209 Works

Radiology Data from The Cancer Genome Atlas Colon Adenocarcinoma [TCGA-COAD] collection

Shanah Kirk, Yueh Lee, Cheryl A. Sadow, Seth Levine, Charles Roche, Ermelinda Bonaccio & Joe Filiippini
The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). Matched TCGA patient identifiers allow researchers to explore the...

Radiology Data from The Cancer Genome Atlas Cervical Kidney renal papillary cell carcinoma [KIRP] collection

Marston Linehan, Rabindra Gautam, Shanah Kirk, Yueh Lee, Charles Roche, Ermelinda Bonaccio, Joe Filippini, Kimberly Rieger-Christ, John Lemmerman & Rose Jarosz
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...

Radiology Data from The Cancer Genome Atlas Rectum Adenocarcinoma [TCGA-READ] collection

Shanah Kirk, Yueh Lee, Cheryl A. Sadow & Seth Levine
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...

Radiology Data from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) collection

Bradley J. Erickson, David Mutch, Lynne Lippmann & Rose Jarosz
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...

Data From NaF_PROSTATE

Karen A Kurdziel, Andrea B. Apolo, Liza Lindenberg, Esther Mena, Yolanda Y. McKinney, Stephen S. Adler, Baris Turkbey, William Dahut, James L. Gulley, Ravi A. Madan, Ola Landgren & Peter L. Choyke
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...

Data From RIDER_Lung CT

Binsheng Zhao, Lawrence H Schwartz & Mark G Kris
The RIDER Lung CT collection was constructed as part of a study to evaluate the variability of tumor unidimensional, bidimensional, and volumetric measurements on same-day repeat computed tomographic (CT) scans in patients with non–small cell lung cancer. Thirty-two patients with non–small cell lung cancer, each of whom underwent two CT scans of the chest within 15 minutes by using the same imaging protocol, were included in this study. Three radiologists independently measured the two greatest...

Data From Soft-tissue-Sarcoma

Clark K Smith K

Data From LungCT-Diagnosis

Clark K Smith K

Data From Mouse-Mammary

Sunny Jansen, Lilia Ileva, Lucy Lu & Terry Van Dyke
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...

Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage

Elizabeth Morris, Elizabeth Burnside, Gary Whitman, Margarita Zuley, Ermelinda Bonaccio, Marie Ganott, Elizabeth Sutton, Jose Net, Kathy Brandt, Hui Li, Karen Drukker, Chuck Perou & Maryellen L. Giger
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...

Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma [CPTAC-LUAD] collection

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma (CPTAC-LUAD) 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...

Data from ACRIN-FLT-Breast

Paul Kinahan, Mark Muzi, Brian Bialecki & Laura Coombs
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...

Standardized representation of the TCIA LIDC-IDRI annotations using DICOM

Andrey Fedorov, Matthew Hancock, David Clunie, Mathias Brochhausen, Jonathan Bona, Justin Kirby, John Freymann, Hugo J.W.L. Aerts, Ron Kikinis & Fred Prior
This dataset contains standardized DICOM representation of the annotations and characterizations collected by the LIDC/IDRI initiative, originally stored in XML and available in the TCIA LIDC-IDRI collection (https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI). Only the nodules that were deemed to be greater or equal to 3 mm in the largest planar dimensions have been annotated and characterized by the expert radiologists performing the annotations. Only those nodules are included in the present dataset.

Data from the ACRIN 6668 Trial NSCLC-FDG-PET

Paul Kinahan, Mark Muzi, Brian Bialecki, Ben Herman & Laura Coombs
Positron Emission Tomography Pre- and Post-treatment Assessment for Locally Advanced Non-small Cell Lung Carcinoma This was a multicenter clinical trial by the ACRIN Cooperative Group (now part of ECOG-ACRIN) and the RTOG Cooperative Group (now part of NRG) using FDG-PET imaging both pre- and post-chemoradiotherapy.The objective of the ACRIN 6668 multi-center clinical trial was to determine if the PET standardized uptake value (SUV) measurement from FDG-PET imaging shortly after treatment is a useful predictor of...

Osteosarcoma UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment

Patrick Leavey, Anita Sengupta, Dinesh Rakheja, Ovidiu Daescu, Harish Babu Arunachalam & Rashika Mishra
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...

