The‌ ‌Cancer‌ ‌Imaging‌ ‌Archive‌ ‌Updates:‌ November 2024

[The Cancer Imaging Program]

The‌ ‌Cancer‌ ‌Imaging‌ ‌Archive‌ ‌Updates:‌ November 2024 Join us in Chicago at the Radiological Society for North America’s Annual Meeting! RSNA Deep Learning Lab series: “Accessing freely available public datasets from The Cancer Imaging Archive (TCIA)” As part of the RSNA Deep Learning Lab series, Justin Kirby will present “Accessing freely available public datasets from The Cancer Imaging Archive (TCIA)” from 9:00-10:00am Central on Tuesday, December 2nd. In this course we will address a variety of use cases for helping data scientists query and download TCIA datasets via Python and Jupyter Notebooks.  Please note that advance registration is required to attend Deep Learning Labs this year.  Don’t delay or you may miss out! Collection Updates Illustrative example dataset of a patient with vertebral lesions with the raw CT, annotated segmentation and identification of vertebral levels, and lesion classification (Lytic, Blastic and Mixed). The “Spine metastatic bone cancer: pre and post radiotherapy CT (Spine-Mets-CT-SEG)” collection provides an annotated imaging dataset of cancerous spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification.  The dataset of 55 CT scans was collected from patients with a large range of primary cancers and corresponding bone metastatic lesions. The “Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations (BraTS-Africa)” dataset is a collection of retrospective pre-operative brain MRI scans, clinically acquired from six diagnostic centers in Nigeria. The scans are from 146 patients who have brain MRIs indicating central nervous system neoplasms, diffuse glioma, low-grade glioma, or glioblastoma/high-grade glioma. There are expert-annotated tumor subregions for each of the 146 cases, along with information regarding study location and scanner type, where available. The BraTS-Africa dataset has a potential for use in research leading towards generalizable and inclusive diagnostic tools applicable across all settings including resource constrained environments, and  contributes to understanding neuroimaging in African settings. The  “Mediastinal Lymph Node Quantification (LNQ): Segmentation of Heterogeneous CT Data (Mediastinal-Lymph-Node-SEG)” dataset contains scans of patients with lymphadenopathy (i.e., swelling of lymph nodes) due to illness or disease, such as cancer or infections.  The cohort is a cross-institutional dataset of chest CT scans acquired from 513 patients during treatment for various cancer types and was curated  for a MICCAI Challenge to help develop new segmentation tools from weakly annotated cases. “The Head and Neck Lymph Nodes Model Dataset used by the Radiation Planning Assistant (RPA-Head-and-Neck-Lymph-Nodes)” has been published on TCIA. This dataset was generated to train the models for deployment in the Radiation Planning Assistant (RPA) related to auto-contouring cervical lymph node levels in the head and neck. TCIA News A frame from left-sided nephrolithiasis and urolithiasis with stones in the left kidney, ureter, and ureterovesical junction as identified by a model created using CT Foundation. Google recently released a new medical foundation tool for 3D CT volumes: CT Foundation.  Their work uses the National Lung Screening Trial and LIDC-IDRI datasets to demonstrate how the tool can be used to rapidly train artificial intelligence models for image analysis of axial CT data. The NCI Office of Data Sharing (ODS) hosted their 2nd Annual Data Sharing Symposium on Oct. 15-16 on NIH’s Bethesda campus. TCIA staff provided in-person demos of our site functionality and information about our datasets.  The NCI Office of Data Sharing (ODS) hosted their 2nd Annual Data Sharing Symposium Cancer Research Data Commons (CRDC) 2024 Fall Symposium The Cancer Imaging Program’s Acting Associate Director, Dr. Lalitha Shankar,  provided TCIA perspectives in the NCI Panel Discussion that concluded the “Cancer Research Data Commons (CRDC) 2024 Fall Symposium”. The original REST API for TCIA DICOM data (“Bindaas”) was retired on November 1, 2024. A migration guide is located here and the newer NBIA API documentation is here.  The Cancer Imaging Archive (TCIA) Stay Connected with The Cancer Imaging Archive  Manage Subscriptions | Help The Cancer imaging Archive is funded in part by Frederick Natational Lab for Cancer Research.

 

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