{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:58:23Z","timestamp":1777377503839,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01749-3","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T08:27:10Z","timestamp":1751358430000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma"],"prefix":"10.1186","volume":"25","author":[{"given":"Shiyan","family":"Song","sequence":"first","affiliation":[]},{"given":"Wenfei","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Xiaochen","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Xiangyu","family":"Che","sequence":"additional","affiliation":[]},{"given":"Qifei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guangzhen","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"1749_CR1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.eururo.2022.08.019","volume":"82","author":"L Bukavina","year":"2022","unstructured":"Bukavina L, Bensalah K, Bray F, Carlo M, Challacombe B, Karam JA, Kassouf W, Mitchell T, Montironi R, O\u2019Brien T, et al. Epidemiology of renal cell carcinoma: 2022 update. Eur Urol. 2022;82:529\u201342. https:\/\/doi.org\/10.1016\/j.eururo.2022.08.019.","journal-title":"Eur Urol"},{"key":"1749_CR2","doi-asserted-by":"publisher","first-page":"425","DOI":"10.3322\/caac.21494","volume":"68","author":"KD Miller","year":"2018","unstructured":"Miller KD, Goding Sauer A, Ortiz AP, Fedewa SA, Pinheiro PS, Tortolero-Luna G, Martinez-Tyson D, Jemal A, Siegel RL. Cancer statistics for Hispanics\/Latinos, 2018. CA Cancer J Clin. 2018;68:425\u201345. https:\/\/doi.org\/10.3322\/caac.21494.","journal-title":"CA Cancer J Clin"},{"key":"1749_CR3","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.ejca.2018.07.005","volume":"103","author":"J Ferlay","year":"2018","unstructured":"Ferlay J, Colombet M, Soerjomataram I, Dyba T, Randi G, Bettio M, Gavin A, Visser O, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries and 25 major cancers in 2018. Eur J Cancer. 2018;103:356\u201387. https:\/\/doi.org\/10.1016\/j.ejca.2018.07.005.","journal-title":"Eur J Cancer"},{"key":"1749_CR4","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1007\/s10147-022-02204-x","volume":"27","author":"T Arai","year":"2022","unstructured":"Arai T, Sazuka T, Sato H, Kato M, Kamada S, Katsura S, Seito A, Miyamoto S, Wakai K, Takeuchi N, et al. A clinical investigation of recurrence and lost follow-up after renal cell carcinoma surgery: a single-center, long-term, large cohort, retrospective study. Int J Clin Oncol. 2022;27:1467\u201376. https:\/\/doi.org\/10.1007\/s10147-022-02204-x.","journal-title":"Int J Clin Oncol"},{"key":"1749_CR5","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.eururo.2015.07.072","volume":"69","author":"L Marconi","year":"2016","unstructured":"Marconi L, Dabestani S, Lam TB, Hofmann F, Stewart F, Norrie J, Bex A, Bensalah K, Canfield SE, Hora M, et al. Systematic review and Meta-analysis of diagnostic accuracy of percutaneous renal tumour biopsy. Eur Urol. 2016;69:660\u201373. https:\/\/doi.org\/10.1016\/j.eururo.2015.07.072.","journal-title":"Eur Urol"},{"key":"1749_CR6","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1038\/s12276-020-00500-y","volume":"52","author":"WS Lee","year":"2020","unstructured":"Lee WS, Yang H, Chon HJ, Kim C. Combination of anti-angiogenic therapy and immune checkpoint Blockade normalizes vascular-immune crosstalk to potentiate cancer immunity. Exp Mol Med. 2020;52:1475\u201385. https:\/\/doi.org\/10.1038\/s12276-020-00500-y.","journal-title":"Exp Mol Med"},{"key":"1749_CR7","doi-asserted-by":"publisher","first-page":"255","DOI":"10.32604\/or.2023.027942","volume":"31","author":"W Ge","year":"2023","unstructured":"Ge W, Song S, Qi X, Chen F, Che X, Sun Y, Wang J, Li X, Liu N, Wang