Surveillance, Epidemiology, And End Results Research Articles

Impact of environmental temperature on the survival outcomes of breast cancer: A SEER-based study.

Experimental evidence in tumor-bearing mouse models shows that exposure to cool, that is, sub-thermoneutral environmental temperature is associated with a higher tumor growth rate and an immunosuppressive tumor immune microenvironment than seen at thermoneutral temperatures. However, the translational significance of these findings in humans is unclear. We hypothesized that breast cancer patients living in warmer climates will have better survival outcomes than patients living in colder climates. A retrospective population-based analysis was conducted on 270,496 stage I-III breast cancer patients, who were retrieved from the Surveillance, Epidemiology and End Results (SEER) over the period from 1996 to 2017. The average annual temperature (AAT) was calculated based on city level data from the National Centers for Environmental Information. A total of 270, 496 patients were analyzed. Temperature as assessed in quartiles. After adjusting for potential confounders, patients who lived in the 3rd and 4th quartile temperature regions with AAT 56.7-62.5°F (3rd quartile) and > 62.5°F (4th quartile) had a 7% increase in the OS compared to patients living at AAT < 48.5°F (1st quartile) (HR 0.93, 95% CI 0.90-0.95 and HR 0.93, 95% CI 0.91-0.96, respectively). For DSS, When comparing AAT quartiles, patients living with AAT in the range of 56.7-62.5°F and > 62.5°F demonstrated a 7% increase each in DSS after adjustment (HR 0.93, 95% CI 0.90-0.96 and HR 0.93, 95% CI 0.90-0.96). Higher environmental temperatures are associated with significantly better OS and DSS in breast cancer patients. Future research is warranted to confirm this observation using large datasets to elucidate the underlying mechanisms and investigate novel therapeutic strategies to minimize this geographic disparity in clinical outcomes.

Patterns of chemotherapy use with primary radiotherapy for localized bladder cancer in patients 65 or older

IntroductionBladder preservation with concurrent chemoradiotherapy after maximum transurethral resection of bladder tumor is an alternative to radical cystectomy in select patients with muscle invasive bladder cancer (MIBC). Concurrent administration of radio-sensitizing chemotherapy and radiation therapy (RT) has been shown to have superior disease control compared with RT alone and can often be administered with modest added toxicity. We sought to describe national patterns of chemotherapy use.MethodsThe linked surveillance, epidemiology, and end results (SEER)-Medicare database was used to identify patients with cT2-4, N0/X, M0/X BC who received radiation between 2004 and 2018. Data on demographics, clinicopathologic factors, therapy and outcomes were extracted. Concurrent utilization of chemotherapy with RT was also identified (CRT). Multivariate logistic regression (MVA) models were used to explore factors associated with receipt of chemotherapy and overall survival (OS).Results2190 patients met inclusion criteria. Of these, 850 (38.8%) received no chemotherapy. Among those receiving chemotherapy, the most frequent regimens were single agent carboplatin, cisplatin, or gemcitabine. Factors that were independently associated with decreased likelihood of chemotherapy use were increasing age (OR 0.93, CI 0.92 – 0.95), Hispanic race (compared with White, OR 0.62, CI 0.39 – 0.99), cT3 or T4 (compared with cT2, OR 0.70, CI 0.55 – 0.90), and lower National Cancer Institute comorbidity index (OR 0.60, CI 0.51 – 0.70) (p &amp;lt; 0.05). Variables independently associated with increased likelihood of receipt of chemotherapy were married status (OR 1.28, CI 1.06 – 1.54), higher socioeconomic status (OR 1.31, CI 1.06 – 1.64), and later year of diagnosis (OR 1.09, CI 1.06 – 1.12). Receipt of concurrent chemotherapy with RT was associated with superior OS compared with RT alone.ConclusionOver a third of patients &amp;gt;/65 years old receiving curative-intent RT for MIBC do not receive concurrent chemotherapy. Considering the improvement in oncologic outcomes with CRT over RT alone and more options, such as low dose gemcitabine which can be administered with modest toxicity, efforts are needed to identify barriers to utilization and increase the use of radio-sensitizing chemotherapy.

Breast cancer and the steadily increasing maternal age: are they colliding?

