Can morphological features differentiate between malignant and benign pulmonary nodules, detected in a screen setting?

S. van Riel, F. Ciompi, M.W. Wille, E. Scholten, N. Sverzellati, S. Rossi, A. Dirksen, M. Brink, R. Wittenberg, M. Naqibullah, M. Prokop, C. Schaefer-Prokop and B. van Ginneken

in: Annual Meeting of the Radiological Society of North America, 2015


PURPOSE Existing nodule classification systems and risk models (e.g., McWilliams model, LungRADS) consider only nodule type, size, growth, and the presence of a spiculated border. However, radiologists consider additional morphological features when assigning a malignancy risk. Goal of the study was to determine the power of additional morphological features to differentiate between benign and malignant nodules. METHOD AND MATERIALS All 60 cancers were selected from the Danish Lung Cancer Screening Trial, in thefirst scan where they were visible, and a benign set of 120 randomly selected and 120 sizematched benign nodules from baseline scans were included, all from different participants. Data had been acquired using a lowdose (16x0.75mm, 120 kVp, 40 mAs) protocol, and 1mm section thickness reconstruction. Seven radiologists were asked to score the presence of morphological features for each nodule referring to density distribution (homogeneous, inhomogeneous, high, low), lesion margin (spiculation, lobulation, demarcation by interlobular septa, sharplydefined, illdefined), lesion surrounding (distortion of the surrounding parenchyma, pleural/fissure retraction, attachment to pleura, fissure or vessel) and lesion architecture (thickened wall of a bulla, bubbles, air bronchogram). Separately per observer and feature, chi square analysis was used to determine the power to discriminate between benign and malignant nodules. Features with a pvalue <0.05 in ≥4 observers are reported. RESULTS Significant differences were seen for inhomogeneous density distribution (p <0.001 0.003) and pleural/fissure retraction (p < 0.001 0.047) in 7 observers. The presence of bubbles (p <0.001 0.025), spiculation (p <0.001), lobulation (p <0.001), and an illdefined nodule border (p<0.0010.012) were significant in 6 observers. The presence of a thickened bulla wall in 5 observers (p<0.0010.042), and air bronchogram (p<0.0010.006) and distortion of surrounding architecture (p<0.0010.004) was significantly different in 4 observers. CONCLUSION We have identified several morphological features that are significantly associated with malignancy of pulmonary nodules, but not included in current risk prediction models. CLINICAL RELEVANCE/APPLICATION Morphological features can be used to differentiate malignant from benign nodules. Further studies will show whether integration of more morphological features will increase the power of risk prediction.