cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7937/k9/tcia.2015.l4fret6z
title: Data From NSCLC-Radiomics-Genomics

description: This collection contains images from 89 non-small cell lung cancer (NSCLC) patients that were treated with surgery. For these patients pretreatment CT scans, gene expression, and clinical data are available. This dataset refers to the Lung3 dataset of the study published in Nature Communications. In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. The dataset described here (Lung3) was used to investigate the association of radiomic imaging features with gene-expression profiles. The Lung2 dataset used for training the radiomic biomarker and consisting of 422 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics. For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School (hugo_aerts@dfci.harvard.edu).
DOI: 10.7937/k9/tcia.2016.8lng8xdr
title: The Cancer Genome Atlas Urothelial Bladder Carcinoma Collection (TCGA-BLCA)

description: The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) 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.ab2nazrp
title: The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)

description: The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) 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.cx6ylsux
title: The Cancer Genome Atlas Sarcoma Collection (TCGA-SARC)

description: The Cancer Genome Atlas Sarcoma (TCGA-SARC) 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.qhsyhjky
title: Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy
title: Breast-MRI-NACT-Pilot

description: This pilot study to investigate the use of serial DCE MRI examinations during neoadjuvant chemotherapy (NACT) for invasive breast cancer recruited 68 patients with stage II or III locally advanced breast cancer. Longitudinal DCE MRI studies of 64 patients are available.
DOI: 10.7937/k9/tcia.2016.yxgr4blu
title: Data from QIBA CT-1C

description: The QIBA CT-1C phantom collection was designed and shared to assist in Characterizing Variability, sans Biology. This data set was contributed by RSNA's Quantitative Imaging Biomarker Alliance activity, Volumetric CT Group 1C. Multiple image sets of the same phantoms were re-scanned across centers to isolate contributors to variability. The goal was to determine necessary control conditions to be documented in QIBA profiles, ensuring that the output for imaging when performed under these conditions will be adequately precise and accurate when scanned on profile-compliant equipment. Much more information about this data set can be found on the QIBA CT-1C wiki page at http://qibawiki.rsna.org/index.php?title=VolCT_-_Group_1C.
DOI: 10.7937/k9/tcia.2017.7hs46erv
title: Data for NSCLC Radiogenomics Collection
title: NSCLC Radiogenomics

description: Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being a non-invasive procedure, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available for biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, segmentation maps of tumors in the CT scans, and quantitative values obtained from the PET/CT scans. Imaging data are also paired with gene mutation, RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between genomic and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
DOI: 10.7937/tcia.2019.woskq5oo
title: Pelvic Reference Data
title: Pelvic-Reference-Data

description: Purpose: Expert selected landmark points on clinical image pairs provide a basis for rigid registration validation. Using combinatorial rigid registration optimization (CORRO) we provide a statistically characterized reference data set for image registration of the pelvis by estimating the underlying ground truth.Methods: Landmark points for each CT/CBCT image pair for 58 pelvic cases were identified. From the identified landmark pairs, combination subsets of k-number of landmark pairs were generated without repeat, to form a k-set for k=4, 8, &12. An affine registration between the image pairs was calculated for each k-combination set (1,900-8,000,000). The mean and the standard deviation of the registration were used as the final registration for each image pair. Joint entropy was employed to measure and compare the quality of CORRO to commercially available software.Results: An average of 154 (range: 91-212) landmark pairs were selected for each CT/CBCT image pair. The mean standard deviation of the registration output decreased as the k-size increased for all cases. In general the joint entropy evaluated was found to be lower than results from commercially available software. Of all 58 cases 58.3 of the k=4, 15 of k=8 and 18.3 of k=12 resulted in the better registration using CORRO as compared to 8.3 from a commercial registration software. The minimum joint entropy was determined for one case and found to exist at the estimated registration mean in agreement with the CORRO approach.Conclusion: The results demonstrate that CORRO works even in the extreme case of the pelvic anatomy where the CBCT suffers from reduced quality due to increased noise levels. The estimated ground truth using CORRO was found to be better than commercially available software for all k-sets tested. Additionally, the k-set of 4 resulted in overall best outcomes when compared to k=8 and 12, which is anticipated because k=8 and 12 are more likely to have combinations that affected the accuracy of the registration.
DOI: 10.7937/tcia.2020.fqn0-0326
title: High-dimensional imaging of colorectal carcinoma and other tumors with 50+ markers

description: We have used CODEX to image 56 proteins simultaneously in 140 tissue regions from the tumor invasive front of 35 advanced-stage colorectal cancer (CRC) patients (17 patients with Crohn's-like reaction (CLR) - leading to high amount of tertiary lymphoid structures (TLS); and 18 patients with diffuse inflammatory infiltration (DII) and no TLS). These patients were selected from an initial cohort of 715 CRC patients. Patients with low-stage CRC (pTNM 0-2), pre-operative chemotherapy, insufficient material, and low immune infiltration were excluded. The 35 resulting patients were matched for age, sex and tumor characteristics. CLR patients had a much better survival compared to DII patients.
DOI: 10.7937/tcia.2020.py71-5978
title: Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population

