{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:14:47Z","timestamp":1772165687381,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Scientific Research Project of Fujian for Youth","award":["no. 2020QNB062"],"award-info":[{"award-number":["no. 2020QNB062"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01715-z","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T07:17:50Z","timestamp":1747379870000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma"],"prefix":"10.1186","volume":"25","author":[{"given":"Xiaoxia","family":"Li","sequence":"first","affiliation":[]},{"given":"Yi","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Shunfa","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Funan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chenchen","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Jianjun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Dengqiang","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"1715_CR1","doi-asserted-by":"crossref","unstructured":"Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7\u201334.","DOI":"10.3322\/caac.21551"},{"issue":"1","key":"1715_CR2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.eururo.2018.08.036","volume":"75","author":"U Capitanio","year":"2019","unstructured":"Capitanio U, Bensalah K, Bex A, et al. Epidemiology of renal cell carcinoma. Eur Urol. 2019;75(1):74\u201384.","journal-title":"Eur Urol"},{"issue":"23","key":"1715_CR3","doi-asserted-by":"publisher","first-page":"2246","DOI":"10.1056\/NEJMoa1611406","volume":"375","author":"A Ravaud","year":"2016","unstructured":"Ravaud A, Motzer RJ, Pandha HS, et al. Adjuvant Sunitinib in High-Risk Renal-Cell carcinoma after nephrectomy. N Engl J Med. 2016;375(23):2246\u201354.","journal-title":"N Engl J Med"},{"issue":"9","key":"1715_CR4","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1038\/s41571-019-0218-0","volume":"16","author":"F Martins","year":"2019","unstructured":"Martins F, Sofiya L, Sykiotis GP, et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat Rev Clin Oncol. 2019;16(9):563\u201380.","journal-title":"Nat Rev Clin Oncol"},{"issue":"7","key":"1715_CR5","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1002\/cncr.11234","volume":"97","author":"BC Leibovich","year":"2003","unstructured":"Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer. 2003;97(7):1663\u201371.","journal-title":"Cancer"},{"issue":"1","key":"1715_CR6","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1097\/01.ju.0000148261.19532.2c","volume":"173","author":"M Sorbellini","year":"2005","unstructured":"Sorbellini M, Kattan MW, Snyder ME, et al. A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma. J Urol. 2005;173(1):48\u201351.","journal-title":"J Urol"},{"issue":"23","key":"1715_CR7","doi-asserted-by":"publisher","first-page":"2062","DOI":"10.1200\/JCO.19.00107","volume":"37","author":"AF Correa","year":"2019","unstructured":"Correa AF, Jegede O, Haas NB, et al. Predicting renal Cancer recurrence: defining limitations of existing prognostic models with prospective Trial-Based validation. J Clin Oncol. 2019;37(23):2062\u201371.","journal-title":"J Clin Oncol"},{"key":"1715_CR8","doi-asserted-by":"crossref","unstructured":"Gillies RJ, Kinahan PE, Hricak H, Radiomics. Images are more than pictures. They Are Data Radiol. 2016;278(2):563\u201377.","DOI":"10.1148\/radiol.2015151169"},{"issue":"12","key":"1715_CR9","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, et al. Radiomics: the Bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749\u201362.","journal-title":"Nat Rev Clin Oncol"},{"key":"1715_CR10","doi-asserted-by":"publisher","first-page":"876664","DOI":"10.3389\/fonc.2022.876664","volume":"12","author":"L Jian","year":"2022","unstructured":"Jian L, Liu Y, Xie Y, Jiang S, Ye M, Lin HMRI. -Based radiomics and urine creatinine for the differentiation of renal Angiomyolipoma with minimal fat from renal cell carcinoma: A preliminary study. Front Oncol. 2022;12:876664.","journal-title":"Front Oncol"},{"issue":"1129","key":"1715_CR11","doi-asserted-by":"publisher","first-page":"20210534","DOI":"10.1259\/bjr.20210534","volume":"95","author":"X Li","year":"2022","unstructured":"Li X, Ma Q, Nie P, Zheng Y, Dong C, Xu W. A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study. Br J Radiol. 