In 2022, there were an estimated 2.48 million new cases of trachea, bronchus, and lung cancer worldwide. Breast cancer was the second most common cancer type at that time with around 2.3 million new cases worldwide.

Number of new cancer cases

Cancer can be caused by internal factors like genetics and mutations, as well as external factors such as smoking and radiation. It occurs in the presence of uncontrolled growth and spread of abnormal cells. However, many cancer cases could be prevented, for example, by omitting cigarette usage and heavy alcohol consumption. Risk of developing cancer tends to increase with age and is most common in older adults. Nevertheless, cancer can develop in individuals of any age. Cancer can be treated through surgery, radiation, and chemotherapy, among other methods.

In the United States, there will be an estimated two million new cancer cases and 611,720 deaths in 2024. Among U.S. men, prostate cancer and lung and bronchus cancers are the most common cancer types as of 2024, totaling an estimated 299,010 and 116,310 cases, respectively. In women, breast cancer and lung and bronchus cancer are the most common newly diagnosed types, totaling 310,720 and 118,270 cases, respectively.

Lung cancer is the deadliest cancer worldwide, accounting for 1.82 million deaths in 2022. The second most deadly form of cancer is colorectum cancer, followed by liver cancer. However, lung cancer is only the sixth leading cause of death worldwide, with heart disease and stroke accounting for the highest share of deaths. Male vs. female cases Given that lung cancer causes the highest number of cancer deaths worldwide, it may be unsurprising to learn that lung cancer is the most common form of new cancer cases among males. However, among females, breast cancer is by far the most common form of new cancer cases. In fact, breast cancer is the most prevalent cancer worldwide, followed by prostate cancer. Prostate cancer is a very close second to lung cancer among the cancers with the highest rates of new cases among men. Male vs. female deaths Lung cancer is by far the deadliest form of cancer among males but is the second deadliest form of cancer among females. Breast cancer, the most prevalent form of cancer among females worldwide, is also the deadliest form of cancer among females. Although prostate cancer is the second most prevalent cancer among men, it is the fifth deadliest cancer. Lung, liver, stomach, colorectum, and oesophagus cancers all have higher deaths rates among males.

Breast cancer was the most prevalent cancer type in 2022 with around 48 women living with this type of cancer per 100,000 population. The 12-month prevalence rate for prostate cancer as of this time was 30.5 per 100,000 population. This statistic shows the number of prevalent cancer cases worldwide in 2022, by type of cancer.

Editor’s Choice

• Global Breast Cancer Market size is expected to be worth around USD 49.2 Bn by 2032 from USD 19.8 Bn in 2022, growing at a CAGR of 9.8% during the forecast period from 2022 to 2032.
• Breast cancer is the most common cancer among women worldwide. In 2020, there were about 2.3 million new cases of breast cancer diagnosed globally.
• Breast cancer is the leading cause of cancer-related deaths in women. In 2020, it was responsible for approximately 685,000 deaths worldwide.
• The survival rate of breast cancer has improved over the years. In the United States, the overall five-year survival rate of breast cancer is around 90%.
• The American Cancer Society recommends annual mammograms starting at age 40 for women at average risk.
• Although rare, breast cancer also occurs in men. Less than 1% of breast cancer cases are diagnosed in males.

(Source: WHO, American Cancer Society)

Lung Cancer Dataset

Introduction:

Lung cancer remains one of the most prevalent and deadly forms of cancer worldwide, posing significant challenges for early detection and effective treatment. To contribute to the global effort in understanding and combating this disease, we are excited to introduce our comprehensive Lung Cancer Dataset, now available on Kaggle.

