{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T05:02:10Z","timestamp":1777006930464,"version":"3.51.4"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate preoperative prediction of lymph node metastasis adjacent to the right recurrent laryngeal nerve (RLN) in esophageal squamous cell carcinoma (ESCC) is crucial for treatment planning. This study aimed to develop and validate an imaging approach that integrates deep learning-based automatic segmentation (nnU-Net) with computed tomography (CT)-derived differential elasticity map (DEM) to predict RLN lymph node metastasis in ESCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 415 patients diagnosed with ESCC. An automatic segmentation model was trained using the nnU-Net framework to delineate lymph nodes near the right RLN. Three-dimensional CT elasticity images were generated from segmented CT voxels, from which radiomic features, including first-order entropy and multi-scale fractal dimensions, were extracted. Statistically significant features were selected using statistical tests and area under the curve (AUC) analyses, and their diagnostic efficacy, probability calibration, and clinical decision-making value were further evaluated in a validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The automatic segmentation model achieved a Dice coefficient of 0.898 \u00b1 0.024. Five DEM-derived radiomic features were ultimately selected: one first-order entropy feature (E_original_firstorder_Entropy) and four fractal dimension-related features. The entropy feature exhibited the highest diagnostic performance (AUC\u00a0=\u00a00.814, sensitivity = 0.895, specificity = 0.709), and fractal dimension-related features were significantly elevated (all P &amp;lt; 0.001) in the metastatic group, indicating increased textural complexity and multi-scale irregularity. Calibration curves demonstrated the robustness of entropy-based probability estimation. Decision curve analysis confirmed the clinical utility of these features, showing positive net benefits across a wide range of threshold probabilities (10%\u201370%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed automated workflow, combining nnU-Net segmentation and DEM-based radiomics, enables accurate and non-invasive prediction of right RLN lymph node metastasis in ESCC. First-order entropy and fractal dimension features offer valuable complementary information beyond conventional radiomics, providing a promising tool for preoperative decision support and personalized treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1748391","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:30:07Z","timestamp":1777005007000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-based automatic segmentation and CT elastography radiomics for preoperative prediction of right recurrent laryngeal nerve lymph node metastasis in esophageal cancer"],"prefix":"10.3389","volume":"16","author":[{"given":"Chao","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingqing","family":"Li","sequence":"additional","affiliation":[{"name":"Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shumin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology Division1, Xiamen Cardiovascular Hospital, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunkui","family":"Ke","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Fujian Provincial Cancer Hospital","place":["Fuzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi\u2019e","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Zhongshan Hospital, Xiamen University School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University","place":["Xiamen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.34301","article-title":"Esophageal cancer in China: Practice and research in the new era","volume":"152","author":"Zhu","year":"2023","journal-title":"Intl J Cancer"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.2147\/CMAR.S182436","article-title":"Anatomy of lymphatic drainage of the esophagus and lymph node metastasis of thoracic esophageal cancer","author":"Wang","year":"2018","journal-title":"CMAR"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0b013e31817bbe59","article-title":"Total number of resected lymph nodes predicts survival in esophageal cancer","volume":"248","author":"Altorki","year":"2008","journal-title":"Ann Surg"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2007.09.003","article-title":"Prognostic significance of the number of lymph node metastases in esophageal cancer","volume":"206","author":"Greenstein","year":"2008","journal-title":"J Am Coll Surgeons"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranon.2018.04.005","article-title":"Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction","volume":"11","author":"Shen","year":"2018","journal-title":"Transl Oncol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20210539","article-title":"Overview of radiomics in prostate imaging and future directions","volume":"95","author":"Cho","year":"2022","journal-title":"Br J Radiol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.4103\/jcrt.jcrt_1755_21","article-title":"A computed tomography-based clinical-radiomics model for prediction of lymph node metastasis in esophageal carcinoma","volume":"17","author":"Li","year":"2021","journal-title":"J Cancer Res Ther"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2024.110225","article-title":"Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer","volume":"113","author":"Jannatdoust","year":"2024","journal-title":"Clin Imaging"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.bioeng.2.1.315","article-title":"Current methods in medical image segmentation","volume":"2","author":"Pham","year":"2000","journal-title":"Annu Rev BioMed Eng"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01178-8","article-title":"Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity in CT images","volume":"38","author":"Cao","year":"2024","journal-title":"J Digit Imaging Inform Med"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15131686","article-title":"Optimizing esophageal cancer diagnosis with computer-aided detection by YOLO models combined with hyperspectral imaging","volume":"15","author":"Weng","year":"2025","journal-title":"Diagnostics"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17122049","article-title":"Evaluation of spectral imaging for early esophageal cancer detection","volume":"17","author":"Chang","year":"2025","journal-title":"Cancers"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.7150\/jca.102759","article-title":"Evaluation of band selection for spectrum-aided visual enhancer (SAVE) for esophageal cancer detection","volume":"16","author":"Chen","year":"2025","journal-title":"J Cancer"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A8293","article-title":"Training and comparison of nnU-Net and DeepMedic methods for autosegmentation of pediatric brain tumors","volume":"45","author":"Vossough","year":"2024","journal-title":"AJNR Am J Neuroradiol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11247334","article-title":"Application of nnU-Net for automatic segmentation of lung lesions on CT images and its implication for radiomic models","volume":"11","author":"Ferrante","year":"2022","journal-title":"JCM"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.09.022","article-title":"A phantom assessment of achievable contouring concordance across multiple treatment planning systems","volume":"117","author":"Pogson","year":"2015","journal-title":"Radiother Oncol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-021-01389-x","article-title":"A deep look into radiomics","volume":"126","author":"Scapicchio","year":"2021","journal-title":"Radiol Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.7150\/thno.30309","article-title":"The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges","volume":"9","author":"Liu","year":"2019","journal-title":"Theranostics"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.04.004","article-title":"Radiomic phenotype features predict pathological response in non-small cell lung cancer","volume":"119","author":"Coroller","year":"2016","journal-title":"Radiother Oncol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-11036-4","article-title":"Diagnostic accuracy of CT and PET\/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer","volume":"35","author":"Li","year":"2024","journal-title":"Eur Radiol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat Commun"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111197","article-title":"Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability","volume":"170","author":"Wu","year":"2024","journal-title":"Eur J Radiol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000971","article-title":"MR elastography in cancer","volume":"58","author":"Guo","year":"2023","journal-title":"Invest Radiol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1111\/bju.12236","article-title":"Elasticity as a biomarker for prostate cancer: a systematic review","volume":"113","author":"Good","year":"2014","journal-title":"BJU Int"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-012-2575-y","article-title":"Can EUS elastography improve lymph node staging in esophageal cancer","volume":"27","author":"Knabe","year":"2013","journal-title":"Surg Endosc"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1039\/D4TB00328D","article-title":"Cellular elasticity in cancer: a review of altered biomechanical features","volume":"12","author":"Radman","year":"2024","journal-title":"J Mater Chem B"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.714660","article-title":"Ultrasound elastography for the evaluation of lymph nodes","volume":"11","author":"Wang","year":"2021","journal-title":"Front Oncol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S0096-3003(98)10096-6","article-title":"Advances in the implementation of the box-counting method of fractal dimension estimation","volume":"105","author":"Foroutan-pour","year":"1999","journal-title":"Appl Math Comput"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2019.06.46","article-title":"A nomogram prediction model for recurrent laryngeal nerve lymph node metastasis in thoracic oesophageal squamous cell carcinoma","volume":"11","author":"Liu","year":"2019","journal-title":"J Thorac Dis"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1136\/thorax-2023-220226","article-title":"Radiomics analysis to predict pulmonary nodule Malignancy using machine learning approaches","volume":"79","author":"Warkentin","year":"2024","journal-title":"Thorax"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2024.10.011","article-title":"A stacked multimodality model based on functional MRI features and deep learning radiomics for predicting the early response to radiotherapy in nasopharyngeal carcinoma","volume":"32","author":"Wang","year":"2025","journal-title":"Acad Radiol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"19259","DOI":"10.1038\/s41598-024-68158-4","article-title":"A reference framework for standardization and harmonization of CT radiomics features on cadaveric sample","volume":"14","author":"Levi","year":"2024","journal-title":"Sci Rep"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1748391\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:30:14Z","timestamp":1777005014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1748391\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":34,"alternative-id":["10.3389\/fonc.2026.1748391"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1748391","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"article-number":"1748391"}}