{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T20:31:27Z","timestamp":1782246687586,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81802572"],"award-info":[{"award-number":["81802572"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-05718-2","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T01:07:26Z","timestamp":1751418446000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer"],"prefix":"10.1038","volume":"15","author":[{"given":"Chi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zewen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peicheng","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yachen","family":"Zang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"issue":"3","key":"5718_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209\u2013249 (2021).","journal-title":"CA Cancer J. Clin."},{"issue":"1","key":"5718_CR2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s12957-019-1573-0","volume":"17","author":"J Xiang","year":"2019","unstructured":"Xiang, J. et al. Transperineal versus transrectal prostate biopsy in the diagnosis of prostate cancer: A systematic review and meta-analysis. World J. Surg. Oncol. 17(1), 31 (2019).","journal-title":"World J. Surg. Oncol."},{"key":"5718_CR3","doi-asserted-by":"publisher","first-page":"16089","DOI":"10.1038\/srep16089","volume":"5","author":"LH Guo","year":"2015","unstructured":"Guo, L. H. et al. Comparison between ultrasound guided transperineal and transrectal prostate biopsy: A prospective, randomized, and controlled trial. Sci. Rep. 5, 16089 (2015).","journal-title":"Sci. Rep."},{"issue":"18","key":"5718_CR4","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1001\/jama.2018.4914","volume":"319","author":"HB Carter","year":"2018","unstructured":"Carter, H. B. Prostate-specific antigen (PSA) screening for prostate cancer: revisiting the evidence. Jama 319(18), 1866\u20131868 (2018).","journal-title":"Jama"},{"issue":"3","key":"5718_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.prnil.2017.03.006","volume":"5","author":"CS Kim","year":"2017","unstructured":"Kim, C. S. et al. Report of the second Asian prostate cancer (A-CaP) study meeting. Prostate Int. 5(3), 95\u2013103 (2017).","journal-title":"Prostate Int."},{"issue":"3","key":"5718_CR6","first-page":"479","volume":"63","author":"Z Liu","year":"2017","unstructured":"Liu, Z. et al. Tumor abnormal protein as a novel biomarker in papillary thyroid carcinoma. Clin. Lab. 63(3), 479\u2013485 (2017).","journal-title":"Clin. Lab."},{"issue":"1","key":"5718_CR7","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1186\/s12885-024-12418-z","volume":"24","author":"L Fu","year":"2024","unstructured":"Fu, L. et al. Clinical application of serum tumor abnormal protein in prostate cancer patients. BMC Cancer 24(1), 665 (2024).","journal-title":"BMC Cancer"},{"key":"5718_CR8","doi-asserted-by":"crossref","unstructured":"Weinreb, J. C. et al. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur. Urol.  69(1), 16\u201340. (2016).","DOI":"10.1016\/j.eururo.2015.08.052"},{"issue":"1","key":"5718_CR9","doi-asserted-by":"publisher","first-page":"9407","DOI":"10.1038\/s41598-020-66255-8","volume":"10","author":"P Movahedi","year":"2020","unstructured":"Movahedi, P. et al. Prediction of prostate cancer aggressiveness using (18)F-fluciclovine (FACBC) PET and multisequence multiparametric MRI. Sci. Rep. 10(1), 9407 (2020).","journal-title":"Sci. Rep."},{"issue":"7553","key":"5718_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521(7553), 436\u2013444 (2015).","journal-title":"Nature"},{"issue":"1","key":"5718_CR11","doi-asserted-by":"publisher","first-page":"14951","DOI":"10.1038\/s41598-024-65354-0","volume":"14","author":"H Sherafatmandjoo","year":"2024","unstructured":"Sherafatmandjoo, H. et al. Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning. Sci. Rep. 14 (1), 14951 (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"5718_CR12","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","volume":"13","author":"KA Tran","year":"2021","unstructured":"Tran, K. A. et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 13(1), 152 (2021).","journal-title":"Genome Med."