description: Radiology imaging is playing an increasingly vital role in the diagnosis of COVID-19 patients and determining therapeutic options, patient care management and new research directions. Publicly available imaging data is essential to drive new research by permitting the creation of large multi-site cohorts for machine learning based analyses. All too frequently rural populations are underrepresented in such public collections. In fact, the literature demonstrates there is very limited data on COVID-19 outcomes in rural populations, while it is well established that such populations have differentially high expression of key co-morbidities. Similarly, while the number of genomes of the SARS-COV-2 virus are rapidly growing in public repositories, few samples represent the variants expressed in rural populations. This gap in available data is of particular importance given that the southern United States, as of July 2020, is the most rapidly expanding COVID-19 hot spot on earth. We have published a collection of radiographic and CT imaging studies for patients who tested positive for COVID-19. Each patient is described by a limited set of clinical data correlates that includes demographics, comorbidities, selected lab data and key radiology findings. These data are cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, uploaded to the Genbank repository. We believe this collection will help to define appropriate correlative data and contribute samples from this normally underrepresented population to the global research community.
DOI: 10.7937/k9/tcia.2015.zf0vlopv
title: NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures (ISBI-MR-Prostate-2013)
title: ISBI-MR-Prostate-2013

description: This data set was created for use in the NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures.  The challenge data set was divided into 3 parts including training, leaderboard and test data sets.  This allowed participants to prepare their algorithms and test their results prior to submitting to a final test phase for selecting the winner.

Image data were selected from PROSTATE-DIAGNOSIS and Prostate-3T collections on TCIA.  Cases consist of axial scans with half obtained at 1.5 T (Philips Achieva) with an endo-rectal receiver coil (fromBostonMedicalCenter) and the other half at 3T (Siemens TIM) with a surface coil (from Radboud University Nijmegen Medical Centre [RUNMC],Netherlands). They were acquired as T2-weighted MR axial pulse sequences with either 4 mm thick slices at 3T or 3 mm thick at 1.5T. 
Each case has had central gland (CG) and peripheral zone (PZ) outlines marked by NB and MR, or HH, GL, or JF.
DOI: 10.7937/k9/tcia.2016.psjoxm47
title: Imaging Features, and Correlations with Genomic and Clinical Data from the TCGA Ovarian Radiology Research Group (TCGA-OV-Radiogenomics)
title: TCGA-OV-Radiogenomics

description: This study was a multi-reader, multi-institutional, IRB-approved retrospective analysis of 93 HGSOC patients with abdominal and pelvic CT scans prior to primary debulking that were available through The Cancer Imaging Archive (TCIA). Eight radiologists from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group developed and subsequently independently recorded the following CT features in each patient: primary ovarian mass(es) characteristics (if present), presence and distribution of peritoneal tumor spread, lymphadenopathy, and distant metastases. Inter-observer agreement for the CT features was assessed, as were associations of these features with time-to-disease progression (TTP) and CLOVAR subtypes and abilities of combinations of these features to predict TTP and CLOVAR subtypes. Results of analyzing this data are published in a manuscript titled Radiogenomics of High-Grade Serous Ovarian Cancer: Multi- Reader Multi-Institutional Study from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group.
DOI: 10.7937/k9/tcia.2016.tnb1kqbu
title: Data From Pancreas-CT

description: The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects. Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy. The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions. Subjects' ages range from 18 to 76 years with a mean age of 46.8 ± 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 − 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage). A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist.
DOI: 10.7937/tcia.2019.36f5o9ld
title: A Single-cell Morphological Dataset of Leukocytes from AML Patients and Non-malignant Controls

description: The Munich AML Morphology Dataset contains 18,365 expert-labeled single-cell images taken from peripheral blood smears of 100 patients diagnosed with Acute Myeloid Leukemia at Munich University Hospital between 2014 and 2017, as well as 100 patients without signs of hematological malignancy. Image acquisition was done using a M8 digital microscope / scanner (Precipoint GmbH, Freising, Germany) at 100-fold optical magnification and oil immersion. Pathological and non-pathological leukocytes were classified into a standard morphological classification scheme derived from clinical practice by trained experts. To quantify inter- and intra-rater variability of examiners, a subset of images was re-annotated up to two times. The dataset has been used by the authors to train a convolutional neural network for single-cell morphology classification.
DOI: 10.25982/33233.606/1831502
title: Genome Extraction from Shotgun Metagenome Sequence Data