Scientific Overview:

This dataset is an invaluable asset in the realm of Health Care, providing a structured foundation for the development of cancer detection models. This dataset exemplifies the variety of symptoms of Lung Cancer. Each category within the dataset—'GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY', 'PEER_PRESSURE', 'CHRONIC_DISEASE', 'FATIGUE', 'ALLERGY', 'WHEEZING', 'ALCOHOL_CONSUMING', 'COUGHING', 'SHORTNESS_OF_BREATH', 'SWALLOWING_DIFFICULTY', 'CHEST_PAIN'—has been carefully curated to encompass a diverse range of symptoms, ensuring that the resulting models are versatile and accurate. This scientific approach not only enhances the dataset's diversity to record symptoms of lung cancer but also contributes to the broader field of AI-driven health technologies, pushing the boundaries of what health care assistants can achieve.

Dataset Composition

The Lung Cancer Dataset includes a diverse array of symptoms essential for comprehensive analysis and model development. The primary categories of data are as follows:

1. Patient Demographics

Age: Provides the age at diagnosis, enabling analysis of age-related incidence and outcomes. Gender: Includes information on patient gender, facilitating gender-based studies. Smoking Status: Categorized as current smoker, former smoker, or non-smoker, this data is critical for evaluating the impact of smoking on lung cancer risk and progression.

2. Medical History

Comorbidities: Details additional health issues such as chronic obstructive pulmonary disease (COPD), which are relevant for treatment planning and prognosis.

3. Clinical Data

Vital Signs: Records of blood pressure, heart rate, respiratory rate, and other vital signs at diagnosis and during treatment.

Implementation Guide for the Mental Health Dataset:

Data Integration

Dataset Acquisition: Obtain the Lung Cancer Dataset. Data Exploration: Familiarize yourself with the structure and contents of the dataset, including symptoms and conclusions related to different conditions.

Preprocessing

Data Cleaning: Remove any irrelevant or redundant entries, and ensure consistency in formatting across the dataset. Tokenization: Break down the symptoms and conclusions into tokens or individual words to facilitate analysis and model training. Normalization: Standardize the text data by converting it to lowercase and removing punctuation or special characters as needed.

Model Training

Choose a Framework: Select a suitable machine learning or natural language processing framework such as TensorFlow, PyTorch, or spaCy. Model Selection: Decide on the type of model to use, such as recurrent neural networks (RNNs), transformers, or sequence-to-sequence models, based on the complexity of the dataset and the desired level of accuracy. Training Process: Train the chosen model using the preprocessed dataset, adjusting hyperparameters as necessary to optimize performance. Evaluation: Assess the performance of the trained model using appropriate metrics such as accuracy, precision, recall, and F1-score.

Deployment

Integration: Integrate the trained model into a chatbot or virtual assistant application using programming languages like Python or JavaScript. User Interface Design: Design an intuitive user interface that allows users to interact with the chatbot and receive responses related to Lung Cancer. Testing: Conduct thorough testing of the deployed chatbot to ensure functionality, accuracy, and responsiveness in providing relevant result. Feedback Mechanism: Implement a feedback mechanism to gather user feedback and improve the chatbot's performance over time.

Continuous Improvement

Monitoring: Continuously monitor the chatbot's performance and user interactions to identify areas for improvement. Data Updates: Periodically update the dataset with new symptoms to ensure accuracy. Model Refinement: Fine-tune the model based on user feedback and additional training data to enhance the chatbot's effectiveness and accuracy in detecting lung cancer. By following this implementation guide, developers can effectively leverage the Lung Cancer Dataset to build and deploy AI-driven chatbots and virtual assistants that offer accurate predictions to users worldwide.

Potential Applications

The extensive nature of the Lung Cancer Dataset supports a wide range of scientific and clinical applications:

Machine Learning Models: Facilitates the development of predictive algorithms for early detection, prognosis, and personalized t...

📄 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.

