Cancer Rates for Lake County Illinois. Explanation of field attributes: Colorectal Cancer - Cancer that develops in the colon (the longest part of the large intestine) and/or the rectum (the last several inches of the large intestine). This is a rate per 100,000. Lung Cancer – Cancer that forms in tissues of the lung, usually in the cells lining air passages. This is a rate per 100,000. Breast Cancer – Cancer that forms in tissues of the breast. This is a rate per 100,000. Prostate Cancer – Cancer that forms in tissues of the prostate. This is a rate per 100,000. Urinary System Cancer – Cancer that forms in the organs of the body that produce and discharge urine. These include the kidneys, ureters, bladder, and urethra. This is a rate per 100,000. All Cancer – All cancers including, but not limited to: colorectal cancer, lung cancer, breast cancer, prostate cancer, and cancer of the urinary system. This is a rate per 100,000.

The dataset contains x-ray images, mammography, from breast cancer screening at the Karolinska University Hospital, Stockholm, Sweden, collected by principal investigator Fredrik Strand at Karolinska Institutet. The purpose for compiling the dataset was to perform AI research to improve screening, diagnostics and prognostics of breast cancer. The dataset is based on a selection of cases with and without a breast cancer diagnosis, taken from a more comprehensive source dataset. 1,103 cases of first-time breast cancer for women in the screening age range (40-74 years) during the included time period (November 2008 to December 2015) were included. Of these, a random selection of 873 cases have been included in the published dataset. A random selection of 10,000 healthy controls during the same time period were included. Of these, a random selection of 7,850 cases have been included in the published dataset. For each individual all screening mammograms, also repeated over time, were included; as well as the date of screening and the age. In addition, there are pixel-level annotations of the tumors created by a breast radiologist (small lesions such as micro-calcifications have been annotated as an area). Annotations were also drawn in mammograms prior to diagnosis; if these contain a single pixel it means no cancer was seen but the estimated location of the center of the future cancer was shown by a single pixel annotation. In addition to images, the dataset also contains cancer data created at the Karolinska University Hospital and extracted through the Regional Cancer Center Stockholm-Gotland. This data contains information about the time of diagnosis and cancer characteristics including tumor size, histology and lymph node metastasis. The precision of non-image data was decreased, through categorisation and jittering, to ensure that no single individual can be identified. The following types of files are available: - CSV: The following data is included (if applicable): cancer/no cancer (meaning breast cancer during 2008 to 2015), age group at screening, days from image to diagnosis (if any), cancer histology, cancer size group, ipsilateral axillary lymph node metastasis. There is one csv file for the entire dataset, with one row per image. Any information about cancer diagnosis is repeated for all rows for an individual who was diagnosed (i.e., it is also included in rows before diagnosis). For each exam date there is the assessment by radiologist 1, radiologist 2 and the consensus decision. - DICOM: Mammograms. For each screening, four images for the standard views were acuqired: left and right, mediolateral oblique and craniocaudal. There should be four files per examination date. - PNG: Cancer annotations. For each DICOM image containing a visible tumor. Access: The dataset is available upon request due to the size of the material. The image files in DICOM and PNG format comprises approximately 2.5 TB. Access to the CSV file including parametric data is possible via download as associated documentation.

Death rate has been age-adjusted to the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Obesity can increase an individual’s lifetime risk of breast cancer. Promoting healthy food retail and physical activity and improving access to preventive care services are important measures that cities and communities can take to prevent breast cancer.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumor size, examined regional LNs, regional positive LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included.

According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms. Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. RSNA collected screening mammograms and supporting information from two sites, totaling just under 20,000 imaging studies.

SEER Limited-Use cancer incidence data with associated population data. Geographic areas available are county and SEER registry. The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute collects and distributes high quality, comprehensive cancer data from a number of population-based cancer registries. Data include patient demographics, primary tumor site, morphology, stage at diagnosis, first course of treatment, and follow-up for vital status. The SEER Program is the only comprehensive source of population-based information in the United States that includes stage of cancer at the time of diagnosis and survival rates within each stage.