Q, et al. Review and prospect of immune checkpoint Blockade therapy represented by PD-1\/PD-L1 in the treatment of clear cell renal cell carcinoma. Oncol Res. 2023;31:255\u201370. https:\/\/doi.org\/10.32604\/or.2023.027942.","journal-title":"Oncol Res"},{"key":"1749_CR8","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/bs.ai.2015.12.002","volume":"130","author":"E Becht","year":"2016","unstructured":"Becht E, Giraldo NA, Germain C, de Reyni\u00e8s A, Laurent-Puig P, Zucman-Rossi J, Dieu-Nosjean M-C, Saut\u00e8s-Fridman C, Fridman WH. Immune contexture, immunoscore, and malignant cell molecular subgroups for prognostic and theranostic classifications of cancers. Adv Immunol. 2016;130:95\u2013190. https:\/\/doi.org\/10.1016\/bs.ai.2015.12.002.","journal-title":"Adv Immunol"},{"key":"1749_CR9","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.ccell.2016.09.011","volume":"30","author":"Y Liu","year":"2016","unstructured":"Liu Y, Cao X. Characteristics and significance of the Pre-metastatic niche. Cancer Cell. 2016;30:668\u201381. https:\/\/doi.org\/10.1016\/j.ccell.2016.09.011.","journal-title":"Cancer Cell"},{"key":"1749_CR10","doi-asserted-by":"publisher","first-page":"e009358","DOI":"10.1136\/jitc-2024-009358","volume":"12","author":"T Yoshida","year":"2024","unstructured":"Yoshida T, Nakamoto T, Atsumi N, Ohe C, Sano T, Yasukochi Y, Tsuta K, Kinoshita H. Impact of LAG-3\/FGL1 pathway on immune evasive contexture and clinical outcomes in advanced urothelial carcinoma. J Immunother Cancer. 2024;12:e009358. https:\/\/doi.org\/10.1136\/jitc-2024-009358.","journal-title":"J Immunother Cancer"},{"key":"1749_CR11","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1186\/s13059-016-1113-y","volume":"17","author":"E Becht","year":"2016","unstructured":"Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, Selves J, Laurent-Puig P, Saut\u00e8s-Fridman C, Fridman WH, et al. Erratum to: estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17:249. https:\/\/doi.org\/10.1186\/s13059-016-1113-y.","journal-title":"Genome Biol"},{"key":"1749_CR12","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.cell.2014.12.033","volume":"160","author":"MS Rooney","year":"2015","unstructured":"Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160:48\u201361. https:\/\/doi.org\/10.1016\/j.cell.2014.12.033.","journal-title":"Cell"},{"key":"1749_CR13","doi-asserted-by":"publisher","first-page":"865596","DOI":"10.3389\/fimmu.2022.865596","volume":"13","author":"Z Wu","year":"2022","unstructured":"Wu Z, Zhou J, Xiao Y, Ming J, Zhou J, Dong F, Zhou X, Xu Z, Zhao X, Lei P, et al. CD20\u2009+\u2009CD22\u2009+\u2009ADAM28\u2009+\u2009B cells in tertiary lymphoid structures promote immunotherapy response. Front Immunol. 2022;13:865596. https:\/\/doi.org\/10.3389\/fimmu.2022.865596.","journal-title":"Front Immunol"},{"key":"1749_CR14","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.critrevonc.2019.03.015","volume":"138","author":"I Gardin","year":"2019","unstructured":"Gardin I, Gr\u00e9goire V, Gibon D, Kirisli H, Pasquier D, Thariat J, Vera P. Radiomics: principles and radiotherapy applications. Crit Rev Oncol Hematol. 2019;138:44\u201350. https:\/\/doi.org\/10.1016\/j.critrevonc.2019.03.015.","journal-title":"Crit Rev Oncol Hematol"},{"key":"1749_CR15","doi-asserted-by":"publisher","first-page":"1239124","DOI":"10.3389\/fonc.2023.1239124","volume":"13","author":"J Shao","year":"2023","unstructured":"Shao J, Wang C, Shu K, Zhou Y, Cheng N, Lai Z, Li K, Xu L, Chen J, Du F, et al. A contrast-enhanced CT-based radiomic nomogram for the differential diagnosis of intravenous leiomyomatosis and uterine leiomyoma. Front Oncol. 