ObjectivesTo quantify the burden and variation trends of cancers in children under 5 years at the global, regional, and national levels from 1990 to 2019.MethodsEpidemiological data for children under 5 years who were diagnosed with any one childhood cancer were obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) from 1990 to 2019. The outcomes were the absolute numbers and rates of incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) for different types of cancer.ResultsIn 2019, 8,774,979.1 incident cases (95% uncertainty interval [UI]: 6,243,599.2 to11,737,568.5) and 8,956,583.8 (6,446,323.9 to 12,364,520.8) prevalent cases of cancer in children under 5 years were identified worldwide; these cancers resulted in 44,451.6 (36,198.7 to 53,905.9) deaths and 3,918,014.8 (3,196,454.9 to 4,751,304.2) DALYs. From 1990 to 2019, although the numbers of incident and prevalent cases only decreased by −4.6% (−7.0 to −2.2) and −8.3% (−12.6 to −3.4), respectively, the numbers of deaths and DALYs clearly declined by −47.8% (−60.7 to −26.4) and −47.7% (−60.7 to −26.2), respectively. In 2019, the middle sociodemographic index (SDI) regions had the highest incidence and prevalence, whereas the low SDI regions had the most mortality and DALYs. Although all of the SDI regions displayed a steady drop in deaths and DALYs between 1990 and 2019, the low-middle and low SDI regions showed increasing trends of incidence and prevalence. Leukemia remained the most common cancer globally in 2019. From 1990 to 2019, the burdens of leukemia, liver cancer, and Hodgkin's lymphoma declined, whereas the incidence and prevalence of other cancers grew, particularly testicular cancer.ConclusionsThe global childhood cancer burden in young children has been steadily decreasing over the past three decades. However, the burdens and other characteristics have varied across different regions and types of cancers. This highlights the need to reorient current treatment strategies and establish effective prevention methods to reduce the global burden of childhood cancer.

According to Cognitive Market Research, the global Cancer Diagnosis market size was USD 109614.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 43845.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 32884.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 25211.34 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 5480.73 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 2192.29 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
The consumables category is the fastest growing segment of the Cancer Diagnosis industry

Market Dynamics of Cancer Diagnosis Market

Key Drivers for Cancer Diagnosis Market

Increasing Rate of Cancer Diagnostics to Boost Market Growth

The rising global incidence of cancer, which affects millions of people a year, is a primary driver of the need for diagnostic testing. Numerous factors contribute to this tendency, such as the aging population, which increases the risk of developing some cancers in older adults. Changes in lifestyle, including poor eating habits, inactivity, and increased use of alcohol and tobacco, have also contributed to an increase in cancer incidence. Environmental factors, such as exposure to chemicals and hazardous compounds, exacerbate the problem and increase the risk of developing cancer. Therefore, as early detection and diagnosis are becoming more and more important to patients and healthcare professionals, effective cancer diagnostics are essential. The market for cancer diagnostics is expanding as a result of the increased emphasis on prompt and precise cancer detection, which highlights the value of novel diagnostic procedures. For Instance, in 2023, the Pan American Health Organization (PAHO) projects that there will be 20 million new cases and 10 million deaths, and by 2040, nearly 30 million cases will be reported annually.

Innovations in Diagnostic Technologies to Drive Market Growth

The market for cancer diagnostics is expanding as a result of advancements in diagnostic technologies that have greatly improved the precision and effectiveness of cancer detection. For example, non-invasive cancer biomarker identification in physiological fluids is made possible by liquid biopsies, which offer vital insights into tumor dynamics and therapy response. In a similar vein, molecular diagnostics has transformed the detection of particular genetic abnormalities and changes linked to different types of cancer, allowing for more individualized treatment strategies. High-resolution images of tumors are provided by advanced imaging methods like MRI and PET scans, which help with accurate staging and localization. Better patient outcomes result from these technical developments because they increase overall diagnosis accuracy and enable early intervention. The ongoing development of these cutting-edge diagnostic instruments is propelling market expansion and revolutionizing cancer treatment.

Restraint Factor for the Cancer Diagnosis Market

The High Price of Cutting-Edge Diagnostic Technology Will Limit Market Growth

The market for cancer diagnostics is severely hampered by the high price of sophisticated diagnostic tools. Advanced diagnostic instruments, such as molecular tests and imaging technologies, are frequently expensive, which limits healthcare facilities' access to them, especially in settings with limited resources. These institutions' capacity to provide thorough cancer screening and diagnostic services is restricted by this financial barrier, which eventually affects patient outcomes. These financial difficulties are further exacerbated by the costs associated with the development, research, and regulatory approval of new diagnostic instruments. Companies have to spend a lot of money to comply with...

