Assessment of Solid Pulmonary Nodules or Masses Using Zero Echo Time MR Lung Imaging: A Prospective Head-to-Head Comparison With CT

Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.

The Response Evaluation Criteria in Solid Tumours (RECIST) were developed and published in 2000, based on the original World Health Organisation guidelines first published in 1981. In 2009, revisions were made (RECIST 1.1) incorporating major changes, including a reduction in the number of lesions to be assessed, a new measurement method to classify lymph nodes as pathologic or normal, the clarification of the requirement to confirm a complete response or partial response and new methodologies for more appropriate measurement of disease progression. The purpose of this paper was to summarise the questions posed and the clarifications provided as an update to the 2009 publication.

To assess the association between CT features and EGFR, ALK, KRAS mutations in non-small cell lung cancer.