  Computed tomography imaging is a standard modality for detecting and
assessing lung cancer. In order to evaluate the malignancy of lung nodules,
clinical practice often involves expert qualitative ratings on several criteria
describing a nodule's appearance and shape. Translating these features for
computer-aided diagnostics is challenging due to their subjective nature and
the difficulties in gaining a complete description. In this paper, we propose a
computerized approach to quantitatively evaluate both appearance distinctions
and 3D surface variations. Nodule shape was modeled and parameterized using
spherical harmonics, and appearance features were extracted using deep
convolutional neural networks. Both sets of features were combined to estimate
the nodule malignancy using a random forest classifier. The proposed algorithm
was tested on the publicly available Lung Image Database Consortium dataset,
achieving high accuracy. By providing lung nodule characterization, this method
can provide a robust alternative reference opinion for lung cancer diagnosis.
