Automated detection of lung cancer-caused metastasis by classifying scintigraphic images using convolutional neural network with residual connection and hybrid attention mechanism

Insights into Imaging

Xianwu Zeng¹  Tongtong Li²  Shaofang Zhao²  Yanru Guo²  Qiang Lin³  Zhengxing Man³  Yongchun Cao³ 

[1] Department of Nuclear Medicine, Gansu Provincial Tumor Hospital, Lanzhou, Gansu, China;School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China;Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China;School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, China;Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, Gansu, China;Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China;

关键词: Bone scan;    Skeletal metastasis;    Lung cancer;    Image classification;    Convolutional neural network;   

DOI  :  10.1186/s13244-022-01162-2

来源: Springer

【 摘 要 】

BackgroundWhole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Convolutional neural network can be used to develop automated classification of images by automatically extracting hierarchal features and classifying high-level features into classes.ResultsUsing convolutional neural network, a multi-class classification model has been developed to detect skeletal metastasis caused by lung cancer using clinical whole-body scintigraphic images. The proposed method consisted of image aggregation, hierarchal feature extraction, and high-level feature classification. Experimental evaluations on a set of clinical scintigraphic images have shown that the proposed multi-class classification network is workable for automated detection of lung cancer-caused metastasis, with achieving average scores of 0.7782, 0.7799, 0.7823, 0.7764, and 0.8364 for accuracy, precision, recall, F-1 score, and AUC value, respectively.ConclusionsThe proposed multi-class classification model can not only predict whether an image contains lung cancer-caused metastasis, but also differentiate between subclasses of lung cancer (i.e., adenocarcinoma and non-adenocarcinoma). On the context of two-class (i.e., the metastatic and non-metastatic) classification, the proposed model obtained a higher score of 0.8310 for accuracy metric.

【 授权许可】

CC BY