Search

The overall survival (OS) improvement after the advent of several novel systemic therapies, designed for treatment of metastatic urothelial carcinoma of the urinary bladder (mUCUB), is not conclusively studied in either contemporary UCUB patients and/or non‐UCUB patients. Within the Surveillance, Epidemiology, and End Results database, contemporary (2017–2020) and historical (2000–2016) systemic therapy‐exposed metastatic UCUB and, subsequently, non‐UCUB patients were identified. Separate Kaplan–Meier and multivariable Cox regression (CRM) analyses first addressed OS in mUCUB and, subsequently, in metastatic non‐UCUB (mn‐UCUB). Of 3443 systemic therapy‐exposed patients, 2725 (79%) harbored mUCUB versus 709 (21%) harbored mn‐UCUB. Of 2725 mUCUB patients, 582 (21%) were contemporary (2017–2020) versus 2143 (79%) were historical (2000–2016). In mUCUB, median OS was 11 months in contemporary versus 8 months in historical patients (Δ = 3 months; p <.0001). After multivariable CRM, contemporary membership status (2017–2020) independently predicted lower overall mortality (OM; hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.60–0.76; p <.001). Of 709 mn‐UCUB patients, 167 (24%) were contemporary (2017–2020) and 542 (76%) were historical (2000–2016). In mn‐UCUB, median OS was 8 months in contemporary versus 7 months in historical patients (Δ = 1 month; p =.034). After multivariable CRM, contemporary membership status (2017–2020) was associated with HR of 0.81 (95% CI = 0.66–1.01; p =.06). In conclusion, contemporary systemic therapy‐exposed metastatic patients exhibited better OS in UCUB. However, the magnitude of survival benefit was threefold higher in mUCUB and approximated the survival benefits recorded in prospective randomized trials of novel systemic therapies. [ABSTRACT FROM AUTHOR]

Background: This study aimed to predict metastasis risk and identify prognostic factors of gastrointestinal signet ring cell carcinoma (SRCC) using data from the SEER database, the largest cancer dataset in North America. Methods: Data were obtained from the SEER database, covering 17 cancer registries from 2004 to 2020. Demographic and clinical data included sex, age, race, tumor location, size, pathological grade, stage, overall survival time, and treatment modalities. Statistical analyses were conducted using SPSS and R software. Propensity Score Matching (PSM) ensured comparable baseline characteristics between gastric cancer (GC) and colorectal cancer (CRC) groups. LASSO regression analysis identified predictors of metastasis, leading to the construction of predictive models using the lrm function in R. Nomograms were visualized with the "rms" package and assessed via ROC curves, calibration curves, and decision curve analysis (DCA). Cox regression analyses identified prognostic indicators for overall survival (OS), and Kaplan–Meier curves compared OS between high-risk and low-risk groups. Results: From 2004 to 2020, 7680 SRCC patients were identified, including 4980 GC and 2700 CRC patients. CRC patients were older and had larger tumors, higher staging, and worse differentiation. Nomograms demonstrated good discriminative ability, with AUCs of 0.704 and 0.694 for GC, and 0.694 and 0.701 for CRC in training and validation cohorts, respectively. The DCA curve indicates that this predictive model has a high gain in predicting metastasis and OS. Conclusions: The nomograms effectively predicted metastasis risk and OS in metastatic SRCC patients, offering clinical utility in stratifying patients and guiding treatment decisions, thereby enhancing personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to identify the risk factors associated with ampullary duodenal carcinoma (a-DC) and develop a clinical model to dynamically and accurately predict the risk of lymph node metastasis (LNM) in a-DC patients. Methods: Data from 4077 patients (2004–2020) were extracted from the Surveillance, Epidemiology, and End Results database to form a training cohort, while 173 cases (2010–2020) from Zhejiang Provincial People's Hospital in China were used as an external validation cohort. A reliable LASSO-logistic method was employed to identify independent risk factors for a-DC LNM, and a nomogram was developed based on these factors to assess the risk of a-DC LNM. The nomogram was evaluated using the Akaike information criterion, misclassification error, area under the curve, and likelihood ratio test. Finally, the nomogram's accuracy and generalizability were externally validated.. Results: After screening using LASSO and logistic regression four variables were identified as independent risk factors for a-DC LNM: sex (P < 0.001), tumor size (P < 0.001), grade (P < 0.001), and tumor extension (P < 0.001). The area under the curve of the nomogram was 74.8% in the training group and 88.9% in the external validation group. The calibration curves demonstrated that the LNM predictions made by the nomogram were in satisfactory agreement with the actual observed LNM. Additionally, the decision curve analysis curves indicated effective clinical utility of the nomogram. Conclusions: A nomogram based on the LASSO-logistic analysis was constructed to predict a-DC LNM, demonstrating good performance and clinical application value. [ABSTRACT FROM AUTHOR]

Background: Difficulty in achieving complete resection leads to a poor prognosis for retroperitoneal soft tissue sarcoma, hence emphasizing the significance of adjuvant treatment. The benefit of preoperative radiotherapy for retroperitoneal liposarcoma was initially demonstrated by the STRASS trial. However, the impact of intraoperative radiotherapy (IORT) on retroperitoneal liposarcoma remains unexplored. Method: Patients with retroperitoneal liposarcoma were identified in the Surveillance, Epidemiology, and End Results (SEER) database, treated between 2000 and 2019. Subsequently, a 1:1 propensity score-matched (PSM) analysis was conducted based on variables identified from a multivariate analysis. T-tests were used to assess differences in normally distributed continuous variables, while the rank-sum test was applied to variables that did not follow a normal distribution. The chi-squared test was utilized to evaluate differences in categorical variables. Ultimately, survival analysis was performed using SPSS to evaluate patient prognosis. Result: A total of 2129 patients with retroperitoneal liposarcoma were included in our study. Age, sex, histology, grading, chemotherapy, and tumor size as independent prognostic risk factors for these patients through multivariate Cox regression analysis. Subsequently, 66 patients were included in the survival analysis through PSM, with 33 patients receiving IORT. Finally, the survival analysis revealed that there was no difference in overall survival among patients with retroperitoneal liposarcoma, regardless of whether they received IORT or not (p= 0.711). Conclusion: As an exploratory study, our findings suggest that patients may not derive benefit from intraoperative radiotherapy. These observations are intended to lay the groundwork for future prospective clinical studies. [ABSTRACT FROM AUTHOR]

Background: Utilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk. Methods: Clinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer. Results: Involving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model's area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach. Conclusion: We created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans. [ABSTRACT FROM AUTHOR]

Purposes: Whether patients with early-onset colorectal cancer (EOCRC) receive chemotherapy has been controversial, so our study aimed to screen patients with EOCRC who benefit from chemotherapy. Methods: A total of 2166 EOCRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into chemotherapy and non-chemotherapy groups, propensity score matching (PSM) was performed to balance the differences between the groups, and the Kaplan–Meier method was used to calculate the cancer-specific survival (CSS) of EOCRC patients. Multifactorial COX regression analysis was used to identify independent prognostic factors for CSS and to construct a nomogram for predicting CSS in EOCRC patients. The overall risk score was calculated based on Nomogram, and EOCRC patients were classified into high-risk and low-risk groups to assess further chemotherapy's therapeutic effect on patients with different risk stratification. Results: Before PSM, patients in the chemotherapy group had poorer CSS (p < 0.001). After PSM, there was no significant difference in patient CSS between the two groups (p = 0.057). Independent prognostic factors (Race, Grade, Pathology, AJCC.N, AJCC.M, CEA, Marital. Status) were screened according to multifactorial COX regression analyses and included in the Nomogram predicting CSS in EOCRC patients. A risk stratification system for EOCRC patients was further developed, and the results showed that chemotherapy had no significant effect on CSS in the low-risk group of patients, but in the high-risk group, chemotherapy significantly improved CSS in EOCRC patients. Conclusions: We developed a clinical risk model by combining different risk factors, which can accurately screen those with high-risk EOCRC for benefit from chemotherapy. For low-risk EOCRC patients, our results did not observe a better survival benefit from chemotherapy, and more prospective studies are needed in the future to prove our conclusions. [ABSTRACT FROM AUTHOR]