The dataset consists of the state wise estimated incidence of breast cancer and cervical cancer in India as per the National Cancer Registry Programme. The estimates are computer using age specific incidence Rate of 28 PBCRs of 2012-2016 and the projected population (person-years). NB: Incidence estimates of breast cancer is available since 2016 while that of cervical cancer is available since 2015.

Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg.

BackgroundThis nationwide study examined breast cancer (BC) incidence and mortality rates in Hungary between 2011–2019, and the impact of the Covid-19 pandemic on the incidence and mortality rates in 2020 using the databases of the National Health Insurance Fund (NHIF) and Central Statistical Office (CSO) of Hungary.MethodsOur nationwide, retrospective study included patients who were newly diagnosed with breast cancer (International Codes of Diseases ICD)-10 C50) between Jan 1, 2011 and Dec 31, 2020. Age-standardized incidence and mortality rates (ASRs) were calculated using European Standard Populations (ESP).Results7,729 to 8,233 new breast cancer cases were recorded in the NHIF database annually, and 3,550 to 4,909 all-cause deaths occurred within BC population per year during 2011-2019 period, while 2,096 to 2,223 breast cancer cause-specific death was recorded (CSO). Age-standardized incidence rates varied between 116.73 and 106.16/100,000 PYs, showing a mean annual change of -0.7% (95% CI: -1.21%–0.16%) and a total change of -5.41% (95% CI: -9.24 to -1.32). Age-standardized mortality rates varied between 26.65–24.97/100,000 PYs (mean annual change: -0.58%; 95% CI: -1.31–0.27%; p=0.101; total change: -5.98%; 95% CI: -13.36–2.66). Age-specific incidence rates significantly decreased between 2011 and 2019 in women aged 50–59, 60–69, 80–89, and ≥90 years (-8.22%, -14.28%, -9.14%, and -36.22%, respectively), while it increased in young females by 30.02% (95%CI 17,01%- 51,97%) during the same period. From 2019 to 2020 (in first COVID-19 pandemic year), breast cancer incidence nominally decreased by 12% (incidence rate ratio [RR]: 0.88; 95% CI: 0.69–1.13; 2020 vs. 2019), all-cause mortality nominally increased by 6% (RR: 1.06; 95% CI: 0.79–1.43) among breast cancer patients, and cause-specific mortality did not change (RR: 1.00; 95%CI: 0.86–1.15).ConclusionThe incidence of breast cancer significantly decreased in older age groups (≥50 years), oppositely increased among young females between 2011 and 2019, while cause-specific mortality in breast cancer patients showed a non-significant decrease. In 2020, the Covid-19 pandemic resulted in a nominal, but not statistically significant, 12% decrease in breast cancer incidence, with no significant increase in cause-specific breast cancer mortality observed during 2020.

This table contains 600 series, with data for years 1997 - 1997 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Prince Edward Island; Newfoundland and Labrador; Nova Scotia ...), Sex (3 items: Both sexes; Females; Males ...), Selected sites of cancer (ICD-9) (4 items: Colorectal cancer; Prostate cancer; Lung cancer; Female breast cancer ...), Characteristics (5 items: Relative survival rate for cancer; High 95% confidence interval; relative survival rate for cancer; Number of cases; Low 95% confidence interval; relative survival rate for cancer ...).

