{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T11:18:55Z","timestamp":1788261535057,"version":"build-2803163510"},"reference-count":36,"publisher":"IOP Publishing","issue":"17","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82172024"],"award-info":[{"award-number":["82172024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Purpose.<\/jats:italic>\n                    Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.\n                    <jats:italic>Methods.<\/jats:italic>\n                    This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human\u2013machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.\n                    <jats:italic>Results.<\/jats:italic>\n                    In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521\u20130.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638\u20130.7781), two-phase models (AUC range: 0.7826\u20130.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853\u20130.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.\n                    <jats:italic>Conclusions<\/jats:italic>\n                    . The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae98dd","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T22:55:11Z","timestamp":1786575311000},"page":"175004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9428-5456","authenticated-orcid":true,"given":"Xiaohui","family":"Cheng","sequence":"first","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7709-3709","authenticated-orcid":true,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"},{"name":"The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi\u2019an Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering, Guangzhou Clinical Research and Transformation Center for Artificial Liver, Institute of Regenerative Medicine, General Surgery Center, Zhujiang Hospital, Southern Medical University"},{"name":"The State Key Laboratory of Organ Failure Research, Southern Medical University"},{"name":"The Guangdong Provincial Key Laboratory for Prevention and Control of Major Liver Diseases, Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenjie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University Shanghai Cancer Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7466-4366","authenticated-orcid":true,"given":"Shulong","family":"Li","sequence":"additional","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"pmbae98ddbib1","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s00330-019-06417-z","type":"journal-article","article-title":"Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network","volume":"30","author":"Aldoj","year":"2020","journal-title":"Eur. 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