Interobserver Variability Prediction of Primary Gross Tumor in a Patient with Non-Small Cell Lung Cancer

SIMPLE SUMMARY: To reduce interobserver variability (IOV) for primary gross tumor volume in a patient with non-small cell lung cancer (NSLCL), the concept of an IOV map was newly proposed using signed Euclidean distance transform, fuzzy set theory, and the IOV prediction network, which could predict an IOV map from the corresponding CT images. The clinical feasibility of reducing IOV with the predicted IOV map was evaluated using a two-dimensional Dice similarity coefficient, the Jaccard index, and Hausdorff distance. Our proposed method can reduce the IOV in a set of NSCLC patients and was statistically verified using a Wilcoxon signed rank test (p < 0.05). ABSTRACT: This research addresses the problem of interobserver variability (IOV), in which different oncologists manually delineate varying primary gross tumor volume (pGTV) contours, adding risk to targeted radiation treatments. Thus, a method of IOV reduction is urgently needed. Hypothesizing that the radiation oncologist’s IOV may shrink with the aid of IOV maps, we propose IOV prediction network (IOV-Net), a deep-learning model that uses the fuzzy membership function to produce high-quality maps based on computed tomography (CT) images. To test the prediction accuracy, a ground-truth pGTV IOV map was created using the manual contour delineations of radiation therapy structures provided by five expert oncologists. Then, we tasked IOV-Net with producing a map of its own. The mean squared error (prediction vs. ground truth) and its standard deviation were 0.0038 and 0.0005, respectively. To test the clinical feasibility of our method, CT images were divided into two groups, and oncologists from our institution created manual contours with and without IOV map guidance. The Dice similarity coefficient and Jaccard index increased by ~6 and 7%, respectively, and the Hausdorff distance decreased by 2.5 mm, indicating a statistically significant IOV reduction (p < 0.05). Hence, IOV-net and its resultant IOV maps have the potential to improve radiation therapy efficacy worldwide.