Full Abstract: Introduction: Triple-negative breast cancer (TNBC) is a highly metastatic type of breast cancer and one of the largest contributors to cancer mortality in women. Unlike other breast cancers, TNBC lacks any approved therapeutic targets. Scientists are rigorously attempting to decipher molecular pathways enriched in TNBC and to design clinically applicable therapeutics. Many TNBC drugs that successfully produce general antitumor effects in vitro fail to display significant long-lasting positive effects at the clinical level. This is in part because they do not effectively suppress the growth of cancer stem cells (CSCs), which have increased ability to evolve into metastatic tumors and are associated with enrichment of immunosuppressive pathways. Moreover, it has been shown that in TNBC, dormant CSCs are able to change their metabolic signature to escape the toxic effects of these drugs; these modified metabolic signatures are shown to be causally associated with increased metastasis. Therefore, a successful, clinically-applicable therapy must have the ability to selectively inhibit CSC growth, the metastatic metabolic signature, and pathways involved in immunosuppression. Objective: This study will evaluate the potential of four recently proposed TNBC treatments—which all successfully reduced tumor viability in vitro and/or in vivo—to inhibit genes involved in CSC survival, metastatic metabolic signature, and tumor immunosuppression. Methods: TNBC cell lines and/or patient-derived xenografts were treated with four different treatments: DCC-2036, 9Gy proton irradiation, miR302b+cisplatin combination, and DFX+doxorubicin combination. Genome-wide mRNA profiling (via either RNA-seq or microarray) was performed on control and treated groups. Data was obtained from publicly-deposited NCBI GEO datasets. We assessed the differential expression of over 40 genes associated with CSC growth, metastatic metabolic modifications, and immunosuppression in TNBC tumors. Limma statistical analysis was performed. GSEA was also used to complement results from individual gene expression analysis. Results: DCC-2036 treatment significantly induced the expression of CSC TNBC biomarkers—such as ALDH2, CD44, CCR5, and SNAI1—and genes associated with TNBC metastatic metabolomic signature—such as PPARGC1A. DCC-2036 showed inconsistent effects on the expression of immunosuppressive markers. 9Gy proton irradiation has mixed effects on the expression of our candidate genes, yet mostly induced the expression of stemness, metastatic, and immunosuppressive markers. miR302b+cisplatin and DFX+doxorubicin both failed to inhibit the candidate genes, yet without significantly inducing their expression. GSEA analysis confirmed the results obtained for all four treatments. Conclusions: Observing cancer rebound in TNBC patients after treatment with traditional cancer drugs is common and often happens when treatments fail to inhibit CSC growth, metabolic pathways associated with metastasis, and oncogenic immunosuppressive pathways. Our analysis shows that all four treatments failed to significantly impact the expression of protein pathways associated with increased metastasis and immunosuppression. It is worth noting that the researchers did report a decrease in tumor viability due to treatment of their experimental models with all four treatments. However, these findings correspond to the viability of the whole cell culture or tumor, not the viability of specifically the CSCs; in TNBC, CSCs make up only a small proportion of the total mass or the tumor, so the reported antiproliferative effects of the treatments do not necessarily suggest the treatment has effectively targeted the CSC population. Therefore, we hypothesize that these non-targeted therapies will likely not show positive effects in clinical studies. Furthermore, none of the researchers performed any assays evaluating CSC growth—such as CSC-labelled flow cytometry—or metastasis—such as secondary tumor transplantation. Therefore, we encourage the researchers to perform more rigorous assays to evaluate the translatable potential of their treatments. Finally, the outline of this study provides a useful rationale for future studies to evaluate emerging TNBC therapies and serves as a motivation for further in-silico research focus.

This table contains 30810 series, with data for years 2001/2003 - 2013/2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (158 items: Canada; Newfoundland and Labrador; Eastern Regional Health Authority, Newfoundland and Labrador; Central Regional Health Authority, Newfoundland and Labrador; ...);  Sex (3 items: Both sexes; Males; Females);  Selected sites of cancer (ICD-O-3) (5 items: All invasive primary cancer sites (including in situ bladder); Colon, rectum and rectosigmoid junction cancer; Bronchus and lung cancer; Female breast cancer; ...);  Characteristics (13 items: Number of new cancer cases; Cancer incidence (rate per 100,000 population); Low 95% confidence interval, cancer incidence (rate per 100,000 population); High 95% confidence interval, cancer incidence (rate per 100,000 population); ...).

Cancer diagnoses and age-standardised incidence rates for all types of cancer by age and sex including breast, prostate, lung and colorectal cancer.

This record contains raw data related to article “Ipsilateral Breast Cancer Recurrence: Characteristics, Treatment, and Long-Term Oncologic Results at a High-Volume Center"

Introduction: Salvage mastectomy is considered the treatment of choice for ipsilateral breast cancer recurrence (IBCR), even if a second breast-conserving surgery (BCS) is feasible. The purpose of this study was to describe the characteristics of IBCR patients, to compare the 2 therapeutic options in terms of long-term outcomes, and to identify independent factors that may predict the type of treatment.

