cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/tcia.2020.nnc2-0461
title: A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis

description: This dataset consists of CT and PET-CT DICOM images of lung cancer subjects with XML Annotation files that indicate tumor location with bounding boxes. The images were retrospectively acquired from patients with suspicion of lung cancer, and who underwent standard-of-care lung biopsy and PET/CT. Subjects were grouped according to a tissue histopathological diagnosis. Patients with Names/IDs containing the letter 'A' were diagnosed with Adenocarcinoma, 'B' with Small Cell Carcinoma, 'C' with Large Cell Carcinoma, and 'G' with Squamous Cell Carcinoma.
DOI: 10.7937/tcia.caem-ys80
title: The Clinical Proteomic Tumor Analysis Consortium Breast Invasive Carcinoma Collection (CPTAC-BRCA)

description: This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium CPTAC Breast Invasive Carcinoma 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/tcia.svqt-q016
title: Canine glioma characterization project for ICDC (ICDC-Glioma) 01
title: ICDC-Glioma

description: This dataset includes baseline pathologic, MRI and genomic features of 85 treatment-naive naturally-occurring canine glioma. The data were collected from 5 different veterinary institutions' patient populations. Clinical MRI are available prior to any treatment, and surgical biopsy and/or tissues collected at necropsy are provided as H stained sections. The tumors have been classified with a harmonized classification scheme developed by veterinary and physician neuropathologists. The genomic features were described using whole exome, whole genome, RNAseq and methylation profiling.
DOI: 10.7937/k9/tcia.2015.1buvfjr7
title: QIN multi-site collection of Lung CT data with Nodule Segmentations (QIN-LungCT-Seg)
title: QIN-LungCT-Seg

description: This dataset (also known as the “moist run” among QIN sites) contains CT images (41 total scans) of non-small cell lung cancer from: the Reference Image Database to Evaluate Therapy Response (RIDER), the Lung Image Database Consortium (LIDC), patients from Stanford University Medical Center and the Moffitt Cancer Center, and the Columbia University/FDA Phantom. In addition, 3 academic institutions (Columbia, Stanford, Moffitt-USF) each ran their own segmentation algorithm on a total of 52 tumor volumes.  Segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as DICOM Segmentation Objects (DSOs) for each tumor volume, for a total of 468 segmentations. This collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments.
DOI: 10.7937/k9/tcia.2016.ndo1mdfq
title: The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV)

description: The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) 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.tygkkfmq
title: The Cancer Genome Atlas Lung Squamous Cell Carcinoma Collection (TCGA-LUSC)

description: The Cancer Genome Atlas Lung Squamous Cell Carcinoma (TCGA-LUSC) 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.2017.0tv7b9x1
title: Long and Short Survival in Adenocarcinoma Lung CTs DICOM

description: The dataset consists of pre-surgical chest CT images of 40 subjects from the H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida. The CT images were acquired by standard-of-care, contrast-enhanced CT scans among patients who had non-small cell cancer with biopsy-verified adenocarcinoma with 2 years of follow-up. A region-growing algorithm segmented the tumor with seed points that were chosen by radiologists. The adenocarcinoma cases are divided into the upper and lower quartiles of survival. Both the lower and upper quartiles have 20 cases. The lower quartile survival timeline is 103 to 498 days while the upper quartile timeline is 1351 to 2163 days. The average survival of the lower and upper quartiles is 288 days and 1569 days respectively. The median survival for the lower and upper quartiles is 289 and 1551 days respectively. The overall mean survival time is 879 days and median survival time is 925 days. Three of these cases, QIN-LSC-0009, QIN-LSC-0014, and QIN-LSC-0064 appear in LungCT-Diagnosis collection (http://doi.org/10.7937/K9/TCIA.2015.A6V7JIWX), while the remaining 37 cases are from the QIN Lung CT collection (http://doi.org/10.7937/K9/TCIA.2015.NPGZYZBZ).
DOI: 10.7937/tcia.2018.jze75u7v
title: Data from NSCLC-Cetuximab
title: NSCLC-Cetuximab

description: This collection contains data from the National Cancer Insitute Clinical Trial NCT00533949, "High-Dose or Standard-Dose Radiation Therapy and Chemotherapy With or Without Cetuximab in Treating Patients With Newly Diagnosed Stage III Non-Small Cell Lung Cancer That Cannot Be Removed by Surgery."  It was sponsored by NCI's Radiation Therapy Oncology Group (RTOG) under study number RTOG-0617.
DOI: 10.7937/tcia.2019.4yibtjno
title: Assessment of Residual Breast Cancer Cellularity after Neoadjuvant Chemotherapy using Digital Pathology