},{"issue":"4","key":"5718_CR13","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1016\/j.eururo.2017.03.049","volume":"72","author":"G Gandaglia","year":"2017","unstructured":"Gandaglia, G. et al. Development and internal validation of a novel model to identify the candidates for extended pelvic lymph node dissection in prostate cancer. Eur. Urol. 72(4), 632\u2013640 (2017).","journal-title":"Eur. Urol."},{"issue":"5","key":"5718_CR14","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/j.eururo.2016.08.015","volume":"71","author":"A Morlacco","year":"2017","unstructured":"Morlacco, A. et al. The incremental role of magnetic resonance imaging for prostate Cancer staging before radical prostatectomy. Eur. Urol. 71(5), 701\u2013704 (2017).","journal-title":"Eur. Urol."},{"issue":"10","key":"5718_CR15","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1056\/NEJMoa1910038","volume":"382","author":"M Ahdoot","year":"2020","unstructured":"Ahdoot, M. et al. MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. N Engl. J. Med. 382(10), 917\u2013928 (2020).","journal-title":"N Engl. J. Med."},{"key":"5718_CR16","doi-asserted-by":"crossref","unstructured":"Cutaia, G. et al. Radiomics and prostate MRI: current role and future applications. J. Imaging  7(2) (2021).","DOI":"10.3390\/jimaging7020034"},{"issue":"2","key":"5718_CR17","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg, A. et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2), 328\u2013338 (2020).","journal-title":"Radiology"},{"issue":"1","key":"5718_CR18","doi-asserted-by":"publisher","first-page":"p2085","DOI":"10.1038\/s41598-021-81272-x","volume":"11","author":"GA Nketiah","year":"2021","unstructured":"Nketiah, G. A. et al. Utility of T(2)-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: A single-arm, multicenter study. Sci. Rep. 11(1), p2085 (2021).","journal-title":"Sci. Rep."},{"issue":"4","key":"5718_CR19","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1038\/s41391-021-00429-x","volume":"25","author":"PK Chiu","year":"2022","unstructured":"Chiu, P. K. et al. Enhancement of prostate cancer diagnosis by machine learning techniques: An algorithm development and validation study. Prostate Cancer Prostatic Dis. 25(4), 672\u2013676 (2022).","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"5718_CR20","doi-asserted-by":"crossref","unstructured":"Donisi, L. et al. A combined radiomics and machine learning approach to distinguish clinically significant prostate lesions on a publicly available MRI dataset. J. Imaging  7(10)  (2021).","DOI":"10.3390\/jimaging7100215"},{"key":"5718_CR21","doi-asserted-by":"publisher","first-page":"117955492211044","DOI":"10.1177\/11795549221104440","volume":"16","author":"C Gu","year":"2022","unstructured":"Gu, C. et al. Quantification of tumor abnormal proteins in the diagnosis and postoperative prognostic evaluation of gastric Cancer. Clin. Med. Insights Oncol. 16, 11795549221104440 (2022).","journal-title":"Clin. Med. Insights Oncol."},{"issue":"10","key":"5718_CR22","first-page":"18528","volume":"8","author":"L Zhang","year":"2015","unstructured":"Zhang, L. et al. Tumor abnormal protein (TAP) examination contributes to primary diagnosis of bladder cancer. Int. J. Clin. Exp. Med. 8(10), 18528\u201318532 (2015).","journal-title":"Int. J. Clin. Exp. Med."},{"issue":"3","key":"5718_CR23","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1148\/radiol.2019190938","volume":"293","author":"P Schelb","year":"2019","unstructured":"Schelb, P. et al. Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology 293(3), 607\u2013617 (2019).","journal-title":"Radiology"},{"key":"5718_CR24","doi-asserted-by":"crossref","unstructured":"Dominguez, I. et al. MRI-Based surrogate imaging markers of aggressiveness in prostate cancer: development of a machine learning model based on radiomic features. Diagnostics (Basel)  13(17)  (2023).","DOI":"10.3390\/diagnostics13172779"},{"issue":"1","key":"5718_CR25","doi-asserted-by":"publisher","first-page":"1286","DOI":"10.1038\/s41598-020-78428-6","volume":"11","author":"YH Fan","year":"2021","unstructured":"Fan, Y. H. et al. The prostate health index aids multi-parametric MRI in diagnosing significant prostate cancer. Sci. Rep. 11(1), 1286 (2021).","journal-title":"Sci. Rep."},{"issue":"1","key":"5718_CR26","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1186\/s13014-016-0718-3","volume":"11","author":"R Shiradkar","year":"2016","unstructured":"Shiradkar, R. et al. Radiomics based targeted radiotherapy planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI. Radiat. Oncol. 