ObjectiveBreast cancer (BC) is one of the most common cancers globally, placing a significant social burden. This study estimates the BC burden in the U.S. from 1990 to 2021 and projects future trends for the next 15 years.MethodsUsing data from the Global Burden of Disease (GBD) 2021 study, we analyzed four measures: prevalence, incidence, death, and disability-adjusted life years (DALYs), stratified by sex, age, U.S. states, and socio-demographic index (SDI).ResultsBC burden in the U.S. has decreased, with reductions in age-standardized rates of prevalence, incidence, mortality, and DALYs for both sexes. The overall age-standardized prevalence rate dropped from 695.0 (653.5–741.5)/100,000 in 1990 to 556.0 (525.2–584.7)/100,000 in 2021. The ASIR declined from 68.3 (65.1–70.3)/100,000 to 51.7 (48.4–54.1)/100,000. Death rates fell from 15.9 (14.9–16.5)/100,000 to 9.4 (8.5–9.9)/100,000, while DALYs decreased from 485.1 (462.9–507.0)/100,000 to 277.4 (260.1–294.8)/100,000 over the same period. Burden varies by state and SDI: in 2021, low-SDI states, Kentucky and Louisiana had the highest prevalence and incidence, while Louisiana and Mississippi had the highest mortality. Projections suggest a continued downward trend through 2036.ConclusionsBC burden in the U.S. decreased overall, but disparities persist across sex, age groups, and states with varying SDI levels. Addressing risk factors and improving healthcare access are essential to further reduce BC burden.

Cancer is one of the biggest health challenges worldwide. As of 2021, around 15% of all deaths were cancer deaths, making it one of the most common causes of death globally.

Cancers are a group of diseases in which abnormal cells multiply rapidly and can grow into tumors. They can develop in different parts of the body and, in some cases, spread to other organs through the blood and lymph systems.

As the global population grows larger and older, the number of cancer cases has also increased. However, the age-standardized death rate from cancer has declined over time in many countries — due to improvements in diagnosis, research, medical advances, and public health efforts, as well as reductions in risk factors such as smoking and some cancer-causing pathogens.

On this page, we explore global data and research on different types of cancer. This can help us better understand the risk factors for cancer, how cancer risks vary across the lifespan, how they differ worldwide, and how they have changed over time.

Breast cancer was the cancer type with the highest rate of death among females worldwide in 2022. That year, there were around 13 deaths from breast cancer among females per 100,000 population. The death rate for all cancers among females was 76.4 per 100,000 population. This statistic displays the rate of cancer deaths among females worldwide in 2022, by type of cancer.

Description: Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases, and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area. The key challenges against it’s detection is how to classify tumors into malignant (cancerous) or benign(non cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (with SVMs) and the Breast Cancer Wisconsin (Diagnostic) Dataset. Acknowledgements: This dataset has been referred from Kaggle. Objective: Understand the Dataset & cleanup (if required). Build classification models to predict whether the cancer type is Malignant or Benign. Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

What is Breast Cancer Dataset?

Breast cancer is the most common cancer amongst women in the world. It accounts for 25% of all cancer cases and affected over 2.1 Million people in 2015 alone. It starts when cells in the breast begin to grow out of control. These cells usually form tumors that can be seen via X-ray or felt as lumps in the breast area.

.

https://user-images.githubusercontent.com/36210723/182301443-382b14e1-71c1-46ac-88f5-e72a9b2083e7.jpg" alt="cancer-1">

.

How to use this dataset

The key challenge against its detection is how to classify tumors into malignant (cancerous) or benign(non-cancerous). We ask you to complete the analysis of classifying these tumors using machine learning (with SVMs) and the Breast Cancer Wisconsin (Diagnostic) Dataset.

Acknowledgments

When we use this dataset in our research, we credit the authors as :

• License : CC BY 4.0.