BackgroundPregnancy-related cancers are mostly breast cancers, and their incidence is likely to increase as a result of the modern trend of delaying childbearing. In particular, advanced maternal age increases breast cancer risk, and younger breast cancer patients are more likely to die and metastasize. This study compared a population with a high incidence of delayed childbearing with another population with a lower mean age at childbirth in order to determine whether breast cancer diagnosis and childbearing age overlap.MethodsWe retrospectively analyzed multiple data sources. The Surveillance, Epidemiology, and End Results (SEER) program, the United States National Center for Health Statistics as part of the National Vital Statistics System, the United Nations Population Division, the GLOBOCAN Cancer Observatory, the CLIO-INFRA project database, the Human Fertility Database, and anonymized local data were used.ResultsAs women’s age at delivery increased, the convergence between their age distribution at breast cancer diagnosis and childbearing increased. In addition, the overlap between the two age distributions increased by more than 200% as the average age at delivery increased from 27 to 35 years.ConclusionsAs women’s average childbearing age has progressively risen, pregnancy and breast cancer age distributions have significantly overlapped. This finding emphasizes the need for increased awareness and educational efforts to inform women about the potential consequences of delayed childbearing. By providing comprehensive information and support, women can make more informed decisions about their reproductive health and cancer prevention strategies.

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning.

Primary pancreatic signet ring cell carcinoma (PSRCC), an extremely rare histologic variant of pancreatic cancer, has a poor prognosis. This study aimed to investigate the prognostic value of chemotherapy in PSRCC. Patients with PSRCC between 2000 and 2019 were identified using the Surveillance Epidemiology and End Results (SEER) database. The main outcomes in this study were cancer-specific survival (CSS) and overall survival (OS). The baseline characteristics of patients were compared using Pearson's Chi-square test. Kaplan-Meier analysis was used to generate the survival curves. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression models, and Random Survival Forest model were used to analyze the prognostic variables for OS and CSS. The variance inflation factors (VIFs) were used to analyze whether there was an overfitting problem. A total of 588 patients were identified. Chemotherapy was an independent prognostic factor for OS and CSS, and significantly associated with OS (HR = 0.33, 95% CI = 0.27-0.40, P <0.001) and CSS (HR = 0.32, 95% CI = 0.26-0.39, P <0.001). Chemotherapy showed beneficial effects on OS and CSS in patients with PSRCC and should be recommended in clinical practice.

A novel nomogram and prognostic factor for metastatic soft tissue sarcoma survival

BackgroundThis study represented the inaugural effort to develop predictive survival nomograms for metastatic soft tissue sarcoma (mSTS) patients in the era of immune checkpoint inhibitors.MethodFrom the Surveillance, Epidemiology, and End Results (SEER) program database, we extracted 3078 eligible patients with mSTS between 2016 and 2022. Kaplan-Meier survival analysis, univariate and multivariable Cox analyses, and univariate and multivariable logistic analyses were conducted. Subsequently, predictive nomograms were constructed. Clinical effectiveness was validated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA) methods.ResultsWe used the SEER database to include 3078 eligible patients with mSTS between 2016 and 2022. All the eligible patients were randomly allocated in a ratio of 6:4 and stratified into a training group (n = 1846) and a validation group (n = 1232). In the multivariate Cox analysis, age, race, marital status, pathological grade, histologic subtype, surgery, and chemotherapy were identified as independent prognostic factors. These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS of mSTS patients. The C-index for the training cohort and the validation cohort was 0.722(95% confidence interval [CI]: 0.708–0.736), and 0.716(95% CI: 0.698–0.734), respectively. The calibration curves for 1-, 3-, and 5-year OS probability demonstrated excellent calibration between the predicted and the actual survival. The AUC values of the nomogram at 1-, 3-, and 5-year were 0.785, 0.767, and 0.757 in the training cohort, 0.773, 0.754, and 0.751 in the validation cohort, respectively. Furthermore, DCA indicated the favorable clinical utility of the nomogram in both cohorts. The risk stratification system was constructed using the established nomogram, which enhanced prediction accuracy, aided clinicians in identifying high-risk patients and informing treatment decisions.ConclusionThis study marked the inaugural effort in constructing predictive survival nomograms mSTS patients in the era of immune checkpoint inhibitors. The robustly constructed nomograms, alongside actual outcomes, offered valuable insights to inform follow-up management strategies.

