{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:18:33Z","timestamp":1790144313467,"version":"4.0.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["95653117\/95653118"],"award-info":[{"award-number":["95653117\/95653118"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["CTRQQR-2021\\100010"],"award-info":[{"award-number":["CTRQQR-2021\\100010"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["RadNet C19942\/A28832"],"award-info":[{"award-number":["RadNet C19942\/A28832"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["RadNet C19942\/A28832"],"award-info":[{"award-number":["RadNet C19942\/A28832"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["CTRQQR-2021\\100010"],"award-info":[{"award-number":["CTRQQR-2021\\100010"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014653","name":"Manchester Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014653","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiol med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Material and methods<\/jats:title>\n                <jats:p>Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01\/12\/2007 and 1\/08\/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (<jats:italic>p<\/jats:italic>\u2009=\u20090.35). The five selected RFs included textural and wavelet-transformed features.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Whole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11547-023-01644-3","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T18:02:13Z","timestamp":1684346533000},"page":"765-774","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings"],"prefix":"10.1007","volume":"128","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5325-3739","authenticated-orcid":false,"given":"Jim","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russell","family":"Frood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"McWilliam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela","family":"Davey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane","family":"Shortall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Swinton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Hulson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catharine M.","family":"West","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Buckley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ananya","family":"Choudhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Hoskin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann","family":"Henry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Scarsbrook","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"issue":"13","key":"1644_CR1","doi-asserted-by":"publisher","first-page":"2785","DOI":"10.1002\/cncr.31551","volume":"124","author":"KA Cronin","year":"2018","unstructured":"Cronin KA, Lake AJ, Scott S, Sherman RL, Noone A-M, Howlader N et al (2018) Annual report to the nation on the status of cancer, part I: national cancer statistics. Cancer 124(13):2785\u20132800","journal-title":"Cancer"},{"issue":"4","key":"1644_CR2","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.eururo.2016.08.003","volume":"71","author":"N Mottet","year":"2017","unstructured":"Mottet N, Bellmunt J, Bolla M, Briers E, Cumberbatch MG, De Santis M et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer part 1: screening diagnosis and local treatment with curative intent. Eur Urol [Internet] 71(4):618\u2013629","journal-title":"Eur Urol [Internet]"},{"issue":"4","key":"1644_CR3","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1016\/j.ijrobp.2010.03.049","volume":"80","author":"V Beckendorf","year":"2011","unstructured":"Beckendorf V, Guerif S, Le Pris\u00e9 E, Cosset JM, Bougnoux A, Chauvet B et al (2011) 70 Gy versus 80 Gy in localized prostate cancer: 5-year results of GETUG 06 randomized trial. Int J Radiat Oncol Biol Phys [Internet] 80(4):1056\u20131063","journal-title":"Int J Radiat Oncol Biol Phys [Internet]"},{"issue":"6","key":"1644_CR4","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1016\/j.juro.2015.06.100","volume":"194","author":"ZS Zumsteg","year":"2015","unstructured":"Zumsteg ZS, Spratt DE, Romesser PB, Pei X, Zhang Z, Kollmeier M et al (2015) Anatomical patterns of recurrence following biochemical relapse in the dose escalation era of external beam radiotherapy for prostate cancer. J Urol [Internet] 194(6):1624\u20131630","journal-title":"J Urol [Internet]"},{"issue":"2","key":"1644_CR5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1056\/NEJMoa1012348","volume":"365","author":"CU Jones","year":"2011","unstructured":"Jones CU, Hunt D, McGowan DG, Amin MB, Chetner MP, Bruner DW et al (2011) Radiotherapy and short-term androgen deprivation for localized prostate cancer. N Engl J Med 365(2):107\u2013118","journal-title":"N Engl J Med"},{"issue":"3","key":"1644_CR6","doi-asserted-by":"publisher","first-page":"e433","DOI":"10.1016\/j.ijrobp.2011.05.037","volume":"82","author":"A Turaka","year":"2012","unstructured":"Turaka A, Buyyounouski MK, Hanlon AL, Horwitz EM, Greenberg RE, Movsas B (2012) Hypoxic prostate\/muscle Po2 ratio predicts for outcome in patients with localized prostate cancer: long-term results. Int J Radiat Oncol 82(3):e433\u2013e439","journal-title":"Int J Radiat Oncol"},{"issue":"13","key":"1644_CR7","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1016\/S1470-2045(14)71021-6","volume":"15","author":"E Lalonde","year":"2014","unstructured":"Lalonde E, Ishkanian AS, Sykes J, Fraser M, Ross-Adams H, Erho N et al (2014) Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol 15(13):1521\u20131532","journal-title":"Lancet Oncol"},{"issue":"7","key":"1644_CR8","doi-asserted-by":"publisher","first-page":"2108","DOI":"10.1158\/1078-0432.CCR-11-2711","volume":"18","author":"M Milosevic","year":"2012","unstructured":"Milosevic M, Warde P, M\u00e9nard C, Chung P, Toi A, Ishkanian A et al (2012) Tumor hypoxia predicts biochemical failure following radiotherapy for clinically localized prostate cancer. Clin Cancer Res 18(7):2108\u20132114","journal-title":"Clin Cancer Res"},{"issue":"2","key":"1644_CR9","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1038\/bjc.2014.604","volume":"112","author":"HB