• This data set is taken from https://data.world/health/breast-cancer-wisconsin by the Donor: Nick Street and the Source: UCI - Machine Learning Repository.

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

This dataset provides a detailed view of global cancer trends across the 50 most populated countries. With 160,000 records, it encompasses a wide range of variables including cancer types, risk factors, healthcare expenditure, and environmental factors. The data is designed to assist researchers, healthcare policymakers, and data scientists in identifying patterns, predicting future trends, and crafting effective cancer control strategies.

Users can access data about cancer statistics, specifically incidence and mortality worldwide for the 27 major types of cancer. Background Cancer Mondial is maintained by the Section of Cancer Information (CIN) of International Agency for Research on Cancer by the World Health Organization. Users can access CIN databases including GLOBOCAN, CI5(Cancer Incidence in Five Continents), WHO, ACCIS(Automated Childhood Cancer Information System), ECO (European Cancer Observatory), NORDCAN and Survcan. User functionality Users can access a variety of databases. CIN Databases: GLOBOCAN provides acces s to the most recent estimates (for 2008) of the incidence of 27 major cancers and mortality from 27 major cancers worldwide. CI5 (Cancer Incidence in Five Continents) provides access to detailed information on the incidence of cancer recorded by cancer registries (regional or national) worldwide. WHO presents long time series of selected cancer mortality recorded in selected countries of the world. Collaborative projects: ACCIS (Automated Childhood Cancer Information System) provides access to data on cancer incidence and survival of children collected by European cancer registries. ECO (European Cancer Observatory) provides access to the estimates (for 2008) of the incidence of, and mortality f rom 25 major cancers in the countries of the European Union (EU-27). NORDCAN presents up-to-date long time series of cancer incidence, mortality, prevalence and survival from 40 cancers recorded by the Nordic countries. SurvCan presents cancer survival data from cancer registries in low and middle income regions of the world. 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.

Cancer Diagnostics Technologies Market size was valued at USD 17.91 Billion in 2024 and is expected to reach USD 51.6 Billion by 2032, growing at a CAGR of 0.139% from 2026 to 2032.

Global Cancer Diagnostics Technologies Market Definition

Cancer is one of the most common widespread diseases in the world. Cancer deaths are prevented and controlled with proper and prior diagnosis.

Bladder Cancer Diagnostics Market Outlook

According to our latest research, the global bladder cancer diagnostics market size reached USD 2.1 billion in 2024, reflecting a robust expansion trajectory. The market is anticipated to grow at a CAGR of 6.9% from 2025 to 2033, propelling its valuation to USD 3.98 billion by 2033. This growth is primarily driven by increasing incidence rates of bladder cancer globally, coupled with ongoing technological advancements in diagnostic modalities and a greater emphasis on early detection to improve patient outcomes.

The rising prevalence of bladder cancer is a significant growth driver for the bladder cancer diagnostics market. According to the World Health Organization, bladder cancer is among the top ten most common cancers worldwide, with a particularly high incidence in aging populations. The growing geriatric demographic, combined with risk factors such as smoking, occupational exposure to carcinogens, and chronic urinary tract infections, has led to an increased demand for accurate and early diagnostic solutions. Furthermore, heightened awareness initiatives by government and non-governmental organizations have contributed to improved screening rates, which in turn, is positively impacting the market’s growth trajectory.

Technological innovation is another critical factor fueling the expansion of the bladder cancer diagnostics market. The integration of advanced imaging techniques, molecular diagnostics, and non-invasive urine-based assays has revolutionized the diagnostic landscape. These innovations not only enable earlier and more precise detection but also facilitate the monitoring of disease recurrence, which is a common challenge in bladder cancer management. Companies are investing heavily in research and development to launch novel products that enhance sensitivity, specificity, and patient comfort, thereby driving adoption across healthcare settings. Additionally, the emergence of artificial intelligence and machine learning algorithms in diagnostic software is further streamlining workflows and improving diagnostic accuracy.