Analysis of risk characteristics for early progression and late progression in locally advanced rectal cancer patients: a large population-based and validated study.

The current study aimed to explore the factors influencing early progression (EP) and late progression (LP) in locally advanced rectal cancer (LARC) patients. The patients were classified into EP and LP groups using one year as a cutoff. The random survival forest model was utilized to calculate the probability of time-to-progression. Besides, inverse probability of treatment weighting (IPTW) analysis and the Surveillance, Epidemiology, and End Results (SEER) were conducted to validate our results. Our study revealed that PNI, CEA level, and pathological stage were independent prognostic factors for PFS both in EP group and LP group. For EP group patients, Group 1 had the highest probability of progression at the 9th month of follow-up, while Group 2 exhibited the highest probability at the 6th month. Group 3, on the other hand, showed two peaks of progression at the 4th and 8th months of follow-up. As for LP group patients, Groups 4, 5, and 6 all exhibited peaks of progression between the 18th and 24th months of follow-up. Furthermore, our results suggested that PNI was also an independent prognostic factor affecting OS in both EP group and LP group. Finally, the analysis of IPTW and SEER database further confirmed our findings. Our results indicated a significant correlation between immune and nutritional status with PFS and OS in both EP and LP groups. These insights can aid healthcare professionals in effectively identifying and evaluating patients' nutritional status, enabling them to develop tailored nutrition plans and interventions.

Postoperative radiotherapy does not improve survival in patients with Masaoka-Koga stage IIB thymomas: A propensity score matching study based on the SEER database.

This study, based on a population, explored the prognostic value of postoperative radiotherapy (PORT) for Masaoka-Koga IIB stage thymomas. Patients diagnosed with thymoma from 2004 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) database were included in the retrospective study. Through propensity score matching, the baseline characteristics of the patients were successfully matched to mitigate the selection bias of PORT. Survival rates and survival curves were compared between the PORT and non-PORT groups, with potential confounding factors addressed using a multivariate Cox regression model. In this study, 785 cases of IIB stage thymoma were included from the SEER database, and 303 patients were successfully matched between PORT and non-PORT groups through propensity score matching, with no significant differences in baseline characteristics. In the PORT and non-PORT groups, 10-year overall survival rates were 65.2% versus 59.6%, and cancer-specific survival rates were 87.0% vs. 84.4%, PORT did not yield statistically significant improvements in overall survival (P = .275) or cancer-specific survival (P = .336) for stage IIB thymomas. Based on the SEER database, the results of our study indicated that PORT does not confer a significant survival benefit for IIB stage thymomas.

Thyroid Cancer Incidence Among Korean Individuals: A Comparison of South Korea and the United States.

To compare thyroid cancer incidence rates and trends between Korean, non-Korean Asian, and non-Hispanic White populations in the United States, and between the US Korean population and the South Korean population. Population-based analysis of cancer incidence data. Cases of thyroid cancer diagnosed during 1999-2014 from the Korean Central Cancer Registry (KCCR) and the Surveillance, Epidemiology, and End Results (SEER) 9 detailed Asian/Pacific Islander subgroup incidence and population dataset were included. Incidence rates were obtained from the datasets, and annual percent change (APC) of the incidence rates was calculated using Joinpoint regression analysis. Thyroid cancer incidence rate for 1999-2014 was significantly higher for South Korea (48.05 [95% CI 47.89-48.22] per 100,000 person-years) than for the US Korean population (11.12 [95% CI 10.49-11.78] per 100,000 person-years), which was slightly higher than the Non-Korean Asian population (10.23 [95% CI 10.02-10.43] per 100,000 person-years), and slightly lower than the Non-Hispanic White population (12.78 [95% CI 12.69-12.87] per 100,000 person-years). Incidence rates in South Korea increased dramatically (average APC 17.9, 95% CI 16.0-19.9), significantly higher than the US Korean population (average APC 5.0, 95% CI 3.1-6.8), which was similar to the non-Korean Asian (average APC 2.5, 95% CI 0.9-4.2) and the non-Hispanic White (average APC 5.1, 95% CI 4.7-5.6) populations. South Korea's high thyroid cancer incidence rates cannot be attributed to genetic factors, but are likely due to health care system factors. 3 Laryngoscope, 2024.

