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TCIA Analysis Results
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features
Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group
Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset
MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death
TCGA Breast Phenotype Research Group Data sets
QIN multi-site collection of Lung CT data with Nodule Segmentations
NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures
Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data from Glioblastoma Multiforme cases
Imaging Features, and Correlations with Genomic and Clinical Data from the TCGA Ovarian Radiology Research Group
Image Data Used in the Simulations of "The Role of Image Compression Standards in Medical Imaging: Current Status and Future Trends"
Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection
Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection
ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection
Long and Short Survival in Adenocarcinoma Lung CTs
NSCLC Radiogenomics: Initial Stanford Study of 26 Cases
Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images
Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting
Data from Head and Neck Cancer CT Atlas
DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets
Standardized representation of the TCIA LIDC-IDRI annotations using DICOM
Crowds Cure Cancer: Data collected at the RSNA 2018 annual meeting
SDTM datasets of clinical data and measurements for selected cancer collections to TCIA
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TCIA Analysis Results
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