cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/k9/tcia.2015.orbjkmux
title: Data From Phantom FDA
title: Phantom FDA

description: As part of a more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule size estimation, we have been conducting phantom CT studies with an anthropomorphic thoracic phantom containing a vasculature insert on which synthetic nodules were inserted or attached. The utilization of synthetic nodules with known truth regarding size and location allows for bias and variance analysis, enabled by the acquisition of repeat CT scans. Using a factorial approach to probe imaging parameters (acquisition and reconstruction) and nodule characteristics (size, density, shape, location), ten repeat scans have been collected for each protocol and nodule layout. The resulting database of CT scans is incrementally becoming available to the public via The Cancer Imaging Archive (TCIA) to facilitate the assessment of lung nodule size estimation methodologies and the development of image analysis software among other possible applications.
DOI: 10.7937/k9/tcia.2016.gkj0zwac
title: The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma Collection (TCGA-UCEC)

description: The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) 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.immqw8uq
title: The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC)

description: The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) 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.2017.3r3fvz08
title: Data from Lung CT Segmentation Challenge 2017 (LCTSC)
title: LCTSC

description: This data set was provided in association with a challenge competition and related conference session conducted at the AAPM 2017 Annual Meeting. The initial winners were announced at the AAPM meeting, but the competition website remains open to others who wish to see how their algorithms perform.Numerous auto-segmentation methods exist for Organs at Risk in radiotherapy. The overall objective of this auto-segmentation grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (OARs) from CT images for thoracic patients in radiation treatment planning. The results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable.
DOI: 10.7937/tcia.2019.4l24tz5g
title: Credence Cartridge Radiomics Phantom CT Scans with Controlled Scanning Approach
title: CC-Radiomics-Phantom-2

description: This collection consists of 251 CT scans of Credence Cartridge Radiomic (CCR) phantom. This texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. This phantom dataset was acquired on 4-8 CT scanners using a set of imaging parameters (e.g., reconstruction Field of View, Slice thickness, reconstruction kernels, mAs, and Pitch). A controlled scanning approach was employed to assess the variability in radiomic features due to each imaging parameter. This dataset will be useful to radiomic research community to identify a subset of robust radiomic features and for establishing the ground truths for future clinical investigations.
DOI: 10.7937/tcia.2019.ho23nxaw
title: Data from THE VICTRE TRIAL: OPEN-SOURCE, IN-SILICO CLINICAL TRIAL FOR EVALUATING DIGITAL BREAST TOMOSYNTHESIS (VICTRE)

description: A total of 2986 subjects, with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm, were simulated and imaged on in-silico versions of DM and DBT systems using fast Monte Carlo x-ray transport. Images were interpreted by a computational reader detecting the presence of lesions. The in-silico trial (VICTRE) was designed to replicate a comparative trial from a previous regulatory submission. The endpoint was the difference in area under the receiver-operating-characteristic curve between modalities (delta-AUC) for lesion detection. Using a fully-crossed design, VICTRE was sized for a standard error (SE) of 0.01 in delta-AUC, half the uncertainty seen in the comparative trial.
DOI: 10.7937/tcia.2020.7yrs-7j97
title: Imaging characterization of a metastatic patient derived model of melanoma: PDMR-425362-245-T

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, melanoma xenograft 425362-245-T. 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 cm3 size; 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.mi4qddhu
title: RIDER PHANTOM MRI

description: The RIDER Phantom MRI data set contains repeat phantom studies. The phantom used for all data acquisitions was a version of the EuroSpin II Test Object 5 as distributed by Diagnostic Sonar, Ltd (Livingston, West Lothian, Scotland). The phantom was comprised of 18 25-mm doped gel filled tubes and 1 20-mm tube containing 0.25 mM GdDTPA. Scanners evaluated: Scanner A – 1.5T GE 8-channel HD with BRM gradient subsystem (33 mT/m amplitude; 120 T/m-s) Scanner B – 1.5T GE 8-channel HD with CRM gradient subsystem (50 mT/m amplitude; 150 T/m-s) Scanner C – 1.5T Siemens Espree (VB13) with 33 mT/m amplitude, 100 T/m-s gradient subsystem Scanner D – 3.0T GE 8-channel HD with TwinSpeed gradients (40 mT/m; 150 T/m-s in zoom mode) For all measurements, an 8-channel phased array head coil was used.
DOI: 10.7937/k9/tcia.2016.acwogbef
title: The Cancer Genome Atlas Cervical Kidney Renal Papillary Cell Carcinoma Collection (TCGA-KIRP)

description: The Cancer Genome Atlas Cervical Kidney renal papillary cell carcinoma (KIRP) 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.ol20zmxg
title: Data from ACRIN-FLT-Breast