Healthcare infrastructure development and increased healthcare expenditure, especially in emerging economies, are also contributing significantly to market growth. Governments are allocating higher budgets for cancer care, and private sector investments in diagnostic centers are on the rise. As reimbursement policies evolve to cover a broader range of diagnostic procedures, patient access is expanding, leading to higher test volumes. The growing trend of personalized medicine and the need for precise tumor characterization are also pushing the demand for advanced diagnostic solutions. Overall, these factors collectively underpin the sustained growth of the bladder cancer diagnostics market.

Regionally, North America continues to dominate the bladder cancer diagnostics market, attributed to its well-established healthcare infrastructure, high awareness levels, and significant investments in research and development. Europe follows closely, supported by robust screening programs and favorable reimbursement policies. The Asia Pacific region, meanwhile, is emerging as a high-growth market, driven by rising cancer incidence, improving healthcare facilities, and increasing government focus on cancer prevention and early detection. Latin America and the Middle East & Africa, though currently holding smaller market shares, are expected to witness accelerated growth due to ongoing healthcare reforms and rising awareness levels.

Product Type Analysis

The bladder cancer diagnostics market by product type is segmented into instruments, consumables, and software. Instruments, which include cystoscopes, imaging devices, and biopsy tools, form the backbone of bladder cancer diagnosis. The increasing adoption of technologically advanced instruments, such as high-definition cystoscopes and digital imaging systems, is enhancing the accuracy and efficiency of diagnostic procedures. Hospitals and diagnostic centers are

Cancer Diagnostics Market size was valued at USD 111.70 Billion in 2024 and is projected to reach USD 188.43 Billion by 2031, growing at a CAGR of 6.77% during the forecast period 2024-2031.

Cancer Diagnostics Market Drivers

Cancer is one of the most popular comprehensive diseases in the world. The process of detecting cancer includes the usage of certain technology and devices specifically used in its diagnosis. Cancer deaths can be prevented and controlled due to proper and prior diagnosis. A cancer diagnosis is defined as the identification of cancer in patients who have developed the signs of the disease. Early diagnosis leads to effective treatment and secures the survival chances of the patient. Effective diagnostic testing is practiced to confirm or exclude the presence of disease, monitor the disease process, and plan for and decide the effectiveness of treatment.

In some cases, it is important to repeat testing when a person's condition has improved, if a sample received was not of good quality, or if an abnormal test result needs to be confirmed. Diagnostic ways for cancer may involve imaging, laboratory tests (including inspections for tumor markers), tumor biopsy, endoscopic examination, surgery, or genetic testing. Cancer diagnosis begins with a physical checkup. The detection of certain biomarkers and proteins that are common in cancer disorders thereby, results in the diagnosis process. Remarkable another process of detecting cancer includes the usage of certain technology and devices particularly used in its diagnosis.

Abstract

HANCOCK is a comprehensive, monocentric dataset of 763 head and neck cancer patients, including diverse data modalities. It contains histopathology imaging (whole-slide images of H&E-stained primary tumors and tissue microarrays with immunohistochemical staining) alongside structured clinical data (demographics, tumor pathology characteristics, laboratory blood measurements) and textual data (de-identified surgery reports and medical histories). All patients were treated curatively, and data span diagnoses from 2005–2019. This multimodal collection enables research into integrative analyses – for example, combining histologic features with clinical parameters for outcome prediction. Early analyses have demonstrated that fusing these modalities improves prognostic modeling compared to single-source data, and that leveraging histology with foundation models can enhance endpoint prediction​. HANCOCK aims to facilitate precision oncology studies by providing a large public resource for developing and benchmarking multimodal machine learning methods in head and neck cancer.

