This dataset (also known as the \u201cmoist run\u201d among QIN sites) contains CT images (41 total scans) of non-small cell lung cancer from: the Reference Image Database to Evaluate Therapy Response (RIDER), the Lung Image Database Consortium (LIDC), patients from Stanford University Medical Center and the Moffitt Cancer Center, and the Columbia University\/FDA Phantom. In addition, 3 academic institutions (Columbia, Stanford, Moffitt-USF) each ran their own segmentation algorithm on a total of 52 tumor volumes.\u00a0 Segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as DICOM Segmentation Objects (DSOs) for each tumor volume, for a total of 468 segmentations. This collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments.<\/p>
Note:<\/strong> <\/u> In December 2018 it was discovered that an update to\u00a0NSCLC Radiogenomics<\/a> mistakenly resulted in the deletion of the segmentation data from this analysis set.\u00a0 As a result, the 10 affected patients and related segmentations are no longer included in the download section below.\u00a0\u00a0<\/p>