The graph presents prostate cancer relative survival rates in the U.S. from 2001 to 2016, showing 1-year, 5-year, and 10-year relative survival percentages based on age groups. The x-axis represents age groups, while the y-axis indicates survival rates at different time intervals. Survival rates remain high across all age groups, with patients aged 65–69 having the highest 10-year survival rate of 99.5%. In contrast, men aged 80 and older have the lowest survival rates, with 92.1% at 1 year and 82.7% at 10 years. The data highlights that younger patients generally experience better long-term survival outcomes.

Cancer was responsible for around *** deaths per 100,000 population in the United States in 2023. The death rate for cancer has steadily decreased since the 1990’s, but cancer still remains the second leading cause of death in the United States. The deadliest type of cancer for both men and women is cancer of the lung and bronchus which will account for an estimated ****** deaths among men alone in 2025. Probability of surviving Survival rates for cancer vary significantly depending on the type of cancer. The cancers with the highest rates of survival include cancers of the thyroid, prostate, and testis, with five-year survival rates as high as ** percent for thyroid cancer. The cancers with the lowest five-year survival rates include cancers of the pancreas, liver, and esophagus. Risk factors It is difficult to determine why one person develops cancer while another does not, but certain risk factors have been shown to increase a person’s chance of developing cancer. For example, cigarette smoking has been proven to increase the risk of developing various cancers. In fact, around ** percent of cancers of the lung, bronchus and trachea among adults aged 30 years and older can be attributed to cigarette smoking. Other modifiable risk factors for cancer include being obese, drinking alcohol, and sun exposure.

The graph illustrates the number of deaths from cancer in the United States over the period from 1999 to 2022. The x-axis represents the years, labeled with two-digit abbreviations from '99 to '22, while the y-axis displays the annual number of cancer-related deaths. Throughout this 24-year span, the number of deaths ranges from a minimum of 549,829 in 1999 to a maximum of 608,366 in 2022. The data shows a gradual increase in annual deaths over the years. Notably, the number surpassed 550,000 in 2000 with 553,080 deaths, reached 574,738 in 2010, and exceeded 600,000 in 2020 with 602,347 deaths. The figures continued to rise, culminating in the highest recorded number of 608,366 deaths in 2022.

Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

📄 Dataset Description: This dataset contains global cancer patient data reported from 2015 to 2024, designed to simulate the key factors influencing cancer diagnosis, treatment, and survival. It includes a variety of features that are commonly studied in the medical field, such as age, gender, cancer type, environmental factors, and lifestyle behaviors. The dataset is perfect for:

Exploratory Data Analysis (EDA)

Multiple Linear Regression and other modeling tasks

Feature Selection and Correlation Analysis

Predictive Modeling for cancer severity, treatment cost, and survival prediction

Data Visualization and creating insightful graphs

Key Features: Age: Patient's age (20-90 years)

Gender: Male, Female, or Other

Country/Region: Country or region of the patient

Cancer Type: Various types of cancer (e.g., Breast, Lung, Colon)

Cancer Stage: Stage 0 to Stage IV

Risk Factors: Includes genetic risk, air pollution, alcohol use, smoking, obesity, etc.

Treatment Cost: Estimated cost of cancer treatment (in USD)

Survival Years: Years survived since diagnosis

Severity Score: A composite score representing cancer severity

This dataset provides a broad view of global cancer trends, making it an ideal resource for those learning data science, machine learning, and statistical analysis in healthcare.

Table Schoenfeld residual test to check the assumption of proportional hazard.

Restricted mean survival time, cumulative survival probability, and log rank test of cervical cancer patients at Felege Hiwot Comprehensive Specialized Hospital Oncology Center, Ethiopia (N = 422, March 2021).

Medical complexity and time to lung cancer treatment. (DO 25 kb)

Heart disease and cancer were the leading causes of death in the United States in 2023. COVID-19 became the third leading cause of death in 2020 and 2021, but by 2023 it was the tenth leading cause. This statistic shows the rates of the 10 leading causes of death in the United States in 2023.