2023;13:1239124. https:\/\/doi.org\/10.3389\/fonc.2023.1239124.","journal-title":"Front Oncol"},{"key":"1749_CR16","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, Haddadi Avval A, Nazari M, Oveisi M, Shiri I, Zaidi H. Overall survival prediction in renal cell carcinoma patients using computed tomography radiomic and clinical information. J Digit Imaging. 2021;34:1086\u201398. https:\/\/doi.org\/10.1007\/s10278-021-00500-y.","journal-title":"J Digit Imaging"},{"key":"1749_CR17","doi-asserted-by":"publisher","first-page":"3691","DOI":"10.1002\/mp.14896","volume":"48","author":"S Shayesteh","year":"2021","unstructured":"Shayesteh S, Nazari M, Salahshour A, Sandoughdaran S, Hajianfar G, Khateri M, Yaghobi Joybari A, Jozian F, Fatehi Feyzabad SH, Arabi H, et al. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer. Med Phys. 2021;48:3691\u2013701. https:\/\/doi.org\/10.1002\/mp.14896.","journal-title":"Med Phys"},{"key":"1749_CR18","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ejmp.2021.03.008","volume":"83","author":"H Arabi","year":"2021","unstructured":"Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med. 2021;83:122\u201337. https:\/\/doi.org\/10.1016\/j.ejmp.2021.03.008.","journal-title":"Phys Med"},{"key":"1749_CR19","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1148\/radiol.2017162823","volume":"285","author":"J Wu","year":"2017","unstructured":"Wu J, Li B, Sun X, Cao G, Rubin DL, Napel S, Ikeda DM, Kurian AW, Li R. Heterogeneous enhancement patterns of Tumor-adjacent parenchyma at MR imaging are associated with dysregulated signaling pathways and poor survival in breast Cancer. Radiology. 2017;285:401\u201313. https:\/\/doi.org\/10.1148\/radiol.2017162823.","journal-title":"Radiology"},{"key":"1749_CR20","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s13058-019-1199-8","volume":"21","author":"M Fan","year":"2019","unstructured":"Fan M, Xia P, Liu B, Zhang L, Wang Y, Gao X, Li L. Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients. Breast Cancer Res. 2019;21:112. https:\/\/doi.org\/10.1186\/s13058-019-1199-8.","journal-title":"Breast Cancer Res"},{"key":"1749_CR21","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1186\/s12885-021-08122-x","volume":"21","author":"D Arefan","year":"2021","unstructured":"Arefan D, Hausler RM, Sumkin JH, Sun M, Wu S. Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes. BMC Cancer. 2021;21:370. https:\/\/doi.org\/10.1186\/s12885-021-08122-x.","journal-title":"BMC Cancer"},{"key":"1749_CR22","doi-asserted-by":"publisher","first-page":"7627","DOI":"10.1002\/cam4.5449","volume":"12","author":"H He","year":"2023","unstructured":"He H, Jin Z, Dai J, Wang H, Sun J, Xu D. Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma. Cancer Med. 2023;12:7627\u201338. https:\/\/doi.org\/10.1002\/cam4.5449.","journal-title":"Cancer Med"},{"key":"1749_CR23","doi-asserted-by":"publisher","first-page":"21861","DOI":"10.1002\/cam4.6757","volume":"12","author":"W Jiang","year":"2023","unstructured":"Jiang W, Wu R, Yang T, Yu S, Xing W. Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics. Cancer Med. 2023;12:21861\u201372. https:\/\/doi.org\/10.1002\/cam4.6757.","journal-title":"Cancer Med"},{"key":"1749_CR24","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12880-024-01212-9","volume":"24","author":"H Qian","year":"2024","unstructured":"Qian H, Ren X, Xu M, Fang Z, Zhang R, Bu Y, Zhou C. Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment. BMC Med Imaging. 2024;24:31. https:\/\/doi.org\/10.1186\/s12880-024-01212-9.","journal-title":"BMC Med Imaging"},{"key":"1749_CR25","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1186\/s12957-021-02259-6","volume":"19","author":"A Kayi Cangir","year":"2021","unstructured":"Kayi Cangir A, Orhan K, Kahya Y, \u00d6zak\u0131nc\u0131 H, Kazak BB, Konuk Balc\u0131 BM, Karasoy D, Uzun \u00c7. CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: impact of surgical modality choice. World J Surg Oncol. 