{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:57:33Z","timestamp":1781715453754,"version":"3.54.5"},"reference-count":43,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Excessive skeletal muscle loss during neoadjuvant concurrent chemoradiotherapy (NACRT) is significantly related to survival outcomes of oesophageal cancer. However, the conventional method for measuring skeletal muscle mass requires computed tomography (CT) images, and the calculation process is labour\u2010intensive. In this study, we built machine\u2010learning models to predict excessive skeletal muscle loss, using only body mass index data and blood laboratory test results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We randomly split the data of 232 male patients treated with NACRT for oesophageal cancer into the training (70%) and test (30%) sets for 1000 iterations. The naive random over sampling method was applied to each training set to adjust for class imbalance, and we used seven different machine\u2010learning algorithms to predict excessive skeletal muscle loss. We used five input variables, namely, relative change percentage in body mass index, albumin, prognostic nutritional index, neutrophil\u2010to\u2010lymphocyte ratio, and platelet\u2010to\u2010lymphocyte ratio over 50\u00a0days. According to our previous study results, which used the maximal\n                      <jats:italic>\u03c7<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      method, 10.0% decrease of skeletal muscle index over 50\u00a0days was determined as the cut\u2010off value to define the excessive skeletal muscle loss.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The five input variables were significantly different between the excessive and the non\u2010excessive muscle loss group (all\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0&lt;\u00a00.001). None of the clinicopathologic variables differed significantly between the two groups. The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve\u00a0=\u00a00.808, 95% confidence interval (CI): 0.708\u20130.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI: 52.6%\u201389.5%) and 74.5% (95% CI: 62.7%\u201386.3%), respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning model using the ensemble of logistic regression and support vector classifier most effectively predicted the excessive muscle loss following NACRT in patients with oesophageal cancer. This model can easily screen the patients with excessive muscle loss who need an active intervention or timely care following NACRT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.12747","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T01:29:07Z","timestamp":1623979747000},"page":"1144-1152","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9515-1623","authenticated-orcid":false,"given":"Han Gyul","family":"Yoon","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5643-5519","authenticated-orcid":false,"given":"Dongryul","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-6127","authenticated-orcid":false,"given":"Jae Myoung","family":"Noh","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4736-8270","authenticated-orcid":false,"given":"Won Kyung","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9683-4111","authenticated-orcid":false,"given":"Jong\u2010Mu","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Division of Hematology\u2010Oncology, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7815-3336","authenticated-orcid":false,"given":"Hong Kwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6583-9104","authenticated-orcid":false,"given":"Jae 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