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study

ObjectiveThe aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model.MethodsCases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model’s predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests.ResultsFollowing the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P &amp;lt; 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram.ConclusionClinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

Incidence and risk factors for bone metastases at presentation in solid tumors

IntroductionBone metastases are associated with increased morbidity and decreased quality of life in patients with solid tumors. Identifying patients at increased risk of bone metastases at diagnosis could lead to earlier interventions. We sought to retrospectively identify the incidence and predictive factors for bone metastases at initial diagnosis in a large population-based dataset.MethodsThe Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients 18 years-old or older diagnosed with solid cancers from 2010 to 2019. Patients with hematologic malignancies and primary tumors of the bone were excluded. We calculated the incidence and predictive factors for bone metastases according to demographic and tumor characteristics.ResultsAmong 1,132,154 patients identified, 1,075,070 (95.0%) had known bone metastasis status and were eligible for the study. Bone metastases were detected in 55,903 patients (5.2% of those with known bone metastases status). Among patients with bone metastases, the most common primary tumors arose from lung (44.4%), prostate (19.3%), breast (12.3%), kidney (4.0%), and colon (2.2%). Bone metastases at presentation were most common in small cell lung cancer (25.2%), non-small cell lung cancer (18.0%), and esophageal adenocarcinoma (9.4%). In addition to stage classification, predictors for bone metastases included Gleason score (OR 95.7 (95% CI 73.1 – 125.4) for Grade Group 5 vs 1 and OR 42.6 (95% CI 32.3 – 55.9) for Group 4 vs 1) and PSA (OR 14.2 (95% CI 12.6 – 16.0) for PSA &amp;gt; 97 vs 0 – 9.9) for prostate cancer, HER2 and hormonal receptor (HR) status (OR 2.2 (95% CI 1.9 – 2.6) for HR+/HER2+ vs HR-/HER2-) for breast cancer, histology (OR 2.5 (95% CI 2.3 – 2.6) for adenocarcinoma vs squamous) for lung cancer, and rectal primary (OR 1.2 (95% 1.1 – 1.4) vs colon primary) and liver metastases (OR 8.6 (95% CI 7.3 – 10.0) vs no liver metastases) for colorectal tumors.ConclusionsBone metastases at presentation are commonly seen in solid tumors, particularly lung, prostate, breast, and kidney cancers. Clinical and pathologic factors are associated with a significantly increased risk for bone metastases.

Do we need reshape rTNM staging system for ipsilateral breast tumor recurrence of breast cancer? A population-based, propensity score matched cohort study.

The aim of the study was to explore the role of recurrent TNM (rTNM) staging in predicting prognosis for ipsilateral breast tumor recurrence (IBTR) and determine the optimal treatment strategy for IBTR. IBTR cases were identified from the Surveillance, Epidemiology, and End Results (SEER) database spanning the years 2000-2018. Cox proportional hazards analysis was performed to examine factors associated with overall survival (OS) and breast cancer-specific survival (BCSS). Propensity score matching (PSM) was employed to match IBTR with primary early breast cancer (EBC) based on clinicopathological characteristics. Investigations into the impact of different therapies were also included. Of the 4375 IBTR cases included in the study, the 5-year OS was 87.1%, 71.6% and 58.7% in rTNM stages I, II and III, respectively. After PSM, while IBTR patients had worse survival to primary EBC patients, prognosis of IBTR for different rTNM stage always closely aligned with the corresponding stage of primary EBC. Repeat breast-conserving surgery (BCS) with radiation therapy was equivalent to mastectomy with respect to OS and BCSS. Chemotherapy was favorable for OS and BCSS in estrogen receptor (ER)-negative IBTR or IBTR occurring within a 60-month interval. rTNM staging system has an outstanding prognostic value for survival outcome of patients with IBTR, and IBTR and primary EBC may have potentially analogous features in the context of TNM staging. BCS plus radiation therapy may be an alternative. IBTR cases who have experienced recurrence with short intervals and with ER-negative tumors might benefit from chemotherapy.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

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. 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=10596, 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. 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. 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.