Background: Limited information is available regarding the relative risks of cancer-specific mortality and noncancer-specific mortality among long-term survivors with differentiated thyroid cancer (DTC). Methods: In this retrospective study, nationwide data from the Surveillance, Epidemiology, and End Results database (1992-2020) were utilized. The Accelerated Failure Time Model was applied to calculate Survival Time Ratios (TR), with the primary focus on mortality resulting from DTC. The competing risks model was employed to investigate the relative risks of various outcomes in DTC patients with a survival duration of 5 years or more. Results: In our cohort, 279 patients succumbed to DTC, while 748 died from other diseases. Notably, in DTC cohorts, noncancer-specific mortality rates were consistently higher than DTC-specific mortality rates across different age groups and genders. The risk of DTC and noncancer-specific mortality varied based on the TNM stage. With more advanced disease stages, the risk of DTC and other cancer-specific mortality gradually increased. The cumulative mortality from other cancer-specific causes was consistently the lowest. Conclusions: In long-term surviving DTC patients, noncancer-specific mortality outweighed DTC-specific mortality irrespective of age and gender. For stage I and II patients, increased attention should be directed toward noncancer-specific mortality in postoperative follow-ups. Conversely, for stage III and IV patients, greater consideration should be given to DTC-related causes of death. In addition, for stage III and IV DTC patients, the risk of death from other cancers was significantly higher than for stages I and II. [ABSTRACT FROM AUTHOR]

Purpose: The overall survival of patients with pancreatic cancer is extremely low. We aimed to establish machine learning (ML) based model to accurately predict three-year survival and prognosis of pancreatic cancer patients. Methods: We analyzed pancreatic cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2021. Univariate and multivariate logistic analysis were employed to select variables. Recursive Feature Elimination (RFE) method based on 6 ML algorithms was utilized in feature selection. To construct predictive model, 13 ML algorithms were evaluated by area under the curve (AUC), area under precision-recall curve (PRAUC), accuracy, sensitivity, specificity, precision, cross-entropy, Brier scores and Balanced Accuracy (bacc) and F Beta Score (fbeta). An optimal ML model was constructed to predict three-year survival, and the predictive results were explained by SHapley Additive exPlanations (SHAP) framework. Meanwhile, 101 ML algorithm combinations were developed to select the best model with highest C-index to predict prognosis of pancreatic cancer patients. Results: A total of 20,064 pancreatic cancer patients from SEER database was consecutively enrolled. We utilized eight clinical variables to establish prediction model for three-year survival. CatBoost model was selected as the best prediction model, and AUC was 0.932 [0.924, 0.939], 0.899 [0.873, 0.934] and 0.826 [0.735, 0.919] in training, internal test and external test sets, with 0.839 [0.831, 0.847] accuracy, 0.872 [0.858, 0.887] sensitivity, 0.803 [0.784, 0.825] specificity and 0.832 [0.821, 0.853] precision. Surgery type had the greatest effects on three-year survival according to SHAP results. For prognosis prediction, "RSF+GBM" algorithm was the best prognostic model with C-index of 0.774, 0.722 and 0.674 in training, internal test and external test sets. Conclusions: Our ML models demonstrate excellent accuracy and reliability, offering more precise personalized prognostic prediction to pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

Background: Epithelioid hemangioendothelioma (EHE), is an uncommon, intermediate-grade malignant vascular tumor that can manifest in diverse organs, including the liver, lungs, and bones. Given its unique malignancy profile and rarity, there lacks a consensus on a standardized treatment protocol for EHE, particularly for hepatic epithelioid hemangioendothelioma (HEHE). This study aims to elucidate factors influencing the clinical prognosis of EHE by analyzing data from the SEER database, complemented with insights from a departmental cohort of 9 HEHE cases. Through this, we hope to shed light on potential clinical outcomes and therapeutic strategies for HEHE. Methods: Using SEER data from 22 registries, we analyzed 313 liver cancer patients with ICD-O-3 9130 and 9133 histology. Twelve variables were examined using Cox regression and mlr3 machine learning. Significant variables were identified and compared. Clinical data, imaging characteristics, and treatment methods of nine patients from our cohort were also presented. Result: In univariate and multivariate Cox regression analyses, Age, Sex, Year of diagnosis, Surgery of primary site, Chemotherapy, and Median household income were closely related to survival outcomes. Among the ten survival-related machine learning models, CoxPH, Flexible, Mboost, and Gamboost stood out based on Area Under the Curve(AUC), Decision Curve Analysis(DCA), and Calibration Curve Metrics. In the feature importance analysis of these four selected models, Age and Surgery of primary site were consistently identified as the most critical factors influencing prognosis. Additionally, the clinical data of nine patients from our cohort not only demonstrated unique imaging characteristics of HEHE but also underscored the importance of surgical intervention. Conclusion: For patients with resectable HEHE, surgical treatment is currently a highly important therapeutic approach. [ABSTRACT FROM AUTHOR]

Despite radiotherapy (RT) is recognized as preferred initial therapy for early-stage low-grade follicular lymphoma (FL) by many international practice guidelines, the medical oncologist has improperly underutilized RT, and diverse management strategies, including systemic therapy (ST), combined modality (CM) and watch and wait (WW), are still used. Except survival outcomes, previous studies concerned little about the treatment-related toxicity, which is also important factor in choosing initial management strategy, especially second primary malignancies (SPMs). The aim of this study was to compare the overall survival (OS) and the SPMs risk between different management strategies, which can provide guidance for the choice of optimal initial management strategy. Data was acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Finally, A total 10,900 patients were identified, in which 930 cases developed SPMs. The use of radiotherapy (RT) has remained consistently low, with a utilization rate of around 20%, while most patients have received watchful waiting (WW) and systemic therapy (ST). In the rituximab era, multivariate analysis indicated that RT exhibited significantly superior OS and did not increase SPMs risk in comparison with ST and WW. At the same time, although there were no significant differences in OS between CM and RT, RT had significantly lower SPMs risk in comparison with CM. The use of RT improved the OS and did not increase the SPMs risk in comparison with other management strategies. Considering the low application rate of RT, oncologists should emphasize and increase the use of RT as an initial management strategy in patients with early-stage low-grade FL. [ABSTRACT FROM AUTHOR]

Although the recent drop in melanoma mortality has been attributed to the introduction of newer therapies, the impact of ongoing public efforts remains unknown. Characterize and model melanoma mortality trends before the era of molecular and immune therapies (1969-2014) in the U.S. and Australia. Differential time series analysis based on population-ascertained melanoma mortality rates from the U.S. and Australia. Mortality rates were modeled and compared to the trajectories of ten other cancers. Melanoma mortality rates have been significantly decelerating since the 1970s in both the U.S. (P <.0001) and Australia (P =.0021). Zero acceleration occurred around 2001 (95% CI: 1996, 2008) for the U.S. and 2004 (95% CI: 1999, 2011) for Australia. Male mortality rates decelerated 3x-4x faster than females in both countries. Melanoma mortality followed a similar quadratic function (R2 > 0.9) to 10 other cancers, albeit with a later inflection point (1986 vs 2001) and broader focal width. Absolute mortality data used without further stratification or considering cancer incidence or covariates. Melanoma deaths have been decelerating for the past 5 decades, reaching an inflection point around 2001, suggesting that mitigating campaigns were already afoot in both the U.S. and Australia before the advent of modern therapies. [ABSTRACT FROM AUTHOR]

Nationwide datasets are frequently used to examine cancer trends and outcomes in the U.S. Understanding the strengths and limitations of the commonly used Surveillance, Epidemiology, and End Results (SEER) Program and the National Cancer Database (NCDB) is important when designing studies and interpreting results. We used colorectal cancer (CRC) as a case study to compare information available. We identified 575,128 (SEER) and 1,578,046 (NCDB) adults diagnosed with CRC between 2004 and 2021. The distribution of age, tumor location, stage, and treatment did not meaningfully differ between SEER and NCDB. SEER represents racially and ethnically diverse populations, including a higher proportion of Hispanic (11.7% vs 5.8%) and Asian/Pacific Islander (8.6% vs 3.3%) persons. SEER includes more information on area-level characteristics, such as county-level measures of poverty, unemployment, and migration and census tract-level measures of socioeconomic status. Age-adjusted incidence, mortality rates, and cause-specific survival are only available in SEER, facilitating detailed analyses of racial, ethnic, and socioeconomic differences in cancer incidence and mortality. NCDB provides information on tumor characteristics and treatment not available in SEER, including microsatellite instability, KRAS mutation, palliative treatment, unplanned readmissions, and 30-day mortality after surgery, facilitating analyses of treatment effectiveness and outcomes. Five-year overall survival was similar in SEER (55.6%) vs NCDB (57.5%). [ABSTRACT FROM AUTHOR]

Background: The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. Methods: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). Results: Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan–Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44–0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19–12.84]). Conclusions: RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans. [ABSTRACT FROM AUTHOR]

