SEER Database Research Articles

Minimum number of lymph nodes to maximize survival in non-metastatic appendiceal adenocarcinoma

IntroductionIn non-metastatic appendiceal adenocarcinoma, the relationship between the number of lymph nodes harvested and overall survival is unknown. The objective of this study was to determine whether the number of lymph nodes harvested impacts survival in patients with mucinous and non-mucinous appendiceal cancer. MethodsRetrospective cohort analysis was conducted in patients who underwent surgery for appendiceal adenocarcinoma in the SEER database from 2000–2019. Exclusion criteria included patients without the number of regional nodes harvested/node positivity, missing survival data, stage IV disease, and certain subtypes of appendiceal tumors. The primary outcome was 10-year overall survival. ResultsA total of 1651 patients were identified, 1101 with mucinous and 550 with non-mucinous cancer. The majority of patients were male (54 %) and between the ages of 50–74 (52 %). Mean ± SD number of lymph nodes harvested was 17.04 ± 10.41. The ten-year survival rates for patients with mucinous appendiceal cancer were 43–74 % and 25–68 % for non-mucinous cancer, depending on AJCC stage. Multivariate Cox-regression analysis displayed that the number of lymph nodes harvested was an independent predictor of survival (p < 0.0001). The optimal threshold predicted by Cox-regression for maximal survival benefit was harvest of a minimum of 15 nodes for mucinous and 12 for non-mucinous cancer. Additionally, for mucinous cancer, per each additional 3 nodes harvested past 15, the 10-year risk of mortality decreased by 7 % (HR 0.93 (0.88 to 0.98), p < 0.001). ConclusionsThe number of lymph nodes harvested is an independent predictor of survival for patients with appendiceal adenocarcinoma. To maximize this survival benefit, a minimum of 15 regional lymph nodes must be harvested for mucinous and 12 for non-mucinous cancer.

An International Registry Study of Early-Stage NSCLC treatment variations (LUCAEUROPE) in Europe and the USA highlighting variations

ObjectiveHarmonized European NSCLC incidence, treatment approach, and survival based on national tumor registries are unclear. Summary background dataSurgery has the potential to cure NSCLC and significantly prolong survival. This large-scale international study aimed to investigate treatment variations in Europe and the USA, as well as the determinants for its utilization. MethodsThe retrospective cohort study analyzed data from six European national population-based cancer registries (Belgium, Denmark, Estonia, Germany, the Netherlands, and Slovenia) and the US SEER database from 2010–2015. ResultsThe study computed cancer incidence, survival, and age-standardized proportions of the use of various therapies. Multivariable logistic regression models were used to assess associations between resection and demographic and clinical parameters. A total of 428,107 records were analyzed. Among all countries, Estonia had the highest surgical resection rate (79.3 %) and the lowest radiation rate (7.3 %) for stage I patients. The Netherlands had the highest rate of radiotherapy across all years of investigation and the lowest surgery rate between 2012 and 2015. The primary treatment for early-stage NSCLC showed significant international variation, with the USA having a decrease in surgical rates from 67.6 % to 59.5 %. Resection was less frequently performed as tumor stage increased, patients aged, other lung cancer besides adenocarcinoma was present, and when the tumor site overlapped multiple lobes. ConclusionsResection rates have declined in some studied European countries and the USA and resection rates vary substantially among countries. Interpretation of current scientific lung cancer evidence and international guidelines results in wide variations in patient treatment.

Exploring the biological behavior differences between retroperitoneal and non-retroperitoneal liposarcomas

