cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/3kq0-yk19
title: Serial Non-contrast Non-gated T2w MRI Datasets of Patient Derived Xenograft Cancer Models for Development of Tissue Characterization Algorithms (PDMR-Texture Analysis)

description: This collection contains serial non-contrast non-gated T2w MRI of 18 patient derived xenograft cancer models. 175 mice were imaged at multiple time points (514 total studies) for researchers to develop algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes) for the improvement in patient care through advances in precision medicine.
Characterization of tissue using non-invasive in vivo imaging techniques is used for detection and measurement of disease burden in oncology. Researchers have developed numerous algorithms, such as neural networks, and classification techniques to improve the characterization (morphological changes) of tissue. Unfortunately, to obtain statistical significance, large datasets are a requirement in this research endeavor due to tumor heterogeneity within the same histologic classification. Pre-clinical patient derived xenograft animal models can be a significant resource by providing collections with a more homogenous tumor genome across the collection with companion genomic and pathologic characterization available (https://pdmr.cancer.gov/), allowing determination of the variability of imaging characteristics.

This dataset of a patient derived xenograft model (below table) can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and cancer model.
DOI: 10.7937/6rda-p940
title: Proteogenomic analysis of chemo-refractory high grade serous ovarian cancer (PTRC-HGSOC)

description: In our study, we have generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiles from high grade serous ovarian cancer (HGSOC) tumor biopsies. All biospecimens are formalin-fixed, parrafin-embedded (FFPE) tissues and annotated for patient sensitivity to platinum chemotherapy (refractory or sensitive). For all 174 tumors that were analyzed, we have H and imaged the first and last sections (“bookend”) cut from each FFPE block to allow study of tumor pathology. These H pathology images are uploaded in this dataset. The bookend FFPE slides were cut at 4 μm thickness using a microtome and mounted on glass slides (Leica Biosystems Cat# 3800040) for H staining. Digital images of the H slides were recorded using a ScanScope AT Slide Scanner (Leica Aperio Technologies, Vista, CA, USA) under 20X objective magnification (0.5 μm resolution). Images were analyzed by HALO Image Analysis Platform software (Indicta Labs, Albuquerque, New Mexico, USA).
DOI: 10.7937/e65c-am96
title: HER2 and trastuzumab treatment response H&E slides with tumor ROI annotations

description: The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations. Here, we are providing the datasets used in the study to facilitate development of other HER2+ diagnosis and trastuzumab response applications.
DOI: 10.7937/k9/tcia.2015.8wg2kn4w
title: Data From RIDER PHANTOM PET-CT

description: The RIDER Phantom PET-CT collection consists of repeat measurement PET/CT phantom scan collections carried out under the aegis of the Society of Nuclear Medicine (SNM) to discern the uniformity of clinical imaging instrumentation at various sites. They were obtained in cooperation with SNM as a resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing. The phantom was manufactured by Sanders Medical(www.sandersmedical.com) in December of 2006. The phantom was based on a NEMA NU-2 IQ phantom (manufactured by Data Spectrum, Durham NC), but with the central 5 cm diameter 'lung' cylinder of the IQ phantom removed. In addition the two larger fillable spheres were changed to hot spheres, as opposed to cold spheres as in the NEMA NU-2 specifications. Nominal target/background ratio was 4:1 with the initial background activity level set to be equivalent to 15 mCi in a 70 Kg patient, With the 271 day half-life of Ge-68 after 6 months the activity will be about 9.5 mCi. After a year it was 6 mCi.
DOI: 10.7937/k9/tcia.2015.u1x8a5nr
title: Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters
title: RIDER Lung CT

description: The RIDER Coffee-break Lung CT (RIDER Lung CT) collection was constructed as part of a study to evaluate the variability of tumor size measurements on same-day repeat CT scans in patients with non–small cell (NSCLC) lung cancer. Patients with NSCLC receiving systemic therapy consented to this Institutional Review Board (IRB)–approved prospective repeat CT can study (ClinicalTrials.gov identifier NCT00579852). Patients were accrued in the clinic by their treating oncologist.

 Thirty-two NSCLC patients, each of whom underwent two CT scans of the chest within 15 minutes by using the same imaging protocol, were included in this study. Each scan was reconstructed at 6 imaging settings, comprising 3 slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and 2 reconstruction algorithms (lung, standard).
DOI: 10.7937/k9/tcia.2016.7o02s9cy
title: Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM)
title: CBIS-DDSM