We aimed to investigate the risk of bladder cancer (BCa) survivors developing or dying from 15 specific-subsequent primary cancers (SPCs). A total of 229,554 BCa survivors were identified from the Surveillance, Epidemiology, and End Results database. Incidence and mortality per 10,000 person-years, absolute excess risk (AER) per 10,000 person-years, standardized incidence ratios, and standardized mortality ratios were calculated. Among BCa survivors, 38,207 developed SPCs and 17,546 died of SPCs. The risk of developing and dying from SPCs was significantly high for 10 and 6 of the 12 common SPCs in men, respectively, while for 6 and 5 of the 14 common SPCs in women, respectively. The SPCs with high risk of development in men were colorectal, breast, liver, and pancreatic cancer, and the ones with high risk of death were liver and pancreatic cancer. Moreover, SPCs with a high risk of development or death among young BCa survivors include ureter, kidney and renal pelvis, and lung cancer. In addition, BCa survivors within 1 year of diagnosis have a significantly higher risk of development and death from ureter, kidney and renal pelvis, prostate, and cervix cancer, but a lower risk of prostate cancer than the general population after 5 years of diagnosis. Lung cancer had a significantly high risk of development but a low risk of death. Among BCa survivors, the risk of developing or dying from few SPCs is significantly high. These findings may provide an important basis for clinical follow-up of BCa survivors. [ABSTRACT FROM AUTHOR]

Background: Pathological subtypes of papillary thyroid carcinoma (PTC) are important factors in thyroid cancer. Some rare subtypes exhibit extensive lymph node metastasis. These pathological subtypes should receive more attention in clinical practice. Methods: Patients with different pathological subtypes of PTC were selected from the SEER database. Logistic regression, random forest, and bootstrap aggregating (bagging) methods were employed to screen for risk factors associated with cervical lymph node metastasis in the training cohort. A nomogram was established based on the model with the largest area under the curve (AUC) and evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical benefit to patients. The nomogram was validated in depth by 200 iterations of tenfold cross-validation. Results: A total of 7,882 patients were included in the analysis, with 5,516 patients in the training group and 2,366 patients in the testing group. The logistic regression model achieved the highest AUC of 0.7396. Sex, age, race, extension (extrathyroidal extension), pathological type, and primary tumour size were identified as independent risk factors for cervical lymph node metastasis (p < 0.05). The calibration curve indicated that the model was well calibrated. DCA indicated that the nomogram model had good clinical practicability. Conclusion: In clinical practice, it is important to consider the pathological subtypes of PTC. The established nomogram can serve as a predictive tool for assessing cervical lymph node metastasis. [ABSTRACT FROM AUTHOR]

Pilocytic astrocytoma (PA) is classified as a Grade I benign neuroglial tumor. The extent of surgical resection is a critical factor influencing the prognosis for patients with PA. In prior researches of PA, the extent of surgical resection is generally categorized into GTR, STR and biopsy. In some researches on brain tumor surgeries, the extent of resection also includes GTL. There is no existing research specifically comparing the efficacy of GTR versus GTL in PA treatment. In this study, the data we used are from the SEER database. We categorized the extent of resection into GTL, GTR, STL, STR, biopsy, and no surgery based on SEER classification of surgical procedures, to investigate the impact of extent of resection on PA patient survival. A multivariate logistic regression model was utilized to acquire odds ratios (OR) for different extent of resection. Survival outcomes across different extent of resection (GTL, GTR, STL, STR, biopsy, no surgery) were assessed using Kaplan–Meier survival curve analysis, with curve comparisons conducted via log-rank tests. The impact of various risk factors on survival was assessed using the Cox proportional hazards model. The hazard ratio (HR) was employed to quantify the influence of one or more factors on overall survival throughout the follow-up period. Multivariate Cox analysis revealed that age, tumor location, extent of resection, as well as the application of radiotherapy and chemotherapy, all significantly impacted prognosis. Compared to GTL, GTR did not significantly increase the risk of mortality (HR 1.17; 95% CI 0.73–1.86, p = 0.5). Furthermore, there was no statistically significant difference between the Kaplan–Meier survival curves of the two groups (p = 0.18). We employed propensity score matching (PSM) to balance the differences in baseline characteristics of patients receiving chemotherapy or radiotherapy. A total of 4429 patients were included in this study. Age, diagnosis period, race, tumor size, and tumor location as influential on the extent of resection. Age, tumor location, extent of resection, and application of radiotherapy and chemotherapy influenced the survival of PA patients. The Kaplan–Meier survival curves revealed that the long-term survival rate for GTR is slightly higher than that for GTL. The PSM analysis revealed that the application of radiotherapy and chemotherapy was associated with the reduction of overall survival in PA patients. In conclusion, there was no significant difference in survival between GTR and GTL, so GTR with less damage was preferred. The application of radiotherapy and chemotherapy can reduce overall survival of patients with PA. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma multiforme (GBM) with synchronous metastasis(SM) is a rare occurrence. We extracted the data of GBM patients from the SEER database to look into the incidence of SM in GBM, determine the prognostic significance of SM in GBM, and assess therapeutic options for patients presenting with SM. Methods: From 2004 to 2015, information on GBM patients was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score matching (PSM) method was employed to mitigate confounding factors between SM and non-SM groups, subsequently investigating the prognostic significance of SM in patients with GBM. Multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic variables for GBM patients with SM. A forest plot was used to visualize the results. Results: A cohort of 19,708 patients was obtained from the database, among which 272 (1.4%) had SM at the time of diagnosis. Following PSM at a 3:1 ratio, in both univariate and multivariate cox regression analysis, SM (HR = 1.27, 95% CI: 1.09-1.46) was found to be an independent predictive predictor for GBM patients. Furthermore, the Cox proportional hazard forest plot demonstrated that independent risk variables for GBM patients with SM included age (Old vs. Young, HR = 1.44, 95% CI: 1.11-1.88), surgery (biopsy vs. no surgery, HR = 0.67, 95% CI: 0.46-0.96;Subtotal resection vs. no surgery, HR = 0.47, 95% CI: 0.32-0.68;Gross total resection vs. no surgery, HR = 0.44, 95% CI: 0.31-0.62), radiotherapy (HR = 0.58, 95% CI: 0.41-0.83), and chemotherapy (HR = 0.51, 95% CI: 0.36-0.72). Conclusion: The predictive value of SM in GBM was determined by this propensity-matched analysis using data from the SEER database. Radiotherapy, chemotherapy, and surgery constitute an effective treatment regimen for patients with SM. A more positive approach toward the use of aggressive treatment for GBM patients with SM may be warranted. [ABSTRACT FROM AUTHOR]

Background: The prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients. Methods: Clinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform. Results: We enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform (https://wubopredict.shinyapps.io/dynnomapp/). Conclusion: The predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging. [ABSTRACT FROM AUTHOR]

