Affiliation:
1. Department of Radiation Oncology and Image‐Applied Therapy Graduate School of Medicine, Shogoin Sakyo‐ku Kyoto Japan
2. Department of Advanced Medical Physics, Graduate School of Medicine Kyoto University, Shogoin Sakyo‐ku Kyoto Japan
3. Department of Radiology National Hospital Organization Kyoto Medical Center Fushimi‐ku Kyoto Japan
4. Department of Radiation Oncology Japanese Red Cross Wakayama Medical Center Wakayama Japan
5. Department of Radiation Oncology National Cancer Center Hospital East Kashiwa Japan
6. Division of Radiation Oncology Chiba Cancer Center Chuo‐ku Chiba Japan
7. Department of Radiation Oncology Kyoto City Hospital Nakagyo‐ku Kyoto Japan
8. Department of Radiation Oncology Kyoto‐Katsura Hospital Nishikyo‐ku Kyoto Japan
9. Department of Radiotherapy Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Bunkyo‐ku Tokyo Japan
Abstract
AbstractBackgroundRadiomics analysis using on‐board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns.PurposeThis study investigated the factors that influence the reproducibility of radiomic features extracted from on‐board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features.MethodsThe phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On‐board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone‐beam computed tomography (kV‐CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV‐CBCT, megavoltage‐CBCT (MV‐CBCT), and megavoltage computed tomography (MV‐CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first‐order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter‐based, and 744 (i.e., 93 × 8) wavelet filter‐based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature.ResultsFor internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter‐tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter‐based and seven wavelet filter‐based features, were indicated as highly reproducible features. The gray‐level run‐length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray‐level dependence matrix (N = 7) and gray‐level co‐occurrence matrix (N = 1) features.ConclusionsWe developed the standard phantom for radiomics analysis of kV‐CBCT, MV‐CBCT, and MV‐CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on‐board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter‐based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.
Funder
Takeda Science Foundation