Clinical and histopathological characteristics of cervical cancer patients in Felege Hiwot Comprehensive Specialized Hospital Oncology Center, Ethiopia (N = 422, March 2021).

Socio-demographic characteristics of cervical cancer patients in Felege Hiwot Comprehensive Specialized Hospital Oncology Center, Ethiopia (N = 422, March 2021).

Network of 44 papers and 68 citation links related to "The monocyte to red blood cell count ratio is a strong predictor of postoperative survival in colorectal cancer patients: The Fujian prospective investigation of cancer (FIESTA) study".

BackgroundLung cancer is the leading cause of cancer-related mortality, and accurate prediction of patient survival can aid treatment planning and potentially improve outcomes. In this study, we proposed an automated system capable of lung segmentation and survival prediction using graph convolution neural network (GCN) with CT data in non-small cell lung cancer (NSCLC) patients.MethodsIn this retrospective study, we segmented 10 parts of the lung CT images and built individual lung graphs as inputs to train a GCN model to predict 5-year overall survival. A Cox proportional-hazard model, a set of machine learning (ML) models, a convolutional neural network based on tumor (Tumor-CNN), and the current TNM staging system were used as comparison.FindingsA total of 1,705 patients (main cohort) and 125 patients (external validation cohort) with lung cancer (stages I and II) were included. The GCN model was significantly predictive of 5-year overall survival with an AUC of 0.732 (p < 0.0001). The model stratified patients into low- and high-risk groups, which were associated with overall survival (HR = 5.41; 95% CI:, 2.32–10.14; p < 0.0001). On external validation dataset, our GCN model achieved the AUC score of 0.678 (95% CI: 0.564–0.792; p < 0.0001).InterpretationThe proposed GCN model outperformed all ML, Tumor-CNN, and TNM staging models. This study demonstrated the value of utilizing medical imaging graph structure data, resulting in a robust and effective model for the prediction of survival in early-stage lung cancer.

BackgroundThe aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G-chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers.Patients and Methods1655 patients after kidney transplantation at our institution with a total of 9,425 person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios (SIRs) of observed malignancies. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G-chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.ResultsCancer incidence rates were almost three times higher as compared to the matched general population (SIR = 2.75; 95%-CI: 2.33–3.21). Significantly increased SIRs were observed for renal cell carcinoma (SIR = 22.46), post-transplant lymphoproliferative disorder (SIR = 8.36), prostate cancer (SIR = 2.22), bladder cancer (SIR = 3.24), thyroid cancer (SIR = 10.13) and melanoma (SIR = 3.08). Independent pre-transplant risk factors for cancer-free survival were age 62.6 years (p = 0.001, HR: 1.29), polycystic kidney disease other than autosomal dominant polycystic kidney disease (ADPKD) (p = 0.001, HR: 0.68), high body mass index in kg/m2 (p

