The recently published "RPA-Head-and-Neck-Lymph-Nodes" dataset includes CTs and RTSTRUCT contours of 7 lymph node levels from 46 subjects with definitive Oropharynx Cancer (OPX) to facilitate improved solutions for auto-contouring. Download it for free at

@yazdan_salimi Yep, we recommend installing 3D Slicer and the TCIABrowser extension for this:

The 🧠BraTS-Africa🌍 dataset is now available at It contains MR images (T1, T1 CE, T2, and T2 FLAIR) and tumor segmentations from 146 patients, which provides insight into unique aspects of African brain cancer cohorts and imaging resource constraints.

"Mediastinal-Lymph-Node-SEG" is now available at It contains chest CT scans from 513 patients with lymphadenopathy due to illness, such as cancer or infections. Segmentations are also included to help develop new tools from weakly annotated cases.

Exciting new work from @Google leveraging our NLST and LIDC-IDRI datasets to help simplify image analysis of axial CT data!

The original REST API for TCIA DICOM data used Bindaas, which was deprecated in June 2022. We will fully retire this API on November 1, 2024. A migration guide is located at and the newer NBIA API documentation is at

Check out this recently published manuscript on TCIA datasets which combined radiomics, clinical and mutation data from CPTAC Pancreatic cancer subjects to study their prognoses! p.s. Thank you @GianMaria_Zeit for following our citation guidelines perfectly!

The NCI Office of Data Sharing (ODS) is hosting their 2nd Annual Data Sharing Symposium on Oct. 15-16 on NIH’s Bethesda campus or online! We'll be there providing in-person demos of TCIA on the 16th. Agenda and registration are at Hope to see you there!

RT @TylerSbrt: Excellent work on 🧠 #AI for tumor tracking and targeting, led by @RadRudie and Louis Gagnon @UCSDCancer @ASTRO_org @ESTRO_R…

We've created a Jupyter Notebook in Python to make it easy to link clinical data in the Genomic Data Commons to images from TCIA! Examples use data from The Cancer Genome Atlas, which provides an opportunity to study links between genotypes & phenotypes.

RT @NIH_CommonFund: We are gathering input on challenges and opportunities related to #ArtificialIntelligence enabled #PrecisionMedicine th…

RT @FredNatLab: 📢 Calling all medical professionals and researchers: Help contribute to #HealthEquity efforts by submitting more demographi…

The "MR imaging of pediatric subjects with high-grade gliomas (DFCI-BCH-BWH-PEDs-HGG)" has been published on TCIA at This data was part of the ASNR-MICCAI BraTS Pediatrics Tumor Challenge which you can learn more about at

New CPTAC-GBM data are helping illuminate key signaling pathways and tumor evolution in high-grade glioma: Access the data used in their study at and

Glioblastoma is the most common and deadliest type of brain cancer. Researchers seeking to improve outcomes for these patients can leverage 14 unique datasets on TCIA including radiology & pathology imaging, analyses, & clinical data for ~2,000 subjects:

The recently published "Histological Hyperspectral Glioblastoma Dataset (HistologyHSI-GB)" dataset contains 13 H&E slides (20x magnification) from glioblastoma subjects which were annotated and used to create 469 hyperspectral images:

Heading to #SIIM24? Register for our Hands-On Data Curation Learning Lab on Thursday morning!

The #SIIMHackathon Dataset uses TCIA's DICOM images to create fictitious, but believable, narratives that illustrate common concepts in radiology to provide a cohesive, rich dataset that will allow people to build interesting applications:

Interested in lung cancer screening? The Imaging Data Commons team applied TotalSegmentator to 126,088 CT series from TCIA's National Lung Screening Trial dataset to segment a total of 9,565,554 anatomic structures which were then processed by pyradiomics!