Abstract
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.
Highlights
Head and neck cancer (HNC) is a type of cancer originating from the tissues and organs of the head and neck with high incidence in Southern China [1]
We proposed an automatic method of gross tumor volume (GTV) delineation for Radiation therapy (RT) planning of HNC based on deep learning (DL) and dual-center PETCT images, aiming to improve the efficiency and accuracy
We developed a deep convolutional neural network (DCNN) for HNC tumor lesion segmentation, and we trained the network based on the positron emission tomography-computed tomography (PET-CT) images and the gold standard of GTV in the training dataset
Summary
Head and neck cancer (HNC) is a type of cancer originating from the tissues and organs of the head and neck with high incidence in Southern China [1]. Radiation therapy (RT) is one of the most e ective therapies, which relies heavily on the contouring of tumor volumes on medical images. It is time-consuming to delineate the tumor volumes manually. The manual delineation is subjective, and the accuracy depends on the experience of the Contrast Media & Molecular Imaging treatment planner. There have been studies reporting the automatic segmentation of tumor lesions on magnetic resonance images of HNC using different methods [2,3,4,5,6,7,8,9,10]
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