Introduction

Head and neck cancer (HNC) is a prevalent malignancy with poor outcomes – it is the 7th most common cancer globally and carries a 5-year survival of only ~25–60% despite modern treatments​. Improving patient prognosis may require personalized, multimodal therapy decisions, using information from pathology, clinical, and other data sources​. However, progress in multimodal prediction has been limited by the lack of large public datasets that integrate these diverse data types​. To our knowledge, existing HNC datasets are either small or incomplete; for example, a radiomics study included 288 oropharyngeal cases​, and a proteomics-focused set with imaging had only 122 cases​. The Cancer Genome Atlas (TCGA) provides multi-omics for >500 HNC cases, but lacks crucial data like pathology reports, blood tests, or comprehensive imaging for each patient​. These limitations hinder robust multimodal research​.

HANCOCK was created to address this gap​. It aggregates 763 patients’ data from a single academic center, capturing a real-world, uniformly treated cohort. The dataset uniquely combines whole slide histopathology images, tissue microarray images, detailed clinical parameters, pathology reports, and lab values in one resource​​. By curating and harmonizing these modalities, HANCOCK enables researchers to explore complex data interdependencies and develop multimodal predictive models. The patient population reflects typical HNC demographics – 80% male, median age 61, with 72% being former or current smokers​ – aligning with expected epidemiology​ and supporting generalizability. In summary, HANCOCK is an unprecedented multimodal HNC dataset that can fuel research in machine learning, prognostic biomarker discovery, and integrative oncology, ultimately advancing personalized head and neck cancer care.

Methods

The following sections describe how the HANCOCK data were collected, processed, and prepared for public sharing.

Subject Inclusion and Exclusion Criteria

Patients included in HANCOCK were those diagnosed with head and neck cancer between 2005 and 2019 at University Hospital Erlangen (Germany) who underwent a curative-intent initial treatment (surgery and/or definitive therapy)​. This encompasses cancers of the oral cavity, oropharynx, hypopharynx, and larynx​. Patients treated palliatively or with recurrent/metastatic disease at presentation were excluded to focus on first-course, curative treatments. The cohort consists of 763 patients (approximately 80% male, 20% female) with a median age of 61 years​. Notably, ~72% have a history of tobacco use​, which is consistent with real-world HNC risk factors. The distribution of tumor subsites and stages reflects typical HNC presentation, and thus the dataset is broadly representative of the general HNC patient population​. Being a single-center dataset, there is limited geographic diversity; however, the homogeneous data acquisition and treatment context reduce variability in data quality. No significant selection biases were introduced aside from the exclusion of non-curative cases – all major HNC subsite cases over the inclusion period were captured, providing a comprehensive real-world sample. Ethical approval was obtained for this retrospective data collection and sharing (Ethics Committee vote #23-22-Br), and all data were fully de-identified prior to release.

Data Acquisition

Histopathology: Tissue specimens from the primary tumors (and involved lymph nodes, if present) were obtained from the pathology archives. All samples were formalin-fixed and paraffin-embedded (FFPE) and stained with hematoxylin and eosin (H&E) following routine protocols​. Digital whole-slide imaging was performed on these histology slides. A total of 709 H&E slides of primary tumor tissue (701 patients had one slide, 8 patients had two slides) were scanned at high resolution using a 3DHISTECH P1000 scanner at an effective 82.44× magnification (0.1213 µm/pixel). Additionally, 396 H&E slides of lymph node metastases were scanned, using two systems: an Aperio Leica GT450 at 40× (0.2634 µm/pixel) and the 3DHISTECH P1000 at ~51× (0.1945 µm/pixel). (Multiple scanners were utilized over the course of the project; all resulting images were cross-verified for quality.) The digital whole slide images (WSIs) are provided in the pyramidal Aperio SVS format, a TIFF-based format compatible with standard viewers.

