{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T20:03:20Z","timestamp":1767902600951,"version":"3.49.0"},"reference-count":40,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100007449","name":"Takeda Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007449","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Radiomics analysis using on\u2010board 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study investigated the factors that influence the reproducibility of radiomic features extracted from on\u2010board 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The phantom was designed to be 35 \u00d7 20 \u00d7 20\u00a0cm with eight types of heterogeneous spheres (\u2300\u00a0=\u00a01, 2, and 3\u00a0cm). On\u2010board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone\u2010beam computed tomography (kV\u2010CBCT) 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\u2010CBCT, megavoltage\u2010CBCT (MV\u2010CBCT), and megavoltage computed tomography (MV\u2010CT) 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\u2010order, 75 texture, 465 (i.e., 93 \u00d7 5) Laplacian of Gaussian (LoG) filter\u2010based, and 744 (i.e., 93 \u00d7 8) wavelet filter\u2010based 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For 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\u2010tube 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\u2010based and seven wavelet filter\u2010based features, were indicated as highly reproducible features. The gray\u2010level run\u2010length matrix (GLRLM) was classified as containing the most frequent features (<jats:italic>N<\/jats:italic>\u00a0=\u00a08), followed by the gray\u2010level dependence matrix (<jats:italic>N<\/jats:italic>\u00a0=\u00a07) and gray\u2010level co\u2010occurrence matrix (<jats:italic>N<\/jats:italic>\u00a0=\u00a01) features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We developed the standard phantom for radiomics analysis of kV\u2010CBCT, MV\u2010CBCT, and MV\u2010CT 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\u2010board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter\u2010based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16376","type":"journal-article","created":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T13:53:36Z","timestamp":1679147616000},"page":"5585-5596","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of reproducible radiomic features from on\u2010board volumetric images: A multi\u2010institutional phantom study"],"prefix":"10.1002","volume":"50","author":[{"given":"Takanori","family":"Adachi","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Image\u2010Applied Therapy Graduate School of Medicine, Shogoin  Sakyo\u2010ku Kyoto 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