description: Breast cancer (BC) is the second most commonly diagnosed cancer in the U.S. with more than 250,000 new cases of invasive breast cancers reported in 2017. The majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy/ NAT) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery. The Post-NAT-BRCA dataset is a collection of representative sections from breast resections in patients with residual invasive BC following NAT. Histologic sections were prepared and digitized to produce high resolution, microscopic images of treated BC tumors. Also included, are clinical features and expert pathology annotations of tumor cellularity and cell types. The Residual Cancer Burden Index (RCBi), is a clinically validated tool for assessment of response to NAT associated with prognosis. Tumor cellularity is one of the parameters used for calculating the RCBi. In this dataset, tumor cellularity refers to a measure of residual disease after NAT, in the form of proportion of malignant tumor inside the tumor bed region; also annotated. (See MD Anderson RCB Calculator for a detailed description of tumor cellularity.) Malignant, healthy, lymphocyte and other labels were also provided for individual cells to aid development of cell segmentation algorithms.
DOI: 10.7937/tcia.2019.8kap372n
title: Data from HEAD-NECK-RADIOMICS-HN1

description: This collection contains clinical data and computed tomography (CT) from 137 head and neck squamous cell carcinoma (HNSCC) patients treated by radiotherapy. For these patients a pre-treatment CT scan was manual delineated by an experienced radiation oncologist of the 3D volume of the gross tumor volume. This dataset refers to the "H&N1" dataset of the study published in Nature Communications (http://doi.org/10.1038/ncomms5006). At time of previous publication, images of one subject had been unintentionally overlooked. In short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer.
DOI: 10.7937/tcia.2019.9stoinf1
title: Data from Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis (TCGA-OV-Proteogenomics)
title: TCGA-OV-Proteogenomics

description: PURPOSE:To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high grade serous ovarian cancer (HGSOC).
This retrospective multi-institutional study enrolled 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast enhanced computed tomography (CT) and extracted 33 imaging traits. In addition all sites of suspected HGSOC were manually segmented and grey-level correlation matrix-based texture features were computed from each tumor site. Three texture features representing inter-site tumor heterogeneity were used for further analysis. Combined analysis of transcriptomics proteomics was used to identify stably expressed proteins between primary tumor sites and metastasis. The correlation between the different imaging traits and texture features with measurement of protein abundance were assessed using Kendall tau rank correlation coefficient and Mann-Whitney U test, whereby the area under the receiver operating characteristic curve (AUC) was reported as a metric of strength and direction of the association. P values &lt; 0.05 were considered significant. 
This study provides first insights on potentially strong associations between standard of care CT imaging traits and CT-based texture measures of tumor burden inter-site heterogeneity and abundance of several associated proteins.
DOI: 10.7937/tcia.2019.j71i4fah
title: Data from CT Phantom Scans for Head, Chest, and Controlled Protocols on 100 Scanners (CC-Radiomics-Phantom-3)

description: This collection consists of a dearth of phantom scans on large samples. This data collection contains 100 scanners using three protocols. This provides population data that can be used to quantify inter-scanner variability. This data can be used to determine how robust specific radiomics or other quantitative imaging signatures are.
DOI: 10.7937/tcia.2019.tt7f4v7o
title: CT-ORG: A Dataset of CT Volumes With Multiple Organ Segmentations 

description: This dataset consists of 140 computed tomography (CT) scans, each with five organs labeled in 3D: lung, bones, liver, kidneys and bladder. The brain is also labeled on the minority of scans which show it.
Patients were included based on the presence of lesions in one or more of the labeled organs. Most of the images exhibit liver lesions, both benign and malignant. Some also exhibit metastatic disease in other organs such as bones and lungs.
The images come from a wide variety of sources, including abdominal and full-body; contrast and non-contrast; low-dose and high-dose CT scans. 130 images are dedicated CTs, the remaining 10 are the CT component taken from PET-CT exams. This makes the dataset ideal for training and evaluating organ segmentation algorithms, which ought to perform well in a wide variety of imaging conditions.
The dataset includes large and easily-located organs such as the lungs, as well as small and difficult ones like the bladder. We hope the dataset will enable widespread adoption of multi-class organ segmentation, as well as competitive benchmarking of algorithms for it.
The data are divided into a testing set of 21 CT scans, and a training set of the remaining 119. For the training set, the lungs and bones were automatically segmented by morphological image processing. For the testing set, the lungs and bones were segmented manually. All other organs were segmented manually in both the training and testing sets. Manual segmentations were done with ITK-SNAP (https://www.itksnap.org), starting with semi-automatic active contour segmentation followed by manual clean-up. The source code for the morphological algorithms is available at:
- https://github.com/bbrister/ctOrganSegmentation.git
Many images were borrowed from the Liver Tumor Segmentation (LiTS) challenge, which the organizers have generously allowed us to distribute. For more information, see the following website and paper:
- https://lits-challenge.com
- Arxiv [1901.04056] The Liver Tumor Segmentation Benchmark (LiTS) (https://arxiv.org/abs/1901.04056)
This work was supported in part by grants from the National Cancer Institute, National Institutes of Health, 1U01CA190214 and 1U01CA187947.
DOI: 10.7937/tcia.2020.6c7y-gq39
title: Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines

description: Automated or semi-automated algorithms intended for chest CT analyses typically require the creation of a 3D map of the thoracic volume as their initial step. Identifying this anatomic region precedes fundamental tasks such as lung structure segmentation, lesion detection, and radiomics feature extraction in analysis pipelines. However, automatic approaches to segment the thoracic volume maps struggle to perform consistently in subjects with diseased lungs, yet this is exactly the circumstance for which pipeline analyses would be most useful. To address this need, we have created a dataset of thoracic volume segmentations on subjects with diseased lungs. These will help the research community compare and contrast their approaches for this foundational processing step on clinically relevant data.This dataset consists of left and right thoracic volume segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC Radiomics collection. Thoracic segmentations include lung parenchyma, tumor, atelectasis, and effusion when present.
DOI: 10.7937/tcia.2020.rmwvzwix
title: GBM-DSC-MRI-DRO
title: Barrow-DRO

description: The standardization of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume (CBV) inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, a digital reference object (DRO) was developed using physiological and kinetic parameters derived from a patient database, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach. The primary, intended use of the DRO is to validate image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas [1,2]. The DRO datasets have also been used as part of the QIN-challenge titled “DSC-DRO Challenge” to evaluate multisite rCBV consistency [3], and for systematic assessment of multi-echo DSC-MRI [4]. References[1] Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. Tomography 2017;3:41–9. doi:10.18383/j.tom.2016.00286.[2] Semmineh NB, Bell LC, Stokes AM, Hu LS, Boxerman JL, Quarles CC. Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI using a population-based digital reference object. Am J Neuroradiol 2018. doi:10.3174/ajnr.A5827.[3] Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, et al. Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). Tomogr (Ann Arbor, Mich) 2019. doi:10.18383/j.tom.2018.00041.[4] Stokes AM, Semmineh NB, Nespodzany A, Bell LC, Quarles CC. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn Reson Med 2020. doi:10.1002/mrm.27914.
DOI: 10.7937/tcia.2021.v4z7-tc39
title: Ultrasound data of a variety of liver masses

description: Data was generated as part of two ongoing clinical trials investigating the use of contrast-enhanced ultrasound to a) characterize indeterminate liver lesions and b) monitor treatment response to loco regional therapy. Ultrasound data was obtained on a variety of state of the art ultrasound scanners with curvilinear probes. Gain, dynamic range, focus position and depth were optimized for image quality by the performing sonographer. Images of the mass in both sagittal and transverse planes were obtained and saved in DICOM format. Full cine-loops of the contrast enhanced ultrasound are also saved in DICOM format. The reference standard used for lesion characterization included tissue pathology and contrast-enhanced cross-sectional imaging within 1 month of the ultrasound exam. The initial treatment response to transarterial chemoembolization is also available for many HCC cases and uses pathology, retreatment angiography, or longer-term tumor response on cross-sectional imaging as a reference standard.

We expect these images can be used in a wide variety of image processing application. We are currently exploring a variety of automated intelligence algorithms for AI-based lesion characterization. Algorithms for object detection and segmentation may also be of interest. As the studies that have generated this data are also ongoing, it is expected that we can add volumetric data, contrast-enhanced ultrasound cine loops, and longer-term treatment response data to this data set in the future.
DOI: 10.7937/tcia.axh3-t579
title: An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies
title: Bone-Marrow-Cytomorphology_MLL_Helmholtz_Fraunhofer

description: The dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the May-Grünwald-Giemsa/Pappenheim stain. The diagnosis distribution in the cohort included a variety of hematological diseases reflective of the sample entry of a large laboratory specialized in leukemia diagnostics. Image acquisition was performed using a brightfield microscope with 40x magnification and oil immersion.
DOI: 10.7937/k9/tcia.2014.8sipiy6g
title: Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage
title: TCGA Breast Phenotype Research Group Data sets (TCGA-Breast-Radiogenomics)
title: TCGA-Breast-Radiogenomics

description: At the time of our study, 108 cases with breast MRI data were available in the TCGA-BRCA collection. In order to minimize variations in image quality across the multi-institutional cases we included only breast MRI studies acquired on GE 1.5 Tesla magnet strength scanners (GE Medical Systems, Milwaukee,Wisconsin, USA) scanners, yielding a total of 93 cases. We then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the TCGA Data Portal (8 patients). After these criteria, a dataset of 84 breast cancer patients resulted, with MRIs from four institutions: Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center, and the Roswell Park Cancer Institute. The resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. The dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. Of these, 73 (87%) were ER+, 67 (80%) were PR+, and 19 (23%) were HER2+. Various types of analyses were conducted using the combined imaging, genomic, and clinical data. Those analyses are described within several manuscripts created by the group
description: Acknowledgement: 
“The authors would like to thank the TCGA Breast Phenotype Research Group for providing the computer-extracted tumor segmentation data used in this study. The tumor segmentation data comes from the University of Chicago lab of Maryellen Giger,
whose lab members participated in the TCGA Breast Phenotype Research Group. In any presentation, poster, paper, etc, the segmentations should be identified as “Chicago Dynamic MRI Explorer 2005 Version”. We would also like to acknowledge The Cancer Imaging Archive and The Cancer Genome Atlas initiatives for making the imaging and the clinical data used in this study publicly available.”
DOI: 10.7937/k9/tcia.2016.f7ppnpnu
title: The Cancer Genome Atlas Rectum Adenocarcinoma Collection (TCGA-READ)

description: The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) 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.