Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study

• Published in: Contrast media and molecular imaging Vol. 2018; no. 2018; pp. 1 - 12
• Main Authors: Li, Qiaoliang, Huang, Bingsheng, Wang, Tian-Fu, Zheng, Liyun, Wong, Ching-Yee Oliver, Feng, Shi-Ting, Ye, Yufeng, Wu, Po-Man, Chen, Zhewei, Huang, Bin, Liu, Yong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; Hindawi Limited
• Subjects: Accuracy; Artificial neural networks; Automation; Brain cancer; Cancer; Cancer therapies; Computed tomography; Contouring; Correlation analysis; Deep Learning; Female; Head & neck cancer; Head and Neck Neoplasms - diagnostic imaging; Humans; Image processing; Image segmentation; International conferences; Male; Medical imaging; Methods; Middle Aged; Models, Theoretical; Neural networks; NMR; Nuclear magnetic resonance; Positron emission; Positron emission tomography; Positron Emission Tomography Computed Tomography; Semantics; Tomography; Tumors
• ISSN: 1555-4309; 1555-4317; 1555-4317
• DOI: 10.1155/2018/8923028

Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.