11(1), 148 (2016).","journal-title":"Radiat. Oncol."},{"issue":"1131","key":"5718_CR27","doi-asserted-by":"publisher","first-page":"20210191","DOI":"10.1259\/bjr.20210191","volume":"95","author":"L Zhang","year":"2022","unstructured":"Zhang, L. et al. Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer. Br. J. Radiol. 95(1131), 20210191 (2022).","journal-title":"Br. J. Radiol."},{"key":"5718_CR28","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.ejrad.2019.05.006","volume":"116","author":"R Cuocolo","year":"2019","unstructured":"Cuocolo, R. et al. Clinically significant prostate cancer detection on MRI: A radiomic shape features study. Eur. J. Radiol. 116, 144\u2013149 (2019).","journal-title":"Eur. J. Radiol."},{"issue":"3","key":"5718_CR29","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s00345-020-03530-3","volume":"39","author":"RC Wu","year":"2021","unstructured":"Wu, R. C. et al. Role of MRI for the detection of prostate cancer. World J. Urol. 39(3), 637\u2013649 (2021).","journal-title":"World J. Urol."},{"issue":"2","key":"5718_CR30","doi-asserted-by":"publisher","first-page":"93","DOI":"10.5152\/tud.2018.56056","volume":"44","author":"HC Demirel","year":"2018","unstructured":"Demirel, H. C. & Davis, J. W. Multiparametric magnetic resonance imaging: overview of the technique, clinical applications in prostate biopsy and future directions. Turk. J. Urol. 44(2), 93\u2013102 (2018).","journal-title":"Turk. J. Urol."},{"key":"5718_CR31","doi-asserted-by":"crossref","unstructured":"Saito, S. et al. Relationship between apparent diffusion coefficient distribution and cancer grade in prostate cancer and benign prostatic hyperplasia. Diagnostics (Basel) 12(2)  (2022).","DOI":"10.3390\/diagnostics12020525"},{"issue":"2","key":"5718_CR32","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1148\/radiol.10091343","volume":"255","author":"DL Langer","year":"2010","unstructured":"Langer, D. L. et al. Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology 255(2), 485\u2013494 (2010).","journal-title":"Radiology"},{"issue":"1","key":"5718_CR33","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/s00330-024-10890-6","volume":"35","author":"G Agrotis","year":"2025","unstructured":"Agrotis, G. et al. Diagnostic performance of ADC and adcratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis. Eur. Radiol. 35(1), 404\u2013416 (2025).","journal-title":"Eur. Radiol."},{"issue":"2","key":"5718_CR34","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.juro.2009.10.046","volume":"183","author":"JI Epstein","year":"2010","unstructured":"Epstein, J. I. An update of the Gleason grading system. J. Urol. 183(2), 433\u2013440 (2010).","journal-title":"J. Urol."},{"issue":"1","key":"5718_CR35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s40644-021-00414-6","volume":"21","author":"D He","year":"2021","unstructured":"He, D. et al. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging  21(1), 46 (2021).","journal-title":"Cancer Imaging"},{"issue":"5","key":"5718_CR36","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1002\/jmri.27486","volume":"53","author":"H Zhang","year":"2021","unstructured":"Zhang, H. et al. Diagnostic nomogram based on intralesional and perilesional radiomics features and clinical factors of clinically significant prostate cancer. J. Magn. Reson. Imaging  53(5), 1550\u20131558 (2021).","journal-title":"J. Magn. Reson. Imaging"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-05718-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-05718-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-05718-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T01:07:30Z","timestamp":1751418450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-05718-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["5718"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-05718-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"26 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by the Ethics and Research Committee of the second affiliated hospital of Soochow university. Informed consent was obtained from all the patients in the study, and all the procedures were in accordance with the principles of the Declaration of Helsinki.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}}],"article-number":"22816"}}