2022;95(1129):20210534.","journal-title":"Br J Radiol"},{"key":"1715_CR12","doi-asserted-by":"publisher","first-page":"854979","DOI":"10.3389\/fonc.2022.854979","volume":"12","author":"Y Gao","year":"2022","unstructured":"Gao Y, Wang X, Wang S, et al. Differential diagnosis of type 1 and type 2 papillary renal cell carcinoma based on enhanced CT radiomics nomogram. Front Oncol. 2022;12:854979.","journal-title":"Front Oncol"},{"key":"1715_CR13","doi-asserted-by":"publisher","first-page":"101924","DOI":"10.1016\/j.compmedimag.2021.101924","volume":"90","author":"MA Hussain","year":"2021","unstructured":"Hussain MA, Hamarneh G, Garbi R. Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging. Comput Med Imaging Graph. 2021;90:101924.","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"1715_CR14","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.1007\/s00330-021-08344-4","volume":"32","author":"NL Demirjian","year":"2022","unstructured":"Demirjian NL, Varghese BA, Cen SY, et al. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur Radiol. 2022;32(4):2552\u201363.","journal-title":"Eur Radiol"},{"issue":"6","key":"1715_CR15","doi-asserted-by":"publisher","first-page":"2690","DOI":"10.1007\/s00261-020-02890-z","volume":"46","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Qian X, Hu J, et al. CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma. Abdom Radiol (NY). 2021;46(6):2690\u20138.","journal-title":"Abdom Radiol (NY)"},{"issue":"1","key":"1715_CR16","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s13244-021-01107-1","volume":"12","author":"Y Xv","year":"2021","unstructured":"Xv Y, Lv F, Guo H, et al. Machine learning-based CT radiomics approach for predicting WHO\/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study. Insights Imaging. 2021;12(1):170.","journal-title":"Insights Imaging"},{"issue":"5","key":"1715_CR17","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1007\/s10278-021-00500-y","volume":"34","author":"Z Khodabakhshi","year":"2021","unstructured":"Khodabakhshi Z, Amini M, Mostafaei S, et al. Overall survival prediction in renal cell carcinoma patients using computed tomography radiomic and clinical information. J Digit Imaging. 2021;34(5):1086\u201398.","journal-title":"J Digit Imaging"},{"key":"1715_CR18","doi-asserted-by":"publisher","first-page":"671420","DOI":"10.3389\/fonc.2021.671420","volume":"11","author":"L Yan","year":"2021","unstructured":"Yan L, Yang G, Cui J, et al. Radiomics analysis of Contrast-Enhanced CT predicts survival in clear cell renal cell carcinoma. Front Oncol. 2021;11:671420.","journal-title":"Front Oncol"},{"issue":"8","key":"1715_CR19","doi-asserted-by":"publisher","first-page":"2949","DOI":"10.1007\/s00259-022-05773-1","volume":"49","author":"G Yang","year":"2022","unstructured":"Yang G, Nie P, Yan L, et al. The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study. Eur J Nucl Med Mol Imaging. 2022;49(8):2949\u201359.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"8","key":"1715_CR20","doi-asserted-by":"publisher","first-page":"5840","DOI":"10.1007\/s00330-023-09551-x","volume":"33","author":"D Deniffel","year":"2023","unstructured":"Deniffel D, McAlpine K, Harder FN, et al. Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions. Eur Radiol. 2023;33(8):5840\u201350.","journal-title":"Eur Radiol"},{"issue":"11","key":"1715_CR21","doi-asserted-by":"publisher","first-page":"6049","DOI":"10.1007\/s00330-019-06084-0","volume":"29","author":"X Wang","year":"2019","unstructured":"Wang X, Zhao X, Li Q, et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol. 2019;29(11):6049\u201358.","journal-title":"Eur Radiol"},{"issue":"10","key":"1715_CR22","doi-asserted-by":"publisher","first-page":"100820","DOI":"10.1016\/j.tranon.2020.100820","volume":"13","author":"Y Zhuo","year":"2020","unstructured":"Zhuo Y, Feng M, Yang S, et al. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol. 2020;13(10):100820.","journal-title":"Transl Oncol"},{"issue":"6","key":"1715_CR23","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.annonc.2020.03.295","volume":"31","author":"Y Jiang","year":"2020","unstructured":"Jiang Y, Wang H, Wu J, et al. Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol. 2020;31(6):760\u20138.","journal-title":"Ann Oncol"},{"key":"1715_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.mri.2021.12.008","volume":"88","author":"J Shi","year":"2022","unstructured":"Shi J, Dong Y, Jiang W, et al. MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study. Magn Reson Imaging. 