Incidence of cardiovascular mortality among head and neck cancer patients

Incidence of cardiovascular mortality among head and neck cancer patients

Construction and validation of an innovative prognostic nomogram for overall survival in cervical cancer patients with lung metastasis: an analysis utilizing the SEER database

PurposeTo facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years.MethodsWe extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).ResultsWe gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts.ConclusionBy utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.

Association between diabetes and subsequent malignancy risk among older breast cancer survivors.

Type II diabetes is associated with cancer risk in the general population but has not been well-studied as a risk factor for subsequent malignancies among cancer survivors. We investigated the association between diabetes and subsequent cancer risk among older (66-84 years), one-year breast cancer survivors within the linked Surveillance Epidemiology and End Results (SEER)-Medicare database using Cox regression analyses to quantify hazard ratios (HR) and corresponding 95% confidence intervals (95%CI). Among 133,324 women, 29.3% were diagnosed with diabetes prior- or concurrent to their breast cancer diagnosis, and 10,452 women developed subsequent malignancies over a median follow-up of 4.3 years. Diabetes was statistically significantly associated with liver (HR = 2.35, 95%CI=1.48-3.74), brain (HR = 1.94, 95%CI=1.26-2.96), and thyroid cancer risks (HR = 1.38, 95%CI=1.01-1.89). Future studies are needed to better understand the spectrum of subsequent cancers associated with diabetes and the role of diabetes medications in modifying subsequent cancer risk, alone or in combination with cancer treatments.

The National Cancer Database Conforms to the Standardized Framework for Registry and Data Quality.

Standardization of procedures for data abstraction by cancer registries is fundamental for cancer surveillance, clinical and policy decision-making, hospital benchmarking, and research efforts. The objective of the current study was to evaluate adherence to the four components (completeness, comparability, timeliness, and validity) defined by Bray and Parkin that determine registries' ability to carry out these activities to the hospital-based National Cancer Database (NCDB). Tbis study used data from U.S. Cancer Statistics, the official federal cancer statistics and joint effort between the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI), which includes data from National Program of Cancer Registries (NPCR) and Surveillance, Epidemiology, and End Results (SEER) to evaluate NCDB completeness between 2016 and 2020. The study evaluated comparability of case identification and coding procedures. It used Commission on Cancer (CoC) standards from 2022 to assess timeliness and validity. Completeness was demonstrated with a total of 6,828,507 cases identified within the NCDB, representing 73.7% of all cancer cases nationwide. Comparability was followed using standardized and international guidelines on coding and classification procedures. For timeliness, hospital compliance with timely data submission was 92.7%. Validity criteria for re-abstracting, recording, and reliability procedures across hospitals demonstrated 94.2% compliance. Additionally, data validity was shown by a 99.1% compliance with histologic verification standards, a 93.6% assessment of pathologic synoptic reporting, and a 99.1% internal consistency of staff credentials. The NCDB is characterized by a high level of case completeness and comparability with uniform standards for data collection, and by hospitals with high compliance, timely data submission, and high rates of compliance with validity standards for registry and data quality evaluation.

Impact of tumor size on overall survival and cancer-specific survival of early-onset colon and rectal cancer: a retrospective cohort study

PurposeThis study aimed to investigate the impact of tumor size on survival in early-onset colon and rectal cancer.MethodsEarly-onset colon and rectal cancer patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Tumor size was analyzed as both continuous and categorical variables. Several statistical techniques, including restricted cubic spline (RCS), Cox proportional hazard model, subgroup analysis, propensity score matching (PSM), and Kaplan–Meier survival analysis, were employed to demonstrate the association between tumor size and overall survival (OS) and cancer-specific survival (CSS) of early-onset colon and rectal cancer.ResultsSeventeen thousand five hundred fifty-one (76.7%) early-onset colon and 5323 (23.3%) rectal cancer patients were included. RCS analysis confirmed a linear association between tumor size and survival. Patients with a tumor size > 5 cm had worse OS and CSS, compared to those with a tumor size ≤ 5 cm for both early-onset colon and rectal cancer. Notably, subgroup analysis showed that a smaller tumor size (≤ 50 mm) was associated with worse survival in stage II early-onset colon cancer, although not statistically significant. After PSM, Kaplan–Meier survival curves showed that the survival of patients with tumor size ≤ 50 mm was better than that of patients with tumor size > 50 mm.ConclusionPatients with tumors larger than 5 cm were associated with worse survival in early-onset colon and rectal cancer. However, smaller tumor size may indicate a more biologically aggressive phenotype, correlating with poorer survival in stage II early-onset colon cancer.

