Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans

Frontiers in Medicine

Medicine

Zubin Ouyang¹  Jing Huang²  Shuliang Guo²  Weiyi Li²  Haiyun Dai²  Min Ao²  Li Yang²  Junhao Mu²  Jingyu Li³  Jiancheng Yang⁴  Kaiming Kuang⁵ 

[1]Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

[2]Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

[3]Dianei Technology, Shanghai, China

[4]School of Computer Science, Wuhan University, Wuhan, China

[5]Dianei Technology, Shanghai, China

[6]Shanghai Jiao Tong University, Shanghai, China

[7]École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

[8]Dianei Technology, Shanghai, China

[9]University of California, San Diego, San Diego, CA, United States

关键词: deep learning;    malignancy;    metastasis;    solid pulmonary nodule;    CT;   

DOI  :  10.3389/fmed.2023.1145846

 received in 2023-01-16, accepted in 2023-04-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human–computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human–computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80–88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine.

【 授权许可】

Unknown   
Copyright © 2023 Mu, Kuang, Ao, Li, Dai, Ouyang, Li, Huang, Guo, Yang and Yang.