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U-Net and DeepLab V3\u2009+\u2009architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The predicted tumor volumes by DeepLab V3\u2009+\u2009model and U-Net model were highly correlated with those delineated manually (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). The DSC of DeepLab V3\u2009+\u2009model was significantly higher than that of U-Net model (0.77 vs 0.75,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05), particularly in those small tumor volumes of\u2009&lt;\u200910 cm\n                      <jats:sup>3<\/jats:sup>\n                      (0.74 vs 0.70,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71\u20130.91) with manual delineation. The radiomics extracted by DeepLab V3\u2009+\u2009model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Both DeepLab V3\u2009+\u2009and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3\u2009+\u2009had a better performance than U-Net.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance statement<\/jats:title>\n                    <jats:p>The deep learning model, DeepLab V3\u2009+\u2009, exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>DeepLab V3<\/jats:italic>\n                      \u2009+\u2009\n                      <jats:italic>and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>DeepLab V3<\/jats:italic>\n                      \u2009+\u2009\n                      <jats:italic>model was more accurate than U-Net in automated segmentation, especially on small tumors.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>DeepLab V3<\/jats:italic>\n                      \u2009+\u2009\n                      <jats:italic>exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-023-09827-2","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T03:03:03Z","timestamp":1687230183000},"page":"6548-6556","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning"],"prefix":"10.1007","volume":"33","author":[{"given":"Yu-Chun","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gigin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumit","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Hua","family":"Yeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiun-Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chien-Yu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsung-Ying","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheung-Fat","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1842-6179","authenticated-orcid":false,"given":"Shu-Hang","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"9827_CR1","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1007\/s10147-020-01671-4","volume":"25","author":"Z Lin","year":"2020","unstructured":"Lin Z, Lin H, Lin C (2020) Dynamic prediction of cancer-specific survival for primary hypopharyngeal squamous cell carcinoma. 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