description: ACRIN 6688, a [18F]-fluorothymidine (FLT) imaging study using positron emission tomography (PET), was designed to evaluate the relationship between [18F] FLT uptake parameters and pathologic complete response to neoadjuvant therapy of the primary tumor in patients with locally advanced breast cancer.Participant Eligibility and Enrollment: Criteria for inclusion were patients with pathologically confirmed breast cancer and determined to be a candidate for primary systemic (neoadjuvant) therapy and for whom surgical resection of residual primary tumor following completion of neoadjuvant therapy is planned. The study was open to enrollment from September 2009 to September 2012. Final accrual to the study was 90 patients, of which 80 were included in the analysis.
DOI: 10.7937/k9/tcia.2018.3r3juisw
title: The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC)

description: This collection contains subjects from the National Cancer Institute?s Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma (CPTAC-UCEC) 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.     More Description       Discovery Study Proteomics/Clinical Data (external) can be found at CPTAC Data Portal (Georgetown) Proteomics Data Commons     Genomics/Clinical Data (external) can be found at Genomics/Clinical Data (external)
DOI: 10.7937/k9/tcia.2018.mr1ckgnd
title: Data From QIN-PROSTATE-Repeatability

description: This is a dataset with multiparametric MRI protocol applied in a test-retest setting, allowing to evaluate repeatability of the MRI-based measurements in the prostate. There is very limited data about the repeatability in mpMRI of the prostate, while such information is critical for imaging biomarker development. Data was provided by Brigham and Women's Hospital, PI Dr. Fiona Fennessy.Type of cancer: Confirmed or suspected prostate cancer
DOI: 10.7937/tcia.2019.b6u7wmqw
title: Imaging characterization of a metastatic patient derived model of bladder cancer: BL0293F (PDMR-BL0293-F563)

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 animal sacrice and extensive pathological examination. We recently identied and characterized a patient derived xenograft model with metastatic potential, bladder xenograft BL0293, developed by Jackson Laboratories and the University of California at Davis. In this study we performed a detailed imaging characterization of this model, which develops spontaneous liver and bone metastases. Using non-contrast T2 weighted MRI, hepatic metastases were demonstrated in over 70 of animals at 52 days post tumor implantation without resection of the xenograft and in 100 of animals at day 52 following resection of the xenograft. T2w turbo spin echo (T2wTSE) sequence was applied in the coronal view with a repetition time (TR) 5333ms, echo time (TE) 65ms, with an in-plane pixel of 0.180 × 0.180 mm2. A Spectral Presaturation with Inversion Recovery (SPIR) sequence (Philips Healthcare, Best, The Netherlands) was used to suppress the fat component and assist in distinguishing fat from cystic mass and tumor tissue. In a group of animals receiving one cycle of effective chemotherapy (Temozolomide; (50 mg/kg; PO, QDx5)] plus [(veliparib, a poly (ADP-ribose) polymerase inhibitor) (7.75 mg/kg; PO; BIDx7)]), no animals demonstrated metastasis by imaging. The imaging characteristics of this model, which is available from the National Cancer Institute Patient-Derived Models Repository (https://pdmr.cancer.gov/), is highly favorable for preclinical research studies of metastatic disease when used in conjunction with non-contrast T2 weighted MRI.
DOI: 10.7937/k9/tcia.2015.08a1ixoo
title: Lung Phantom

description: The FDA anthropomorphic thorax phantom with 12 phantom lesions of different sizes (10 and 20 mm in effective diameter), shapes (spherical, elliptical, lobulated, and spiculated), and densities (−630,−10, and +100 HU) was scanned at Columbia University Medical Center on a 64-detector row scanner (LightSpeed VCT, GE Healthcare, Milwaukee, WI). The CT scanning parameters were 120 kVp, 100 mAs, 64x0.625 collimation, and pitch of 1.375. The images were reconstructed with the lung kernel using 1.25 mm slice thickness. This data set was provided to TCIA for use in the National Cancer Institute's Quantiatitive Imaging Network (QIN) Lung CT Segmentation Challenge.
DOI: 10.7937/k9/tcia.2015.aqiidcnm
title: A new 2.5 D representation for lymph node detection in CT (CT Lymph Nodes)
title: CT Lymph Nodes

description: This collection consists of Computed Tomography (CT) images of the mediastinum and abdomen in which lymph node positions are marked by radiologists at the National Institutes of Health, Clinical Center. Radiologists at the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory labeled a total of 388 mediastinal lymph nodes in CT images of 90 patients and a total of 595 abdominal lymph nodes in 86 patients. The collection is aimed at the medical image computing community for developing and assessing computer-aided detection methods. Automated detection of lymph nodes can be an important clinical diagnostic tool but is very challenging due to the low contrast of surrounding structures in CT and to their varying sizes, poses, shapes and sparsely distributed locations. This data set is made available to make direct comparison to other detection methods in order to advance the state of the art.
DOI: 10.7937/k9/tcia.2015.lo9ql9sx
title: Data From LIDC-IDRI

