Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society

PMID: 39966960

Abstract

BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant risk of GGOs.

Authors

Yuhang Liu

School of Computer Science and Technology, North University of China, Taiyuan, China.
Jian Wang

Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
Bulin Du

Department of Nuclear Medicine, The First Hospital of China Medical University, No. 155 Nanjing St, Shenyang, 110001, China.
Yaming Li

Department of Nuclear Medicine, The First Hospital of China Medical University, No. 155 Nanjing St, Shenyang, 110001, China. ymli2001@163.com.
Xuena Li

Department of Nuclear Medicine, The First Hospital of China Medical University, No. 155 Nanjing St, Shenyang, 110001, China. lixuenacmunm@163.com.

Keywords

Adult Aged Convolutional Neural Networks Female Fluorodeoxyglucose F18 Humans Lung Neoplasms Male Middle Aged Neural Networks, Computer Positron Emission Tomography Computed Tomography Radiopharmaceuticals Retrospective Studies Solitary Pulmonary Nodule

External Resources

View on PubMed Access via DOI PubMed (39966960)

Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.

Abstract

Authors

Keywords

External Resources

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