Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

• Published in: Medical physics (Lancaster) 2020-04, Vol.47 (4), p.1738-1749
• Main Authors: Wang, Jun, Chen, Xiaorong, Lu, Hongbing, Zhang, Lichi, Pan, Jianfeng, Bao, Yong, Su, Jiner, Qian, Dahong
• Format: Article
• Language: English
• Subjects: computed tomography; Deep Learning; Humans; Image Processing, Computer-Assisted - methods; invasiveness classification; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Neoplasm Invasiveness; pulmonary nodule; Tomography, X-Ray Computed
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.14068

Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of 73.4\%±1.4 with an AUC of 81.3\%±2.2. These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of 69.1\%, 69.3\%, and 67.9\%, respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinica