  Early detection of lung cancer is essential in reducing mortality. Recent
studies have demonstrated the clinical utility of low-dose computed tomography
(CT) to detect lung cancer among individuals selected based on very limited
clinical information. However, this strategy yields high false positive rates,
which can lead to unnecessary and potentially harmful procedures. To address
such challenges, we established a pipeline that co-learns from detailed
clinical demographics and 3D CT images. Toward this end, we leveraged data from
the Consortium for Molecular and Cellular Characterization of Screen-Detected
Lesions (MCL), which focuses on early detection of lung cancer. A 3D
attention-based deep convolutional neural net (DCNN) is proposed to identify
lung cancer from the chest CT scan without prior anatomical location of the
suspicious nodule. To improve upon the non-invasive discrimination between
benign and malignant, we applied a random forest classifier to a dataset
integrating clinical information to imaging data. The results show that the AUC
obtained from clinical demographics alone was 0.635 while the attention network
alone reached an accuracy of 0.687. In contrast when applying our proposed
pipeline integrating clinical and imaging variables, we reached an AUC of 0.787
on the testing dataset. The proposed network both efficiently captures
anatomical information for classification and also generates attention maps
that explain the features that drive performance.
