Bram van Ginneken

MSc

Radboud University Medical Center

Professor of Functional Image Analysis

NLST (Learn more about this study)

NLST-74

May 23, 2014

The role of morphologic computed tomography characteristics in the management of subsolid nodules

Lung cancer is the leading cause of cancer death. Prognosis is strongly correlated to disease stage. Unfortunately most patients are diagnosed with advanced disease, resulting in poor survival. Adenocarcinoma represents the most frequent subtype of lung cancer (40%). The early stages of the cancer sequence from atypical adenomatoid hyperplasia to invasive adenocarcinoma present as subsolid pulmonary nodules on Computed Tomography (CT) [1,2]. Subsolid lesions are however difficult to detect and easily missed on CT images.

The goal of this project is to gain more insight into the morphologic CT characteristics of subsolid nodules in a screening population. In addition to descriptive characteristics, mathematic descriptors of e.g. size will be calculated using computer assisted detection (CAD) techniques. This will allow us to study possible predictors based on morphologic characteristics that might discriminate (pre)malignant nodules from benign diseases, and predict progression into invasive cancer. Furthermore, predictors for growth and persistence may also be established.

References:
1. Godoy MCB, Naidich DP. Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: recommended interim guidelines for assessment and management. Radiology. 2009 Dec; 253(3):606-22.
2. Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS; ELCAP Group. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am Roentgenol. 2002 May;178(5):1053-7.

- Establish predictors based on morphologic CT characteristics (descriptive characteristics and mathematic descriptors using CAD techniques) for transient versus persistent, growth and outcome of subsolid lesions in the NLST.

- Using the morphological predictors models will be developed for subsolid nodules detected at baseline and follow-up scans to predict vanishing nodules, nodule persistence and nodule growth. Using the same morphological predictors, a model to predict outcome endpoints will be developed.