A measure of the number of adults diagnosed with breast, lung or colorectal cancer in a year who are still alive five years after diagnosis.

ONS still publish survival percentages for individual types of cancers. These can be found at: http://www.ons.gov.uk/ons/rel/cancer-unit/cancer-survival/cancer-survival-in-england--patients-diagnosed-2007-2011-and-followed-up-to-2012/index.html

A time series for five-year survival figures for breast, lung and colorectal cancer individually (previous NHS Outcomes Framework indicators 1.4.ii, 1.4.iv and 1.4.vi) is still published and can be found under the link 'Indicator data - previous methodology (.xls)' below.

Purpose

This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with breast, lung or colorectal cancer.

Current version updated: May-14

Next version due: To be confirmed

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

A measure of the number of adults diagnosed with breast, lung or colorectal cancer in a year who are still alive one year after diagnosis.

ONS still publish survival percentages for individual types of cancers. These can be found at: http://www.ons.gov.uk/ons/rel/cancer-unit/cancer-survival/cancer-survival-in-england--patients-diagnosed-2007-2011-and-followed-up-to-2012/index.html

A time series for one-year survival figures for breast, lung and colorectal cancer individually (previous NHS Outcomes Framework indicators 1.4.i, 1.4.iii and 1.4.v) is still published and can be found under the link 'Indicator data - previous methodology (.xls)' below.

Purpose

This indicator attempts to capture the success of the NHS in preventing people from dying once they have been diagnosed with breast, lung or colorectal cancer.

Current version updated: Feb-14

Next version due: To be confirmed

One woman in nine can expect to develop breast cancer during her lifetime and one in 25 will die from the disease. Statistically low incidences of breast cancer are found in Newfoundland and Labrador, the territories, and northern areas of most provinces. Otherwise, each province has one or more pockets of significantly high breast cancer incidence. These are often located in more southerly areas, but they do not seem to be restricted to either urban or rural areas alone. Breast cancer rates are a health status indicator. They can be used to help assess health conditions. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

Source RSNA Screening Mammography Breast Cancer Detection

Processing of the huge 314GB+ Dataset (Include 54713 Images) of this competition into TFRecords for fast dataloading during training.

All images are resized to 768x1280 and saved in 100 TFRecords, making each TFRecord contain roughly 548 images as 8.6GB+ Dataset.

TFRecords have the benefit of loading large chunks of data containing many samples instead of loading every image and label seperately.

Dataset Description

Note: The dataset for this challenge contains radiographic breast images of female subjects. The goal of this competition is to identify cases of breast cancer in mammograms from screening exams. It is important to identify cases of cancer for obvious reasons, but false positives also have downsides for patients. As millions of women get mammograms each year, a useful machine learning tool could help a great many people. This competition uses a hidden test. When your submitted notebook is scored the actual test data (including a full length sample submission) will be made available to your notebook.

Files

[train/test]_images/[patient_id]/[image_id].dcm The mammograms, in dicom format. You can expect roughly 8,000 patients in the hidden test set. There are usually but not always 4 images per patient. Note that many of the images use the jpeg 2000 format which may you may need special libraries to load.

sample_submission.csv A valid sample submission. Only the first few rows are available for download.

[train/test].csv Metadata for each patient and image. Only the first few rows of the test set are available for download.

site_id - ID code for the source hospital. patient_id - ID code for the patient. image_id - ID code for the image. laterality - Whether the image is of the left or right breast. view - The orientation of the image. The default for a screening exam is to capture two views per breast. age - The patient's age in years. implant - Whether or not the patient had breast implants. Site 1 only provides breast implant information at the patient level, not at the breast level. density - A rating for how dense the breast tissue is, with A being the least dense and D being the most dense. Extremely dense tissue can make diagnosis more difficult. Only provided for train. machine_id - An ID code for the imaging device. cancer - Whether or not the breast was positive for malignant cancer. The target value. Only provided for train. biopsy - Whether or not a follow-up biopsy was performed on the breast. Only provided for train. invasive - If the breast is positive for cancer, whether or not the cancer proved to be invasive. Only provided for train. BIRADS - 0 if the breast required follow-up, 1 if the breast was rated as negative for cancer, and 2 if the breast was rated as normal. Only provided for train. prediction_id - The ID for the matching submission row. Multiple images will share the same prediction ID. Test only. difficult_negative_case - True if the case was unusually difficult. Only provided for train.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-segmentation.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-objdetect.