description: This CBIS-DDSM (Curated Breast Imaging Subset of DDSM) is an updated and standardized version of the Digital Database for Screening Mammography (DDSM). The DDSM is a database of 2,620 scanned film mammography studies. It contains normal, benign, and malignant cases with verified pathology information. The scale of the database along with ground truth validation makes the DDSM a useful tool in the development and testing of decision support systems. The CBIS-DDSM collection includes a subset of the DDSM data selected and curated by a trained mammographer. The images have been decompressed and converted to DICOM format. Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included. Published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. Few well-curated public datasets have been provided for the mammography community. These include the DDSM, the Mammographic Imaging Analysis Society (MIAS) database, and the Image Retrieval in Medical Applications (IRMA) project. Although these public data sets are useful, they are limited in terms of data set size and accessibility. For example, most researchers using the DDSM do not leverage all its images for a variety of historical reasons. When the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. Additionally, the DDSM images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction. This causes an inability to directly compare the performance of methods or to replicate prior results. The CBIS-DDSM collection addresses that challenge by publicly releasing an curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography.
DOI: 10.7937/k9/tcia.2016.9zfrvf1b
title: The Cancer Genome Atlas Thyroid Cancer Collection (TCGA-THCA)

description: The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) 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.
DOI: 10.7937/k9/tcia.2016.eln8ygle
title: Data from 4D Lung Imaging of NSCLC Patients

description: This data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. The images include four-dimensional (4D) fan beam (4D-FBCT) and 4D cone beam CT (4D-CBCT). All patients underwent concurrent radiochemotherapy to a total dose of 64.8-70 Gy using daily 1.8 or 2 Gy fractions. 4D-FBCT images were acquired on a 16-slice helical CT scanner (Brilliance Big Bore, Philips Medical Systems, Andover, MA) as respiration-correlated CTs with 10 breathing phases (0 to 90%, phase-based binning) and 3 mm slice thickness. 4D-FBCT images were acquired during simulation, prior to therapy, and used for therapy planning. In 14 of the 20 subjects, 4D-FBCTs were also acquired on the same scanner weekly during therapy. 4D-CBCT images were acquired on a commercial CBCT scanner (On-Board Imager™, Varian Medical Systems, Inc.). An external surrogate (Real-time Position Management, Varian Medical Systems, Inc.) was integrated into the CBCT acquisition system to stamp each CBCT projection with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with half bow-tie filter. The technique was 125 kVp, 20 mA, and 20 ms in a single 360° slow gantry arc. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases (0 to 90%, phase-based binning) and reconstructed with an in-house FDK reconstruction algorithm. Audio-visual biofeedback was performed for all 4D-FBCT and 4D-CBCT acquisitions in all subjects. A single Radiation Oncologist delineated targets and organs at risk in all 4D-FBCT and a limited number of 4D-CBCT images, on all 10 phases per scan. Seven of the subjects had gold coils implanted as fiducial markers in or near the tumor.
DOI: 10.7937/k9/tcia.2016.hjjhboxz
title: The Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD)

description: The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) 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.
DOI: 10.7937/t062-8262
title: Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

description: This is a sample collection of synthetic 3D digital reference objects (DROs) intended for standardization of quantitative imaging feature extraction pipelines. We have developed a software toolkit for the creation of DROs with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. This collection includes objects with a range of values for the various feature categories and many combinations of these categories.
DOI: 10.7937/tcia.2019.of2w8lxr
title: SN-CanData: White Blood Cancer Dataset of B-ALL and MM for Stain Normalization

description: Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with B-lineage acute lymphoid leukemia and multiple myeloma as per the standard guidelines. Slides were stained using Jenner-Giemsa and H&E stains. Images were captured at 1000x magnification using Nikon Eclipse-200 microscope equipped with a digital camera. Images were captured in raw BMP format with a size of 2560x1920 pixels. In all, this dataset consists of 90 images of B-ALL, 100 images of MM, and 116 images of H&E. Both MM and B-ALL images have sufficient variability from one image to another image to rigorously test any stain normalization methodology developed.
DOI: 10.7937/tcia.nbb4-4655
title: PROSTATEx Zone Segmentations

description: This collection contains prostate’s zonal segmentation for 98 cases randomly selected from the SPIE-AAPM-NCI PROSTATEx Challenge. The four-class segmentation encompasses the peripheral zone, transition zone, fibromuscular stroma and the distal prostatic urethra. As underlying images, we used transversal T2w scans.  Segmentations were created by a medical student with experience in prostate segmentation and an expert urologist who instructed the student and double-checked the segmentations in the end. The DICOM representation of these segmentations were generated with dcmqi.
DOI: 10.7937/tcia.rmv0-9y95
title: QIBA Anthropomorphic Abdominal Phantom CT Scans