Purpose: Pancreatic ductal adenocarcinoma (PDAC) can be classified into distinct histological subtypes based on the WHO nomenclature. The aim of this study was to compare the prognosis of conventional PDAC (cPDAC) against the other histological variants at the population level. Methods: The Surveillance, Epidemiology and End Results (SEER) database was used to identify patients with microscopically confirmed PDAC. These patients were divided into 9 histological subgroups. Overall survival was assessed using the Kaplan-Meier method and Cox regression models stratified by tumor histology. Results: A total of 159,548 patients with PDAC were identified, of whom 95.9% had cPDAC, followed by colloid carcinoma (CC) (2.6%), adenosquamous carcinoma (ASqC) (0.8%), signet ring cell carcinoma (SRCC) (0.5%), undifferentiated carcinoma (UC) (0.1%), undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) (0.1%), hepatoid carcinoma (HC) (0.01%), medullary carcinoma of the pancreas (MCP) (0.006%) and pancreatic undifferentiated carcinoma with rhabdoid phenotype (PUCR) (0.003%). Kaplan-Meier curves showed that PUCR had the worst prognosis (median survival: 2 months; 5-year survival: 0%), while MCP had the best prognosis (median survival: 41 months; 5-year survival: 33.3%). In a multivariable Cox model, several histological subtypes (i.e. CC, ASqC, SRCC, UCOGC) were identified as independent predictors of overall survival when compared to cPDAC. Conclusion: PDAC is a heterogenous disease and accurate identification of variant histology is important for risk stratification, as these variants may have different biological behavior. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The use of nomograms in predicting the prognosis of early‐stage non‐small cell lung cancer (NSCLC), particularly in elderly patients, is not widespread. A validated prognostic model specifically for NSCLC patients over 80 years old holds promising potential for clinical application in forecasting patient outcomes.The prognostic value of various factors for NSCLC patients aged 80 and above was evaluated using data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2017). Kaplan–Meier (KM) curves, Cox proportional hazards regression models, and nomogram were utilized to evaluate the impact of each factor on cancer‐specific survival (CSS).A cohort comprising 7045 individuals was selected for inclusion in the analysis. Through rigorous statistical analysis, 10 independent prognostic factors were identified and incorporated into the nomogram. The nomogram's receiver operating characteristic (ROC) curve area under the curve (AUC) was higher than that of the AJCC 7th edition TNM staging system's predicted CSS (0.744 versus 0.602), establishing the superior prognostic value of the nomogram.We have successfully created a highly accurate and discriminative nomogram that enables oncologists to predict the survival outcome of each individual patient with I/II NSCLC who is 80 years or older. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify a specific SCLC population that would benefit from surgery. Methods: This study utilized patient data retrieved from the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. To mitigate clinical biases, the propensity score matching (PSM) technique was employed. Separate cohorts were aligned using PSM according to the AJCC 8th edition TNM classification. The Kaplan–Meier method and a competing risk model were applied to evaluate overall survival (OS) and lung cancer–specific survival (LCSS), respectively. Outcomes: Among the 3394 patients with potentially resectable SCLC included in the study, 3062 underwent chemoradiotherapy and 332 underwent surgical treatment with adjuvant chemotherapy. Surgery was associated with better OS (median OS: 49 months; 95% CI: 35–63 months vs. 27 months; 95% CI: 21–33 months, p < 0.001) and LCSS (SHR, 0.578; 95% CI: 0.411–0.815, p < 0.001) in stage I patients after PSM. However, there was no significant difference in OS and LCSS between the surgery and nonsurgery groups in stage II and III patients after PSM. In the entire cohort, lobectomy was associated with improved OS (median OS: 48.6 vs. 28.7 months, p < 0.0001), but not LCSS (SHR, 0.696; 95% CI: 0.466–1.040, p = 0.078) compared with sublobar resection after PSM. Conclusion: Surgery with adjuvant chemotherapy significantly improved the survival prognosis of patients with early‐stage SCLC. However, surgical treatment should be carefully considered in patients with stage II/III disease. Lobectomy is oncologically equal to sublobar resection. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of sentinel lymph node biopsy (SLNB) versus axillary lymph node dissection (ALND) in managing early‐stage male breast cancer (MBC) patients with T1‐2 tumors and limited lymph node metastasis, all receiving radiotherapy, remains uncertain. This study examines trends and survival outcomes for SLNB and ALND in the United States. Methods: We conducted a retrospective analysis of the Surveillance, Epidemiology, and End Results (SEER) data from 2010 to 2020 for MBC patients with T1‐2 tumors and 1–2 positive lymph nodes undergoing radiotherapy. Patients were classified by nodes removed (SLNB ≤5, ALND ≥10), comparing overall survival (OS) and breast cancer‐specific survival (BCSS) between the groups before and after propensity score matching. Results: Of 299 MBC patients analyzed, SLNB usage increased from 18.8% in 2010 to 61.0% in 2020. Multivariable logistic regression highlighted significant associations of SLNB use with diagnosis year, race, surgery type, positive lymph node count, and metastasis size. No significant differences in 5‐year OS (77.98% SLNB vs. 85.85% ALND, p = 0.337) or BCSS (91.54% SLNB vs. 94.97% ALND, p = 0.214) were observed. Propensity score matching (96 patients per group) confirmed similar 5‐year OS (83.9% for SLNB vs. 82.0% for ALND, p = 0.925) and BCSS (90.1% for SLNB vs. 96.9% for ALND, p = 0.167). Conclusion: SLNB and ALND provide comparable survival outcomes in early‐stage MBC patients with limited lymph node metastasis undergoing radiotherapy. The increased utilization of SLNB supports its consideration to reduce surgical morbidity in selected MBC patients despite limited direct evidence. [ABSTRACT FROM AUTHOR]

Using a multi-institutional oncology database, we investigate the survival rates and the impacts of demographic, clinical, and management characteristics on overall survival among adult patients diagnosed with spinal ependymoma. Utilizing the SEER registry, patients with histologically or radiologically confirmed ependymomas were included. Factors impacting overall survival were analyzed using Kaplan-Meier survival curves and log-rank statistical analyses. A total of 1,580 patients were included. Their mean ± standard deviation age was 46.68 ± 15.96 years, and 51.1% were women. Gross total resection (GTR) was achieved in 66.4% of patients. The 5- and 10-year survival rates were 96.7% and 95.4%, respectively. A multivariable backward Cox regression showed that age ≥65 years was a significant predictor for mortality (hazard ratio [HR]: 3.93; 95% confidence interval [CI]: 2.21–7.00; P < 0.001). Likewise, tumor grade 3 (HR: 6.36; 95% CI: 1.95–20.76; P = 0.002), tumor grade 4 (HR: 7.74; 95% CI: 3.97–15.11; P < 0.001), presence of extra-neural metastasis (HR: 13.81; 95% CI: 3.67–51.96; P < 0.001), and receiving radiotherapy (HR: 2.50; 95% CI: 1.50–4.19; P < 0.001) were significant risk factors for mortality, while GTR was significantly associated with improved overall survival compared with subtotal resection or nonsurgical management (HR: 0.42; 95% CI: 0.25–0.73; P = 0.002). There were no significant effects for gender, race, marital status, income, residential area, chemotherapy, tumor size, and the presence of other benign or malignant tumors on the survival hazards (P > 0.05 for each). Early diagnosis and surgical management of spinal ependymomas, such as GTR, were associated with remarkable survival benefits. Old age, high-grade spinal ependymoma, and extra-neural metastasis were associated with worse overall survival, whereas radiotherapy's role remains unclear. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, researchers have begun to investigate the relation between sex and cancer. We wanted to look at the relation between sex and non-small cell lung cancer among high-risk patients to see if this relationship needs further exploration. In order to do this, we chose to look at large publicly available databases that contained information on lung cancer incidence. Our research found indications that females have an elevated risk of lung cancer incidence in comparison with males within high-risk populations. These results suggest that the difference in sex-specific cancer biomechanisms should be further investigated and explored. Background/Objectives: Non-small cell lung cancer (NSCLC) has seen a relative rise in incidence among females versus males in recent years, although males still have a higher overall incidence. However, it is unclear whether this trend is consistent across all populations. Therefore, we retrospectively examined this relationship in two large high-risk clinical cohorts. Methods: First, we analyzed lung cancer incidence among individuals with a smoking history of over 40 pack-years in the National Lung Screening Trial (NLST). Then, we investigated the incidence of second primary NSCLC in patients who underwent lobectomy for previous stage I lung cancer using the Surveillance, Epidemiology, and End Results (SEER) database. We performed both univariate and multivariable time-to-event analyses to investigate the relationship between sex and lung cancer incidence. Results: In the NLST cohort (n = 37,627), females had a higher risk of developing primary NSCLC than males (HR = 1.11 [1.007–1.222], p = 0.035) after adjusting for age and pack-year history. In the SEER cohort (n = 19,327), females again exhibited an increased risk of developing a second primary lung cancer (HR = 1.138 [1.02–1.269], p = 0.021), after adjusting for age, race, grade, and histology. Conclusions: Our analysis reveals that females have a modestly higher lung cancer incidence than males in high-risk populations. These findings underscore the importance of further researching the underlying cellular processes that may cause sex-specific differences in lung cancer incidence. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to explore the prognostic factors and survival patterns based on the histological type for the perimenopausal endometrial carcinoma (PIPEC) patients treated with hysterectomy. Methods: The PIPEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Methods of random survival forest (RSF) and Cox regression were used to identify the possible prognostic factors of PIPEC patients. Then overall survival (OS) and cancer-specific survival (CSS) of PIPEC data were analyzed by histological types with regional lymph nodes status and SEER-stage to investigate the survival patterns of the PIPEC patients. Results: A total of 14,178 PIPEC patients were included in the study. We found tumor size, grade, histology, SEER-stage, AJCC-stage, AJCC-T stage, metastasis to distant organs and regional lymph nodes status had a significant survival outcome for PIPEC both for OS and CSS (all p < 0.05). Regardless of regional lymph nodes status and SEER-stage for OS and CSS, the low-grade endometrioid carcinoma had the best prognosis outcome, followed by the mix cell adenocarcinoma and high-grade endometrioid carcinoma, while the carcinosarcoma and undifferentiated carcinoma had relatively poor prognosis outcome. And the survival patterns of different histological types of PIPEC were diverse and changed along with the time. Conclusion: We identified the possible prognostic factors of PIPEC patients treated with hysterectomy. And survival analysis based on the regional lymph nodes status and SEER-stage revealed the different histological types of PIPEC had diverse survival patterns, which will be helpful for guiding clinical practice. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to test for survival differences according to adjuvant chemotherapy (AC) status in radical nephroureterectomy (RNU) patients with pT2–T4 and/or N1–2 upper tract urothelial carcinoma (UTUC). Patients and Methods: Within the Surveillance, Epidemiology, and End Results database (SEER, 2007–2020), patients with UTUC treated with AC versus RNU alone were identified. Kaplan–Meier plots and multivariable Cox regression models addressed cancer-specific mortality (CSM). Results: Of 1995 patients with UTUC, 804 (40%) underwent AC versus 1191 (60%) RNU alone. AC rates increased from 36.1 to 57.0% over time in the overall cohort [estimated annual percentage changes (EAPC) ± 4.5%, p < 0.001]. The increase was from 28.8 to 50.0% in TanyN0 patients (EAPC ± 7.8%, p < 0.001) versus 50.0–70.9% in TanyN1–2 patients (EAPC ± 2.3%, p = 0.002). Within 698 patients harboring TanyN1–2 stage, median CSM was 31 months after AC versus 16 months in RNU alone (Δ = 15 months, p < 0.0001) and AC independently predicted lower CSM [hazard ratio (HR) 0.64; p < 0.001]. Similarly, within subgroup analyses according to stage, relative to RNU alone, AC independently predicted lower CSM in T2N1–2 (HR 0.49; p = 0.04), in T3N1–2 (HR 0.72; p = 0.015), and in T4N1–2 (HR 0.49, p < 0.001) patients. Conversely, in all TanyN0 as well as in all stage-specific subgroup analyses addressing N0 patients, AC did not affect CSM rates (all p > 0.05). Conclusions: In RNU patients, AC use is associated with significantly lower CSM in lymph-node-positive (N1–2) patients but not in lymph-node-negative patients (N0). The distinction between N1–2 and N0 regarding the effect of AC on CSM applied across all T stages from T2 to T4, inclusively. [ABSTRACT FROM AUTHOR]

