Clinically annotated database of all patients seen in the Oncology Dept and/or presented at a Cancer Multidisciplinary meeting at SVHM. Details pertaining to the diseasse, treatment and outcome are included.

IntroductionCutaneous angiosarcoma (cAS) is an aggressive vascular tumor that originates from vascular or lymphatic epithelial cells. To date, the cAS literature has been limited in a small number with single-center experiences or reports due to its rarity and the optimal treatment strategy is still in dispute. This study aimed to conduct a systematic review and compare the effect of available treatments retrieved from observational studies and Surveillance, Epidemiology, and End Results (SEER) program.MethodsThe authors performed a systematic review in the PubMed, Embase and MEDLINE database identifying the researches assessing the treatment for cAS patients. Clinical and treatment information of patients who had been diagnosed with a primary cAS were also obtained from the SEER program.ResultsThirty-two studies were eligible but only 5 of which with 276 patients were included in meta-analysis since the unclear or unavailable information. The risk ratio of 5-year death for surgery, surgery with radiotherapy and surgery with chemotherapy were 0.84, 0.96, and 0.69. Meanwhile, in SEER database, there are 291 metastatic and 437 localized patients with cAS. The localized patients receiving surgery showed a significantly worse overall survival result when compared with the surgery combined with RT: hazard ratio: 1.6, 95% confidential interval: 1.05, 2.42, P = 0.03.ConclusionIn conclusion, our study provided a detailed picture of the effectiveness of present treatments for localized and metastatic cAS patients. The CT could be inappropriate in localized patients. For metastatic patients, the surgery combined RT was recommended compared with surgery alone since its enhanced OS prognosis. Yet, more novel-designed clinical trials with specific targeted populations and rigorous conducting are needed for a solid conclusion on which would be a better treatment strategy.

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.

Dataset on chemotherapeutic drug responses in TCGA cancer patients, cross-referenced for a hit in TCIA.at database, consisting of clinical (TCGA), cancer tissue gene-expression (TCGA) and tumor-immunome (TCIA) features. The dataset consists of 5 common chemotherapy agents, 3 CRC agents (FOLFOX, 5FU, Oxaliplatin) and 2 Lung agents (Carboplatin, Cisplatin). FOLFOX as a combinational therapy or regimen, was compiled from timings of monotherapies given to patients and as such is a novel dataset derived from TCGA data. FOLFOX dataset is primarily firstline treatment, while other drugs are not to be interpreted as firstline treatments. Drug datasets are individually available in own CSV files.

Citation
Dalibor Hrg, Balthasar Huber, Lukas A. Huber. (2020). TCGA Chemotherapy Response Dataset. Zenodo. http://doi.org/10.5281/zenodo.3719291

The results here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

License
CC BY-SA 4.0 International https://creativecommons.org/licenses/by-sa/4.0. Authors take no liability for any use of this data.

Contributions
D. Hrg and B. Huber acknowledge major and equal work effort: data understanding, data science and dataset preparation (monotherapies and FOLFOX); L. A. Huber: help with dictionary of drug names and curration/cleaning of FOLFOX entries, clinical validation.

