Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: a comparative study of capsule net, convolutional neural network, and radiomics.

Background: Discriminating the subtypes of non-small cell lung cancer (NSCLC) based on computed tomography (CT) images is a challenging task for radiologists. Although several machine learning methods such as radiomics, and deep learning methods such as convolutional neural networks (CNNs) have been proposed to explore the problem, large sample sizes are required for effective training, and this may not be easily achieved in single-center datasets.

Methods: In this study, an automated subtype recognition model with capsule net (CapsNet) was developed for the subtype discrimination of NSCLC. CapsNet utilizes an activity vector to record the relative spatial relationship of image elements that can subsequently better delineate the global image characteristics. CT images of 72 adenocarcinoma (AC) and 54 squamous cell carcinoma (SCC) patients were retrospectively collected from a single clinical center. The cancer region on the CT image was manually segmented for every subject by an experienced radiologist, and CapsNet, CNN, and four radiomics models were then used to construct the recognition model.

Results: The study demonstrated that CapsNet achieved the best discriminative performance (accuracy 81.3%, specificity 80.7%, sensitivity 82.2%) although its area under the curve was just marginally better than that of the optimal random forest (RF) based radiomics model. Not surprisingly, the performance of the CNN was only comparable to the other radiomics models.

Conclusions: This study demonstrated that CapsNet is a viable potential framework for discriminating the subtypes of NSCLC, and its use could be extended to the recognition of other diseases especially in limited single-center datasets.