2022;88:1\u20138.","journal-title":"Magn Reson Imaging"},{"key":"1715_CR25","doi-asserted-by":"publisher","first-page":"110095","DOI":"10.1016\/j.ejrad.2021.110095","volume":"146","author":"M Ma","year":"2022","unstructured":"Ma M, Gan L, Liu Y, et al. Radiomics features based on automatic segmented MRI images: prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy. Eur J Radiol. 2022;146:110095.","journal-title":"Eur J Radiol"},{"issue":"1","key":"1715_CR26","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s13244-024-01739-z","volume":"15","author":"X Li","year":"2024","unstructured":"Li X, Lin J, Qi H, et al. Radiomics predict the WHO\/ISUP nuclear grade and survival in clear cell renal cell carcinoma. Insights Imaging. 2024;15(1):175.","journal-title":"Insights Imaging"},{"issue":"2","key":"1715_CR27","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MYA. Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155\u201363.","journal-title":"J Chiropr Med"},{"issue":"2","key":"1715_CR28","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","volume":"86","author":"PE Shrout","year":"1979","unstructured":"Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420\u20138.","journal-title":"Psychol Bull"},{"issue":"9","key":"1715_CR29","doi-asserted-by":"publisher","first-page":"e2015927","DOI":"10.1001\/jamanetworkopen.2020.15927","volume":"3","author":"Y Hu","year":"2020","unstructured":"Hu Y, Xie C, Yang H, et al. Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. JAMA Netw Open. 2020;3(9):e2015927.","journal-title":"JAMA Netw Open"},{"key":"1715_CR30","doi-asserted-by":"publisher","first-page":"742547","DOI":"10.3389\/fonc.2021.742547","volume":"11","author":"H Zhang","year":"2021","unstructured":"Zhang H, Yin F, Chen M, et al. Development and validation of a CT-Based radiomics nomogram for predicting postoperative Progression-Free survival in stage I-III renal cell carcinoma. Front Oncol. 2021;11:742547.","journal-title":"Front Oncol"},{"key":"1715_CR31","doi-asserted-by":"publisher","first-page":"579619","DOI":"10.3389\/fonc.2020.579619","volume":"10","author":"B Kang","year":"2020","unstructured":"Kang B, Sun C, Gu H, et al. T1 stage clear cell renal cell carcinoma: A CT-Based radiomics nomogram to estimate the risk of recurrence and metastasis. Front Oncol. 2020;10:579619.","journal-title":"Front Oncol"},{"issue":"9","key":"1715_CR32","first-page":"1358","volume":"41","author":"H Zhang","year":"2021","unstructured":"Zhang H, Yin F, Chen M, et al. [Predicting postoperative recurrence of stage I-II renal clear cell carcinoma based on preoperative CT radiomics feature nomogram]. Nan Fang Yi Ke Da Xue Xue Bao. 2021;41(9):1358\u201365.","journal-title":"Nan Fang Yi Ke Da Xue Xue Bao"},{"issue":"13","key":"1715_CR33","doi-asserted-by":"publisher","first-page":"2968","DOI":"10.1007\/s00259-020-04864-1","volume":"47","author":"JC Peeken","year":"2020","unstructured":"Peeken JC, Shouman MA, Kroenke M, et al. A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients. Eur J Nucl Med Mol Imaging. 2020;47(13):2968\u201377.","journal-title":"Eur J Nucl Med Mol Imaging"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01715-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01715-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01715-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T07:17:54Z","timestamp":1747379874000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01715-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,16]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1715"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01715-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3995515\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,16]]},"assertion":[{"value":"28 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 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 study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the institutional Ethics Committee in Zhongshan Hospital Fudan University, and the committee\u2019s reference number was B2021-608R.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The need for written informed consent was waived with the confirmation of patient data confidentiality by the institutional Ethics Committee for this retrospective study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"167"}}