Background: The real-time prognostic data of patients with poorly differentiated thyroid carcinoma (PDTC) after surviving for several years was unclear. This study aimed to employ a novel method to dynamically estimate survival for PDTC patients. Methods: A total of 913 patients diagnosed with PDTC between 2014 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, was recruited in our study. Kaplan--Meier method was used to estimate the overall survival (OS). The conditional survival (CS) outcomes of PDTC were analyzed and CS rates were calculated using the formula CS(y/x) = OS(y+x)/OS(x), whereby CS(y/x) denotes the probability of a patient enduring an additional y years subsequent to surviving x years following the diagnosis of PDTC. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify prognostic predicters and multivariate Cox regression was utilized to develop a CS-nomogram. Finally, the performance of this model was evaluated and validated. Results: Kaplan--Meier survival analysis unveiled patient outcomes demonstrating an OS rate of 83%, 75%, and 60% respectively at the end of 3, 5, and 10 years. The novel CS analysis highlighted a progressive enhancement in survival over time, with the 10-year cumulative survival rate progressively augmenting from its initiation of 60% to 66%, 69%, 73%, 77%, 81%, 83%, 88%, 93%, and finally 97% (after surviving for 1-9 years, respectively) each year. And then 11 (11/15) predictors including age at diagnosis, sex, histology type, SEER stage, T stage, N stage, M stage, tumor size, coexistence with other malignancy, radiotherapy and marital status, were selected by LASSO analysis under the condition of lambda.min. Multivariate Cox regression analysis further highlighted the significant impact of all these predictors on the OS of PDTC and we successfully established and validated a novel CS-nomogram for real-time and dynamic survival prediction. Conclusions: This was the first study to analyze the CS pattern and demonstrate a gradual improvement in CS over time in long-term PDTC survivors. We then successfully developed and validated a novel CS-nomogram for individualized, dynamic, and real-time survival forecasting, empowering clinicians to adapt and refine the patient-tailored treatment strategy promptly with consideration of evolving risks. [ABSTRACT FROM AUTHOR]

Background: Spinal Ependymoma (SP-EP) is the most commonly occurring tumor affecting the spinal cord. Prompt diagnosis and treatment can significantly enhance prognostic outcomes for patients. In this study, we conducted a comprehensive analysis of RNA sequencing data, along with associated clinical information, from patients diagnosed with SP-EP. The aim was to identify key genes that are characteristic of the disease and develop a survival-related nomogram. Methods: We first accessed the Gene Expression Integrated Database (GEO) to acquire the microarray dataset pertaining to SP-EP. This dataset was then processed to identify differentially expressed genes (DEGs) between SP-EP samples and normal controls. Furthermore, machine learning techniques and the CIBERSORT algorithm were employed to extract immune characteristic genes specific to SP-EP patients, thereby enhancing the characterization of target genes. Next, we retrieved comprehensive information on patients diagnosed with SP-EP between 2000 and 2020 from the Surveillance, Epidemiology, and End Results Database (SEER). Using this data, we screened for predictive factors that have a significant impact on patient outcomes. A nomogram was constructed to visualize the predicted overall survival (OS) rates of these patients at 3, 5, and 8 years post-diagnosis. Finally, to assess the reliability and clinical utility of our predictive model, we evaluated it using various metrics including the consistency index (C-index), time-dependent receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: A total of 5,151 DEGs were identified between the SP-EP sample and the normal sample. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that these DEGs were primarily involved in cellular processes, including cell cycle regulation and cell sensitivity mechanisms. Furthermore, immune infiltration analysis was utilized to identify the core gene CELF4. Regarding the survival rates of patients with SP-EP, the 3-year, 5-year, and 8-year survival rates were 72.5, 57.0, and 40.8%, respectively. Diagnostic age (p < 0.001), gender (p < 0.001), and surgical approach (p < 0.005) were identified as independent prognostic factors for OS. Additionally, a nomogram model was constructed based on these prognostic factors, demonstrating good consistency between predicted and actual results in the study's validation process. Notably, the study also demonstrated that more extensive surgical resection could extend patients' OS. Conclusion: Through bioinformatics analysis of microarray datasets, we identified CELF4 as a central gene associated with immune infiltration among DEGs. Previous studies have demonstrated that CELF4 may play a pivotal role in the pathogenesis of SP-EP. Furthermore, this study developed and validated a prognostic prediction model in the form of a nomogram utilizing the SEER database, enabling clinicians to accurately assess treatment risks and benefits, thereby enhancing personalized therapeutic strategies and prognosis predictions. [ABSTRACT FROM AUTHOR]

Background: This study aimed to identify prognostic factors for survival and develop a prognostic nomogram to predict the survival probability of elderly patients with lower-grade gliomas (LGGs). Methods: Elderly patients with histologically confirmed LGG were recruited from the Surveillance, Epidemiology, and End Results (SEER) database. These individuals were randomly allocated to the training and validation cohorts at a 2:1 ratio. First, Kaplan-Meier survival analysis and subgroup analysis were performed. Second, variable screening of all 13 variables and a comparison of predictive models based on full Cox regression and LASSO-Cox regression analyses were performed, and the key variables in the optimal model were selected to construct prognostic nomograms for OS and CSS. Finally, a risk stratification system and a web-based dynamic nomogram were constructed. Results: A total of 2307 elderly patients included 1220 males and 1087 females, with a median age of 72 years and a mean age of 73.30 ± 6.22 years. Among them, 520 patients (22.5%) had Grade 2 gliomas, and 1787 (77.5%) had Grade 3 gliomas. Multivariate Cox regression analysis revealed four independent prognostic factors (age, WHO grade, surgery, and chemotherapy) that were used to construct the full Cox model. In addition, LASSO-Cox regression analysis revealed five prognostic factors (age, WHO grade, surgery, radiotherapy, and chemotherapy), and a LASSO model was constructed. A comparison of the two models revealed that the LASSO model with five variables had better predictive performance than the full Cox model with four variables. Ultimately, five key variables based on LASSO-Cox regression were utilized to develop prognostic nomograms for predicting the 1-, 2-, and 5-year OS and CSS rates. The nomograms exhibited relatively good predictive ability and clinical utility. Moreover, the risk stratification system based on the nomograms effectively divided patients into low-risk and high-risk subgroups. Conclusion: Variable screening based on LASSO-Cox regression was used to determine the optimal prediction model in this study. Prognostic nomograms could serve as practical tools for predicting survival probabilities, categorizing these patients into different mortality risk subgroups, and developing personalized decision-making strategies for elderly patients with LGGs. Moreover, the webbased dynamic nomogram could facilitate its use in the clinic. [ABSTRACT FROM AUTHOR]

Background: Primary malignant melanoma (MM) of skin threatens health, especially in the older population, causing a significant risk of early death. The purpose of this study was to establish a diagnostic nomogram to predict the early mortality risk in older patients with primary skin MM and to determine the independent risk factors of cancer-specific early death in such patients. Methods: The Surveillance, Epidemiology and End Results (SEER) database provided the clinical and pathological characteristics of older patients with primary skin MM from 2000 to 2019. Initially, a 7:3 random assignment was used to place the recruited patients into training and validation cohorts. Then, the independent risk variables of cancer-specific early death in those individuals were determined using univariate and multivariate logistic regression analysis. Those patients' diagnostic nomograms were constructed using the acquired independent risk variables. Ultimately, the performance of the newly created diagnostic nomogram was verified using calibration curves, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Results: In this study, 2,615 patients in total were included. Age, histology, liver metastasis, tumor stage, surgery, therapy, and radiation were found to be independent risk factors following statistical analysis, with a special emphasis on early death in older patients with primary skin MM. A diagnostic nomogram for the cancer-specific early death risk was created and validated based on these variables. High agreement was reported between the expected and actual probabilities in the calibration curves. Area under the curves (AUC) of the novel created diagnostic nomogram was greater than that of each independent risk factor, with AUCs for the training and validation cohorts being 0.966 and 0.971, respectively. The nomogram had a high value for its applicability in clinical settings, according to DCA. Conclusion: In older patients with primary skin MM, the current study created a diagnostic nomogram to predict the probability of cancer-specific early death. Because of the nomograms' good performance, physicians will be better able to identify older patients who are at a high risk of early death and treat them individually to increase their survival benefit. [ABSTRACT FROM AUTHOR]

