In European radiology experimental
BACKGROUND : Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs).
METHODS : We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training.
RESULTS : Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p ≥ 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data.
CONCLUSIONS : GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics.
Wang Zhixiang, Zhang Zhen, Feng Ying, Hendriks Lizza E L, Miclea Razvan L, Gietema Hester, Schoenmaekers Janna, Dekker Andre, Wee Leonard, Traverso Alberto
2022-Nov-30
Deep learning, Lung, Neural networks (computer), Solitary pulmonary nodule, Tomography (x-ray computed)