Contact & Maintenance
dalibor.hrg@gmail.com
dalibor.hrg@i-med.ac.at

IntroductionWe conducted an extensive, sex-oriented real-world data analysis to explore the impact and safety of non-steroidal anti-inflammatory drugs (NSAIDs) and selective COX-2 inhibitors (coxibs) on cancer treatment outcomes. This is particularly relevant given the role of the COX-2/PGE2 pathway in tumor cell resistance to chemotherapy and radiotherapy.MethodsThe study applied a retrospective cohort design utilizing the TriNetX research database consisting of patients receiving cancer treatment in 2008-2022. The treated cohorts included patients who were prescribed with coxibs, aspirin or ibuprofen, while individuals in the control cohort did not receive these medicines during their cancer treatment. A 1:1 propensity score matching technique was used to balance the baseline characteristics in the treated and control cohorts. Then, Cox proportional hazards regression and logistic regression were applied to assess the mortality and morbidity risks among patient cohorts in a 5-year follow-up period.ResultsUse of coxibs (HR, 0.825; 95% CI 0.792-0.859 in females and HR, 0.884; 95% CI 0.848-0.921 in males) and ibuprofen (HR, 0.924; 95% CI 0.903-0.945 in females and HR, 0.940; 95% CI 0.917-0.963 in males) were associated with improved survival. Female cancer patients receiving aspirin presented increased mortality (HR, 1.078; 95% CI 1.060-1.097), while male cancer patients also had improved survival when receiving aspirin (HR, 0.966; 95% CI 0.951-0.980). Cancer subtype specific analysis suggests coxibs and ibuprofen correlated with survival, though ibuprofen and aspirin increased emergency department visits’ risk. Secondary analyses, despite limited by small cohort sizes, suggest that COX inhibition post-cancer diagnosis may benefit patients with specific cancer subtypes.DiscussionSelective COX-2 inhibition significantly reduced mortality and emergency department visit rates. Further clinical trials are needed to determine the optimal conditions for indication of coxibs as anti-inflammatory adjuvants in cancer treatment.

The Edinburgh Ovarian Cancer Database was founded by Professor John Smyth in 1984 with the main aim of tracking the disease course of every ovarian cancer patient in the South-East of Scotland (Lothian, Fife, Borders and Dumfries and Galloway). Clinical, pathological, genetic, surgical and treatment information is recorded. The database tracks the patient’s disease course including therapies, responses to treatment, progression episodes, radiological investigations, tumour marker results and ultimately cause of death. It has been and continues to be a huge resource for retrospective research, sample collection and uniform prospective data collection. The data helps identify patients suitable for particular therapy options and clinical trials. There are over 4500 patients documented to date. Data is curated by a team of 2 data managers who source data from patient case notes, electronic patient records, SCI-Store, APEX, the Scottish morbidity registers and from Scotland’s genetic services. Going forward some areas of the database will be populated using automated feeds from various national, regional and bespoke databases and EPRs.

Established in 2019, the Ovarian Cancer Database builds on 40 years of data collection across the region of the South East Scotland Cancer Network. The database holds data on diagnosis, treatment and outcomes of patients undergoing care within the region.

Global Cancer Registry Software Market size valued at US$ 85.14 Million in 2023, set to reach US$ 204.07 Million by 2032 at a CAGR of about 10.2% from 2024 to 2032.

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.

The Cancer in Young People in Canada (CYP-C) program maintains a national childhood cancer surveillance and research database that is available to researchers seeking to improve cancer diagnosis, treatment, and outcomes.

Raw data and edited data on the observational CICARO-study on chemotherapy-induced cognitive impairment and its long-term development in patients with breast cancer

Neocryptolepine is a natural alkaloid isolated from the African climbing plant Paeonia lactiflora, belonging to the indole quinoline alkaloid class. This compound has become a natural precursor widely studied by medicinal chemists due to its diverse biological activities, especially its potential applications in anti-tumor, anti-inflammatory, anti malaria and other fields. As a natural product with multiple biological activities,Neocryptolepine has great potential in cancer treatment research. Through in-depth research and development of the Neocryptolepine, it may provide new treatment options for cancer patients in the future.Cancer, as a global health challenge, has long plagued the medical community and patients. It is a disease caused by the unlimited proliferation, invasion, and metastasis of abnormal cells, which can affect any part of the human body. With the change of lifestyle, the aggravation of environmental pollution and the trend of aging population, the incidence rate of cancer has increased year by year and has become the second leading cause of death in the world. Despite its enormous potential in cancer treatment, the diversity, mechanisms, and unknown targets of action make it extremely challenging to obtain Neocryptolepine anti-cancer pathways from it. In addition, it is difficult to search for systematic information on anti-cancer Neocryptolepine from a large amount of information such as the internet. Neocryptolepine derivatives, as a natural compound, have shown great potential and diversity in cancer treatment. Despite facing challenges in screening and utilization, they remain important resources for drug development.In order to construct the NDADS database, authoritative literature search websites such as Pubmed and Google Scholar were used to systematically collect key information on the generic name, anti-tumor activity, cancer type, mechanism of action, and targets of Neocryptolepine and its derivatives using keywords such as Neocryptolepine, Cancer, and Target. On this basis, all data were integrated and included in the data of 85 Neocryptolepine derivatives in the laboratory, ultimately forming a database containing information on 203 anti-tumor compounds derived from Neocryptolepine derivatives. In order to integrate and evaluate numerous research resources and results, the Neocryptolepine derivatives anti-tumor database can provide rich retrieval and analysis tools, such as cross database retrieval, citation retrieval, journal retrieval, etc., enabling users to easily search for anti-tumor related information of Neocryptolepine derivatives. Supplement the current inclusion status, covering the names, structures, molecular weights, activities, functions, cancer types, cancer cells, targets/signaling pathways, references, and corresponding website sources of various compounds. This interface supports the query function for the content of the Neocryptolepine derivatives mentioned above. Therefore, the anti-tumor database of the Neocryptolepine derivatives will help to study the potential of Neocryptolepine derivatives in the treatment of cancer from multiple aspects such as activity, structure, method of action, and target, assisting in cancer treatment and improving cancer survival rate.

The Cancer Registry Software Market is segmented by type, deployment model, database type, functionality, and end user.Type:Integrated SoftwareStandalone SoftwareDeployment Model:Cloud-BasedOn-PremiseDatabase Type:CommercialPublicFunctionality:Patient Care ManagementProduct Outcome EvaluationCancer Reporting to Meet State, Federal RegulationsMedical ResearchClinical StudiesEnd User:HospitalsMedical PracticesGovernment OrganizationsResearch CentersPharmaceutical, Biotechnology, Medical Device CompaniesOthers Recent developments include: In May 2022, Veeva Systems has declared that Lucid Diagnostics Inc., a commercial-stage, which is a cancer prevention medical diagnostics company with an owned subsidiary of PAVmed Inc., has selected Veeva Vault CDMS for capturing electronic data, coding, and data cleaning in the upcoming research EsoGuard in patients who are undergoing standard for, and management of, esophageal adenocarcinoma., In May 2022, Taipei Veteran’s General Hospital is a first-class medical center and teaching hospital in Taiwan. TVGH and a major electronics brand joined forces in AI-based medicine for intensive care of early warning systems for shock and an individualized cancer treatment system., Together, the hospital-private sector collaboration declared their result in the field of AI-based precision medicine. They established a warning system for shock in intensive care patients. In addition, the collaboration has led to the analysis of patients with hepatobiliary cancer, rectal, and gastrointestinal. It is used to predict a patient’s disease duration and treatment outcomes. . Notable trends are: The rapid adoption of digitalization in healthcare to drive market growth.

Cancer Registry Software Market size was valued at USD 71.35 Million in 2023 and is projected to reach USD 156.71 Million by 2031, growing at a CAGR of 11.4% from 2024 to 2031.

Key Market Drivers

• Rising Global Cancer Incidence: The rising global prevalence of cancer is a major driver of the cancer registry software market. According to the World Health Organization (WHO), cancer is the biggest cause of death worldwide, accounting for approximately 10 million deaths in 2020. The International Agency for Research on Cancer (IARC) predicts that the worldwide cancer burden will reach 28.4 million cases in 2040, up 47% from 2020. To efficiently track and manage cancer data, sophisticated registry systems are required given the expanding cancer burden.