Background: Traditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients. Methods: Data on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified. Results: In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated. Conclusion: This study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram. [ABSTRACT FROM AUTHOR]

Simple Summary: The incidence and mortality rates of cancer in the Surveillance, Epidemiology, and End Results (SEER) Program for adolescent and young adult (AYA) patients show distinct patterns among early-onset cancers. For some cancers, AYA cancer rates varied by age group, sex, race, ethnicity and geography. Monitoring the rates and time trends of AYA cancer emphasizes the distinct health concern for this age group. (1) Background: The incidence rate of early onset-cancer (<50) has increased since 1995. Among younger people, cancers in AYAs (aged 15–39 y) are often biologically distinct tumors from those treated in the pediatric and older adult population. The current study describes trends in the United States for the most recent years including the first year of the COVID-19 epidemic. We aimed to describe the recent incidence and mortality trends of cancers in AYAs (aged 15–39 y). (2) Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER 22) from 1 January 2016 to 31 December 2021. Age-adjusted incidence and mortality rates were assessed by SEER*Stat 8.4.3 for major cancer types by sex, race/ethnicity, age, and metropolitan/nonmetropolitan status. Time trends of age-adjusted incidence and mortality rates were examined by sex and metropolitan/nonmetropolitan status. (3) Results: Age-adjusted overall cancer incidence and mortality rates were stable during this study period. The age-adjusted incidence rates declined significantly for ependymoma, melanoma, carcinomas of lung, bronchus, and trachea, unspecified malignant neoplasms, and non-Hodgkin's lymphoma. Significant increases were found for gastrointestinal tract cancers and non-Kaposi sarcomas. The age-adjusted mortality rate decreased for acute myeloid leukemia, melanoma, carcinomas of liver and intrahepatic bile ducts, kidney and, in women, leukemia. For some cancers, rates differed by sex, race, ethnicity, and geography. Monitoring the rates and time trends of AYA cancer emphasizes the distinct health concern for this age group. [ABSTRACT FROM AUTHOR]

This study aims to develop and validate a nomogram for predicting overall survival (OS) in Asian patients with Esophageal Cancer (EC). Data from Asian EC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial variable selection, followed by multivariate Cox regression analysis to identify independent prognostic factors. A nomogram was subsequently constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves and calibration curves, while the clinical utility of the nomogram was assessed through decision curve analysis (DCA). The LASSO regression and multivariate Cox regression analysis identified age, sex, marital status, tumor size, M stage, surgery, and chemotherapy as independent prognostic factors. The ROC curve results demonstrated that the area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year OS in the training cohort were 0.770, 0.756, and 0.783, respectively. In the validation cohort, the AUC values were 0.814, 0.763, and 0.771, respectively. Calibration curves indicated a high concordance between predicted and actual OS. The DCA demonstrated that the nomogram has significant clinical applicability. This nomogram provides reliable predictions and valuable guidance for personalized survival estimates and high-risk patient identification. [ABSTRACT FROM AUTHOR]

Background: The necessity and therapeutic value of lymph node dissection (LND) in early stage T1 MTC patients remain controversial. Methods: Patients with T1MTC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Poisson regression analysis was utilized to investigate promotive factors for lymph node metastasis in T1MTC patients. Fisher's exact test was employed to calculate baseline differences between non-LND and LND groups. Propensity score match (PSM) was used to control baseline bias. Survival outcomes were calculated by KaplanMeier method and log-rank test. Multivariable Cox regression assessed the prognostic impact of LND across subgroups. Results: Of 3298 MTC cases, 50.4% were T1MTC. The lymph node metastasis rate increased along with the T stage (from 22.2% to 90.5%). Among 1231 T1MTC patients included after exclusion criteria, 72.0% underwent LND and 22.0% had lymph node metastasis. Patients aged younger than 44 years (RR=1.700, p<0.001), male (RR=1.832, p<0.001), and with tumor larger than 10mm (RR=2.361, p<0.001) were more likely to have lymph node metastasis, while elderly patients (p<0.001) and those with microcarcinoma (p<0.001) were more likely to undergo non-LND procedures. LND provided no OS or DSS benefit over non-LND before and after propensity score match (matched 10-year OS/DSS: LND 83.8/96.2% vs non-LND 81.9/99.3%, p>0.05). Subgroup analyses revealed no prognostic gain with LND in any subgroup (p>0.05). Conclusion: Nearly half of MTC patients were diagnosed at T1 stage and had low lymph node risk. Different from ATA guidelines, avoiding routine LND conferred similar prognosis to standard procedures while potentially improving quality of life. Large-scale prospective multi-center studies should be conducted to further validate these findings. [ABSTRACT FROM AUTHOR]

Purpose: The study aimed to develop a nomogram model for individual prognosis prediction in patients with hormone receptors positive (HR+) mucinous breast carcinoma (MBC) and assess the value of neoadjuvant chemotherapy (NAC) in this context. Methods: A total of 6,850 HR+ MBC patients from the SEER database were identified and randomly (in a 7:3 ratio) divided into training cohorts and internal validation cohorts. 77 patients were enrolled from the Chongqing University Cancer Hospital as the external validation cohort. Independent risk factors affecting overall survival (OS) were selected using univariate and multivariate Cox regression analysis, and nomogram models were constructed and validated. A propensity score matching (PSM) approach was used in the exploration of the value of NAC versus adjuvant chemocherapy (AC) for long-term prognosis in HR+ MBC patients. Results: Multivariate Cox regression analysis showed 8 independent prognostic factors: age, race, marital status, tumor size, distant metastasis, surgery, radiotherapy, and chemotherapy. The constructed nomogram model based on these 8 factors exhibited good consistency and accuracy. In the training group, internal validation group and external validation group, the high-risk groups demonstrated worse OS (p<0.0001). Subgroup analysis revealed that NAC had no impact on OS (p = 0.18), or cancer specific survival (CSS) (p = 0.26) compared with AC after PSM. Conclusions: The established nomogram model provides an accurate prognostic prediction for HR+ MBC patients. NAC does not confer long-term survival benefits compared to AC. These findings provide a novel approach for prognostic prediction and clinical practice. [ABSTRACT FROM AUTHOR]

Hodgkin lymphoma (HL) is a rare malignancy affecting the lymphatic system. Our study examined the incidence rates of adult HL based on sex, race/ethnicity, age, and histological subgroups in the United States (US) from 2000 to 2020. Data for this study were extracted from the Surveillance, Epidemiology, and End Results 22 database. HL patients were identified utilizing the International Classification of Diseases for Oncology version 3 and categorized as classical HL, lymphocyte-rich/mixed cell/lymphocyte depleted, nodular sclerosis, classical HL, not otherwise specified, and nodular lymphocyte-predominant HL. The study reported average annual percent change (AAPC). All estimates were presented as counts and age-standardized incidence rates (ASIRs) per 100,000 individuals. Between 2000 and 2019, a total of 70,924 cases of HL were reported in the US. Classical HL was the predominant subtype (94.27%), and most incident cases were among non-Hispanic Whites (66.92%) and those aged 20–29 years (24.86%). The ASIR per 100,000 population was 3.83 for men and 2.92 for women. Both sexes showed declines in the AAPCs between 2000 and 2019 (− 0.64% [− 0.99, − 0.28] and − 0.40% [− 0.77, − 0.03] for men and women, respectively). There was a significant decrease in ASIRs after COVID-19 among both sexes (percent change: − 7.49% [− 11.58, − 3.40]). Throughout all age groups, men had a higher incidence rate compared to women, except for those aged 20–29 years. Although the overall HL incidence rate was lowered in the study period from 2000 to 2019, a dramatic decrease in ASIRs of HL patients following COVID-19 pandemic was observed. [ABSTRACT FROM AUTHOR]