BackgroundLiposarcoma is a malignant tumor that originates from adipose tissue and can occur in any part of the body. There is currently no clear conclusion on whether there are significant differences in prognosis between liposarcoma at different anatomical locations, especially retroperitoneal liposarcoma (RLPS) and non retroperitoneal liposarcoma (NRLPS). The aim of this study is to reveal whether there are differences in prognosis between these two locations of liposarcoma, and further explore the fundamental reasons behind these differences. MethodsWe conducted an in-depth investigation into the factors affecting the prognosis of patients with liposarcoma by analyzing the data from the Surveillance, Epidemiology, and End Results Program (SEER) database. Then, we used propensity score matching (PSM) to balance these prognostic factors for comparative analysis of survival between RLPS and NRLPS. In addition, by analyzing transcriptome and whole exome data from TCGA and the Japan Genotypic Phenotype Archive (JGA), we identified genes with significant expression differences and explored changes in the immune microenvironment. ResultThrough analysis of RLPS and NRLPS patients in the SEER database, we observed significant prognostic differences between the two groups, with RLPS exhibiting worse prognosis (p < 0.001). Even after adjusting for confounding factors through PSM, these survival rate differences remained significant, with RLPS still showing worse prognosis (p = 0.017). Furthermore, our analysis of transcriptomic data led to the identification of 467 differentially expressed genes. Additionally, we noted significant differences in the immune microenvironment and whole exome sequencing data between the two groups. ConclusionThere are significant differences between patients with RLPS and NRLPS. Therefore, from clinical research to treatment strategies, RLPS and NRLPS should be considered as two distinct types of tumors, necessitating differentiated approaches for their study and treatment.

Role of elective neck dissection in cT2N0 maxillary sinus squamous cell carcinoma

Our objective was to examine the impact of elective neck dissection (END) on the prognosis of patients with cT2N0 maxillary sinus squamous cell carcinoma (MS-SCC) and to determine factors that predict the occurrence of occult metastasis in this patient population. A retrospective analysis was conducted using data from the SEER database. Patients with cT2N0 MS-SCC were included in the study and divided into two groups: those who received END and those who did not. The impact of END on disease-specific survival (DSS) and overall survival (OS) was assessed using propensity score matching. Multivariate logistic regression analysis was performed to determine predictors for occult metastasis. A total of 180 patients were included in the study, with 40 cases receiving END. Following propensity score matching, patients treated with END and those without showed similar DSS and OS rates. Occult metastasis was observed in 9 patients, corresponding to a rate of 22.5%. High-grade tumors were independently associated with a higher risk of occult metastasis compared to low-grade tumors (hazard ratio 1.52, 95% confidence interval 1.17–2.00). cT2 MS-SCC carries an occult metastasis rate of 22.5%, with histologic grade being the primary determinant of occult metastasis. END does not confer a significant survival benefit in this patient population.

Rural Patients With Melanoma Exhibit Primary Tumors in High-Risk Locations and Reduced Disease-specific Survival: A Retrospective Cohort Analysis of the 2000 to 2020 SEER Database.

Rural Patients With Melanoma Exhibit Primary Tumors in High-Risk Locations and Reduced Disease-specific Survival: A Retrospective Cohort Analysis of the 2000 to 2020 SEER Database.

Conditional survival and the prognostic value of serum carcinoembryonic antigen level in oldest old with colorectal cancer

BackgroundTo evaluate the clinical value of serum CEA levels and their implications on the diagnostic value of the conventional TNM staging system in the oldest-old patients with colorectal cancer (CRC).MethodsThe recruited subjects were colorectal cancer patients aged 85 and older. The cutoff value for normal CEA level is 5 ng/mL. Patients with elevated CEA levels were categorized as stage C1, and those with normal CEA levels as stage C0. A number of Cox proportional hazard regression models were established to evaluate the prognosis of different prognostic factors with hazard ratios (HRs) and 95% confidence intervals (CIs). The Kaplan–Meier method was utilized to display the disparate prognostic impact of multiple clinicopathological factors with the log-rank test.ResultsA total of 17,359 oldest-old patients diagnosed with CRC were recruited from the SEER database. The conditional survival of oldest-old patients with CRC was dismal with a 1-year conditional survival of only 11%, 18%, and 30% for patients surviving 1, 3, and 5 years, respectively. Patients with stage C1 exhibited a 48.5% increased risk of CRC-specific mortality compared with stage C0 (HR = 1.485, 95%CI = 1.393–1.583, using stage C0 patients as the reference, P < 0.001). All the stage C0 patients indicated lower HRs relative to the corresponding stage C1 patients.ConclusionsDismal conditional survival of oldest-old patients with CRC should be given additional consideration. C stage influences the prognosis of oldest-old patients with CRC.