description: Uncultivated Bacteria and Archaea comprise the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only recently become possible. To realize the hope of capturing Earth’s microbial genetic complement, technologies that accelerate recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform (http://www.kbase.us/). In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAG genomes, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq, and others. While portions of such capabilities are individually available from other resources, the combination of the intuitive usability, data interoperability, and integration of tools in a freely available compute resource makes KBase a uniquely powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended, with additional MAG recovery and analysis tools, via the KBase SDK (Software Development Kit).
DOI: 10.7937/k9/tcia.2015.foqeujvt
title: Data From PROSTATE-DIAGNOSIS

description: Prostate cancer T1- and T2-weighted magnetic resonance images (MRIs) were acquired on a 1.5 T Philips Achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after I.V. administration of 0.1 mmol/kg body weight of Gadolinium-DTPA (pentetic acid).
DOI: 10.7937/k9/tcia.2018.odu24gze
title: The Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma Collection (CPTAC-CM)
title: CPTAC-CM

description: This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) 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/k9/tcia.2018.smt36lpn
title: Fused Radiology-Pathology Lung (Lung-Fused-CT-Pathology)
title: Lung-Fused-CT-Pathology

description: Data collection and analysis was provided by Case Western Reserve University. This is the first attempt of mapping the extent of Invasive Adenocarcinoma onto in vivo lung CT. The mappings constitute ground truth of disease and may be used to further investigate the imaging signatures of Invasive Adenocarcinoma in ground glass pulmonary nodules. Patient with small ground glass nodules with &gt;2 histology slices per nodule were included. Patients with solid large nodules (&gt;40mm), with &lt;3 histology slices or with histology slices showing substantial artifacts were excluded from this study.
DOI: 10.60707/cy5c-qh62
title: NANO SCALE THERMAL CONDUCTIVITY CONTRAST MEASUREMENTS OF BENNU SAMPLE OREX-800055-9 USING AN ATOMIC FORCE MICROSCOPE
DOI: 10.25934/00001246
title: To Evaluate the Pharmacokinetics, Safety and Tolerability of Retosiban (GSK221149) Co-administered With EFAVIRENZ

description: This will be a randomized single sequence open label study. This study is designed to determine if chronic dosing with efavirenz (EFZ) will have an effect on the pharmacokinetics (PK) of intravenously-administered retosiban in healthy volunteers. The study consists of screening (28 days), treatment (1 dosing session) and follow-up (7 to 14 days) period, and the total duration of study participation for each subject will be approximately 8 weeks. During the treatment period, subjects will be admitted to the clinical research unit the day before dosing (Day 1) and will remain until completion of the last assessment on Day 20. All subjects will receive on Day 1, a 6 milligrams (mg) bolus of retosiban for 5 minutes (min), followed by a 6 mg/hour (hr) infusion for 12 hrs. On Day 2, a washout day will occur. On Days 3-17, subjects will receive EFZ 600 mg once daily in the evening. On Day 18, subjects will receive a 6 mg bolus of retosiban for 5 mins, followed by a 6 mg/hr infusion for 12 hrs plus a 600 mg dose of EFZ.
DOI: 10.25934/00003767
title: NUTRItional Adequacy Therapeutic Enhancement in the Critically Ill: A Randomized Double Blind, Placebo-controlled Trial of the Motilin Receptor Agonist GSK962040. The NUTRIATE Study

description: This is a multi-center, parallel group, placebo-controlled and active-compared, randomized study to assess the ability of GSK962040 to enhance the delivery of enteral feed to critically ill subjects that are predisposed to developing feeding intolerance (e.g., percentage of goal volume); enhance gastric emptying in this population; and provide preliminary evidence of the drug's effect on outcomes of therapy (length of stay in the Intensive Care Unit [ICU], time on ventilator, ICU acquired infections, and 60-day mortality). Other aims are evaluation of GSK962040 safety, tolerability and pharmacokinetics upon repeat dosing in a critically ill population. After meeting eligibility criteria, male and female subjects will be randomized to either receive GSK962040 (50 milligram [mg]) once daily (OD) via naso-gastric (NG) or orogastric (OG) feeding tube (oral solution), or placebo by the same route. If subjects develop intolerance to enteral feeding at any point up to Dose 5 of study medication (inclusive), study treatments will switch such that those originally receiving GSK962040 will receive metoclopramide (10 mg, intravenous [iv], every 6 hours) and those subjects originally randomized to receive placebo will receive GSK962040 (50 mg, via NG, OD). Additionally, if subjects develop intolerance prior to any treatment, they will be randomized to receive either GSK962040 (50 mg, via NG, OD) or metoclopramide (10 mg, iv, every 6 hours). The study will consist of a screening/baseline assessment, a treatment period (up to 7 days in duration), and a 4-day post treatment safety follow-up assessment. The duration of each subject's participation in the study from screening to follow-up safety assessment will be up to approximately 2 weeks. In addition, mortality will be assessed 60 days after admission to the ICU.