{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T14:01:49Z","timestamp":1789740109700,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82430065"],"award-info":[{"award-number":["82430065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Whole\u2010body bone scintigraphy (WBS) remains a widely used first\u2010line screening tool for bone metastasis, but differentiating MDP\u2010avid metastatic lesions from benign bone abnormalities in non\u2010small cell lung cancer (NSCLC) remains challenging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and externally validate a radiomics and deep learning bone signature (RDB) derived from planar WBS images for prediction of MDP\u2010avid bone metastasis and to explore its prognostic value in NSCLC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective analysis was conducted on NSCLC patients who underwent WBS imaging across two centers. Eight hundred twenty\u2010nine patients from Center 1 were used for training and internal validation, whereas 574 patients from Center 2 were used for external validation. Radiomics features from 2 segmentation methods and deep learning features were extracted from planar WBS images. The RDB nomogram was generated from clinical, radiomics, and deep learning features using the best\u2010performing machine learning algorithm. Model performance was assessed using the area under the receiver operating characteristic curve. Shapley additive explanations were used to interpret the model output, and Kaplan\u2010Meier survival analysis evaluated prognostic stratification. The locked model was also translated into a research\u2010use\u2010only local desktop application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There were 720 patients with bone metastasis (202 solitary and 518 multiple) and 683 without bone metastasis. The final RDB nomogram included 8 radiomics features, 11 deep learning features, and 2 clinical factors. The RDB nomogram achieved an area under the curve of 0.857 (95% CI, 0.811\u20130.904) in the internal validation cohort and 0.870 (95% CI, 0.840\u20130.900) in the external validation cohort for predicting MDP\u2010avid bone metastasis. The Rad\u2010score was the most influential predictor. Kaplan\u2010Meier analysis showed significant survival differences for solitary\u2010lesion patients in both centers. A deployable desktop implementation of the final model was completed to support reproducibility and external use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The RDB nomogram is a promising noninvasive tool for predicting MDP\u2010avid bone metastasis in NSCLC patients and may provide prognostic value, particularly for solitary lesions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70730","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T03:46:52Z","timestamp":1785815212000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable radiomics\u2013deep learning nomogram from whole\u2010body bone scintigraphy for MDP\u2010avid bone metastasis prediction in NSCLC"],"prefix":"10.1002","volume":"27","author":[{"given":"Weihao","family":"Zhai","sequence":"first","affiliation":[{"name":"Department of Radiology Second Affiliated Hospital of Naval Medical University  Shanghai China"},{"name":"College of Health Sciences and Engineering University of Shanghai for Science and Technology  Shanghai China"},{"name":"Department of Nuclear Medicine Huadong Hospital Affiliated to Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology Second Affiliated Hospital of Naval Medical University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology Second Affiliated Hospital of Naval Medical University  Shanghai China"},{"name":"College of Health Sciences and Engineering University of Shanghai for Science 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