Assessment of algorithmic performance in PET-CT segmentation of solitary lung tumors

Background: PET-CT (Positron emission tomography–computed tomography), a nuclear medicine imaging technique is preferably used for lung tumor detection. However, manual image analysis may be time-consuming. To enhance efficiency, many automated segmentation algorithms have been developed. This study aims to develop a radiomics agreement-based method to evaluate segmentation algorithms and to compare the performance of two deep learning-based algorithms nnU-Net (No-new-net Convolutional Neural Network) and RECOMIA (Research Consortium for Medical Image Analysis Lung Cancer “Lung Cancer FDG-PET/CT v1.5”) for PET-CT solitary lung tumor segmentation. Methods and results: PET-CT images of 221 patients at the University of Debrecen’s Hospital were collected and anonymized. The predictions of segmented lung tumors done by nnU-Net and RECOMIA were compared with the manual segmented masks via Wilcoxon signed-rank test and Pearson correlation values, based on eight groups of radiomics parameters calculated by PyRadiomics software. The combined p-values were calculated using Brown’s method, which were 0.481 and