{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T15:35:48Z","timestamp":1772033748687,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University","award":["2024-LCYJ-PY-13"],"award-info":[{"award-number":["2024-LCYJ-PY-13"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Nucl Med"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s12149-025-02133-5","type":"journal-article","created":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T05:34:55Z","timestamp":1763789695000},"page":"336-348","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prognostic value of a combined model integrating clinical and PET radiomics parameters in metastatic melanoma: A dual-center retrospective study"],"prefix":"10.1007","volume":"40","author":[{"given":"Ruihe","family":"Lai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zekun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dandan","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhi","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianqian","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongyang","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"2133_CR1","doi-asserted-by":"publisher","first-page":"e008611","DOI":"10.1136\/jitc-2023-008611","volume":"12","author":"L Zhao","year":"2024","unstructured":"Zhao L, Ren Y, Zhang G, Zheng K, Wang J, Sha H, et al. Single-arm study of camrelizumab plus apatinib for patients with advanced mucosal melanoma. J Immunother Cancer. 2024;12:e008611.","journal-title":"J Immunother Cancer"},{"key":"2133_CR2","doi-asserted-by":"publisher","first-page":"388","DOI":"10.3390\/diagnostics12020388","volume":"12","author":"A Flaus","year":"2022","unstructured":"Flaus A, Habouzit V, de Leiris N, Vuillez J-P, Leccia M-T, Simonson M, et al. Outcome prediction at patient level derived from pre-treatment 18F-FDG PET due to machine learning in metastatic melanoma treated with anti-PD1 treatment. Diagnostics. 2022;12:388.","journal-title":"Diagnostics"},{"key":"2133_CR3","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/S0140-6736(23)00821-8","volume":"402","author":"GV Long","year":"2023","unstructured":"Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402:485\u2013502.","journal-title":"Lancet"},{"key":"2133_CR4","doi-asserted-by":"publisher","first-page":"3709","DOI":"10.1007\/s00259-023-06327-9","volume":"50","author":"C Sachpekidis","year":"2023","unstructured":"Sachpekidis C, Stein-Thoeringer CK, Kopp-Schneider A, Weru V, Dimitrakopoulou-Strauss A, Hassel JC. Can physiologic colonic [18F]FDG uptake in PET\/CT imaging predict response to immunotherapy in metastatic melanoma? Eur J Nucl Med Mol Imaging. 2023;50:3709\u201322.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12040929","volume":"12","author":"L Filippi","year":"2022","unstructured":"Filippi L, Bianconi F, Schillaci O, Spanu A, Palumbo B. The role and potential of 18F-FDG PET\/CT in malignant melanoma: prognostication, monitoring response to targeted and immunotherapy, and radiomics. Diagnostics. 2022;12:929.","journal-title":"Diagnostics"},{"key":"2133_CR6","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1007\/s00259-020-04792-0","volume":"47","author":"R Nakamoto","year":"2020","unstructured":"Nakamoto R, Zaba LC, Rosenberg J, Reddy SA, Nobashi TW, Davidzon G, et al. Prognostic value of volumetric PET parameters at early response evaluation in melanoma patients treated with immunotherapy. Eur J Nucl Med Mol Imaging. 2020;47:2787\u201395.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR7","doi-asserted-by":"publisher","first-page":"2573","DOI":"10.1007\/s00259-020-05103-3","volume":"48","author":"K Prigent","year":"2021","unstructured":"Prigent K, Lasnon C, Ezine E, Janson M, Coudrais N, Joly E, et al. Assessing immune organs on 18F-FDG PET\/CT imaging for therapy monitoring of immune checkpoint inhibitors: inter-observer variability, prognostic value and evolution during the treatment course of melanoma patients. Eur J Nucl Med Mol Imaging. 2021;48:2573\u201385.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR8","first-page":"256","volume":"38","author":"A Bianchi","year":"2023","unstructured":"Bianchi A, De Rimini ML, Sciuto R, Annovazzi A, Di Traglia S, Bauckneht M, et al. Can baseline [18F]FDG PET\/CT predict response to immunotherapy after 6 months and overall survival in patients with lung cancer or malignant melanoma? A multicenter retrospective study. Cancer Biother Radiopharm. 2023;38:256\u201367.","journal-title":"Cancer Biother Radiopharm"},{"key":"2133_CR9","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.cell.2017.01.018","volume":"168","author":"N McGranahan","year":"2017","unstructured":"McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613\u201328.","journal-title":"Cell"},{"key":"2133_CR10","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1007\/s00259-021-05242-1","volume":"48","author":"G Wu","year":"2021","unstructured":"Wu G, Jochems A, Refaee T, Ibrahim A, Yan C, Sanduleanu S, et al. Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging. 2021;48:3961\u201374.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR11","doi-asserted-by":"publisher","first-page":"5095","DOI":"10.1038\/s41467-022-32816-w","volume":"13","author":"W Huang","year":"2022","unstructured":"Huang W, Jiang Y, Xiong W, Sun Z, Chen C, Yuan Q, et al. Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer. Nat Commun. 2022;13:5095.","journal-title":"Nat Commun"},{"key":"2133_CR12","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.semcancer.2023.07.003","volume":"95","author":"Y Meng","year":"2023","unstructured":"Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: technical advances and clinical application. Semin Cancer Biol. 2023;95:75\u201387.","journal-title":"Semin Cancer Biol"},{"key":"2133_CR13","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.semcancer.2020.04.002","volume":"72","author":"A Conti","year":"2021","unstructured":"Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol. 2021;72:238\u201350.","journal-title":"Semin Cancer Biol"},{"key":"2133_CR14","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.omtn.2023.06.001","volume":"33","author":"G Chu","year":"2023","unstructured":"Chu G, Ji X, Wang Y, Niu H. Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer. Mol Ther Nucleic Acids. 2023;33:110\u201326.","journal-title":"Mol Ther Nucleic Acids"},{"key":"2133_CR15","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1111\/pcmr.13184","volume":"37","author":"M Lever","year":"2024","unstructured":"Lever M, Bogner S, Giousmas M, Mairinger FD, Baba HA, Richly H, et al. Prognostic value of clinical and radiomic parameters in patients with liver metastases from uveal melanoma. Pigment Cell Melanoma Res. 2024. https:\/\/doi.org\/10.1111\/pcmr.13184.","journal-title":"Pigment Cell Melanoma Res"},{"key":"2133_CR16","doi-asserted-by":"publisher","first-page":"704607","DOI":"10.3389\/fonc.2021.704607","volume":"11","author":"A Guerrisi","year":"2021","unstructured":"Guerrisi A, Russillo M, Loi E, Ganeshan B, Ungania S, Desiderio F, et al. Exploring CT texture parameters as predictive and response imaging biomarkers of survival in patients with metastatic melanoma treated with PD-1 inhibitor nivolumab: A pilot study using a Delta-Radiomics approach. Front Oncol. 2021;11:704607.","journal-title":"Front Oncol"},{"key":"2133_CR17","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1007\/s11548-022-02662-8","volume":"17","author":"G Ungan","year":"2022","unstructured":"Ungan G, Lavandier A-F, Rouanet J, Hordonneau C, Chauveau B, Pereira B, et al. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J Comput Assist Radiol Surg. 2022;17:1867\u201377.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2133_CR18","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1001\/jamaoncol.2021.6818","volume":"8","author":"L Dercle","year":"2022","unstructured":"Dercle L, Zhao B, G\u00f6nen M, Moskowitz CS, Firas A, Beylergil V, et al. Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis. JAMA Oncol. 2022;8:385\u201392.","journal-title":"JAMA Oncol"},{"key":"2133_CR19","first-page":"3065","volume":"13","author":"J McGale","year":"2023","unstructured":"McGale J, Hama J, Yeh R, Vercellino L, Sun R, Lopci E, et al. Artificial intelligence and radiomics: clinical applications for patients with advanced melanoma treated with immunotherapy. Diagn Basel Switz. 2023;13:3065.","journal-title":"Diagn Basel Switz"},{"key":"2133_CR20","doi-asserted-by":"crossref","unstructured":"Nioche C, Orlhac F, Boughdad S, Reuz\u00e9 S, Goya-Outi J, Robert C, et al. LIFEx: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786\u20139.","DOI":"10.1158\/0008-5472.CAN-18-0125"},{"key":"2133_CR21","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/s00259-014-2961-x","volume":"42","author":"R Boellaard","year":"2015","unstructured":"Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W, et al. FDG PET\/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328\u201354.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR22","first-page":"369","volume":"52","author":"F Tixier","year":"2011","unstructured":"Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges J-P, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med Off Publ Soc Nucl Med. 2011;52:369\u201378.","journal-title":"J Nucl Med Off Publ Soc Nucl Med"},{"key":"2133_CR23","doi-asserted-by":"publisher","first-page":"6953","DOI":"10.1007\/s00330-022-08830-3","volume":"32","author":"Y Zheng","year":"2022","unstructured":"Zheng Y, Zhou D, Liu H, Wen M. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors. Eur Radiol. 2022;32:6953\u201364.","journal-title":"Eur Radiol"},{"key":"2133_CR24","doi-asserted-by":"publisher","first-page":"2904","DOI":"10.1007\/s00259-021-05220-7","volume":"48","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Ma X-L, Zhang T, Wang J, Zhang T, Tian R. Use of radiomics based on 18F-FDG PET\/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging. 2021;48:2904\u201313.