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

作者全名："Mu, Junhao; Kuang, Kaiming; Ao, Min; Li, Weiyi; Dai, Haiyun; Ouyang, Zubin; Li, Jingyu; Huang, Jing; Guo, Shuliang; Yang, Jiancheng; Yang, Li"

作者地址："[Mu, Junhao; Ao, Min; Li, Weiyi; Dai, Haiyun; Huang, Jing; Guo, Shuliang; Yang, Li] Chongqing Med Univ, Affiliated Hosp 1, Dept Resp & Crit Care Med, Chongqing, Peoples R China; [Kuang, Kaiming; Li, Jingyu; Yang, Jiancheng] Dianei Technol, Shanghai, Peoples R China; [Kuang, Kaiming] Univ Calif San Diego, San Diego, CA USA; [Ouyang, Zubin] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China; [Li, Jingyu] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China; [Yang, Jiancheng] Shanghai Jiao Tong Univ, Shanghai, Peoples R China; [Yang, Jiancheng] Ecole Polytech Fed Lausanne, Lausanne, Switzerland"

通信作者："Yang, L (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Resp & Crit Care Med, Chongqing, Peoples R China.; Yang, JC (通讯作者)，Dianei Technol, Shanghai, Peoples R China.; Yang, JC (通讯作者)，Shanghai Jiao Tong Univ, Shanghai, Peoples R China.; Yang, JC (通讯作者)，Ecole Polytech Fed Lausanne, Lausanne, Switzerland."

来源：FRONTIERS IN MEDICINE

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000999576700001

JCR分区：Q1

影响因子：3.1

年份：2023

卷号：10

期号：　

开始页：　

结束页：　

文献类型：Article

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

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."

基金机构："Program for National Natural Science Foundation of China [82203181]; Chongqing Science and Technology Commission; Chongqing People's Municipal Government [cstc2019jscxmsxmX0184]; Senior Medical Talents of Chongqing for Young and Middle-aged [0202czzx2108, 2020GDRC029]; Youth Innovation in Future Medicine; Chongqing Medical University [W0102]; First Affiliated Hospital of Chongqing Medical University [XKST134]"

基金资助正文："The Program for National Natural Science Foundation of China (82203181), the Chongqing Science and Technology Commission, the Chongqing People's Municipal Government (cstc2019jscxmsxmX0184), the Senior Medical Talents of Chongqing for Young and Middle-aged (0202czzx2108: 2020GDRC029), the Youth Innovation in Future Medicine, the Chongqing Medical University (W0102), and the Discipline Innovation Fund of Discipline Cultivation Project from the First Affiliated Hospital of Chongqing Medical University (XKST134) supported the conduct of the study but had no involvement in the study design, implementation, or manuscript writing."