Survival Outcomes and Prognostic Factors in Therapy-Related Acute Myeloid Leukemia: A SEER Database Study, 2000-2020

IntroductionWith advances in therapeutics and longer survival across different cancer spectrums, the incidence of therapy-related acute myeloid leukemia (tAML) has continued to rise. This study aims to evaluate the trend of survival outcomes and their association with sociodemographic factors in tAML over the last 20 years. MethodsWe identified tAML patients between 2000 and 2020 from the Surveillance, Epidemiology, and End Results database. Patients were divided into 4 age groups: 18-39, 40-59, 60-69, and >= 70 years, and 4 diagnostic periods: 2000-2005, 2006-2010, 2011-2015, and 2016-2020. Overall survival (OS) was compared using Kaplan Meier and log-rank methods. ResultsThe 1-year (and 5-year) OS in patients with tAML was 59.3% (33.7%), 48.2% (24.8%), 37.2% (11.1%), and 32.9% (5.5%) in age groups 18-39, 40-59, 60-69, and >=70 years, respectively. The 1-year (and 5-year) OS based on the year of diagnosis was 20.9% (13.2%), 36.8% (15.2%), 41.9% (13.88%), and 40.4% (not reached) for 2000-2005, 2006-2010, 2011-2015, and 2016-2020 respectively. Among the youngest cohort aged 18-39 years, 1-year OS was 35.7%, 57.7%, 66.7%, and 59.6%, respectively, in 4 diagnostic periods, whereas 1-year OS was 10.5%, 23.9%, 32.2%, and 36.9%, respectively, in the oldest cohort aged >=70 years. Age, year of diagnosis, and geographic location were independent prognostic markers of OS. ConclusionOur study demonstrates a significant improvement in the 1-year OS of tAML patients over the last decade, but the long-term prognosis remains dismal. Older patients continue to show improved survival in recent years with the addition of newer intensive and nonintensive options.

A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database

Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan–Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients’ overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery.

Comparing the oncologic outcomes of local tumor destruction vs. local tumor excision vs. partial nephrectomy in T1a solid renal masses: a population-based cohort study from the SEER database- correspondence.

Comparing the oncologic outcomes of local tumor destruction vs. local tumor excision vs. partial nephrectomy in T1a solid renal masses: a population-based cohort study from the SEER database- correspondence.

Comparative efficacy of lobar resection and sublobar resection in patients with stage IA lung invasive mucinous adenocarcinoma: insights from the SEER database with propensity score matching

Comparative efficacy of lobar resection and sublobar resection in patients with stage IA lung invasive mucinous adenocarcinoma: insights from the SEER database with propensity score matching

Survival of female patients with breast cancer in the reproductive age group: A retrospective study using SEER database

ABSTRACT Background: Breast cancer is the most prevalent cancer in women worldwide. The progressive rise in breast cancer incidence in young and reproductive-aged women is a cause for concern. Objectives: The primary objective of this study was to investigate the survival of patients with breast cancer in women from the reproductive age group (15-49 years). The secondary objective was to identify significant factors associated with their survival. Materials and Methods: This retrospective study was performed in the Department of Statistics, Utkal University, Odisha, India. We utilized data from the Surveillance, Epidemiology, and End Results (SEER) database, specifically from November 2020, covering 2000 - 2018, maintained by the National Cancer Institute. The Kaplan-Meier non-parametric estimation technique was used for survival estimation, and standard Cox proportional hazard (PH) and Cox PH with a gamma frailty term were employed to determine significant potential determinants. Results: Among women with breast cancer in the reproductive age group, the average survival time was 97.4 months (8.1 years), and the overall survival probability was 0.836. Young women (&lt; 40 years) with breast cancer had poorer survival and a higher risk of death. An increasing incidence of HR+/HER2-subtype of breast cancer was present in 65% of women in this age group. Women with distant metastasis faced a significantly elevated risk (hazard ratio [HR], 66.66, 95% confidence interval [CI], 61.11–72.71), approximately 67 times higher, compared to those with localized staged breast cancer. Furthermore, the ER−/PR+/HER2- group (HR, 6.50; 95% CI, 5.25-8.03) had a similar increased risk of mortality as those in the triple-negative group (HR, 6.25; 95% CI, 5.83-6.71). Conclusion: Young women (15–49 years) consistently have a higher risk of breast cancer recurrence or death as compared to older women (&gt; 49 years). This study provides evidence that the ER/PR/HER2 status is an important prognostic factor and should be considered in the management and treatment decisions for patients with breast cancer in this population.