In addition to full slides, tissue microarrays (TMAs) were constructed from each patient’s tumor block to sample important regions. For each case, two cylindrical core biopsies (diameter 1.5 mm) were taken – one from the tumor center and one from the invasive tumor front. These cores were assembled into TMA blocks and stained on separate slides with a panel of eight stains: H&E plus immunohistochemical (IHC) markers targeting various immune cells and tumor biomarkers. The IHC markers include CD3, CD8, CD56, CD68, CD163, PD-L1, and MHC-1, which label T cells (CD3, CD8), natural killer cells (CD56), monocytes/macrophages (CD68, CD163), and a tumor immune checkpoint ligand (PD-L1), as well as MHC class I expression. Each core appears on up to 8 stained TMA slides (one per stain), yielding up to 16 TMA images per patient (two cores × eight stains). In the dataset, TMA images are provided for both the tumor-center and tumor-front cores; these too are digitized high-resolution images (consistent microscope settings, ~40×). The combination of WSIs and TMAs yields a rich imaging dataset: 701 patients have at least one primary tumor WSI (62 patients lack WSIs due to unavailable tissue), and all patients have TMA core images unless the tumor block was exhausted. This imaging data offers both broad tissue context from WSIs and targeted cellular detail from TMAs. Manual tumor region annotations are also included for the primary tumor WSIs (see Data Analysis below).

Clinical and Pathology Data: A wide array of non-imaging data was extracted from hospital information systems and pathology reports for each patient. Key demographic variables (age, sex, etc.) and tumor pathology details were collected, including primary tumor site, histologic subtype, grade, TNM stage, resection margin status, depth of invasion, perineural and lymphovascular invasion, and nodal metastasis status. These pathology parameters were recorded in a structured format for each case​​. Standard clinical coding systems were used where applicable: e.g., diagnoses are coded with ICD-10 codes and procedures with OPS codes (the German procedure classification system)​. The dataset includes these codes for each patient’s conditions and treatments. Comprehensive laboratory blood test results at diagnosis or pre-treatment were also compiled, covering complete blood counts, coagulation measures, electrolytes, kidney function, C-reactive protein, and other relevant analytes. Reference ranges for each lab parameter are provided alongside the values to indicate whether a result was normal or abnormal. Most patients have a full panel of these lab results, though some values are missing if a test was not clinically indicated; the dataset notes availability per patient. All structured data have been cleaned and validated – for example, harmonizing category values and checking consistency (e.g. TNM stages align with recorded tumor sites).

Textual Data (Surgical Reports and Histories): Unstructured clinical text was also included to add rich context on treatment details. Surgery reports (operative notes) from the primary tumor resection and associated medical history summaries were retrieved from the hospital’s electronic records. For each patient, the operative report from their first definitive surgery and the corresponding

This dataset contains real-world information about colorectal cancer cases from different countries. It includes patient demographics, lifestyle risks, medical history, cancer stage, treatment types, survival chances, and healthcare costs. The dataset follows global trends in colorectal cancer incidence, mortality, and prevention.

Use this dataset to build models for cancer prediction, survival analysis, healthcare cost estimation, and disease risk factors.

Dataset Structure Each row represents an individual case, and the columns include:

Patient_ID (Unique identifier) Country (Based on incidence distribution) Age (Following colorectal cancer age trends) Gender (M/F, considering men have 30-40% higher risk) Cancer_Stage (Localized, Regional, Metastatic) Tumor_Size_mm (Randomized within medical limits) Family_History (Yes/No) Smoking_History (Yes/No) Alcohol_Consumption (Yes/No) Obesity_BMI (Normal/Overweight/Obese) Diet_Risk (Low/Moderate/High) Physical_Activity (Low/Moderate/High) Diabetes (Yes/No) Inflammatory_Bowel_Disease (Yes/No) Genetic_Mutation (Yes/No) Screening_History (Regular/Irregular/Never) Early_Detection (Yes/No) Treatment_Type (Surgery/Chemotherapy/Radiotherapy/Combination) Survival_5_years (Yes/No) Mortality (Yes/No) Healthcare_Costs (Country-dependent, $25K-$100K+) Incidence_Rate_per_100K (Country-level prevalence) Mortality_Rate_per_100K (Country-level mortality) Urban_or_Rural (Urban/Rural) Economic_Classification (Developed/Developing) Healthcare_Access (Low/Moderate/High) Insurance_Status (Insured/Uninsured) Survival_Prediction (Yes/No, based on factors)