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR25","doi-asserted-by":"publisher","first-page":"2902","DOI":"10.1007\/s00259-022-05717-9","volume":"49","author":"C Jiang","year":"2022","unstructured":"Jiang C, Li A, Teng Y, Huang X, Ding C, Chen J, et al. Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging. 2022;49:2902\u201316.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR26","doi-asserted-by":"publisher","first-page":"5730","DOI":"10.1007\/s00330-022-08668-9","volume":"32","author":"C Jiang","year":"2022","unstructured":"Jiang C, Huang X, Li A, Teng Y, Ding C, Chen J, et al. Radiomics signature from [18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma. Eur Radiol. 2022;32:5730\u201341.","journal-title":"Eur Radiol"},{"key":"2133_CR27","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441\u20136.","journal-title":"Eur J Cancer"},{"key":"2133_CR28","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749\u201362.","journal-title":"Nat Rev Clin Oncol"},{"key":"2133_CR29","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/s00259-018-4138-5","volume":"46","author":"SH Moon","year":"2019","unstructured":"Moon SH, Kim J, Joung J-G, Cha H, Park W-Y, Ahn JS, et al. Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging. 2019;46:446\u201354.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR30","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s00259-012-2247-0","volume":"40","author":"S Chicklore","year":"2013","unstructured":"Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET\/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133\u201340.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2133_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.crad.2016.09.013","volume":"72","author":"E Sala","year":"2017","unstructured":"Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017;72:3\u201310.","journal-title":"Clin Radiol"},{"key":"2133_CR32","doi-asserted-by":"publisher","first-page":"4122","DOI":"10.1038\/s41467-023-39933-0","volume":"14","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, Carrillo-Perez F, Pizurica M, Heiland DH, Gevaert O. Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun. 2023;14:4122.","journal-title":"Nat Commun"},{"key":"2133_CR33","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/j.ccell.2021.07.009","volume":"39","author":"D Pe\u2019er","year":"2021","unstructured":"Pe\u2019er D, Ogawa S, Elhanani O, Keren L, Oliver TG, Wedge D. Tumor heterogeneity. Cancer Cell. 2021;39:1015\u20137.","journal-title":"Cancer Cell"},{"key":"2133_CR34","doi-asserted-by":"crossref","unstructured":"Jiang Z, Jia R, Zhang L et al. Pre-trained tabular transformer for real-time, efficient, stable radiomics data processing: a comprehensive study. 2023 IEEE International Conference on E-health Networking, Application & Services (Healthcom). IEEE, 2023: 276\u2013281.","DOI":"10.1109\/Healthcom56612.2023.10472397"},{"key":"2133_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2025.108859","volume":"269","author":"Q Wei","year":"2025","unstructured":"Wei Q, Zeng S, Jiang Z, Xu F, Shi S, Wen W, et al. Topological representation based on wavelet transform as a novel imaging biomarker for tumor diagnosis in ultrasound images: a comprehensive study. Comput Methods Programs Biomed. 2025;269:108859.","journal-title":"Comput Methods Programs Biomed"},{"key":"2133_CR36","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1007\/s00259-024-07024-x","volume":"52","author":"C Jiang","year":"2025","unstructured":"Jiang C, Jiang Z, Zhang X, Qu L, Fu K, Teng Y, et al. Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer\/T-cell lymphoma. Eur J Nucl Med Mol Imaging. 2025;52:1739\u201350.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"4","key":"2133_CR37","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.26599\/BDMA.2024.9020058","volume":"7","author":"Z Jiang","year":"2024","unstructured":"Jiang Z, Cheng D, Qin Z, et al. TV-SAM: increasing zero-shot segmentation performance on multimodal medical images using GPT-4 generated descriptive prompts without human annotation. Big Data Min Anal. 2024;7(4):1199\u2013211.","journal-title":"Big Data Min Anal"},{"issue":"2","key":"2133_CR38","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1002\/med4.21","volume":"1","author":"H Ali","year":"2023","unstructured":"Ali H. Generative pre-trained transformer 4 in healthcare: challenges, opportunities, and recommendations. Med Adv. 2023;1(2):163\u20136.","journal-title":"Med Adv"},{"key":"2133_CR39","doi-asserted-by":"publisher","first-page":"4156","DOI":"10.1007\/s00330-020-07507-z","volume":"31","author":"M Pi\u00f1eiro-Fiel","year":"2021","unstructured":"Pi\u00f1eiro-Fiel M, Moscoso A, Lado-Cacheiro L, Pombo-Pas\u00edn M, Rey-Bretal D, G\u00f3mez-Lado N, et al. Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer? Eur Radiol. 2021;31:4156\u201365.","journal-title":"Eur Radiol"},{"key":"2133_CR40","doi-asserted-by":"publisher","first-page":"387","DOI":"10.2967\/jnumed.116.181859","volume":"58","author":"F Orlhac","year":"2017","unstructured":"Orlhac F, Nioche C, Soussan M, Buvat I. Understanding changes in tumor texture indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med. 2017;58:387\u201392.","journal-title":"J Nucl Med"},{"key":"2133_CR41","doi-asserted-by":"publisher","first-page":"18795","DOI":"10.1038\/s41598-021-98310-3","volume":"11","author":"A Flaus","year":"2021","unstructured":"Flaus A, Habouzit V, De Leiris N, Vuillez JP, Leccia MT, Perrot JL, et al. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep. 2021;11:18795.","journal-title":"Sci Rep"},{"key":"2133_CR42","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.2214\/AJR.18.19796","volume":"212","author":"Y Sanli","year":"2019","unstructured":"Sanli Y, Leake J, Odu A, Xi Y, Subramaniam RM. Tumor heterogeneity on FDG PET\/CT and immunotherapy: an imaging biomarker for predicting treatment response in patients with metastatic melanoma. AJR Am J Roentgenol. 2019;212:1318\u201326.","journal-title":"AJR Am J Roentgenol"},{"key":"2133_CR43","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.suronc.2019.05.005","volume":"29","author":"P Sanghani","year":"2019","unstructured":"Sanghani P, Ti AB, Kam King NK, Ren H. Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients. Surg Oncol. 2019;29:178\u201383.","journal-title":"Surg Oncol"},{"key":"2133_CR44","doi-asserted-by":"publisher","first-page":"815","DOI":"10.21873\/anticanres.13180","volume":"39","author":"M Katsurada","year":"2019","unstructured":"Katsurada M, Nagano T, Tachihara M, Kiriu T, Furukawa K, Koyama K, et al. Baseline tumor size as a predictive and prognostic factor of immune checkpoint inhibitor therapy for non-small cell lung cancer. Anticancer Res. 2019;39:815\u201325.","journal-title":"Anticancer Res"},{"key":"2133_CR45","doi-asserted-by":"publisher","first-page":"e179854","DOI":"10.1172\/jci.insight.179854","volume":"9","author":"R Rosberg","year":"2024","unstructured":"Rosberg R, Smolag KI, Sj\u00f6lund J, Johansson E, Bergelin C, Wahld\u00e9n J, et al. Hypoxia-induced complement component 3 promotes aggressive tumor growth in the glioblastoma microenvironment. JCI Insight. 2024;9:e179854.","journal-title":"JCI Insight"},{"key":"2133_CR46","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJWL Aerts","year":"2014","unstructured":"Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.","journal-title":"Nat Commun"},{"key":"2133_CR47","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1016\/j.ejca.2007.01.031","volume":"43","author":"S-Y Suh","year":"2007","unstructured":"Suh S-Y, Ahn H-Y. Lactate dehydrogenase as a prognostic factor for survival time of terminally ill cancer patients: a preliminary study. Eur J Cancer. 2007;43:1051\u20139.","journal-title":"Eur J Cancer"},{"key":"2133_CR48","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41571-022-00686-2","volume":"19","author":"G Claps","year":"2022","unstructured":"Claps G, Faouzi S, Quidville V, Chehade F, Shen S, Vagner S, et al. The multiple roles of LDH in cancer. Nat Rev Clin Oncol. 2022;19:749\u201362.","journal-title":"Nat Rev Clin Oncol"},{"key":"2133_CR49","first-page":"472","volume":"67","author":"JE Gershenwald","year":"2017","unstructured":"Gershenwald JE, Scolyer RA, Hess KR, Sondak VK, Long GV, Ross MI, et al. Melanoma staging: evidence\u2010based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin. 2017;67:472\u201392.","journal-title":"CA Cancer J Clin"}],"container-title":["Annals of Nuclear Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12149-025-02133-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12149-025-02133-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12149-025-02133-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T14:02:43Z","timestamp":1772028163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12149-025-02133-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,22]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2133"],"URL":"https:\/\/doi.org\/10.1007\/s12149-025-02133-5","relation":{},"ISSN":["0914-7187","1864-6433"],"issn-type":[{"value":"0914-7187","type":"print"},{"value":"1864-6433","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,22]]},"assertion":[{"value":"22 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethical approval for this study was obtained from the Nanjing Drum Tower hospital ethics committee (approval number: 2024-553-01). Written informed consent was obtained for anonymised patient information to be published in this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"\u00a0Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Generative AI use"}}]}}