The survival prediction of advanced colorectal cancer received neoadjuvant therapy—a study of SEER database

PurposeThe aim of study was to screen factors associated with the overall survival of colorectal cancer patients with lymph nodes metastasis who received neoadjuvant therapy and construct a nomogram model.MethodsAll enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model.ResultsA total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients.ConclusionA nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.

Analysis of Risk Factors for Secondary Endometrial Cancer-Related Death: A SEER-Based Study.

The aim of this study was to analyze the survival of patients with endometrial cancer diagnosed after a prior cancer and identify risk factors of endometrial cancer death in this population. Totally 1371 women diagnosed with second primary endometrial cancer (SPEC) between 2004 and 2015 were identified using the SEER database. Clinicopathological characteristics were collected, and Fine and Gray regression model was employed to assess the impact of treatment for the first primary cancer (FPC) and SPEC on the mortality of endometrial cancer patients. After propensity score matching (PSM), patients diagnosed with single primary endometrial cancer and SPEC between 2004 and 2015 were included as the second cohort. Kaplan-Meier and Cox survival risk models were used to assess the influence of previous cancer history on survival. Patients previously diagnosed as lung cancer exhibited the lowest overall survival (OS). A diagnostic interval of ≥3 years was significantly associated with higher mortality from SPEC compared with that <3 years. Surgical treatment for SPEC was linked to a reduced risk of endometrial cancer-specific mortality (ECSM) and non-ECSM. Conversely, radiotherapy and chemotherapy were associated with an increased risk of ECSM. The 1-, 3-, and 5-year OS rates of patients with SPEC were significantly lower than those with single primary endometrial cancer whether before or after PSM. Univariate and multivariate analyses further demonstrated that endometrial cancer, either as FPC or SPEC, was independently associated with an increased risk for endometrial cancer-specific survival (ECSS) and OS. Chemotherapy and radiotherapy for SPEC can elevate the risk of ECSM. Whether as FPC or SPEC, endometrial cancer is demonstrated to be a significant independent risk factor for ECSS and OS.

Prognostic factors and treatment strategies of limited-stage primary esophageal small-cell carcinoma-a SEER database analysis.

Primary esophageal small-cell carcinoma (PESC) is a rare tumor with poor efficacy, and there is currently no standardized treatment method. Our aim is to explore the prognostic factors and possible optimal treatment modalities for limited-stage PESC. We retrospectively searched the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2019 for data of patients with limited-stage PESC. Kaplan-Meier method was used to plot survival curves, calculate survival rates, and Log-rank was used to test the differences among survival curves. Prognostic factors were explored through univariate and multivariate Cox regression survival analyses; Cox regression survival analysis was also conducted to analyze the risk of death among treatment groups and compare the survival differences among each treatment group. The non-single treatment (ST) group was defined as the comprehensive treatment (CT) group and it was compared against the ST group. A total of 186 cases of limited-stage PESC were included in the study, there were differences in survival time among different groups due to differences in age, year, median household income, and N stage (P<0.001, P=0.041, P=0.002, P=0.001). The median overall survival (mOS) of the surgical group (19 months) was longer than that of the nonsurgical group (11 months) (P=0.01). The mOS of the chemotherapy group (16 months) was longer than that of the non-chemotherapy group (4 months) (P<0.001). The mOS of the radiotherapy group (16 months) was longer than that of the non-radiotherapy group (8 months) (P<0.001). Univariate analysis showed that age ≥80 years (P=0.006), year (1997-2007) (P=0.01), year (2008-2019) (P=0.01), N2 (P=0.003), surgery (P=0.02), radiotherapy (P<0.001), and chemotherapy (P<0.001) were prognostic factors affecting overall survival (OS) in limited-stage PESC patients. Multivariate analysis showed that SEER stage (P=0.02), age (P=0.007), radiotherapy (P<0.001), surgery (P=0.006), and chemotherapy (P<0.001) were independent prognostic factors affecting OS in patients of limited-stage PESC. Prognosis was better in the non-monotherapy group than in each monotherapy group. The CT group is superior to the ST group (P<0.001). The surgery combined with chemotherapy (SC) group had the longest mOS and the highest reduced risk of death, but there was no statistical difference. SEER stage, age, radiotherapy, chemotherapy, and surgery were independent prognostic factors in limited-stage patients; CT outperformed ST; the SC group had the longest median survival, but showed no statistical difference.

