1. Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach
- Author
-
Carlos E. Cardenas, Beth M. Beadle, Adam S. Garden, Heath D. Skinner, Jinzhong Yang, Dong Joo Rhee, Rachel E. McCarroll, Tucker J. Netherton, Skylar S. Gay, Lifei Zhang, and Laurence E. Court
- Subjects
Cancer Research ,medicine.medical_specialty ,medicine.medical_treatment ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Radiation treatment planning ,Lymph node ,Radiation oncologist ,Retrospective Studies ,Radiation ,business.industry ,Radiotherapy Planning, Computer-Assisted ,Head and neck cancer ,Reproducibility of Results ,medicine.disease ,Radiation therapy ,Data set ,medicine.anatomical_structure ,Oncology ,Head and Neck Neoplasms ,030220 oncology & carcinogenesis ,Test set ,Pharynx ,Radiology ,Lymph Nodes ,business ,Tomography, X-Ray Computed ,Test data - Abstract
Purpose To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflow. Methods and Materials Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 radiation oncologists as being “clinically acceptable without requiring edits,” “requiring minor edits,” or “requiring major edits.” Results When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, Conclusions We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated radiation treatment planning.
- Published
- 2020