Breast Cancer

https://cdn.sanity.io/images/0vv8moc6/targetedonc/9b5d50af939b7d1e584b5773acf8d903c3e32a6f-471x621.jpg?auto=format">

Data Set Information:

There are 10 predictors, all quantitative, and a binary dependent variable, indicating the presence or absence of breast cancer. The predictors are anthropometric data and parameters which can be gathered in routine blood analysis. Prediction models based on these predictors, if accurate, can potentially be used as a biomarker of breast cancer. There's a story behind every dataset and here's your opportunity to share yours.

Attribute Information:

Quantitative Attributes: * Age (years) * BMI (kg/m2) * Glucose (mg/dL) * Insulin (µU/mL) * HOMA * Leptin (ng/mL) * Adiponectin (µg/mL) * Resistin (ng/mL) * MCP-1(pg/dL)

Labels:

• 1=Healthy controls
• 2=Patients

Source:

Miguel Patrício(miguelpatricio '@' gmail.com), José Pereira (jafcpereira '@' gmail.com), Joana Crisóstomo (joanacrisostomo '@' hotmail.com), Paulo Matafome (paulomatafome '@' gmail.com), Raquel Seiça (rmfseica '@' gmail.com), Francisco Caramelo (fcaramelo '@' fmed.uc.pt), all from the Faculty of Medicine of the University of Coimbra and also Manuel Gomes (manuelmgomes '@' gmail.com) from the University Hospital Centre of Coimbra

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

One woman in nine can expect to develop breast cancer during her lifetime and one in 25 will die from the disease. Statistically low incidences of breast cancer are found in Newfoundland and Labrador, the territories, and northern areas of most provinces. Otherwise, each province has one or more pockets of significantly high breast cancer incidence. These are often located in more southerly areas, but they do not seem to be restricted to either urban or rural areas alone. Breast cancer rates are a health status indicator. They can be used to help assess health conditions. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

Context

The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer

Analysis of ‘Breast Cancer (METABRIC)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gunesevitan/breast-cancer-metabric on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

--- Original source retains full ownership of the source dataset ---

The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Breast Phenotype Research Group.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcanc-ultrasound-class.

##

The current study aims to build the first digitalized mammogram dataset for breast cancer in Saudi Arabia, depend on the BI-RADS categories, to solve the availability problem of local public datasets by collecting, categorizing, and annotating mammogram images, supporting the medical field by providing physicians with different diagnosed cases especially in Saudi Arabia The dataset was collected from Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University in Jeddah, Saudi Arabia, from April 2019 to March 2020 and the annotated was between April and June 2020. The dataset contains 1521 cases; all cases include images with two types of views (CC and MLO) for both breasts (right and left), making a total of 6109 mammogram images. The dataset was classified into 0 to 5 categories in accordance with BI-RADS

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

BackgroundThe past decade has witnessed an improvement in survival rates for breast cancer, with significant inroads achieved in diagnosis and treatment approaches. Even though chemotherapy is effective for this patient population, cardiotoxicity remains a major challenge, especially in older people. It has been established that cardiovascular events are a major cause of death in older female primary breast cancer patients that underwent chemotherapy. In the present study, the independent prognostic factors were identified to develop a novel nomogram for predicting long-term heart disease-specific survival (HDSS) and improving patient management.MethodOlder female primary breast cancer patients that underwent chemotherapy from 2010 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. HDSS was the primary endpoint of this study. Univariate and multivariate Cox regression analyses were conducted on the training cohort to identify independent prognostic factors of HDSS and construct a nomogram to predict the 5- and 8-year HDSS. The performance of the constructed nomogram was evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analyses. Finally, a risk classification system was constructed to assist in patient management.ResultA total of 16,340 patients were included in this study. Multivariate Cox regression analysis identified six independent prognostic factors: age, race, tumor stage, marital status, surgery, and radiotherapy. A nomogram based on these six factors yielded excellent performance, with areas under the curve of the ROC for 5- and 8-year HDSS of 0.759 and 0.727 in the training cohort and 0.718 and 0.747 in the validation cohort. Moreover, the established risk classification system could effectively identify patients at low-, middle-, and high- risk of heart disease-associated death and achieve targeted management.ConclusionIndependent prognostic factors of HDSS in older female primary breast cancer patients that underwent chemotherapy were determined in this study. A novel nomogram for predicting 5- and 8-year HDSS in this patient population was also established and validated to help physicians during clinical decision-making and screen high-risk patients to improve outcomes.

