  We present a hybrid algorithm to estimate lung nodule malignancy that
combines imaging biomarkers from Radiologist's annotation with image
classification of CT scans. Our algorithm employs a 3D Convolutional Neural
Network (CNN) as well as a Random Forest in order to combine CT imagery with
biomarker annotation and volumetric radiomic features. We analyze and compare
the performance of the algorithm using only imagery, only biomarkers, combined
imagery + biomarkers, combined imagery + volumetric radiomic features and
finally the combination of imagery + biomarkers + volumetric features in order
to classify the suspicion level of nodule malignancy. The National Cancer
Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to
train and evaluate the classification task. We show that the incorporation of
semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the
available training sample size of the LIDC-IDRI thereby further improving the
accuracy of malignancy estimation of most of the models tested although there
is no significant improvement with the use of KNN semi-supervised learning if
image classification with CNNs and volumetric features are combined with
descriptive biomarkers. Unexpectedly, we also show that a model using image
biomarkers alone is more accurate than one that combines biomarkers with
volumetric radiomics, 3D CNNs, and semi-supervised learning. We discuss the
possibility that this result may be influenced by cognitive bias in LIDC-IDRI
because malignancy estimates were recorded by the same radiologist panel as
biomarkers, as well as future work to incorporate pathology information over a
subset of study participants.