Patients and methods: A total of 309 IBCR patients who underwent either repeat BCS or mastectomy were identified. All the analyzed patients with IBCR had true recurrence.

Results: Repeat BCS and salvage mastectomy were performed in 143 and 166 patients, respectively. Age < 65 years (59.6% vs 37.1% if age ≥ 65 years; odds ratio, 2.374; 95% confidence interval, 0.92-5.24; P = .018) and disease-free interval < 24 months (15.7% vs 10.5% if disease-free interval ≥ 24 months; odds ratio, 2.705; 95% confidence interval, 1.42-5.97; P = .007) were found to significantly increase the probability of receipt of mastectomy. Disease-free survival rates at 3, 5, and 10 years were 79.2%, 68.2%, and 36.9%; and 77.2%, 65.9%, and 55.3% in patients receiving repeat BCS or mastectomy, respectively (P = .842). Overall survival rates at 3, 5, and 10 years were 95.4%, 91.4%, and 68.5%; and 87.3%, 69.3%, and 57.9%, respectively, in patients receiving repeat BCS or mastectomy (P = .018).

Conclusion: Salvage mastectomy should not be considered the only treatment option for IBCR. A second BCS can still be evaluated and proposed to IBCR patients, with acceptable locoregional control and survival. The risk of poor long-term prognosis after mastectomy should be shared with the patient.

This dataset shows the female breast age adjusted invasive cancer incidence rates in United States in the year 2013.

This dataset presents the footprint of female cancer incidence statistics in Australia for all cancers combined and the 11 top cancer groupings (breast, cervical, colorectal, leukaemia, lung, lymphoma, melanoma of the skin, ovary, pancreas, thyroid and uterus) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Incidence data refer to the number of new cases of cancer diagnosed in a given time period. It does not refer to the number of people newly diagnosed (because one person can be diagnosed with more than one cancer in a year). Cancer incidence data come from the Australian Institute of Health and Welfare (AIHW) 2012 Australian Cancer Database (ACD).

Abstract Introduction There is a need to develop methods to evaluate public health interventions. Therefore, this work proposed an intervention analysis on time series of breast cancer mortality rates to assess the effects of an action of the Brazilian Screening Programme. Methods The analysed series was the monthly female breast cancer mortality rates from January 1996 to March 2016. The intervention was the establishment of the National Information System on Breast Cancer in June 2009. The Box-Tiao approach was used to build a Global Intervention Model (GIM) composed of a component that fits the series without the intervention, and a component that fits the effect with the intervention. The intervention’s response time was estimated and used to define the length of the residual series to assess the predictive accuracy of the GIM, which was compared to a one-step-ahead forecasting approach. Results The pre-intervention period was fitted to a SARIMA (0,1,2) (1,1,1)12 model and the intervention’s effect to an ARIMA (1,1,0) model. The intervention led to an increase in the mortality rates, and its response time was 24 months. The forecast error (MAPE) for the GIM was 3.14%, and for the one-step-ahead forecast it was 2.15%. Conclusion This work goes one step further in relation to the studies carried out to evaluate the Breast Cancer Screening Programme in Brazil, considering that it was possible to quantify the effects and the response time of the intervention, demonstrating the potential of the proposed method to be used to evaluate health interventions.

aBecause of rounding, percentages may not add to 100%.bControls were matched to cases on this variable.

Rate: Number of deaths among females due to breast cancer per 100,000 female population.

Definition: Number of deaths per 100,000 with malignant neoplasm (cancer) of the female breast as the underlying cause (ICD-10 codes: C33-C34).

Data Sources:

(1) Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed Mortality File. CDC WONDER On-line Database accessed at http://wonder.cdc.gov/cmf-icd10.html

(2) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(3) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

The main purpose of this study is to evaluate the safety and effectiveness of the study drug known as abemaciclib in participants with hormone receptor positive breast cancer, non-small cell lung cancer (NSCLC), or melanoma that has spread to the brain.