Cancer Imaging Archive Updates: July 2020
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Tacconelli, Michelle (NIH/NCI) [C]
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TCIA Update: July 2020 |
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We're excited to announce our first TCIA dataset from China! "A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis" includes 363 subjects with tumor locations annotated by 5 thoracic radiologists to facilitate training Deep Learning models: https://doi.org/10.7937/TCIA.2020.NNC2-0461 |
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The slides and video from the July 22nd CPTAC Imaging SIG presentation about CPTAC-LUAD proteogenomic analyses with Shankha Satpathy are now available at https://wiki.cancerimagingarchive.net/x/FR0lAw. You can also access slides and recordings here from previous presentations. |
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Interested in working with TCIA data using our REST API? Make sure you drop by our "Data Analysis Center" page at https:// wiki.cancerimagingarchive.net/x/x49XAQ where you'll find community-contributed code to jump start your work in R, Python and Julia programming languages. |
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MRQy (https://github.com/ccipd/MRQy) is a tool that analyzes MR imaging and generates noise/information measurements for quality assessment. Its developers applied their tool to 3 of our collections and shared the output in this new Analysis Result dataset: https://wiki.cancerimagingarchive.net/x/ soYvB |
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The AAPM RT-MAC Grand Challenge 2019 enabled comparison of auto-segmentation algorithms to delineate organs at risk or tumors in Head&Neck MRIs for radiation treatment planning. RTSTRUCTS previously embargoed from the test phase are now available: https://doi.org/10.7937/ tcia.2019.bcfjqfqb |
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NCI’s Patient-Derived Models Repository and the Cancer Imaging Archive announce identification and imaging characterization of metastatic patient-derived models available to the scientific community: https://buff.ly/3gBq4gw Imaging from 4 PDMR mouse models are now available in TCIA, which can be used in evaluating therapies to reduce metastatic spread. |
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This new Analysis Results dataset explored the lack of standardized data capture for non-image data accompanying various TCIA breast cancer collections to address the desire for semantic interoperability by aligning common clinical metadata elements: https://doi.org/10.7937/TCIA.2019.wgllssg1 |
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Stay Connected with TCIA |
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