Abstract
Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.
Highlights
Integration of positron emission tomography (PET) in radiation treatment planning became an essential part for many tumor entities, such as head and neck cancer [1], brain tumors [2] and many more [3]
The absolute and relative standard deviation in consecutive measurements were the lowest in threshold-based segmentation and higher in all textural-based segmentation algorithms, with the highest values found for the Entropy-based method
It was found that all three textural-based segmentation algorithms showed on average a larger volume compared to the threshold-based segmentation algorithm
Summary
Integration of positron emission tomography (PET) in radiation treatment planning became an essential part for many tumor entities, such as head and neck cancer [1], brain tumors [2] and many more [3]. Besides the many reported advantages of using PET in treatment planning [3,6], the method of how to delineate the clinical target volume based on FDG-PET images is still an open question and the focus of many discussions [7,8]. Manual delineation is still considered fine as long as standardization in image viewing is kept at a high level, semiautomatic or even automatic algorithms may be preferred. They show a lower variability but are faster in processing the images. Several methods for such automatic or semiautomatic segmentation of PET images have been proposed. A high number of more sophisticated algorithms have been presented, including fuzzy logic [14] and deep learning [15]
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