Introduction: Uterine carcinosarcoma is an aggressive gynecologic malignancy that accounts for 5% of all gynecological malignancies. There is a disproportion in its incidence and mortality among different races. This study describes demographic and clinicopathological factors and racial disparities affecting the survival of patients with uterine carcinosarcoma. Methods: Data on uterine carcinosarcoma patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2020. Results: Of the 11,338 patients identified, the median age at diagnosis was 68 years, and the five-year cause-specific survival (CSS) rate was 38.7%. for all races. Compared with Asian patients (39.5%, 95% CI, 36.0–43.4%), Hispanic patients (39.4%, 95% CI, 36.5–42.5%), and White patients (37.9%, 95% CI, 36.7–39.2%), Black patients accounted for 21% of the patients and had a significantly lower 5-year CSS (95% CI, 27.2–31.2%). The CSS rates were 84.4% (95% CI, 83.3–85.6%) for localized tumors, 68.5% (95% CI, 66.9–70.1%) for regional tumors, and 39.0% (95% CI, 36.9–41.2%) for distant tumors. Multimodal treatment involving chemotherapy, surgery, and radiation improved the overall one- and five-year survival rates by 88.2% (95% CI, 87.0–89.5%) and 52.8% (95% CI, 50.7–55.1%), respectively, across all disease stages. Multivariate analysis identified age >60 years, Black race, tumor size >4 cm, and distant metastases as independent risk factors for mortality (p < 0.0001). Conclusions: This large database study presents the most up-to-date epidemiological information regarding cases of uterine carcinosarcoma. The findings suggest that a combination of surgery, chemotherapy, and radiation may be most efficacious in treating this malignancy, especially in patients with distant disease. [ABSTRACT FROM AUTHOR]

(1) Background: This study seeks to employ a machine learning (ML) algorithm to forecast the risk of distant metastasis (DM) in patients with T1 and T2 gallbladder cancer (GBC); (2) Methods: Data of patients diagnosed with T1 and T2 GBC was obtained from SEER, encompassing the period from 2004 to 2015, were utilized to apply seven ML algorithms. These algorithms were appraised by the area under the receiver operating characteristic curve (AUC) and other metrics; (3) Results: This study involved 4371 patients in total. Out of these patients, 764 (17.4%) cases progressed to develop DM. Utilizing a logistic regression (LR) model to identify independent risk factors for DM of gallbladder cancer (GBC). A nomogram has been developed to forecast DM in early T-stage gallbladder cancer patients. Through the evaluation of different models using relevant indicators, it was discovered that Random Forest (RF) exhibited the most outstanding predictive performance; (4) Conclusions: RF has demonstrated high accuracy in predicting DM in gallbladder cancer patients, assisting clinical physicians in enhancing the accuracy of diagnosis. This can be particularly valuable for improving patient outcomes and optimizing treatment strategies. We employ the RF algorithm to construct the corresponding web calculator. [ABSTRACT FROM AUTHOR]

Background and Aim: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. Methods: In this population‐based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long‐short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. Results: In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897–0.916), 0.908 (95% CI 0.899–0.918), 0.910 (95% CI 0.901–0.919), and 0.793 (95% CI 0.769–0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. Conclusions: Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients. [ABSTRACT FROM AUTHOR]

Limited knowledge exists regarding lung metastases from cancer of unknown primary (CUPL), particularly concerning young patients. This study aims to investigate the clinicopathologic features and prognostic factors of CUPL patients, with a specific focus on comparing the survival outcomes across different age groups. We conducted a retrospective analysis of patients diagnosed with CUPL between 2010 and 2020, utilizing the SEER database. Clinical characteristics among different age groups were compared. Prognostic factors influencing overall survival (OS) in CUPL patients were assessed through Cox regression analysis, while competing risks analysis was employed to evaluate cancer-specific survival (CSS) prognostic factors. A comparison of survival differences between age groups was conducted utilizing the Kaplan–Meier and Cumulative Incidences Function. A total of 2,474 patients with CUPL were included in this study, predominantly in the middle-aged and elderly demographic. The median survival time was a mere 1 month, with a one-year OS rate of 11 % and a one-year CSS rate of 13.8 %. Age, tumor histological typing and grading, liver metastasis, bone metastasis, radiotherapy, and chemotherapy were identified as independent prognostic factors affecting both OS and CSS. Despite the small representation of young patients (<40 years old) at 3 %, their OS and CSS rates significantly surpassed those of middle-aged (40–70 years old) and elderly patients (>70 years old). This advantage persists among patients undergoing radiation and chemotherapy. While exceedingly uncommon among young patients, the prognosis for survival is more favorable than in middle-aged and elderly patients. Administration of radiotherapy and chemotherapy emerges as a potential avenue to enhance the survival prognosis for CUPL patients. [ABSTRACT FROM AUTHOR]

Background: Large population-based registries, such as the Surveillance, Epidemiology and End Results (SEER) Registry, help in the study of rare tumors, including medullary thyroid cancer (MTC), but lack data to understand the natural history of the disease. The Medullary Thyroid Cancer Collaborative Registry (MTCCoRe) is an exhaustive multi-institutional collection of demographic, clinical, and pathological data. To determine the extent to which MTCCoRe represents the real-world MTC population, we compared the characteristics of patients enrolled in MTCCoRe with patients enrolled in population-based cancer registries. Methods: Comparison of demographic and clinical characteristics of MTC patients who were enrolled in MTCCoRe, Texas Cancer Registry (TCR), California Cancer Registry (CCR), and SEER between 1995 and 2018. Results: A total of 1416 patients were identified in MTCCoRe, 329 in TCR, 2105 in CCR, and 3820 in SEER. Percentages of patients 20–54 years in MTCCoRe were 58.0%, 50.2% in TCR, 47.2% in CCR, and 44.8% in SEER (p < 0.0001). About half of the patients were female (55.9% in MTCCoRe, 61.4% in TCR, 59% in CCR, and 57.5% in SEER (p = 0.3). Percentages of Hispanic and Black patients differed among cohorts (10.1% and 3.8% for MTCCoRe, 23.7% and 8.2% for TCR, 24.8% and 4.9% in CCR, and 15.9% and 8.2% for SEER, respectively; p < 0.001). MTCCoRe patients presented with more advanced T and N classifications than patients in the other registries (MTCCoRe, 28.6% T3-4 and 49.4% N1; TCR, 12.7% and 32.2%; CCR, 18.6% and 32.4%; and SEER, 24% and 37.8%; p < 0.0001). Prevalence of M1 disease was 10% in MTCCoRe, 11.9% in TCR, 14.1% in CCR, and 9.5% in SEER (p < 0.0001). In the MTCCoRe, 11.4% underwent systemic therapy (compared with 0.3% in TCR and 5.6% in CCR). Conclusions: The clinicodemographic profile of patients with MTC enrolled in a multi-institutional registry differs from those enrolled in population-based databases, with lower proportions of Hispanic and Black patients but additive data on treatment modalities. Moving forward, MTCCoRe and other registry and clinical trial enrollment efforts should intentionally include underrepresented groups via community engagement techniques, patient stakeholder involvement, and inclusion of languages other than English in study materials to yield more generalizable results and conclusions. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the impacts of the number of positive lymph nodes (NPLN) and lymph node ratio (LN ratio) for patients with hypopharyngeal squamous cell carcinoma (HPSCC) based on SEER database, which were validated in the real-world data of China. Methods: A total of 520 patients from SEER database were analyzed. Then 195 patients with pathologically stage III or IV HPSCC in our center were retrospectively studied. Results: In the SEER database, NPLN ≥ 3 was found in 36.9% of patients. Multivariate analysis revealed that LN ratio ≥ 0.138 was significant with poorer overall survival (OS) (hazard ratio [HR] = 1.525, p = 0.001) and cancer-specific survival (CSS) (HR = 1.697, p < 0.001), so was the NPLN ≥ 3 (HR = 1.388, p = 0.013; HR = 1.479, p = 0.008). Patients with NPLN ≥ 3 were found in 103 (52.8%) in our center. Multivariate analysis confirmed a significant association regarding OS (p = 0.005) or CSS (p = 0.003) between patients with LN ratio ≥ 0.138 or not. In addition, disease recurrence rate differed significantly between the patients with NPLN ≥ 3 (27.2%) and NPLN < 3 (14.1%, p = 0.026). Moreover, postoperative chemoradiotherapy (CCRT) was significantly associated with better prognosis in patients with NPLN ≥ 3. Conclusion: In the SEER database, NPLN ≥ 3 and LN ratio ≥ 0.138 were independent poor prognostic factors for patients with HPSCC. Whereas identifying worldwide cut-off values for LN ratio is difficult and surgeon-dependent. In our cohort, adjuvant CCRT was beneficial for OS in patients with NPLN ≥ 3. [ABSTRACT FROM AUTHOR]