Clinical characteristics and prognosis of lung metastases from unknown primary cancer sites

Abstract Objectives 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. Methods 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. Results 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 (&lt;40 years old) at 3 %, their OS and CSS rates significantly surpassed those of middle-aged (40–70 years old) and elderly patients (&gt;70 years old). This advantage persists among patients undergoing radiation and chemotherapy. Conclusions 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.

Comparison of long-term survival of neoadjuvant therapy plus surgery versus upfront surgery and the role of adjuvant therapy for T1b-2N0-1 esophageal cancer: a population study of the SEER database and Chinese cohort.

For stage T1b-2N0-1 esophageal cancer, the impact of neoadjuvant therapy plus surgery (NS), surgery alone (SA), and surgery plus adjuvant therapy (ST) on cancer-specific survival (CSS) and overall survival (OS) is uncertain. Stage T1b-2N0-1 esophageal cancer patients from the SEER database and two Chinese cancer centers were included in this study. The Kaplan-Meier method was used to plot survival curves, which were compared using the log-rank test. Propensity score matching was used to equalize differences between the groups. Cox proportional hazards regression models were used to analyze prognostic factors. A nomogram for OS was developed after screening the variables using the Cox proportional hazards regression model and the least absolute shrinkage and selection operator. The performance of the nomogram was assessed by the Harrell concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic curve, calibration plots, and decision curve analysis. After propensity score matching analysis, the 3-year CSS and OS rates in the NS group compared to the SA group were 80.3% versus 62.1% (P=0.016) and 75.8% versus 55.5% (P=0.006), the 3-year CSS and OS rates in the NS group compared to the ST group were 71.3% versus 68.3% (P=0.560) and 69.8% versus 62.9% (P=0.330), the 3-year CSS and OS rates in the SA group compared to the ST group were 54.6% versus 66.7% (P=0.220) and 50.2% versus 57.9% (P=0.290), respectively. The predictive nomogram for OS in T1b-2N0-1 patients ultimately incorporated five clinicopathological variables: T stage, N stage, age, examined lymph nodes , and therapy modality. The nomogram C-index for predicting OS was 0.648, 0.663, and 0.666 in the training group, external validation group-1, and external validation group-2, respectively. The 1-, 3-, and 5-year predicted AUC values of the OS prediction model were 0.659, 0.639, and 0.612 for the training group, and 0.786, 0.758, and 0.692 for validation group-1, and 0.805, 0.760, and 0.693 for validation group-2, respectively. For patients with stage T1b-2N0-1 esophageal cancer, neoadjuvant therapy significantly improves prognosis compared to surgery alone, those presenting with positive lymph nodes after upfront surgery can achieve survival benefits from adjuvant therapy.

Impact of surgical compliance on survival prognosis of patients with ovarian cancer and associated influencing factors: A propensity score matching analysis of the SEER database