Nomogram for predicting survival in T1–T2 stage patients with supraglottic squamous cell carcinoma

BackgroundSupraglottic squamous cell carcinoma (SGSCC) is characterized by low differentiation, rapid growth, and inconspicuous initial manifestations. Early detection and prompt treatment can significantly improve survival rates. The main focus of treatment is to maintain optimal laryngeal function.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, we conducted univariate and multivariate Cox regression analyses to identify independent prognostic factors for T1–T2 SGSCC. We also enrolled 109 patients with T1–T2 SGSCC from the First Affiliated Hospital of Xinjiang Medical University as an external validation set. In addition, we developed a nomogram to predict the prognosis of T1–T2 SGSCC, assessed the predictive accuracy and discriminatory ability of the nomogram using the area under the curve (AUC), C-index, receiver operating characteristic (ROC) curve and calibration curve, and confirmed the clinical validity of the nomogram using decision curve analysis (DCA).ResultsOur investigation identified nine prognostic indicators for T1–T2 SGSCC: age (≥ 65 years), marital status, American Joint Committee on Cancer (AJCC) stage (II–IV), grade (III–IV), M stage (M1), radiotherapy, chemotherapy, sex (female), and surgery. These variables were used to create accurate nomograms that predict overall and specific survival rates at 1, 3, and 5 years. The nomograms demonstrated superior prognostic value and accuracy compared to AJCC staging. Laryngectomy with partial laryngectomy is the preferred treatment option for T1–T2 SGSCC cases, providing superior overall survival (OS) and cancer-specific survival (CSS). Radiotherapy also improves OS and CSS. Our results were based on a comprehensive analysis of various indicators, including the C-index, ROC curve, calibration curve, and DCA curve.ConclusionNomograms provide significant advantages in treatment decision making and diagnosis. Laryngectomy with partial laryngectomy is the most appropriate method for T1–T2 SGSCC cases. However, radiotherapy can also be used. Thus, patients with T1–T2 SGSCC should be evaluated to determine if combination therapy is the optimal treatment approach. Nevertheless, further research is needed to understand the role of chemotherapy. Overall, this study identified nine key predictors of future outcomes, aiding healthcare professionals in assessing risks and making treatment decisions for T1–T2 SGSCC patients.

Factors Affecting Survival Outcomes in Neuroendocrine Tumor of the Appendix over the Past Two Decades

Background: Appendiceal neuroendocrine tumors (NETs) rank as the third most frequent neoplasm affecting the appendix, originating from enterochromaffin cells. This study aims to evaluate the influence of various prognostic factors on the mortality rates of patients diagnosed with NETs of the appendix. Methods: Conducted retrospectively, the study involved 3346 patients, utilizing data sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Our analysis centered on investigating demographic characteristics, clinical features, overall mortality (OM), and cancer-specific mortality (CSM) among the cohort. Variables showing a p-value &lt; 0.1 in the univariate Cox regression were incorporated into the multivariate Cox regression analysis. A Hazard Ratio (HR) &gt; 1 indicated an unfavorable prognosis. Results: In the multivariate analysis, higher OM and CSM were observed in males, older age groups, tumors with distant metastasis, poorly differentiated tumors, and those who underwent chemotherapy. Non-Hispanic Black individuals showed elevated mortality rates. Conclusion: Delayed diagnosis may contribute to the increased mortality in this community. Improved access to healthcare and treatment is crucial for addressing these disparities. Larger prospective studies are needed to pinpoint the underlying causes of elevated mortality in non-Hispanic Black populations, and randomized controlled trials (RCTs) are warranted to evaluate therapies for advanced-stage appendix NETs.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.