Abstract

This dataset includes MR imaging from 203 glioma patients with 617 different post-treatment MR time points, and tumor segmentations. Clinical data includes patient demographics, genomics, and treatment details. Preprocessing of MR images followed a standardized pipeline with automatic tumor segmentation based on nnUNet deep learning approach. The automatic tumor segmentations were manually validated and refined by neuroradiologists.

The heterogeneity of glioma imaging characteristics and management strategies contributes to a lack of reliable findings when evaluating treatment outcomes with conventional MRI, and the overlapping imaging features of radiation necrosis and tumor progression post-treatment can be particularly challenging for radiologists. This robust dataset should contribute to the development of AI models to improve evaluation of treatment outcomes.

Introduction

The dataset consists of institutional review board-approved retrospective analysis of pathologically proven glioma patients at University Hospital of The University of Missouri - Anatomic Pathology CoPathPlus database was used to collect glioma cases over the last 10 years.

Sharing segmented postoperative glioma data with clinical information significantly accelerates research and improves clinical practice by providing a comprehensive, readily available dataset. This eliminates the time-consuming burden of manual segmentation, enhances the accuracy and consistency of tumor delineation, and allows researchers to focus on analysis and interpretation, ultimately driving the development of more accurate segmentation algorithms, predictive models for personalized treatment strategies, and improved patient outcome predictions. Standardized longitudinal follow-up and benchmarking capabilities further facilitate multi-center studies and objective evaluation of treatment efficacy, leading to advancements in glioma biology and personalized patient care.

Methods

The following subsections provide information about how the data were selected, acquired, and prepared for publication.

Subject Inclusion and Exclusion Criteria

The selection criteria for the CoPath Natural Language II Search included accession dates ranging from 01/01/2021 to 02/20/2024. To ensure all relevant diagnoses for this study were included; three separate keyword searches were performed using "glioma", "astrocytoma", and "glioblastoma". The search only included keyword results that were present in the Final Diagnoses. "Glioma" returned 85 cases; "Astrocytoma" returned 67 cases; and "Glioblastoma" returned 215 cases. Following the exclusion of duplicate cases, those missing any of the four requisite MR imaging sequences, and cases that failed processing through our pipeline, our final cohort comprised 203 patients.

Data Acquisition

Radiology: MRI studies on our McKesson Radiology 12.2 Picture archiving and communication system (PACS) (Change Healthcare Radiology Solutions, Nashville, Tennessee, U.S) were exported. The image exportation process involved multiple personnels of varying ranks, including medical graduates, radiology residents, neuroradiology fellows, and neuroradiologists. Our team exported the four basic conventional MR sequences including T1, T1 with IV gadolinium-based contrast agent administration, T2, and Fluid Attenuated Inversion Recovery (FLAIR) into a HIPPA compliant MU secured research server.

For each patient, the images were thoroughly checked for including up to six post-treatment images as available. The post-treatment images were captured on different dates, though not all patients had the maximum number of follow-up images; some had as few as one post-treatment follow-up MRI. For patients with more frequent follow-up MRIs, the immediate post-operative scan, at least one time point of progression and another follow-up study. The MR images were comprehensively reviewed to exclude significantly motion degraded or suboptimal studies.

The majority of the studies were conducted using Siemens MRI machines 97.47%, n=579 with a smaller proportion performed on MRI machines from other vendors: GE (2.02%, n=12) and Philips (0.51%, n=3). Table 1 shows the distribution of studies across different Siemens MR machines. Regarding the magnetic field strength, 1.5T MRIs accounted for 48.14% (n=1,126), 3T MRIs accounted for 45.08% (n=318), and 3T MRIs accounted for 45.08% (n=261). Table 2 summarizes the MRI parameters of each MR sequence.

Our team made efforts to obtain 3D sequences whenever available. Scans were performed using 3D acquisition methods in 40.28% of cases (n=975) and 2D acquisition methods in 59.82% of cases (n=1,419). In cases where 3D images were not available, 2D images were utilized instead. Table 3 summarizes the counts and percentage of studies performed with 2D vs 3D acquisition across different MR sequences.

