{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T21:36:21Z","timestamp":1792532181414,"version":"4.5.0"},"reference-count":46,"publisher":"American Medical Association (AMA)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women\u2019s Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Overall survival and treatment toxicity outcomes of HNSCC.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n\u2009=\u200996) and internal test set (n\u2009=\u200948) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n\u2009=\u2009377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson\n r<\/jats:italic>\n \u2009=\u20090.99;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04;\n P<\/jats:italic>\n \u2009=\u2009.04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89;\n P<\/jats:italic>\n \u2009=\u2009.006) than no sarcopenia.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n This prognostic study\u2019s findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.28280","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T11:07:49Z","timestamp":1691665669000},"page":"e2328280","source":"Crossref","is-referenced-by-count":49,"title":["Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer"],"prefix":"10.1001","volume":"6","author":[{"given":"Zezhong","family":"Ye","sequence":"first","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anurag","family":"Saraf","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yashwanth","family":"Ravipati","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Hoebers","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"Catalano","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts"},{"name":"Department of Data Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Zha","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Zapaishchykova","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"},{"name":"Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jirapat","family":"Likitlersuang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Guthier","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy B.","family":"Tishler","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan D.","family":"Schoenfeld","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle N.","family":"Margalit","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert I.","family":"Haddad","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond H.","family":"Mak","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Naser","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kareem A.","family":"Wahid","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaakko","family":"Sahlsten","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University School of Science, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel","family":"Jaskari","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University School of Science, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimmo","family":"Kaski","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalto University School of Science, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antti A.","family":"M\u00e4kitie","sequence":"additional","affiliation":[{"name":"Department Otorhinolaryngology\u2013Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clifton D.","family":"Fuller","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo J. W. L.","family":"Aerts","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"},{"name":"Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, the Netherlands"},{"name":"Department of Radiology, Brigham and Women\u2019s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin H.","family":"Kann","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts"},{"name":"Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"issue":"3","key":"zoi230815r1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.v71.3","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"zoi230815r2","doi-asserted-by":"publisher","first-page":"v184","DOI":"10.1093\/annonc\/mdq185","article-title":"Squamous cell carcinoma of the head and neck: EHNS-ESMO-ESTRO clinical practice guidelines for diagnosis, treatment and follow-up.","volume":"21","author":"Gr\u00e9goire","year":"2010","journal-title":"Ann Oncol"},{"issue":"1","key":"zoi230815r3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0360-3016(99)00558-1","article-title":"Toxicity in head and neck cancer: a review of trends and issues.","volume":"47","author":"Trotti","year":"2000","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"1","key":"zoi230815r4","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1093\/ageing\/afy169","article-title":"Sarcopenia: revised European consensus on definition and diagnosis.","volume":"48","author":"Cruz-Jentoft","year":"2019","journal-title":"Age Ageing"},{"issue":"4","key":"zoi230815r5","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.jamda.2011.01.003","article-title":"Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences.","volume":"12","author":"Fielding","year":"2011","journal-title":"J Am Med Dir Assoc"},{"issue":"4","key":"zoi230815r6","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1159\/000356760","article-title":"Sarcopenia, cachexia and aging: diagnosis, mechanisms and therapeutic options\u2014a mini-review.","volume":"60","author":"Ali","year":"2014","journal-title":"Gerontology"},{"issue":"12","key":"zoi230815r7","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.3390\/cancers11121861","article-title":"Cachexia and sarcopenia in older adults with cancer: a comprehensive review.","volume":"11","author":"Dunne","year":"2019","journal-title":"Cancers (Basel)"},{"issue":"1","key":"zoi230815r8","doi-asserted-by":"publisher","DOI":"10.1111\/ecc.v30.1","article-title":"Sarcopenia as an early complication of patients with head and neck cancer with dysphagia.","volume":"30","author":"Silva","year":"2021","journal-title":"Eur J Cancer Care (Engl)"},{"key":"zoi230815r9","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.radonc.2020.03.014","article-title":"Impact of sarcopenia on survival and late toxicity in head and neck cancer patients treated with radiotherapy.","volume":"147","author":"van Rijn-Dekker","year":"2020","journal-title":"Radiother Oncol"},{"issue":"1","key":"zoi230815r10","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s12877-016-0349-4","article-title":"Sarcopenia in daily practice: assessment and management.","volume":"16","author":"Beaudart","year":"2016","journal-title":"BMC Geriatr"},{"issue":"6","key":"zoi230815r11","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1001\/jamaoncol.2015.6339","article-title":"Association of body composition with survival and locoregional control of radiotherapy-treated head and neck squamous cell carcinoma.","volume":"2","author":"Grossberg","year":"2016","journal-title":"JAMA Oncol"},{"issue":"7","key":"zoi230815r12","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1001\/jamaoto.2019.1185","article-title":"Association between sarcopenia and mortality in patients undergoing surgical excision of head and neck cancer.","volume":"145","author":"Stone","year":"2019","journal-title":"JAMA Otolaryngol Head Neck Surg"},{"key":"zoi230815r13","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.oraloncology.2016.09.006","article-title":"Feasibility of using head and neck CT imaging to assess skeletal muscle mass in head and neck cancer patients.","volume":"62","author":"Swartz","year":"2016","journal-title":"Oral Oncol"},{"key":"zoi230815r14","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.812159","article-title":"Establishment and validation of pre-therapy cervical vertebrae muscle quantification as a prognostic marker of sarcopenia in patients with head and neck cancer.","volume":"12","author":"Olson","year":"2022","journal-title":"Front Oncol"},{"issue":"1","key":"zoi230815r15","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1002\/hed.v44.1","article-title":"The association of pretreatment low skeletal muscle mass with chemotherapy dose-limiting toxicity in patients with head and neck cancer undergoing primary chemoradiotherapy with high-dose cisplatin.","volume":"44","author":"Bril","year":"2022","journal-title":"Head Neck"},{"key":"zoi230815r16","doi-asserted-by":"publisher","first-page":"S802","DOI":"10.1016\/S0167-8140(21)07417-X","article-title":"PO-0966 sarcopenia as prognostic marker in elderly HNSCC patients undergoing (chemo-)radiation.","volume":"161","author":"Haehl","year":"2021","journal-title":"Radiother Oncol"},{"key":"zoi230815r17","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.radonc.2019.04.023","article-title":"The impact of sarcopenia on tolerance of radiation and outcome in patients with head and neck cancer receiving chemoradiation.","volume":"137","author":"Ganju","year":"2019","journal-title":"Radiother Oncol"},{"key":"zoi230815r18","doi-asserted-by":"publisher","first-page":"S30","DOI":"10.1016\/j.injury.2022.05.004","article-title":"Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients.","volume":"53","author":"Ackermans","year":"2022","journal-title":"Injury"},{"key":"zoi230815r19","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ejrad.2018.10.031","article-title":"Intra- and interobserver variability in skeletal muscle measurements using computed tomography images.","volume":"109","author":"Perthen","year":"2018","journal-title":"Eur J Radiol"},{"issue":"4","key":"zoi230815r20","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1007\/s00405-019-05307-w","article-title":"Interobserver agreement of skeletal muscle mass measurement on head and neck CT imaging at the level of the third cervical vertebra.","volume":"276","author":"Bril","year":"2019","journal-title":"Eur Arch Otorhinolaryngol"},{"issue":"9","key":"zoi230815r21","doi-asserted-by":"publisher","first-page":"e657","DOI":"10.1016\/S2589-7500(22)00129-7","article-title":"Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.","volume":"4","author":"Hosny","year":"2022","journal-title":"Lancet Digit Health"},{"issue":"3","key":"zoi230815r22","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.210285","article-title":"Deep learning-based detection of intravenous contrast enhancement on CT scans.","volume":"4","author":"Ye","year":"2022","journal-title":"Radiol Artif Intell"},{"issue":"3","key":"zoi230815r24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.07.1398","article-title":"Deep learning for automated outcome prediction in oropharyngeal cancer from tumor and lymph node imaging data.","volume":"114","author":"Ye","year":"2022","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"6","key":"zoi230815r25","doi-asserted-by":"publisher","first-page":"e360","DOI":"10.1016\/S2589-7500(23)00046-8","article-title":"Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.","volume":"5","author":"Kann","year":"2023","journal-title":"Lancet Digit Health"},{"issue":"6","key":"zoi230815r26","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1158\/2767-9764.CRC-22-0152","article-title":"Multi-institutional prognostic modeling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics.","volume":"3","author":"Kazmierski","year":"2023","journal-title":"Cancer Res Commun"},{"issue":"3","key":"zoi230815r27","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.acra.2019.03.011","article-title":"A machine learning algorithm to estimate sarcopenia on abdominal CT.","volume":"27","author":"Burns","year":"2020","journal-title":"Acad Radiol"},{"key":"zoi230815r28","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.580806","article-title":"A deep learning model to automate skeletal muscle area measurement on computed tomography images.","volume":"11","author":"Amarasinghe","year":"2021","journal-title":"Front Oncol"},{"issue":"1","key":"zoi230815r29","doi-asserted-by":"publisher","first-page":"124","DOI":"10.2214\/AJR.21.26486","article-title":"Fully automated deep learning tool for sarcopenia assessment on CT: L1 versus L3 vertebral level muscle measurements for opportunistic prediction of adverse clinical outcomes.","volume":"218","author":"Pickhardt","year":"2022","journal-title":"AJR Am J Roentgenol"},{"key":"zoi230815r30","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.930432","article-title":"Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer.","volume":"12","author":"Naser","year":"2022","journal-title":"Front Oncol"},{"issue":"6","key":"zoi230815r31","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1016\/j.eururo.2014.11.025","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.","volume":"67","author":"Collins","year":"2015","journal-title":"Eur Urol"},{"issue":"6","key":"zoi230815r32","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.","volume":"26","author":"Clark","year":"2013","journal-title":"J Digit Imaging"},{"issue":"1","key":"zoi230815r33","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1038\/s41597-022-01587-w","article-title":"Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer.","volume":"9","author":"Wahid","year":"2022","journal-title":"Sci Data"},{"issue":"7","key":"zoi230815r37","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/S1470-2045(08)70153-0","article-title":"Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study.","volume":"9","author":"Prado","year":"2008","journal-title":"Lancet Oncol"},{"issue":"6","key":"zoi230815r38","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1002\/hed.24760","article-title":"Body mass index and prognosis in patients with head and neck cancer.","volume":"39","author":"Gama","year":"2017","journal-title":"Head Neck"},{"issue":"pt 1","key":"zoi230815r39","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1348\/000711006X126600","article-title":"Computing inter-rater reliability and its variance in the presence of high agreement.","volume":"61","author":"Gwet","year":"2008","journal-title":"Br J Math Stat Psychol"},{"issue":"2","key":"zoi230815r40","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1002\/lary.v131.2","article-title":"Radiologically defined sarcopenia affects survival in head and neck cancer: a meta-analysis.","volume":"131","author":"Wong","year":"2021","journal-title":"Laryngoscope"},{"issue":"8","key":"zoi230815r41","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1016\/j.jand.2020.03.021","article-title":"The association between computed tomography-defined sarcopenia and outcomes in adult patients undergoing radiotherapy of curative intent for head and neck cancer: a systematic review.","volume":"120","author":"Findlay","year":"2020","journal-title":"J Acad Nutr Diet"},{"issue":"9","key":"zoi230815r42","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1016\/j.ijom.2020.12.005","article-title":"Impact of muscle volume loss on acute oral mucositis in patients undergoing concurrent chemoradiotherapy after oral cancer resection.","volume":"50","author":"Yamaguchi","year":"2021","journal-title":"Int J Oral Maxillofac Surg"},{"issue":"11","key":"zoi230815r43","doi-asserted-by":"publisher","first-page":"4000","DOI":"10.1002\/hed.v41.11","article-title":"Sarcopenia, a strong determinant for prolonged feeding tube dependency after chemoradiotherapy for head and neck cancer.","volume":"41","author":"Karsten","year":"2019","journal-title":"Head Neck"},{"key":"zoi230815r44","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.oraloncology.2017.05.012","article-title":"Low skeletal muscle mass is a predictive factor for chemotherapy dose-limiting toxicity in patients with locally advanced head and neck cancer.","volume":"71","author":"Wendrich","year":"2017","journal-title":"Oral Oncol"},{"issue":"2","key":"zoi230815r45","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.clnu.2019.02.029","article-title":"Low muscle mass is associated with early termination of chemotherapy related to toxicity in patients with head and neck cancer.","volume":"39","author":"Sealy","year":"2020","journal-title":"Clin Nutr"},{"key":"zoi230815r46","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.radonc.2021.01.024","article-title":"Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer: a new concept for individually optimised treatment.","volume":"157","author":"Van den Bosch","year":"2021","journal-title":"Radiother Oncol"},{"key":"zoi230815r23","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/978-3-031-27420-6_6","volume-title":"Head and Neck Tumor Segmentation and Outcome Prediction","author":"Jain","year":"2023"},{"key":"zoi230815r34","first-page":"2261","author":"Huang","year":"2017"},{"key":"zoi230815r35","first-page":"234","volume-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015","author":"Ronneberger","year":"2015"},{"key":"zoi230815r36","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/978-3-030-01201-4_22","volume-title":"OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis","author":"Bridge","year":"2018"}],"container-title":["JAMA Network Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/articlepdf\/2808141\/ye_2023_oi_230815_1690904133.7071.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T16:13:32Z","timestamp":1702916012000},"score":1,"resource":{"primary":{"URL":"https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2808141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":46,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8,1]]}},"URL":"https:\/\/doi.org\/10.1001\/jamanetworkopen.2023.28280","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.03.01.23286638","asserted-by":"object"}]},"ISSN":["2574-3805"],"issn-type":[{"value":"2574-3805","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]}}}