This map uses age-standardized ratios to further aid in regional comparisons. A value of 1.0 would indicate that the region rate is identical to the overall Canadian rate; a value greater than 1.0 would indicate that the rate for that region is higher than the Canadian rate; and, in turn, a ratio value less than 1.0 would indicate that the rate for the specific region is lower than the Canadian rate. Statistically low incidences of breast cancer are found in Newfoundland and Labrador, the territories, and northern areas of most provinces. Otherwise, each province has one or more pockets of significantly high breast cancer incidence. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

This map uses age-standardized ratios to further aid in regional comparisons. A value of 1.0 would indicate that the region rate is identical to the overall Canadian rate; a value greater than 1.0 would indicate that the rate for that region is higher than the Canadian rate; and, in turn, a ratio value less than 1.0 would indicate that the rate for the specific region is lower than the Canadian rate. Statistically low incidences of breast cancer are found in Newfoundland and Labrador, the territories, and northern areas of most provinces. Otherwise, each province has one or more pockets of significantly high breast cancer incidence. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

This dataset consists of 256 breast ultrasound scans collected from 256 patients and 266 benign and malignant segmented lesions. It includes patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or biopsy result. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image via a freehand annotation and labeled according to BIRADS features. The tumor histopathological classification is stated for patients who underwent a biopsy. Patient-level labels include clinical data such as age, breast tissue composition, signs and symptoms. Image-level freehand annotations identify the tumor and other abnormal areas in the image. The tumor and image are labeled with BIRADS category, 7 BIRADS descriptors, and interpretation of critical findings as presence of breast diseases. Additional labels include the method of verification, tumor classification and histopathological diagnosis.

Since the role of machine learning and theoretical computing towards the development of augmented inference in the field of cancer detection is indisputable, the quality of the data used to develop any explainable augmented inference methods is extremely important. This dataset can be used as an external testing set for assessing a model’s performance and for developing explainable AI or supervised machine learning models for the detection, segmentation and classification of breast abnormalities in ultrasound images.

A detailed description of this dataset can be found here and should be cited along with the citation of the data:
Pawłowska, A., Ćwierz-Pieńkowska, A., Domalik, A., Jaguś, D., Kasprzak, P., Matkowski, R., Fura, Ł., Nowicki, A., & Zolek, N. A Curated benchmark dataset for ultrasound based breast lesion analysis. Sci Data 11, 148 (2024). https://doi.org/10.1038/s41597-024-02984-z.

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Demographic Summary of Available Imaging

Characteristic	Value (N = 984)
Age (years)	Mean ± SD: 53.2 ± 11 Median (IQR): 53 (45-60) Range: 25-86
Sex	Female: 984 (100%)
Race	White: 898 (91.3%) Black: 46 (4.7%) Asian: 22 (2.2%) Native Hawaiian or Other Pacific Islander: 1 (0.1%) More than One: 5 (0.5%) Unknown: 12 (1.2%)
Ethnicity	Hispanic: 38 (3.9%) Non-Hispanic: 940 (95.5%) Unknown: 6 (0.6%)

This dataset relates to NCI Clinical trial, "Magnetic Resonance Imaging in Women Recently Diagnosed With Unilateral Breast Cancer (ACRIN-6667)". The dataset consists of 984 patients but only 969 were included in the primary data analysis due to study criteria.

Even after careful clinical and mammographic evaluation, cancer is found in the contralateral breast in up to 10% of women who have received treatment for unilateral breast cancer. ACRIN 6667 was conducted to determine whether magnetic resonance imaging (MRI) could improve on clinical breast examination and mammography in detecting contralateral breast cancer soon after the initial diagnosis of unilateral breast cancer. Additional information about the trial is available in the Study Protocol and Case Report Forms.

METHODS

A total of 969 women with a recent diagnosis of unilateral breast cancer and no abnormalities on mammographic and clinical examination of the contralateral breast underwent breast MRI. The diagnosis of MRI-detected cancer was confirmed by means of biopsy within 12 months after study entry. The absence of breast cancer was determined by means of biopsy, the absence of positive findings on repeat imaging and clinical examination, or both at 1 year of follow-up.

RESULTS

MRI detected clinically and mammographically occult breast cancer in the contralateral breast in 30 of 969 women who were enrolled in the study (3.1%). The sensitivity of MRI in the contralateral breast was 91%, and the specificity was 88%. The negative predictive value of MRI was 99%. A biopsy was performed on the basis of a positive MRI finding in 121 of the 969 women (12.5%), 30 of whom had specimens that were positive for cancer (24.8%); 18 of the 30 specimens were positive for invasive cancer. The mean diameter of the invasive tumors detected was 10.9 mm. The additional number of cancers detected was not influenced by breast density, menopausal status, or the histologic features of the primary tumor.

CONCLUSIONS

MRI can detect cancer in the contralateral breast that is missed by mammography and clinical examination at the time of the initial breast-cancer diagnosis.

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).