Clinical: Basic demographic data, clinical data points, and tumor pathology were obtained through review of the electronic medical record (EMR). Clinical data points included the date of diagnosis, date of first surgery or treatment, date and characterization of first and/or subsequent disease progression and/or recurrence, and date of any follow-up resections. Survival information included the date of death and, if that was unknown, the date of last known contact while alive. Disease progression and/or recurrence was characterized as imaging only, clinical only, or both based on information obtained through review of each patient’s clinical notes, brain imaging, and clinical impression as documented by the primary care team. Brief summaries of the reasoning behind each characterization were also included. Patients with no further clinical contact beyond their primary treatment were documented as “lost to follow-up.” Pathological information was obtained through review of the initial pathology note and any subsequent addenda for each tumor sample and included final tumor diagnosis, grade, and any identified genetic mutations. This information was then compiled into a spreadsheet for analysis.

Data Analysis

The image data underwent preprocessing using the Federated Tumor Segmentation (FeTS) tool. The pipeline began with converting DICOM files to the Neuroimaging Informatics Technology Initiative (NIfTI) format, ensuring the removal of any remaining PHI not eliminated by the anonymization/de-identification tool. The converted NIfTI images were then resampled to an isotropic 1mm³ resolution and co-registered to the standard anatomical human brain atlas, SRI24. A deep learning brain extraction method was applied to strip the skull and extracranial tissues, thereby mitigating any potential facial reconstruction or recognition risks.

The preprocessed images were segmented using a deep network based on nnU-Net, resulting in four distinct labels that correspond to different components of each tumor:

• Label 1: Non-enhancing Tumor Core (NETC). This label identifies non-enhancing components within the tumor, such as cystic, necrotic, or hemorrhagic portions.
• Label 2: Surrounding Non-enhancing FLAIR Hyperintensity (SNFH). This label represents both non-enhancing infiltrative tumor components and peritumoral vasogenic edema.
• Label 3: Enhancing Tissue (ET). This label highlights the viable nodular-enhancing components of the tumor.
• Label 4: Resection Cavity (RC). This label covers post-surgical changes, including recent changes like blood products and air foci, as well as chronic changes with materials isointense to CSF signal.

A spreadsheet is also provided that includes tumor volumes and signal intensity of different tumor components across various MR sequences.

Usage Notes

Each scan was manually exported using the built-in McKesson DICOM export tool into separate folders labeled as post-treatment 1, post-treatment 2, etc. In a subsequent step, a subset of the data was selected to contribute for the development of FeTS 2 toolbox. Consequently, the naming convention was updated to replace "post-treatment" with "timepoint" (e.g., post-treatment 1 became timepoint 1) to adhere to the instructions of the FeTS development team. Each sequence was saved in its own folder within these categories to a HIPPA compliant and secured server within the University of Missouri network. Exportation was conducted in DICOM format, maintaining the original image compression settings to preserve quality. To ensure patient privacy and HIPPA compliance, all images were anonymized and all protected health information (PHI) e.g. patient name, MRN, accession number, etc. were deleted from the metadata DICOM headers.

The folders are labeled in the following structure:

• Main folder: PatientID_XXXX
• Subfolders: Timepoint_X, Timepoint_X
• Each time point folder has the NIfTI images associated with the respective timepoints.