Our goal was to assess the impact of childhood/adolescent cancer history on overall survival (OS) and disease-specific survival (DSS) in patients with parotid mucoepidermoid carcinoma (MEC). Patients who underwent surgical treatment for primary parotid MEC and those with a second malignancy of parotid MEC were retrospectively identified from the Surveillance, Epidemiology, and End Results (SEER) database. The primary outcome variables were OS and DSS. The hazard ratios (HRs) of these survival rates associated with cancer history were analysed using Cox regression models. In total, 2681 patients were included, 263 of whom had a second malignancy. The 10-year OS rates in the primary (72%) and second malignancy groups (59%) were significantly different. Cox regression confirmed that a history of cancer tended to decrease OS (p = 0.062, HR: 1.28, 95% confidence interval: 0.99 to 1.64). Subgroup analyses showed that a history of solid tumour as opposed to haematological cancer predicted worse OS, with central nervous system tumours exhibiting a more significant influence than others (p = 0.030 vs p = 0.088). Cancer history was not related to DSS. A history of childhood/adolescent cancer negatively influenced the prognosis of patients with parotid MEC, and this effect was primarily driven by a history of solid malignancy. [ABSTRACT FROM AUTHOR]

Asian American, Native Hawaiian, and Pacific Islander (AANHPI) patient populations are often defined as one monolithic group in medical research despite cultural, socioeconomic, and clinical heterogeneity. Although the general AANHPI population is underrepresented in reception of postmastectomy breast reconstruction, existing literature has not characterized the disaggregation of such rates for AANHPI ethnic subgroups. Patients who underwent mastectomy were identified in the 2007 to 2020 registries within the Surveillance, Epidemiology and End Results database. Patients were stratified by race and ethnicity, and additional demographic and oncologic variables were collected. Multivariate binary logistic regression was conducted to assess for reception of postmastectomy immediate breast reconstruction (p < 0.05). Among 33,422 AANHPI patients who underwent mastectomy, South Asian patients were associated with the highest breast reconstruction rates (33%) and Melanesians with the lowest (15%). Overall, AANHPI patients were associated with a lower breast reconstruction rate than non-Hispanic Whites (27% vs. 35%; p < 0.001). This difference increased from 6.4% in 2007 to 10% in 2020. After controlling for demographic and oncologic covariates, all AANHPI ethnic subgroups predicted a lower likelihood of breast reconstruction than non-Hispanic Whites (p < 0.001). Odds ratios for reconstruction ranged from 0.17 [95% confidence interval (95% CI), 0.11–0.27] for Melanesian patients to 0.45 (95% CI, 0.42–0.48) for South Asian patients. Disparities in the receipt of immediate breast reconstruction exist within the AANHPI patient population in the United States. This analysis supported the need for disaggregation in plastic surgery research for improved knowledge and targeted interventions. [ABSTRACT FROM AUTHOR]

Purpose: Adrenocortical carcinoma (ACC) is an uncommon adrenal gland endocrine tumor that has a poor prognosis in children. We aimed to conduct a population-based cohort study to predict overall survival (OS) in pediatric patients with ACC. Methods: We used the Surveillance, Epidemiology, and End Results (SEER) database to conduct a retrospective cohort research on pediatric patients diagnosed with ACC between 1975 and 2018. We examined demographic characteristics, tumor stage and size, treatment options, and survival results. Kaplane-Meier estimations were used to generate survival curves based on several parameters. To compare survival curves, the log-rank test was applied.Cox proportional-hazards regression was used to determine the variables related with OS. In addition, we created a nomogram to predict overall survival in pediatric ACC patients. Results: A total of 143 pediatric ACC patients were identified. Females were the most impacted (60.8%). Overall 1 year, 3 year, and 5 year survival rates were 75.0%, 57.6%, and 53.7% for all patients, respectively. In comparison to older patients (5–19 years), younger patients (≤ 4 years) were shown to have more positive characteristics, including a higher likelihood of local disease (29.4% vs. 14%, P < 0.001), tumors less than 10 cm (23.1% vs. 14.7%, P < 0.001), and improved overall survival (5 year OS 89.6% vs. 27.7%, P < 0.001). Age at diagnosis, SEER stage, and surgery were significant independent predictors of OS in this model, according to the results of Cox proportional hazard regression. After that, we developed a nomogram for predicting OS in children with ACC. Patients older than 4 years old had a higher chance of dying. Furthermore, the higher the SEER stage, the higher the risk of death. Patients who do not have surgery have a worse survival rate than those who do. Conclusions: Our study revealed that age at diagnosis, SEER stage, and surgery were found to be the most important predictors of the overall survival of pediatric ACC. These findings contribute to the existing body of knowledge and emphasize the importance of continued research to advance our understanding of pediatric ACC and improve patient care. [ABSTRACT FROM AUTHOR]

Background: Colorectal adenosquamous carcinoma (ASC) is a rare subtype of colorectal carcinoma. This study presents findings from a large database query to highlight the demographic, clinical, and pathological factors, prognosis, and survival of colorectal ASC. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients with colorectal ASC diagnosed between 2000 and 2020 and assess factors associated with overall survival (OS) and cause-specific survival (CSS). Results: Among 284 identified cases, the median age of diagnosis was 64 years. The majority of patients were White (69.0%), with income ≤ $70,000 (62.3%), and lived in metropolitan areas (85.6%). Regarding tumor characteristics, the majority of tumors were poorly differentiated (49.6%), regional stage (39.8%), size of > 4.0 cm (41.5%), and had a negative lymph node status (47.2%). Primary sites were the rectum (35.2%) and colon (64.8%). In patients with primary site to the rectum, the majority of treatment modality was multimodal therapy (40.0%). The main treatment modality for the primary site to the colon was surgery only (46.2%), followed by surgery + chemotherapy (34.2%). The overall 5-year survival was 31.3 (95% C.I. 28.4–34.2) and the 5-year cause-specific survival (CSS) was 40.1% (95% C.I. 36.9–43.3). Multivariate analysis showed age ≥ 60 years, regional stage, and distant stage were negative prognostic factors. An income of > $70,000, multimodal therapy, and surgery with chemotherapy were positive prognostic factors. Conclusion: Colorectal adenosquamous carcinomas are more common in the non-Hispanic White populations and appear more frequently later in life (based on the median age of diagnosis at 64). Factors that contributed to a worse prognosis were an age of diagnosis ≥ 60 years, regional stage, and distant stage. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging. Methods: Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification. Results: While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata. Conclusions: Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer. [ABSTRACT FROM AUTHOR]

Copyright of Saudi Medical Journal is the property of Saudi Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aims/Background We aimed to investigate the impact of postoperative chemotherapy (POCT) on survival in patients with primary central nervous system lymphoma (PCNSL) using data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods This study included 786 PCNSL patients, of which 605 received chemotherapy after surgery, and 181 did not. Data from the SEER registry database (2007–2020) were used to analyze PCNSL. Baseline information, including age, sex, race, marital status, primary tumour site, histological type, summary stage, surgical procedures, chemotherapy, and radiotherapy, was analyzed. Propensity Score Matching (PSM) (1:1) was employed to balance the effects of confounding variables between the two groups. Subsequently, Cox regression and bidirectional stepwise regression were used to identify independent prognostic factors. Kaplan-Meier (K-M) survival curves were constructed to assess the impact of POCT on patient prognosis. Additionally, two cases of PCNSL with typical magnetic resonance imaging appearances were presented. Results Multivariate Cox regression results revealed that age older than 60 years (hazard ratio [HR] = 1.786; 95% confidence interval [CI]: 1.272–2.509; p = 0.001) and absence of POCT (HR = 2.841; 95% CI: 2.159–3.738; p < 0.001) were independent prognostic risk factors, while primary tumour locations in the meninges (HR = 0.136; 95% CI: 0.032–0.569; p = 0.006) and other nervous system regions (HR = 0.552; 95% CI: 0.326–0.936; p = 0.027), as well as histological morphologies such as diffuse large B-cell lymphoma (HR = 0.233; 95% CI: 0.128–0.425; p < 0.001) and non-Hodgkin lymphoma (HR = 0.559; 95% CI: 0.356–0.876; p = 0.011), were associated with favourable patient outcomes. K-M curves demonstrated that the group undergoing POCT had a significantly more favourable prognosis compared to the non-POCT group, before (HR = 0.454; 95% CI: 0.343–0.600; p < 0.0001) or after PSM (HR = 0.580; 95% CI: 0.431–0.780; p < 0.0001). For patients with PCNSL, those with tumours located in the infratentorial region (HR = 0.231; 95% CI: 0.078–0.682; p = 0.046), supratentorial region (HR = 0.250; 95% CI: 0.163–0.383; p < 0.0001), overlapping brain regions (HR = 0.201; 95% CI: 0.056–0.727; p = 0.0058), and those who underwent biopsy (HR = 0.740; 95% CI: 0.463–1.182; p = 0.003), subtotal resection (STR) (HR = 0.490; 95% CI: 0.265–0.906; p = 0.0064), or gross total resection (GTR) (HR = 0.613; 95% CI: 0.292–1.287; p = 0.0003) had better prognoses in the postoperative chemotherapy group compared to the non-chemotherapy group. Conclusion POCT significantly improves the prognosis of PCNSL patients and identifies the characteristics of the benefiting population. This information aids clinical practitioners in designing personalized treatment plans for individuals and advancing precise treatment. [ABSTRACT FROM AUTHOR]