{"id":5668,"date":"2023-09-04T03:16:39","date_gmt":"2023-09-04T03:16:39","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/lungct-diagnosis\/"},"modified":"2023-09-13T12:02:18","modified_gmt":"2023-09-13T12:02:18","slug":"lungct-diagnosis","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/lungct-diagnosis\/","title":{"rendered":"LUNGCT-DIAGNOSIS"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Lung Cancer"],"citations":[4604,4605,2925],"collection_doi":"10.7937\/K9\/TCIA.2015.A6V7JIWX","collection_download_info":"Click the Versions tab for more info about data releases.","collection_downloads":[5213,5214,5215,5217],"full_export":"
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Summary<\/span><\/span><\/h1>

All the images are diagnostic contrast enhanced CT scans. The images were retrospectively acquired, to ensure sufficient patient follow-up. Slice thickness is variable : between 3 and 6 mm. All images were done at diagnosis and prior to surgery. The objective of the study was to extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival. \u00a0<\/span><\/span><\/p>

Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity and intratumor density variation using routinely obtained diagnostic CT scans. The features systematically scored tumors and identified imaging phenotypes which exhibited survival differences. The features were extracted from routinely obtained CT images and were reproducible and stable despite the inherent clinical image acquisition variability. Our results suggest that quantitative imaging features can be used as an additional diagnostic tool in management of lung adenocarcinomas. More information is available in the related publication (see Citation tab below).<\/span><\/span><\/p>

Acknowledgements<\/h3>

We would like to acknowledge the individual and institution that have provided data for this collection:<\/p>