An independent, not-for-profit consortium to accelerate research, and improve treatment for patients affected with the most commonly-diagnosed cancers in Asia by generating a genomic data resource for the most prevalent cancers in Asia. ACRG is focusing its initial efforts on Asian liver, gastric and lung cancers. Goals * Generate comprehensive genomics data sets for Asia-prevalent cancers * Conduct all research under good clinical practices and in accordance with local laws * Uncover key mutations and pathways for developing targeted therapies * Discover molecular tumor classifiers for patient stratification * Discover prognostic markers to identify high-risk patients * Freely share resulting raw data with scientific community to empower researchers globally and enable development of new diagnostics and medicines * Publish data analysis results jointly in prominent scientific journals Over the next two years, Lilly, Merck and Pfizer have committed to create an extensive pharmacogenomic cancer database that will be composed of data from approximately 2,000 tissue samples from patients with lung and gastric cancer that will be made publicly available to researchers and, over time, further populated with clinical data from a longitudinal analysis of patients. Comparison of the contrasting genomic signatures of these cancers could inform new approaches to treatment. Lilly has assumed responsibility for ultimately providing the data to the research public through an open-source concept managed by Lilly''''s Singapore research site. Moreover, Lilly, Merck and Pfizer will each provide technical and intellectual expertise. One dataset can be found at http://gigadb.org/dataset/100034

The Veterans Affairs Central Cancer Registry (VACCR) receives and stores information on cancer diagnosis and treatment constraints compiled and sent in by the local cancer registry staff at each of the 132 Veterans Affairs Medical Centers that diagnose and/or treat Veterans with cancer. The information sent is encoded to meet the site-specific requirements for registry inclusion as established by several oversight bodies, including the North American Association of Central Cancer Registries, the American College of Surgeons' Commission on Cancer, and the American Joint Commission on Cancer, among others. The information is obtained from a wide variety of medical record documents at the local medical center pertaining to each Veterans Health Administration (VHA) cancer patient. The information is then transmitted to the VACCR. Details collected include extensive demographics, cancer identification, extent of disease and staging, first course of treatment, and outcomes. Data extraction is available to researchers with VA approved Institutional Review Board studies, peer review, and Data Use Agreements.

The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) 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 CIP TCGA Radiology Initiative.

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

In his transparency and open data letter to Cabinet Ministers on 7 July 2011, the Prime Minister made a commitment to make clinical audit data available from the national audits within the National Clinical Audit and Patient Outcomes Programme. What information is being made available? Audit participation by NHS Trust and network, and data completeness for the key fields. Measures of aspects of the care given to patients. Information about care outcomes and treatment. These data do not list individual patient information, nor do they contain any patient identifiable data. The National Head and Neck Cancer Audit focuses on cancer sites within the head and neck (excluding tumours of the brain and thyroid cancers). It describes a group of cancers (larynx, oral cavity, oropharynx, hypopharynx, nasopharynx, major salivary gland nose and sinuses and cancer of the bones of the jaw) that have many common features but also important differences in biological behaviour. The Head and Neck Cancer Audit database contains a vast amount of information on more than 54,000 head and neck cancer cases, with 7,700 cases of cancer of the glottic larynx, more than 7,500 cases of oral tongue cancer, more than 14,000 cases of larynx cancer, and almost 17,500 cases of oral cavity cancer. The National Head and Neck Cancer Audit 2014, Tenth Annual Report provided information that represents 7875 cases of head and neck cancer from England and 554 cases from Wales. The report, therefore, represents a comprehensive overview of head and neck cancer care, and this supporting dataset contains those England indicators in the main report and supplementary report files. The National Head and Neck Cancer Audit was commissioned and sponsored by the Healthcare Quality Improvement Partnership (HQIP) and developed in partnership with the British Association of Head and Neck Oncologists (BAHNO). The Health and Social Care Information Centre provided project management and technical infrastructure. Accessing the data Each year data from the National Head and Neck Cancer Audit is made available in CSV format. The data are also being made available on the data.gov website. Which level of data is being reported? Trusts and networks within England are included. Trusts and Strategic Clinical Networks / Integrated Cancer Systems are identified by name and their national code. What period does the data cover? The National Head and Neck Cancer Audit 2013-2014 covered the time period from 1st November 2013 to 31st October 2014.