2021;19:147. https:\/\/doi.org\/10.1186\/s12957-021-02259-6.","journal-title":"World J Surg Oncol"},{"key":"1749_CR26","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, Zegers CML, Gillies R, Boellard R, Dekker A, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441\u20136. https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036.","journal-title":"Eur J Cancer"},{"key":"1749_CR27","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1186\/s13059-016-1092-z","volume":"17","author":"Y \u015eenbabao\u011flu","year":"2016","unstructured":"\u015eenbabao\u011flu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, Miao D, Ostrovnaya I, Drill E, Luna A, et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016;17:231. https:\/\/doi.org\/10.1186\/s13059-016-1092-z.","journal-title":"Genome Biol"},{"key":"1749_CR28","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/s13244-023-01557-9","volume":"14","author":"S Wang","year":"2023","unstructured":"Wang S, Zhu C, Jin Y, Yu H, Wu L, Zhang A, Wang B, Zhai J. A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma. Insights Imaging. 2023;14:207. https:\/\/doi.org\/10.1186\/s13244-023-01557-9.","journal-title":"Insights Imaging"},{"key":"1749_CR29","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s40644-024-00768-7","volume":"24","author":"J Wu","year":"2024","unstructured":"Wu J, Li J, Huang B, Dong S, Wu L, Shen X, Zheng Z. Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment. Cancer Imaging. 2024;24:124. https:\/\/doi.org\/10.1186\/s40644-024-00768-7.","journal-title":"Cancer Imaging"},{"key":"1749_CR30","doi-asserted-by":"publisher","first-page":"216380","DOI":"10.1016\/j.canlet.2023.216380","volume":"573","author":"X Fan","year":"2023","unstructured":"Fan X, Li J, Huang B, Lu H, Lu C, Pan M, Wang X, Zhang H, You Y, Wang X, et al. Noninvasive radiomics model reveals macrophage infiltration in glioma. Cancer Lett. 2023;573:216380. https:\/\/doi.org\/10.1016\/j.canlet.2023.216380.","journal-title":"Cancer Lett"},{"key":"1749_CR31","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.jacr.2023.04.024","volume":"21","author":"F Sodagari","year":"2024","unstructured":"Sodagari F, Davenport MS, Asch D, Cavallo JJ, Cohan RH, Ellis JH, Pahade JK. A survey of practicing radiologists on the use of premedication before intravenous iodinated contrast medium administration. J Am Coll Radiol. 2024;21:795\u2013804. https:\/\/doi.org\/10.1016\/j.jacr.2023.04.024.","journal-title":"J Am Coll Radiol"},{"key":"1749_CR32","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1038\/nrc3245","volume":"12","author":"WH Fridman","year":"2012","unstructured":"Fridman WH, Pag\u00e8s F, Saut\u00e8s-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12:298\u2013306. https:\/\/doi.org\/10.1038\/nrc3245.","journal-title":"Nat Rev Cancer"},{"key":"1749_CR33","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/s41581-020-00359-2","volume":"17","author":"E Jonasch","year":"2021","unstructured":"Jonasch E, Walker CL, Rathmell WK. Clear cell renal cell carcinoma ontogeny and mechanisms of lethality. Nat Rev Nephrol. 