1. Multimodality Attention-Guided 3-D Detection of Nonsmall Cell Lung Cancer in 18F-FDG PET/CT Images
- Author
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Ling Chen, Wentao Zhu, Huafeng Liu, Lijuan Yu, Hongwei Ye, Kanfeng Liu, Jingsong Li, Hui Shen, and Kui Zhao
- Subjects
medicine.diagnostic_test ,Computer science ,business.industry ,Pattern recognition ,medicine.disease ,Convolutional neural network ,Atomic and Molecular Physics, and Optics ,Multi modality ,Feature (computer vision) ,Positron emission tomography ,medicine ,Radiology, Nuclear Medicine and imaging ,Fdg pet ct ,Artificial intelligence ,Non small cell ,Sensitivity (control systems) ,business ,Lung cancer ,Instrumentation - Abstract
In this article, a three-dimensional detection framework for detecting non-small cell lung cancer (NSCLC) in 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images and guided by a multi-modality attention fusion is proposed. A total of 250 18F-FDG PET/CT scans between January 1, 2015 and December 31, 2019 of patients who had histopathologically proven NSCLC were acquired. A customized dual-path 3D CenterNet is used for NSCLC detection. Moreover, we propose a multi-modality attention module that adaptively refines the multi-modality feature map fusion. Since the 3D convolutional neural network (CNN) requires many graphic memories and sliding windows, a 384×384×32 patch size is designed and used for training and testing. Five-fold cross-validation is applied for this study. The sensitivity and false positive per scan (FPPS) obtained by our proposed method are 0.96 and 1.04, respectively. Our method significantly outperforms 3D CenterNet in terms of sensitivity (P =.031). This module demonstrates the potential to be implemented in other multi-modality applications. Our result performs competitively against other lung cancer detections. Furthermore, case studies show that the proposed method can detect difficult-to-diagnose NSCLCs. Our result shows the proposed method can help radiologists and medical physicists diagnose NSCLC.
- Published
- 2022