DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules

Chengdi Wang; Jun Shao; Xiuyuan Xu; Le Yi; Gang Wang; Congchen Bai; Jixiang Guo; Yanqi He; Lei Zhang; Zhang Yi; Weimin Li

doi:10.3389/fonc.2022.683792

DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules

Front Oncol. 2022 May 11:12:683792. doi: 10.3389/fonc.2022.683792. eCollection 2022.

Authors

Chengdi Wang¹, Jun Shao¹, Xiuyuan Xu², Le Yi², Gang Wang³, Congchen Bai⁴, Jixiang Guo², Yanqi He¹, Lei Zhang², Zhang Yi², Weimin Li¹

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, China.
² Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
³ Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
⁴ Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China.

Abstract

Objectives: Distinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction.

Methods: A total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled. The different radiological manifestations were identified mainly as various nodules densities and morphological features. Then, these nodules were classified into benign and malignant groups, both of which were subdivided into finer specific pathological types. Here, we proposed a deep convolutional neural network for the assessment of lung nodules named DeepLN to identify the radiological features and predict the pathologic subtypes of pulmonary nodules.

Results: In terms of density, the area under the receiver operating characteristic curves (AUCs) of DeepLN were 0.9707 (95% confidence interval, CI: 0.9645-0.9765), 0.7789 (95%CI: 0.7569-0.7995), and 0.8950 (95%CI: 0.8822-0.9088) for the pure-ground glass opacity (pGGO), mixed-ground glass opacity (mGGO) and solid nodules. As for the morphological features, the AUCs were 0.8347 (95%CI: 0.8193-0.8499) and 0.9074 (95%CI: 0.8834-0.9314) for spiculation and lung cavity respectively. For the identification of malignant nodules, our DeepLN algorithm achieved an AUC of 0.8503 (95%CI: 0.8319-0.8681) in the test set. Pertaining to predicting the pathological subtypes in the test set, the multi-task AUCs were 0.8841 (95%CI: 0.8567-0.9083) for benign tumors, 0.8265 (95%CI: 0.8004-0.8499) for inflammation, and 0.8022 (95%CI: 0.7616-0.8445) for other benign ones, while AUCs were 0.8675 (95%CI: 0.8525-0.8813) for lung adenocarcinoma (LUAD), 0.8792 (95%CI: 0.8640-0.8950) for squamous cell carcinoma (LUSC), 0.7404 (95%CI: 0.7031-0.7782) for other malignant ones respectively in the malignant group.

Conclusions: The DeepLN based on deep learning algorithm represented a competitive performance to predict the imaging characteristics, malignancy and pathologic subtypes on the basis of non-invasive CT images, and thus had great possibility to be utilized in the routine clinical workflow.

Keywords: artificial intelligence; computed tomography; deep learning; pathological subtypes; pulmonary nodules.