{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T23:18:29Z","timestamp":1785799109603,"version":"3.56.0"},"reference-count":31,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Science and Technology Planning Project of the Jiangxi Provincial Health Commission","award":["202410394"],"award-info":[{"award-number":["202410394"]}]},{"name":"The Science and Technology Planning Project of the Jiangxi Provincial Health Commission","award":["202510509"],"award-info":[{"award-number":["202510509"]}]},{"name":"The Science and Technology Planning Project of the Jiangxi Provincial Administration of Traditional Chinese Medicine","award":["2025021340"],"award-info":[{"award-number":["2025021340"]}]},{"name":"The Science and Technology Planning Project of the Jiangxi Provincial Administration of Traditional Chinese Medicine","award":["2025021565"],"award-info":[{"award-number":["2025021565"]}]},{"name":"The Open Research Fund of Jiangxi Cancer Hospital & Institute","award":["KFJJ2023YB16"],"award-info":[{"award-number":["KFJJ2023YB16"]}]},{"name":"The \u201cFive-level Progressive\u201d Talent Cultivation Project of Jiangxi Cancer Hospital & Institute","award":["WCDJ2024QH03"],"award-info":[{"award-number":["WCDJ2024QH03"]}]},{"name":"The Start-up Fund for Doctoral Research of Jiangxi Cancer Hospital","award":["BSQDJ2024007"],"award-info":[{"award-number":["BSQDJ2024007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate prediction of pathological complete response (pCR) following neoadjuvant therapy (NAT) is critical for optimizing treatment in breast cancer. This study develops and validates an interpretable, cost-effective machine learning (ML) model integrating computed tomography (CT)-based body composition parameters with routine inflammatory and nutritional biomarkers to predict pCR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this retrospective single-center study (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0189; January 2019\u2013June 2023), patients were divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0142) and independent temporal test (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a047) sets. CT-based body composition parameters and blood test variables were analyzed. Independent predictors were identified\n                      <jats:italic>via<\/jats:italic>\n                      Least Absolute Shrinkage and Selection Operator and multivariate logistic regression. Eight ML algorithms were compared, and the optimal model was selected based on Area Under the Curve (AUC), calibration, and clinical utility. SHapley Additive exPlanations (SHAP) analysis visualized predictive contributions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six independent predictors were identified: visceral adipose tissue density, skeletal muscle density, intramuscular adipose tissue content, albumin-to-alkaline phosphatase ratio, systemic inflammation response index, and molecular subtype. The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance, achieving an area under the curve (AUC) of 0.888 (95% CI [0.837\u20130.939]) in internal validation and 0.831 (95% CI [0.723\u20130.938]) in the independent test set. The model exhibited good calibration (Brier score = 0.180). SHAP analysis highlighted the contribution of host-related factors alongside tumor biology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This interpretable ML model effectively integrates host-related body composition and inflammatory-nutritional markers to predict pCR. By utilizing routinely available data, this approach offers a practical, accessible tool for initial risk stratification, complementing existing imaging-based strategies and supporting personalized clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj.21051","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:54:23Z","timestamp":1774860863000},"page":"e21051","source":"Crossref","is-referenced-by-count":1,"title":["Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study"],"prefix":"10.7717","volume":"14","author":[{"given":"Linhua","family":"Zhong","sequence":"first","affiliation":[{"name":"Department of Radiology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical 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Nanchang, Jiangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"10.7717\/peerj.21051\/ref-1","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1200\/JCO.19.02309","article-title":"Estrogen and progesterone receptor testing in breast 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