{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:08:53Z","timestamp":1789013333730,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","issue":"16","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study aimed to integrate positron emission tomography\/computed tomography (PET\/CT) metrics and genetic mutations to optimize the risk stratification for diffuse large B\u2010cell lymphoma (DLBCL) patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The data of 94 primary DLBCL patients with baseline PET\/CT examination completed in the Shandong Cancer Hospital and Institute (Jinan, China) were analyzed to establish a training cohort. An independent cohort of 45 DLBCL patients with baseline PET\/CT examination from other hospitals was established for external validation. The baseline total metabolic tumor volume (TMTV) and the largest distance between two lesions (Dmax) standardized by patient body surface area (SDmax) were calculated. The pretreatment pathological tissues of all patients were sequenced by a lymphopanel including 43 genes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The optimal TMTV cutoff was 285.3\u2009cm<jats:sup>3<\/jats:sup> and the optimal SDmax cutoff was 0.135\u2009m<jats:sup>\u22121<\/jats:sup>. <jats:italic>TP53<\/jats:italic> status was found as an independent predictive factor significantly affecting complete remission (<jats:italic>p<\/jats:italic>\u2009=\u20090.001). TMTV, SDmax, and <jats:italic>TP53<\/jats:italic> status were the main factors of the nomogram and could stratify the patients into four distinct subgroups based on their predicted progression\u2010free survival (PFS). The calibration curve demonstrated satisfactory agreement between the predicted and actual 1\u2010year PFS of the patients. The receiver operating characteristic curves showed this nomogram based on PET\/CT metrics and <jats:italic>TP53<\/jats:italic> mutations had a better predictive ability than the clinic risk scores. Similar results were identified upon external validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The nomogram based on imaging factors and <jats:italic>TP53<\/jats:italic> mutations could lead to a more accurate selection of DLBCL patients with rapid progression, to increase tailor therapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.6295","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T10:59:15Z","timestamp":1687863555000},"page":"16734-16743","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A nomogram for predicting the rapid progression of diffuse large B\u2010cell lymphoma established by combining baseline <scp>PET<\/scp>\/<scp>CT<\/scp> total metabolic tumor volume, lesion diffusion, and <scp><i>TP53<\/i><\/scp> mutations"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7382-4623","authenticated-orcid":false,"given":"Cong","family":"Liu","sequence":"first","affiliation":[{"name":"National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer Tianjin Medical University Cancer Institute and Hospital  Tianjin China"},{"name":"Department of Radiation Oncology Tianjin Medical University  Tianjin China"},{"name":"Department of Internal Medicine\u2010Oncology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyue","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0787-8089","authenticated-orcid":false,"given":"Zhenjiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5390-7269","authenticated-orcid":false,"given":"Baosheng","family":"Li","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer Tianjin Medical University Cancer Institute and Hospital  Tianjin China"},{"name":"Department of Radiation Oncology Tianjin Medical University  Tianjin China"},{"name":"Department of Radiation Oncology, 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