PurposeTo evaluate the impact of surgical compliance on overall survival (OS) and cancer-specific survival (CSS) in ovarian cancer patients and identify factors influencing surgical compliance. Materials and methodsData from patients with ovarian cancer in the SEER database (2004–2015) were analyzed to compare the characteristics of patients with high and low surgical compliance. Kaplan-Meier curves and Cox regression models were used to assess the impact of surgical compliance on survival outcomes. Nomograms incorporating surgical compliance and independent prognostic factors were constructed to predict OS and CSS and were validated using internal validation sets. Predictive accuracy was evaluated using Harrell's concordance index (C-index), decision curve analysis (DCA), receiver operating characteristic (ROC) curves, and calibration plots. Binary logistic regression analysis identified factors significantly affecting surgical compliance, and propensity score matching (PSM) was used to adjust for confounders. ResultsAmong the 41,859 patients, 783 (1.87 %) demonstrated poor surgical compliance, while 41,076 (98.13 %) exhibited good compliance. Surgical compliance has emerged as an independent prognostic indicator for ovarian cancer. Patients with high compliance had significantly better OS and CSS rates (P < 0.0001). The prognostic models were internally validated and showed strong discriminative and calibration capabilities. Factors affecting compliance included older age, advanced pathological stage, metastasis, elevated CA-125 levels, and lower income. After PSM, Kaplan-Meier analysis revealed significantly improved survival in patients with good compliance (P < 0.0001). ConclusionSurgical compliance is a pivotal and independent predictor of overall and cancer-specific survival in patients undergoing OC. Factors contributing to lower surgical compliance include advanced age, later tumor stage, metastatic spread, elevated CA-125 levels, and reduced family income.

Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database

BackgroundStudies have found that first primary cancer (FPC) survivors are at high risk of developing second primary breast cancer (SPBC). However, there is a lack of prognostic studies specifically focusing on patients with SPBC. MethodsThis retrospective study used data from Surveillance, Epidemiology and End Results Program. We selected female FPC survivors diagnosed with SPBC from 12 registries (from January 1998 to December 2018) to construct prognostic models. Meanwhile, SPBC patients selected from another five registries (from January 2010 to December 2018) were used as the validation set to test the model's generalization ability. Four machine learning models and a Cox proportional hazards regression (CoxPH) were constructed to predict the overall survival of SPBC patients. Univariate and multivariate Cox regression analyses were used for feature selection. Model performance was assessed using time-dependent area under the ROC curve (t-AUC) and integrated Brier score (iBrier). ResultsA total of 10,321 female FPC survivors with SPBC (mean age [SD]: 66.03 [11.17]) were included for model construction. These patients were randomly split into a training set (mean age [SD]: 65.98 [11.15]) and a test set (mean age [SD]: 66.15 [11.23]) with a ratio of 7:3. In validation set, a total of 3,638 SPBC patients (mean age [SD]: 66.28 [10.68]) were finally enrolled. Sixteen features were selected for model construction through univariate and multivariable Cox regression analyses. Among five models, random survival forest model showed excellent performance with a t-AUC of 0.805 (95 %CI: 0.803 - 0.807) and an iBrier of 0.123 (95 %CI: 0.122 - 0.124) on testing set, as well as a t-AUC of 0.803 (95 %CI: 0.801 - 0.807) and an iBrier of 0.098 (95 %CI: 0.096 - 0.103) on validation set. Through feature importance ranking, the top one and other top five key predictive features of the random survival forest model were identified, namely age, stage, regional nodes positive, latency, radiotherapy, and surgery. ConclusionsThe random survival forest model outperformed CoxPH and other machine learning models in predicting the overall survival of patients with SPBC, which was helpful for the monitoring of high-risk populations.

A temporal and multinational assessment of acute myeloid leukemia (AML) cancer incidence, survival, and disease burden

Understanding the global epidemiology of AML is critical for assessing therapeutic demand and informing healthcare resource allocation. This study estimated current and future AML incidence in 27 countries, described AML survival trends in the United States, and calculated average years of life lost (AYLL). Incidence rates were age-standardized using rates from IARC’s Cancer Incidence in Five Continents and SEER databases and ranged from 0.70 to 3.23 cases per 100,000 persons. Crude incidence rates were projected from 2024 to 2040; growth varied from +1% to +46%. Median overall survival was derived from SEER databases and increased from 4 to 11 months over the last 40 years. Median AYLL of 18.6 years was estimated for 27 countries. This study projected significant growth in new AML diagnoses over the next two decades. Despite improvements in survival over the last four decades, median survival among AML patients remains poor highlighting the need for novel treatments.

Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis

Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767–0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.