Lung, Computer applications, Oncology, PET-CT, Nuclear medicine conventional, Staging, Computer Applications-Detection, diagnosis, Decision analysis, Cancer
A. W. Sauter, G. Sommer, T. J. Weikert, K. S. Mader, J. Cyriac, A. Wallnoefer, B. Stieltjes; Basel/CH
We could demonstrate that there is a wide array of radiomic features that allow a precise,
accurate subcategorization beyond the conventional diameter measurements without requiring manual examination of the tissue morphology.
ROC analysis indicates that these features are important candidates for both refinement of clinical staging and implementation of software algorithms that can automatically detect and stage NSCLC.
Given the large degree of uncertainty and difficulty in staging by standard guidelines,
the use of clearly defined radiomics metrics which can be automatically calculated could simplify the task of staging patients.
Introducing such metrics into clinical training and ultimately clinical settings could reduce misstaging and improve patient care.