over time stratified by socioeconomic status (SES), histologic type, geographic location, and race/ethnicity. Results: Between 2000 and 2009, there was an average annual increase of 5.8% in thyroid cancer incidence (average annual percent change (AAPC): 5.8, p < 0.05). Subsequently, there was a modest rise (AAPC: 1.1, p < 0.05) from 2010 to 2015, followed by a significant annual decrease of 4.8% from 2016 to 2020 (AAPC: -4.8, p < 0.05). The joinpoint regression models identified prominent inflection points around 2009 and 2015, aligning with the years of the ATA's cancer management revisions. These intricate dynamics in thyroid cancer incidence trends from 2000 to 2020 were shaped by SES and histologic, geographic, and racial/ethnic factors. Conclusions: Thyroid cancer incidence trends over the past two decades can be partially explained by the changes in thyroid cancer screening and management recommendations. These findings underscore the importance of cancer management strategies and highlight the need for targeted interventions to address disparities in thyroid cancer incidence across minority demographic groups.