SDTM datasets of clinical data and measurements for selected cancer collections to TCIA [Dataset]

Hubert Hickman, Wendy Ver Hoef, Smita Hastak, Jon Neville, David Clunie, Ulrike Wagner & Edward Helton
The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying various TCIA breast cancer collections and the IVY-Gap brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement...

Imaging characterization of a metastatic patient derived model of adenocarcinoma colon: PDMR-997537-175-T

James L. Tatum, Joseph D. Kalen, Lilia V. Ileva, Lisa A. Riffle, Saito Keita, Nimit Patel, Paula M. Jacobs, Chelsea Sanders, Amy James, Simone Difilippantonio, Lai Thang, Melinda G. Hollingshead, Jessica Phillips, Yvonne Evrard, David A. Clunie, Yanling Liu, Christian Suloway, Kirk E. Smith, Ulrike Wagner, John B. Freymann, Justin Kirby & James H. Doroshow
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 colon xenograft 997537-175-T. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous...

High-dimensional imaging of colorectal carcinoma and other tumors with 50+ markers

Christian M. Schürch, Salil Bhate, Graham Barlow, Darci Phillips, Luca Noti, Inti Zlobec, Pauline Chu, Sarah Black, Janos Demeter, David McIlwain, Nikolay Samusik, Yury Goltsev & Garry Nolan
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...

Multi-institutional Paired Expert Segmentations and Radiomic Features of the Ivy GAP Dataset

Sarthak Pati, Ruchika Verma, Hamed Akbari, Michel Bilello, Virginia B. Hill, Chiharu Sako, Ramon Correa, Niha Beig, Ludovic Venet, Siddhesh Thakur, Prashant Serai, Sung Min Ha, Geri D. Blake, Russell Taki Shinohara, Pallavi Tiwari & Spyridon Bakas
This dataset comprises two paired sets of expert segmentation labels for tumor sub-compartments of the pre-operative multi-institutional scans of the Ivy Glioblastoma Atlas Project (Ivy GAP) collection of The Cancer Imaging Archive (TCIA). These labels have been approved by independent expert board-certified neuroradiologists at the Hospital of the University of Pennsylvania and at Case Western Reserve University. Furthermore, for each of the paired sets of approved labels, a diverse comprehensive panel of radiomic features is...

Comparison of mIF versus mIHC for immune markers in head and neck carcinoma

Robbert J.C. Slebos, Janis V. de la Iglesia, Gerrick A. Aden-Buie, Ritu Chaudhary, Juan C. Hernandez-Prera, Christine H. Chung & Travis A. Gerke
With advancement of immunotherapies, there has been a paradigm shift in the standard of care for cancer treatment and in the focus of cancer research leveraging the immune system in the tumor immune microenvironment. Therefore, accurate characterization of the tumor microenvironmnent in each disease site is extremely important. We compared multiplex immunofluorescence assay (mIF) using multispectral microscopy and multiplex immunohistochemistry assay (mIHC). Here we report the comparison of these two assays regarding data acquisition, image...

Data from the Clinical Proteomic Tumor Analysis Consortium Breast Invasive Carcinoma [CPTAC-BRCA] collection

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...

Data from Ivy GAP

Nameeta Shah, Xu Feng, Michael Lankerovich, Ralph B. Puchalski & Bart Keogh
This data collection consists of MRI/CT scan data for brain tumor patients that form the cohort for the resource Ivy Glioblastoma Atlast Project (Ivy GAP) as summarized below. There are 399 scans for 41 patients that include pre-surgery, post-surgery and follow up scans. The Ivy Glioblastoma Atlas Project (Ivy GAP) is a collaborative partnership between the Ben and Catherine Ivy Foundation, which generously provided the financial support, the Allen Institute for Brain Science, and the...

Data From REMBRANDT

Lisa Scarpace, Adam E. Flanders, Rajan Jain, Tom Mikkelsen & David W. Andrews
Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical...

A new 2.5 D representation for lymph node detection in CT

Holger Roth, Le Lu, Ari Seff, Kevin M Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey & Ronald M. Summers
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...

Registration Year

  • 2021
    11
  • 2020
    28
  • 2019
    50
  • 2018
    20
  • 2017
    13
  • 2016
    40
  • 2015
    36
  • 2014
    11

Resource Types

  • Dataset
    188
  • Image
    9
  • Other
    1