2021;17:245\u201361. https:\/\/doi.org\/10.1038\/s41581-020-00359-2.","journal-title":"Nat Rev Nephrol"},{"key":"1749_CR34","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s13059-017-1180-8","volume":"18","author":"Y \u015eenbabao\u011flu","year":"2017","unstructured":"\u015eenbabao\u011flu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, Miao D, Ostrovnaya I, Drill E, Luna A, et al. Erratum to: tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2017;18:46. https:\/\/doi.org\/10.1186\/s13059-017-1180-8.","journal-title":"Genome Biol"},{"key":"1749_CR35","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1016\/j.ccell.2021.02.013","volume":"39","author":"DA Braun","year":"2021","unstructured":"Braun DA, Street K, Burke KP, Cookmeyer DL, Denize T, Pedersen CB, Gohil SH, Schindler N, Pomerance L, Hirsch L, et al. Progressive immune dysfunction with advancing disease stage in renal cell carcinoma. Cancer Cell. 2021;39:632\u2013e6488. https:\/\/doi.org\/10.1016\/j.ccell.2021.02.013.","journal-title":"Cancer Cell"},{"key":"1749_CR36","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1038\/s41591-018-0014-x","volume":"24","author":"M Binnewies","year":"2018","unstructured":"Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24:541\u201350. https:\/\/doi.org\/10.1038\/s41591-018-0014-x.","journal-title":"Nat Med"},{"key":"1749_CR37","doi-asserted-by":"publisher","first-page":"e000185","DOI":"10.1136\/esmoopen-2017-000185","volume":"2","author":"A Rodriguez-Vida","year":"2017","unstructured":"Rodriguez-Vida A, Hutson TE, Bellmunt J, Strijbos MH. New treatment options for metastatic renal cell carcinoma. ESMO Open. 2017;2:e000185. https:\/\/doi.org\/10.1136\/esmoopen-2017-000185.","journal-title":"ESMO Open"},{"key":"1749_CR38","doi-asserted-by":"publisher","first-page":"eabm6420","DOI":"10.1126\/scitranslmed.abm6420","volume":"14","author":"P Msaouel","year":"2022","unstructured":"Msaouel P, Goswami S, Thall PF, Wang X, Yuan Y, Jonasch E, Gao J, Campbell MT, Shah AY, Corn PG, et al. A phase 1\u20132 trial of sitravatinib and nivolumab in clear cell renal cell carcinoma following progression on antiangiogenic therapy. Sci Transl Med. 2022;14:eabm6420. https:\/\/doi.org\/10.1126\/scitranslmed.abm6420.","journal-title":"Sci Transl Med"},{"key":"1749_CR39","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1038\/s41392-024-01990-2","volume":"9","author":"L Gu","year":"2024","unstructured":"Gu L, Peng C, Liang Q, Huang Q, Lv D, Zhao H, Zhang Q, Zhang Y, Zhang P, Li S, et al. Neoadjuvant Toripalimab plus axitinib for clear cell renal cell carcinoma with inferior Vena Cava tumor thrombus: NEOTAX, a phase 2 study. Signal Transduct Target Ther. 2024;9:264. https:\/\/doi.org\/10.1038\/s41392-024-01990-2.","journal-title":"Signal Transduct Target Ther"},{"key":"1749_CR40","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1056\/NEJMoa1712126","volume":"378","author":"RJ Motzer","year":"2018","unstructured":"Motzer RJ, Tannir NM, McDermott DF, Ar\u00e9n Frontera O, Melichar B, Choueiri TK, Plimack ER, Barth\u00e9l\u00e9my P, Porta C, George S, et al. Nivolumab plus ipilimumab versus Sunitinib in advanced Renal-Cell carcinoma. N Engl J Med. 2018;378:1277\u201390. https:\/\/doi.org\/10.1056\/NEJMoa1712126.","journal-title":"N Engl J Med"},{"key":"1749_CR41","doi-asserted-by":"publisher","first-page":"e004440","DOI":"10.1136\/jitc-2021-004440","volume":"10","author":"M Lopez de Rodas","year":"2022","unstructured":"Lopez de Rodas M, Nagineni V, Ravi A, Datar IJ, Mino-Kenudson M, Corredor G, Barrera C, Behlman L, Rimm DL, Herbst RS, et al. Role of tumor infiltrating lymphocytes and Spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer. J Immunother Cancer. 2022;10:e004440. https:\/\/doi.org\/10.1136\/jitc-2021-004440.","journal-title":"J Immunother Cancer"},{"key":"1749_CR42","doi-asserted-by":"publisher","first-page":"e004803","DOI":"10.1136\/jitc-2022-004803","volume":"10","author":"JW Carlisle","year":"2022","unstructured":"Carlisle JW, Jansen CS, Cardenas MA, Sobierajska E, Reyes AM, Greenwald R, Del Balzo L, Prokhnevska N, Kucuk O, Carthon BC, et al. Clinical outcome following checkpoint therapy in renal cell carcinoma is associated with a burst of activated CD8 T cells in blood. J Immunother Cancer. 