description: This database contains a collection of three sets of CT scan images acquired from an anthropomorphic abdominal phantom with removable liver inserts. The anthropomorphic phantoms were designed by a group of scientists from FDA and Columbia University Medical Center and custom manufactured by QRM (Moehrendorf, Germany). Data sets #1 (AP phantom) & #2 (PVP phantom): Two liver inserts, each containing 19 embedded synthetic lesions with known volumes of varying diameter (6–40 mm), shape, contrast (10–65 HU), and density (homogenous and mixed) were designed to have liver parenchyma and lesion CT values simulating arterial phase (AP phantom) and portal venous phase (PVP phantom) imaging, respectively. The two phantoms were scanned using two 64-slice multi-detector helical CT scanners (GE 750HD and Siemens Biograph mCT) across a wide range of abdominal imaging protocols, including three eﬀective mAs (50, 100, 250), two pitches (GE/Siemens: 1.375 and 0.983/1.35 and 1.0), four slice thicknesses (GE/Siemens: 0.625, 1.25, 2.5, and 5 mm/0.6, 1.5, 3, and 5 mm), and two convolution kernels (GE/Siemens: Standard and Soft/B20f and B30f). Two repeated scans were performed for each protocol and for each phantom. Dataset #3 (IR phantom): One non-uniform liver insert was filled with an in-house made gelatin-based background, and varied concentrations of gelatin, salt, and water were used to mimic normal liver tissue and focal fat at the arterial phase. Ten spherical lesions of five sizes (20, 14, 10, 7, and 5 mm in diameter) and two radiodensities (95 and 110 HU) were placed on the border between the fatty and normal parenchyma. The phantom was imaged with a CT scanner (GE 750HD) across a set of imaging protocols, including three eﬀective mAs (50, 100, 250), three slice thicknesses (1.25, 2.5, and 5 mm) and five reconstruction algorithms (standard, AISR30%, AISR50%, ASIR70%, and VEO). Five repeated scans were performed for each protocol.
DOI: 10.7937/tcia.xc7a-qt20
title: Expert tumor annotations and radiomic features for the ISPY1/ACRIN 6657 trial data collection

description: This dataset enhances the ISPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. This dataset includes a) uniformly processed scans that are harmonized to match the intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions.

The segmentations for the ISPY1/ACRIN 6657 dataset currently hosted on TCIA’s website describe a) the tumor volume of interest (VOI) and b) functional tumor volume (FTV).

The provided tumor VOI is a 3D rectangular box enclosing the enhancing tumor region, while including peritumoral tissue. The VOI provides a general guideline of where the tumor is located within patient anatomy, but it does not delineate tumor boundaries or shape.
The FTV segmentations describe only enhancing voxels in the tumor, i.e., defined by peak enhancement or signal enhancement ratio criteria.
These currently provided segmentations do not include non-enhancing portions of the tumor volume, which represent a significant portion of the disease burden that needs to be studied to better understand and quantify the disease.

The segmentations in these new analysis results are for the entire 3D primary lesion, including both the enhancing and the non-enhancing tumor regions, therefore defining the structural tumor volume (STV). These STV annotations were generated by manually delineating the primary lesion volume, after confirming the location of the primary lesion from the provided VOI and FTV. The STV annotations were reviewed and approved by a board-certified, fellowship-trained breast radiologist, and are statistically significantly different from FTV.

We believe these STV annotations will allow analyses of the entire disease burden and analyses of tumor heterogeneity regarding contrast uptake, contributing to further expanding our mechanistic understanding of the disease potentially leading to improved patient management.
DOI: 10.7937/tcia.yzwq-zz63
title: The Clinical Proteomic Tumor Analysis Consortium Colon Adenocarcinoma Collection (CPTAC-COAD)

description: This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium CPTAC Colon Adenocarcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.
Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype", and is being made available on a release schedule that is coordinated with the CPTAC program releases of proteomic and genomic data.  A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page. 
Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.  For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.
DOI: 10.7937/wtec-mn22
title: Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer (ARAR0331)

description: This collection contains data from the Children’s Oncology Group (COG) Clinical Trial NCT00274937, “Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer". It was sponsored by NCI and performed by the Children's Oncology Group under study number ARAR0331. This phase III trial is studying how well radiation therapy, amifostine, and chemotherapy work in treating young patients with newly diagnosed nasopharyngeal cancer. Radiation therapy uses high-energy x-rays to kill tumor cells. Drugs, such as amifostine, may protect normal cells from the side effects of radiation therapy. Drugs used in chemotherapy, such as cisplatin and fluorouracil, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Giving radiation therapy together with amifostine and chemotherapy may kill more tumor cells.
DOI: 10.7937/z1k6-vd17
title: Hyperspectral Histological Images for Diagnosis of Human Glioblastoma (HistologyHSI-GB)

description: Hyperspectral imaging technology combines the main features of imaging and spectroscopy, allowing simultaneous analysis of the morphological features and chemical composition of samples in a non-contact way. The information provided by hyperspectral imaging can be used to detect patterns, cells, or biomarkers to identify diseases. This dataset is composed of hyperspectral histological images of glioblastoma brain tumors, which can be used by the research community to analyze the potential of hyperspectral images for diverse histological applications. The dataset contains 469 annotated hyperspectral images corresponding to 13 histological slides (1 per patient). The hyperspectral images are captured with a 20× magnification in the spectral range from 400 – 1000 nm. Each hyperspectral image has been annotated based on the diagnosis of the slides by skilled histopathologists into the categories of glioblastoma and no tumor according to the World Health Organization classification of tumors of the nervous system.
DOI: 10.7937/bh0r-y074
title: Highly multiplexed spatially resolved immune cell atlas of hepatocellular carcinoma (CODEX imaging of HCC)

description: Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity.