cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/k9/tcia.2015.9p42kse6
title: TCIA Mouse-Mammary Collection

description: This collection consists of magnetic resonance images (MRI) of genetically engineered mouse models (GEMMs) of breast cancer. These images were acquired as part of a Department of Defense (DOD) Breast Cancer Research Program (BCRP) Postdoctoral Award W81XWH-12-1-0307 entitled “Investigating Ductal Carcinoma in Situ Using Noninvasive Imaging of Genetically Engineered Mouse Models A particular emphasis of this project was to study the earliest stages of breast cancer—preinvasive ductal carcinoma in situ (DCIS)—and to interrogate the underlying genetic events that influence progression into invasive disease. In particular, we focused on the role of perturbed Rb, p53 and BRCA1 functionality and how these pathways, acting alone and in combination, can influence the development and progression of DCIS. GEMMs serve as an excellent model system wherein genetic changes can be controlled and manipulated over time. In vivo MRI is a superb technique for noninvasively tracking and characterizing these microscopic early stage cancers as they develop, change and transition into lethal invasive disease.
DOI: 10.7937/k9/tcia.2015.h1sxnuxl
title: RIDER Breast MRI

description: Ideally a patient’s response to neoadjuvant chemotherapy could be observed noninvasively, in the first 2-3 weeks of treatment using an imaging to provide feedback related to the effectiveness of the chosen chemotherapy regimen. This capability would permit individuation of patient care by supporting the opportunity to tailor chemotherapy to a each patient’s response. Functional diffusion mapping (fDM), now called Parametric Response Mapping (PRM) has been proposed as an MRI imaging biomarker for quantifying early brain tumor response to therapy [1-3]. This approach quantifies local apparent diffusion coefficient (ADC) changes in tumors using a voxel-based analysis implemented by rigid registration of the patient’s head between interval exams. The RIDER Breast MRI data set extended this approach by demonstrating ADC changes in 3 of 5 primary breast cancer patients measured in response to onset of neoadjuvant chemotherapy from interval exams separated by only 8-11 days.
DOI: 10.7937/k9/tcia.2015.isoqthko
title: Data From NaF PROSTATE
title: NaF PROSTATE

description: This is a collection of F-18 NaF positron emission tomography/computed tomography (PET/CT) images in patients with prostate cancer, with suspected or known bone involvement. Imaging was performed on a Phillips Gemini TF PET/CT scanner based on 4x4x22mm LYSO (lutetium yttrium orthosilicate) crystal detection elements covering 18cm axial field of view (FOV) and 57cm imaging transaxial FOV. The time of flight resolution is 585ps. The scanner achieves a spatial resolution of 4.8mm at the center of the FOV. Data were reconstructed using the RAMLA iterative OSEM algorithm using 3 iterations and 33 subsets. The scanner uses CT based attenuation correction, along with randoms, normalization, dead time, and a model based scatter correction. The CT component of the scanner is a 16 slice helical CT. The CT images were generated using a low X-ray dose of 120KV, 60mAs setting. The prescribed injected dose was 3mCi IV. Some of the patients had 2 baseline studies within 14 days of each other (with no intervening interventions). Many have follow-up PET/CT imaging performed following therapy (varied) at 6 +/-2months and 12 +/- 2 months.
DOI: 10.7937/k9/tcia.2015.uzlsu3fl
title: SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset

description: As part of the 2015 SPIE Medical Imaging Conference, SPIE – with the support of American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI) – conducted a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of malignant and benign lung nodules. The LUNGx Challenge provides a unique opportunity for participants to compare their algorithms to those of others from academia, industry, and government in a structured, direct way using the same data sets.
10 contrast-enhanced CT scans (5 scans with malignant nodules and 5 with benign nodules, prefix “CT-Training”) are a calibration dataset, representative of the technical properties of the test dataset (60 scans, prefix “LUNGx”). Dataset contains: Images, Nodule locations and 'truth' labels. 
If you use these data outside the LUNGx Challenge please acknowledge the SPIE, the NCI, the AAPM, and The University of Chicago. An article describing the lessons learned is at https://doi.org/10.1117/1.JMI.2.2.020103.  The corresponding scientific manuscript is at https://doi.org/10.1117/1.JMI.3.4.044506 .  For the full data set, including the answers to the test data, see the SPIE-AAPM-NCI Lung Nodule Classification Challenge page.
DOI: 10.7937/k9/tcia.2016.v6pbvtdr
title: The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC)

description: The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) 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.vptnrgfy
title: The Cancer Genome Atlas Esophageal Carcinoma Collection (TCGA-ESCA)

description: The Cancer Genome Atlas Esophageal Carcinoma (TCGA-ESCA) 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.yu3rbczn
title: The Cancer Genome Atlas Kidney Chromophobe Collection (TCGA-KICH)

description: The Cancer Genome Atlas Kidney Chromophobe (TCGA-KICH) 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/tcia.2019.3xbn2jcc
title: Breast Metastases to Axillary Lymph Nodes

description: The detection of breast cancer metastases to lymph nodes is of great prognostic value for patient treatment. Using machine learning to detect metastatic breast cancer to lymph nodes can increase efficiency of pathologist diagnosis and ultimately ensure patients are accurately staged for prospective treatment. This dataset allows for the objective comparison of breast cancer metastases detection algorithms.The dataset consists of 130 de-identified whole slide images of H&E stained axillary lymph node specimens from 78 patients. Metastatic breast carcinoma is present in 36 of the WSI from 27 patients. No patient inclusion/exclusion criteria were followed. No slide inclusion/exclusion criteria were followed. The slides were scanned at Memorial Sloan Kettering Cancer Center (MSKCC) with Leica Aperio AT2 scanners at 20x equivalent magnification (0.5 microns per pixel). Together with the slides, the class label of each slide, either positive or negative for breast carcinoma, is given. The slide class label was obtained from the pathology report of the respective case.
DOI: 10.7937/tcia.2020.pcak-8z10
title: Imaging characterization of a metastatic patient derived model of adenocarcinoma pancreas: PDMR-292921-168-R

description: Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 292921-168-R. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 cm3size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.
DOI: 10.7937/k9/tcia.2015.qjtv5il5
title: Data From Prostate-3T

description: Prostate transversal T2-weighted magnetic resonance images (MRIs) acquired on a 3.0T Siemens TrioTim using only a pelvic phased-array coil were acquired for prostate cancer detection. The data was provided to TCIA as part of an ISBI challenge competition in 2013.
DOI: 10.7937/k9/tcia.2015.zpukhckb
title: QIN PET Phantom

description: This collection consists of positron emission tomography (PET) phantom scans originally utilized by the Quantitative Imaging Network (QIN) PET Segmentation Challenge to assess the variability of segmentations and subsequently derived quantitative analysis results on phantom PET scans with known ground truth. The phantom was provided by Dr. Sunderland at the University of Iowa (supported by grant R01CA169072 - Harmonized PET Reconstructions for Cancer Clinical Trials) and is based on the NEMA IEC Body Phantom SetTM (Model PET/IEC-BODY/P) with a set of 6 custom made (via rapid prototyping) spheres & ellipses (Fig. 1). The phantom was scanned at two QIN sites (University of Iowa and University of Washington) with different scanners, following a protocol that yields four image sets (Fig. 2) per site. The DICOM PET images are organized as shown in Fig. 3. These figures are located in the Detailed Description tab below.
DOI: 10.7937/k9/tcia.2016.6046gudv
title: Data From PROSTATE-MRI

description: This collection of prostate Magnetic Resonance Images (MRIs) was obtained with an endorectal and phased array surface coil at 3T (Philips Achieva). Each patient had biopsy confirmation of cancer and underwent a robotic-assisted radical prostatectomy. A mold was generated from each MRI, and the prostatectomy specimen was first placed in the mold, then cut in the same plane as the MRI. The data was generated at the National Cancer Institute, Bethesda, Maryland, USA between 2008-2010.
DOI: 10.7937/k9/tcia.2016.gdhl9kim
title: The Cancer Genome Atlas Stomach Adenocarcinoma Collection (TCGA-STAD)

description: 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.
DOI: 10.7937/k9/tcia.2017.klxwjj1q
title: Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection
title: BraTS-TCGA-GBM

description: This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) collection, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-GBM collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.
DOI: 10.7937/k9/tcia.2018.sc20fo18
title: The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA)
title: CPTAC-PDA

description: This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) 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 Phase 3 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. CPTAC Phase 3 collects data from ten cancer types. In TCIA, imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype". CPTAC Phase 3 Imaging data is made available on TCIA each quarter as it is collected. 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/tcia.2019.bvhjhdas
title: Osteosarcoma UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment

description: Osteosarcoma is the most common type of bone cancer that occurs in adolescents in the age of 10 to 14 years. The dataset is composed of Hematoxylin and eosin (H&E) stained osteosarcoma histology images. The data was collected by a team of clinical scientists at University of Texas Southwestern Medical Center, Dallas. Archival samples for 50 patients treated at Children’ s Medical Center, Dallas, between 1995 and 2015, were used to create this dataset. The images are labelled as Non-Tumor, Viable Tumor and Necrosis according to the predominant cancer type in each image. The annotation was performed by two medical experts. All images were divided between two pathologists for the annotation activity. Each image had a single annotation as any given image was annotated by only one pathologist. The dataset consists of 1144 images of size 1024 X 1024 at 10X resolution with the following distribution: 536 (47) non-tumor images, 263 (23) necrotic tumor images and 345 (30) viable tumor tiles.
DOI: 10.7937/tcia.2020.9era-gg29
title: Standardization in Quantitative Imaging: A Multi-center Comparison of Radiomic Feature Values

description: This dataset was used by the NCI's Quantitative Imaging Network (QIN) PET-CT Subgroup for their project titled: Multi-center Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Datasets. The purpose of this project was to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included common image data sets and standardized feature definitions.
DOI: 10.7937/tcia.2020.gqry-nc81
title: CT Images in COVID-19

description: This retrospective NIfTI image dataset consists of unenhanced chest CTs from 632 patients with COVID-19 infections. The images were acquired at the point of care in an outbreak setting from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.
Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT.  CT exams were performed with intravenous contrast and a soft tissue reconstruction algorithm.  The DICOM images were subsequently converted into NIfTI format. 

A multidisciplinary team trained several models using portions of this TCIA data set, along with additional CTs and manually annotated images from other sources. A classification model derived in part from this data is described at:  https://doi.org/10.1038/s41467-020-17971-2.  The NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at https://ngc.nvidia.com/catalog/containers/nvidia:clara:ai-covid-19. This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.  In addition, a web-based version of this model (for research use only) with drag-and-drop functionality for evaluating individual scans can be found at https://marketplace.arterys.com/model/nvidiacovidCT.  Uploading a CT yields a return email that contains results.
DOI: 10.7937/tcia.2020.t90f-wb82
title: AI-ready re-stained and co-registered multiplex dataset for head-and-neck carcinoma (HNSCC-mIF-mIHC-comparison)

description: We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement &gt; 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images.
DOI: 10.7937/k9/tcia.2015.nwtesay1
title: Data From CT COLONOGRAPHY (ACRIN 6664)
title: CT COLONOGRAPHY

description: The CT Colonography collection consists of a limited portion of The National CT Colonography Trial (ACRIN PROTOCOL 6664). The collection is offered to enlarge the broader research community's ability to develop CAD algorithms for polyp detection.
The National CT Colonography Trial (ACRIN 6664) collection contains 825 cases of CT colonography imaging with accompanying spreadsheets that provide polyp descriptions and their location within the colon segments.  Additional information about the trial is available in the Study Protocol and Case Report Forms.

Main Objective: To clinically validate widespread use of computerized tomographic colonography (CTC) in a screening population for the detection of colorectal neoplasia.

Participants: Male and female outpatients, aged 50 years or older, scheduled for screening colonoscopy, who have not had a colonoscopy in the past five years.

Study Design Summary: The study addresses aspects of central importance to the clinical application of CTC in several interrelated but independent parts that will be conducted in parallel. In Part I, the clinical performance of the CTC examination will be prospectively compared in a blinded fashion to colonoscopy. In Part II, optimization of the CT technique will be performed in view of new technological advances in CT technology. In Part III, lesion detection will be optimized by studying the morphologic features of critical lesion types and in the development of a database for computer-assisted diagnosis. In Part IV, patient preferences and cost-effectiveness implications of observed performance outcomes will be evaluated using a predictive model.