Aim: To report treatment patterns and quality of life (QoL) in HER2-negative advanced breast cancer patients. Methods: Data were drawn from a cross-sectional survey in Europe and USA. Results: Hormone plus targeted therapy was the most frequent first-line (1L, 62%) and second-line (2L, 45%) treatment for HR+/HER2-patients. Chemotherapy was most frequent at third-line or greater (3L+, 39%) for HR+/HER2- patients, 2L (51%) and 3L+ (48%) for triple negative breast cancer (TNBC) patients. Time to progression was 13.8 (2L) and 11.0 (3L+) months for HR+/HER2- patients. No comparisons were observed for TNBC patients. EQ-5D-5L scores were highest in patients at 1L and lowest at 3L+. Conclusion: Reduced QoL and treatment response were reported in patients at later lines of therapy. Breast cancer is the most common cancer in women. Differences in survival are seen depending on how widespread or advanced the cancer is, how many different treatments the patient has been given, as well as whether certain receptors on the tumor are present or absent. Many new treatments are available which can target these receptors. These treatments have improved survival in patients with advanced breast cancer, but other benefits for the patient are not always clear. In addition, differences between countries are possible as official guidance can vary. This study aimed to understand these issues, by asking physicians and their patients across Europe and USA for their views on quality of life and satisfaction with their treatments. We found that, in general, physicians prescribed treatments as recommended in the treatment guidelines. As breast cancer progressed and treatment stopped working, patients were switched on to different treatments. Survival, quality of life and treatment satisfaction were all worse in patients who had switched treatments. It appears that the patients lose confidence that their new treatment will work to improve their quality of life. We also saw differences in some of these outcomes between Europe and USA, which were likely due to differences in the treatment guidelines between countries. Both quality of life and treatment satisfaction are important for the well-being of patients with advanced breast cancer as they now live longer with these new treatments. This should be considered by physicians and taken into account for future work. Given the rapidly evolving treatment landscape for patients with advanced breast cancer (ABC), the aim of this analysis was to describe treatment patterns, quality of life (QoL) outcomes, and treatment satisfaction at subsequent treatment lines (first- [1L], second- [2L]; third/later- [3L+] lines) among patients with hormone-receptor positive (HR+)/human epidermal growth factor-negative (HER2-) and triple negative breast cancer (TNBC) in Europe (France, Italy and Spain) and USA in a real-world setting. Data were drawn from the Advanced Breast Cancer (ABC) Disease Specific Programme™, a point-in-time survey of physicians and their consulting patients conducted in Europe and the USA 2019–2020. Median time to progression at 2L and 3L was 13.8 and 11.0 months for patients with HR+/HER2, and 8.0 and 4.6 months, respectively for TNBC patients; patient overall response rates at 1L, 2L and 3L+ were 54, 53 and 42%, respectively. The average EuroQol-5 dimensions-5 level (EQ-5D-5L) health utility score of HR+/HER2- and TNBC patients was 0.77 at 1L, 0.70 and 0.71 at 2L, and 0.65 and 0.79 at 3L+, respectively; EQ-5D-5L visual analog scale mean score was 70.9, 67.4, and 63.4 for HR+/HER2- patients, and 69.4, 65.2, and 65.8 for TNBC patients, at 1L, 2L and 3L+, respectively. The Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) global health status mean score was 64.6, 60.0 and 57.7 for HR+/HER2- patients, and 64.0, 61.2 and 59.3 for TNBC patients, at 1L, 2L and 3L+, respectively; there was a small meaningful difference between scores at 1L and at 2L and 3L+. EORTC scores were generally higher at 1L and lower at 3L+ in HR+/HER2- patients' functioning and symptoms. This differed for TNBC patients where scores were higher in 3L+ patients compared with 2L patients for physical, role, emotional, cognitive and social functioning. Meaningful differences in the scores at 2L and/or 3L+ were seen for most functioning scales, particularly in cognitive functioning and social functioning, and over half of the symptom scales in HR+/HER2- patients. Physicians were ‘satisfied’ or ‘very satisfied’ with current treatment for 77%, HR+/HER2- and 68% TNBC patients on 1L, 73% HR+/HER2- and 57% TNBC patients on 2L, and 59% HR+/HER2- and 54% TNBC patients on 3L+; likewise, patients' treatment satisfaction was reduced for both patient populations. This analysis demonstrated that patients with HR+/HER2- and triple negative ABC treated with 3L+ therapy, compared with patients treated with 1L or 2L therapies, reported a reduction in QoL, while concurrently, time to progression was shorter and overall response rate was poorer; similarly, satisfaction with treatment was reduced for both physicians and patients.

Network of 42 papers and 69 citation links related to "Treatment Patterns, Costs, and Survival among Medicare-Enrolled Elderly Patients Diagnosed with Advanced Stage Gastric Cancer: Analysis of a Linked Population-Based Cancer Registry and Administrative Claims Database".

BackgroundGlobally, the incidence and mortality due to esophageal cancer are increasing, particularly in low- and middle-income countries. Cancer of the esophagus is the eighth in incidence and seventh in cancer mortality in Ethiopia. A few studies have shown an increasing burden, however, little is known about the survival pattern and its determinants among esophageal cancer patients in Ethiopia. Therefore, we assessed the survival pattern and its determinants among esophageal cancer patients.MethodsWe conducted a retrospective cohort study among 349 esophageal cancer patients who were diagnosed at or referred to Tikur Anbessa Specialized Hospital, Ethiopia from January 2010 to May 2017. Using an abstraction form, nurses who were working at the oncology department extracted the data from patient charts. To estimate and compare the probability of survival among covariate categories, we performed a Kaplan–Meier survival analysis with the log-rank test. To identify the prognostic determinants of survival, we performed a multivariable Cox proportional regression analysis.ResultsThe median follow-up time was 32 months with interquartile range of 15 to 42. Overall, the median survival time after diagnosis with esophageal cancer was 4 months with one-, two- and three-year survival of 14.4, 6.3, and 2.4% respectively. In the multivariable Cox proportional hazards model, receiving chemotherapy [Adjusted Hazard Ratio (AHR)=0.36, 95%CI: 0.27–0.49], radiotherapy [AHR=0.38, 95%CI: 0.23–0.63] and surgery [AHR=0.70, 95%CI: 0.54–0.89] were statistically significant.ConclusionsIn Ethiopia, esophageal cancer patients have a very low one-, two- and three-year survival. Despite a very low overall survival, patients who received either chemotherapy, radiotherapy or surgery showed a better survival compared with those who did not receive any treatment. Hence, it is essential to improve the survival of patients with esophageal cancer through early detection and timely initiation of the available treatment options.

Network of 39 papers and 76 citation links related to "Survival of Blacks and Whites After a Cancer Diagnosis".

ObjectiveTo determine whether transthyretin (TTR) influences the prognosis of patients with colorectal cancers and establish a predictive model based on TTR.MethodsBetween January 2013 and February 2019, the clinical data of 1322 CRC patients aged from 18 years to 80 years who underwent surgical treatment were retrospectively analyzed. The preoperative TTR level, clinicopathological data, and follow-up data were recorded. The X-tile program was used to determine the optimal cut-off value. Cox proportional hazard regression analysis was conducted to evaluate the correlation between the TTR and the cumulative incidence of cancer-specific survival (CSS). Nomograms were then developed to predict CSS. Furthermore, an additional cohort of 377 CRC patients enrolled between January 2014 and December 2015 was included as an external validation.ResultsBased on the optimal cut-off value of 121.3 mg/L, we divided the patients into the TTR-lower group (<121.3 mg/L) and the TTR-higher group (≥121.3 mg/L). Comparative analysis revealed that the TTR-higher group exhibited a younger demographic, a higher prevalence of low colorectal cancers, an elevated R0 resection rate, superior differentiation, earlier stage and lower levels of carcinoembryonic antigen (CEA) in contrast to the TTR-lower group. The Cox multivariable analysis underscored the significance of TTR and various clinicopathological factors, encompassing age, tumor location, R0 resection status, differentiation grade, disease stage, postoperative chemoradiotherapy, and preoperative CEA levels, as substantial prognostic indicators. The postoperative survival nomogram, when internally and externally assessed, demonstrated commendable performance across multiple metrics, including the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Compared with other models, the proportional hazards model combined with TTR demonstrates superior performance in terms of C-index, AUC, calibration chart, and DCA within the prognostic column chart.ConclusionsThe preoperative TTR was identified as a prognostic factor for predicting the long-term prognosis of CRC patients who underwent surgical treatment, supporting its role as a prognostic biomarker in clinical practice.

Background: Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.Results: In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9–94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 marker genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific.Conclusion: Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.