2022;10:e004803. https:\/\/doi.org\/10.1136\/jitc-2022-004803.","journal-title":"J Immunother Cancer"},{"key":"1749_CR43","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1038\/s41591-020-0839-y","volume":"26","author":"DA Braun","year":"2020","unstructured":"Braun DA, Hou Y, Bakouny Z, Ficial M, Sant\u2019 Angelo M, Forman J, Ross-Macdonald P, Berger AC, Jegede OA, Elagina L, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 Blockade in advanced clear cell renal cell carcinoma. Nat Med. 2020;26:909\u201318. https:\/\/doi.org\/10.1038\/s41591-020-0839-y.","journal-title":"Nat Med"},{"key":"1749_CR44","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1080\/02664763.2021.2017413","volume":"50","author":"S Chang","year":"2023","unstructured":"Chang S, Li D, Qi Y. Pearson\u2019s goodness-of-fit tests for sparse distributions. J Appl Stat. 2023;50:1078\u201393. https:\/\/doi.org\/10.1080\/02664763.2021.2017413.","journal-title":"J Appl Stat"},{"key":"1749_CR45","doi-asserted-by":"publisher","first-page":"e173","DOI":"10.1016\/S1470-2045(14)71116-7","volume":"16","author":"VP Balachandran","year":"2015","unstructured":"Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than Meets the eye. Lancet Oncol. 2015;16:e173\u2013180. https:\/\/doi.org\/10.1016\/S1470-2045(14)71116-7.","journal-title":"Lancet Oncol"},{"key":"1749_CR46","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1177\/0272989X14547233","volume":"35","author":"B Van Calster","year":"2015","unstructured":"Van Calster B, Vickers AJ. Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Mak. 2015;35:162\u20139. https:\/\/doi.org\/10.1177\/0272989X14547233.","journal-title":"Med Decis Mak"},{"key":"1749_CR47","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.surg.2023.04.058","volume":"174","author":"ZM Alhulaili","year":"2023","unstructured":"Alhulaili ZM, Linnemann RJ, Dascau L, Pleijhuis RG, Klaase JM. A transparent reporting of a multivariable prediction model for individual prognosis or diagnosis analysis to evaluate the quality of reporting of postoperative pancreatic fistula prediction models after pancreatoduodenectomy: A systematic review. Surgery. 2023;174:684\u201391. https:\/\/doi.org\/10.1016\/j.surg.2023.04.058.","journal-title":"Surgery"},{"key":"1749_CR48","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1148\/radiol.2019192515","volume":"294","author":"DA Bluemke","year":"2020","unstructured":"Bluemke DA, Moy L, Bredella MA, Ertl-Wagner BB, Fowler KJ, Goh VJ, Halpern EF, Hess CP, Schiebler ML, Weiss CR. Assessing radiology research on artificial intelligence: A brief guide for authors, reviewers, and Readers-From the radiology editorial board. Radiology. 2020;294:487\u20139. https:\/\/doi.org\/10.1148\/radiol.2019192515.","journal-title":"Radiology"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01749-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01749-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01749-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T08:27:11Z","timestamp":1751358431000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01749-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1749"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01749-3","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"27 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 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":"All datasets in this study were downloaded from public databases, including the TCGA () and TCIA () databases. These public databases allow researchers to download and analyse public datasets for scientific purposes.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","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":"213"}}