  Early detection of lung cancer has been proven to decrease mortality
significantly. A recent development in computed tomography (CT), spectral CT,
can potentially improve diagnostic accuracy, as it yields more information per
scan than regular CT. However, the shear workload involved with analyzing a
large number of scans drives the need for automated diagnosis methods.
Therefore, we propose a detection and classification system for lung nodules in
CT scans. Furthermore, we want to observe whether spectral images can increase
classifier performance. For the detection of nodules we trained a VGG-like 3D
convolutional neural net (CNN). To obtain a primary tumor classifier for our
dataset we pre-trained a 3D CNN with similar architecture on nodule
malignancies of a large publicly available dataset, the LIDC-IDRI dataset.
Subsequently we used this pre-trained network as feature extractor for the
nodules in our dataset. The resulting feature vectors were classified into two
(benign/malignant) and three (benign/primary lung cancer/metastases) classes
using support vector machine (SVM). This classification was performed both on
nodule- and scan-level. We obtained state-of-the art performance for detection
and malignancy regression on the LIDC-IDRI database. Classification performance
on our own dataset was higher for scan- than for nodule-level predictions. For
the three-class scan-level classification we obtained an accuracy of 78\%.
Spectral features did increase classifier performance, but not significantly.
Our work suggests that a pre-trained feature extractor can be used as primary
tumor origin classifier for lung nodules, eliminating the need for elaborate
fine-tuning of a new network and large datasets. Code is available at
\url{https://github.com/tueimage/lung-nodule-msc-2018}.
