{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:54:40Z","timestamp":1785578080738,"version":"3.56.0"},"reference-count":20,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto\u2010segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials\/methods<\/jats:title><jats:p>The data of 10 lung cancer patients\u2019 CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one ICC per radiomics feature and per tumor for nine segmentations and 36 ICCs per radiomics feature for 36 pairs of nine segmentations. Meanwhile, there were 360 DSC, sDSC, and HD values calculated for 36 pairs for 10 tumors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The ICC of radiomics features exhibited greater sensitivity to segmentation changes than DSC and sDSC. The ICCs of the wavelet\u2010LLL first order Maximum, wavelet\u2010LLL glcm MCC, wavelet\u2010LLL glcm Cluster Shade features ranged from 0.130 to 0.997, 0.033 to 0.978, and 0.160 to 0.998, respectively. On the other hand, all DSC and sDSC were larger than 0.778 and 0.700, respectively, while HD varied from 0 to 1.9\u00a0mm. The results indicated that the radiomics features could capture subtle variations in tumor segmentation characteristics, which could not be easily detected by DSC and sDSC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study demonstrates the superiority of radiomics features with ICC as a measure for evaluating a physician's tumor segmentation ability and the performance of auto\u2010segmentation tools. Radiomics features offer a more sensitive and comprehensive evaluation, providing valuable insights into tumor characteristics. Therefore, the new metrics can be used to evaluate new auto\u2010segmentation methods and enhance trainees' segmentation skills in medical training and education.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14442","type":"journal-article","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T21:04:01Z","timestamp":1719176641000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation"],"prefix":"10.1002","volume":"25","author":[{"given":"Rukhsora","family":"Akramova","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8086-6442","authenticated-orcid":false,"given":"Yoichi","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4871620"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rpor.2019.01.003"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2021.12.003"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15102677"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.05.003"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880\u2010015\u20100068\u2010x"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2023.01.016"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/clpt.2008.161"},{"key":"e_1_2_9_11_1","unstructured":"NolanT.RIDER collections: the cancer imaging archive (TCIA).2023. 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