description: The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule &gt; or =3 mm," "nodule &lt;3 mm," and "non-nodule &gt; or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.
DOI: 10.7937/k9/tcia.2015.npgzyzbz
title: Data From QIN LUNG CT
title: QIN LUNG CT

description: The Computed tomography (CT) Image data was obtained on patients diagnosed with Non-Small Cell Lung Cancer (NSCLC) with mixed stage & histology from the H. Lee Moffitt Cancer Center and Research Institute. Scans were obtained from patients who underwent surgical resection and had corresponding pre-surgery diagnostic CTs. The scans were de-identified following HIPPA guidelines to protect patient privacy. The data was shared with the QIN collaborators for research purpose complying with collaborative data sharing policy of the H. Lee Moffitt Total Cancer Care (TCC) .
DOI: 10.7937/k9/tcia.2015.ofip7tvm
title: Data From RIDER Lung PET-CT

description: The RIDER Lung PET-CT collection was shared to facilitate the RIDER PET/CT subgroup activities. The PET/CT subgroup was responsible for: (1) archiving de-identified DICOM serial PET/CT phantom and lung cancer patient data in a public database to provide a resource for the testing and development of algorithms and imaging tools used for assessing response to therapy, (2) conducting multiple serial imaging studies of a long half-life phantom to assess systemic variance in serial PET/CT scans that is unrelated to response, and (3) identifying and recommending methods for quantifying sources of variance in PET/CT imaging with the goal of defining the change in PET measurements that may be unrelated to response to therapy, thus defining the absolute minimum effect size that should be used in the design of clinical trials using PET measurements as end points.
DOI: 10.7937/k9/tcia.2015.sdnrqxxr
title: Data From BREAST-DIAGNOSIS

description: The Breast-Diagnosis collection contains cases that are high-risk normals, DCIS, fibroids and lobular carcinomas. Each case has 3 or more distinct MR pulse sequences from a Phillips 1.5 T (usual sequences are labeled T2, STIR and BLISS but may occasionally include other pulse sequences and digital mammogram of tumor specimen). Multiple time point studies on the same patient are possible. The following is relevant to analyzing the contrast dynamics of the BLISS pulse sequences. The pulse sequence parameters (repetition, echo time, etc.) can be extracted from the DICOM tags. The contrast aspects are as follows: The volume of Magnevist (Bayer) gadolinium contrast injected into the brachial vein is based on a rule of thumb which in ml's is 10% of the patient wright in POUNDS (NOT Kilograms as is recorded in the DICOM patient weight tag. Hence the injected volume for a 150 lb patient is 15 ml. (the DICOM tag entry on that patient will read "68"). The injection itself is 6 or 7 seconds, at a rate of 3cc per second. The first dynamic sequence is started 1 minute after the injection is started. Slice and pulse parameters are accessible in the DICOM tags.
DOI: 10.7937/k9/tcia.2016.hdhpgjlk
title: Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials
title: ISPY1

description: ACRIN 6657 was designed as a prospective study to test MRI for ability to predict response to treatment and risk-of-recurrence in patients with stage 2 or 3 breast cancer receiving neoadjuvant chemotherapy (NACT). ACRIN 6657 was conducted as a companion study to CALGB 150007, a correlative science study evaluating tissue-based biomarkers in the setting of neoadjuvant treatment of breast cancer. Collectively, CALGB 150007 and ACRIN 6657 formed the basis of the multicenter Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and moLecular Analysis (I-SPY TRIAL) breast cancer trial, a study of imaging and tissue-based biomarkers for predicting pathologic complete response (pCR) and recurrence-free survival (RFS). Participant Eligibility and Enrollment: Criteria for inclusion were patients enrolling on CALGB 150007 with T3 tumors measuring at least 3 cm in diameter by clinical exam or imaging and receiving neoadjuvant chemotherapy with an anthracycline-cyclophosphamide regimen alone or followed by a taxane. Pregnant patients and those with ferromagnetic prostheses were excluded from the study. The study was open to enrollment from May 2002 to March 2006. 237 patients were enrolled, of which 230 met eligibility criteria.
DOI: 10.7937/k9/tcia.2017.gjq7r0ef
title: Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection
title: BraTS-TCGA-LGG

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) Low Grade Glioma (LGG) 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-LGG 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: 10.7910/dvn/bp9fqt
title: Replication Data for: AF JGR 2019b

description: Data for AF JGR 2019b