Ragnum","year":"2015","unstructured":"Ragnum HB, Vlatkovic L, Lie AK, Axcrona K, Julin CH, Frikstad KM et al (2015) The tumour hypoxia marker pimonidazole reflects a transcriptional programme associated with aggressive prostate cancer. Br J Cancer 112(2):382\u2013390","journal-title":"Br J Cancer"},{"issue":"4","key":"1644_CR10","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1016\/j.ijrobp.2007.01.018","volume":"68","author":"PJ Hoskin","year":"2007","unstructured":"Hoskin PJ, Carnell DM, Taylor NJ, Smith RE, Stirling JJ, Daley FM et al (2007) Hypoxia in prostate cancer: correlation of BOLD-MRI with pimonidazole immunohistochemistry-initial observations. Int J Radiat Oncol Biol Phys [Internet] 68(4):1065\u20131071","journal-title":"Int J Radiat Oncol Biol Phys [Internet]"},{"issue":"12","key":"1644_CR11","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"4","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol [Internet] 4(12):749\u2013762","journal-title":"Nat Rev Clin Oncol [Internet]"},{"issue":"6","key":"1644_CR12","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1007\/s00261-019-02028-w","volume":"44","author":"Z Bodalal","year":"2019","unstructured":"Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R (2019) Radiogenomics: bridging imaging and genomics. Abdom Radiol 44(6):1960\u20131984","journal-title":"Abdom Radiol"},{"issue":"5","key":"1644_CR13","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1002\/jmri.27692","volume":"54","author":"SJ Hectors","year":"2021","unstructured":"Hectors SJ, Chen C, Chen J, Wang J, Gordon S, Yu M et al (2021) Magnetic resonance imaging radiomics-based machine learning prediction of clinically significant prostate cancer in equivocal <scp>PI-RADS<\/scp> 3 lesions. J Magn Reson Imaging 54(5):1466\u20131473","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"1644_CR14","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.ccell.2018.06.006","volume":"34","author":"ZA Binder","year":"2018","unstructured":"Binder ZA, Thorne AH, Bakas S, Wileyto EP, Bilello M, Akbari H et al (2018) Epidermal growth factor receptor extracellular domain mutations in glioblastoma present opportunities for clinical imaging and therapeutic development. Cancer Cell 34(1):163-177.e7","journal-title":"Cancer Cell"},{"issue":"11","key":"1644_CR15","doi-asserted-by":"publisher","first-page":"3643","DOI":"10.1007\/s00259-021-05371-7","volume":"48","author":"M Kirienko","year":"2021","unstructured":"Kirienko M, Sollini M, Corbetta M, Voulaz E, Gozzi N, Interlenghi M et al (2021) Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur J Nucl Med Mol Imaging 48(11):3643\u20133655","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"1644_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.102841","volume":"57","author":"CS Fjeldbo","year":"2020","unstructured":"Fjeldbo CS, Hompland T, Hillestad T, Aarnes E-K, G\u00fcnther C-C, Kristensen GB et al (2020) Combining imaging- and gene-based hypoxia biomarkers in cervical cancer improves prediction of chemoradiotherapy failure independent of intratumour heterogeneity. EBioMedicine 57:102841","journal-title":"EBioMedicine"},{"issue":"1","key":"1644_CR17","doi-asserted-by":"publisher","first-page":"4861","DOI":"10.1038\/s41467-020-18703-2","volume":"11","author":"M Fan","year":"2020","unstructured":"Fan M, Xia P, Clarke R, Wang Y, Li L (2020) Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer. Nat Commun 11(1):4861","journal-title":"Nat Commun"},{"key":"1644_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.ebiom.2018.04.019","volume":"31","author":"L Yang","year":"2018","unstructured":"Yang L, Roberts D, Takhar M, Erho N, Bibby BAS, Thiruthaneeswaran N et al (2018) Development and validation of a 28-gene hypoxia-related prognostic signature for localized prostate cancer. EBioMedicine [Internet] 31:182\u2013189","journal-title":"EBioMedicine [Internet]"},{"issue":"2","key":"1644_CR19","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200029","volume":"2","author":"J Mongan","year":"2020","unstructured":"Mongan J, Moy L, Kahn CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2(2):e200029","journal-title":"Radiol Artif Intell"},{"issue":"21","key":"1644_CR20","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104\u2013e107","journal-title":"Cancer Res"},{"issue":"1","key":"1644_CR21","doi-asserted-by":"publisher","first-page":"12340","DOI":"10.1038\/s41598-020-69298-z","volume":"10","author":"A Carr\u00e9","year":"2020","unstructured":"Carr\u00e9 A, Klausner G, Edjlali M, Lerousseau M, Briend-Diop J, Sun R et al (2020) Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep 10(1):12340","journal-title":"Sci Rep"},{"issue":"2","key":"1644_CR22","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/42.836373","volume":"19","author":"LG Nyul","year":"2000","unstructured":"Nyul LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imaging 19(2):143\u2013150","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"1644_CR23","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A et al (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 295(2):328\u2013338","journal-title":"Radiology"},{"key":"1644_CR24","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J (2011) Scikit-learn: Machine Learning in Python. J Mach Learn Res [Internet] 12:2825\u20132830","journal-title":"J Mach Learn Res [Internet]"},{"issue":"3","key":"1644_CR25","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837\u2013845","journal-title":"Biometrics"},{"issue":"2","key":"1644_CR26","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.euo.2022.10.001","volume":"6","author":"DMC Poon","year":"2022","unstructured":"Poon DMC, Yuan J, Yang B, Kerkmeijer LGW, Kishan AU, Murthy V et al (2022) Magnetic Resonance Imaging\u2013guided Focal Boost to Intraprostatic Lesions Using External Beam Radiotherapy for Localized Prostate Cancer: A Systematic Review and Meta-analysis. Eur Urol Oncol 6(2):116\u2013127","journal-title":"Eur Urol Oncol"},{"issue":"1035","key":"1644_CR27","doi-asserted-by":"publisher","first-page":"20130753","DOI":"10.1259\/bjr.20130753","volume":"87","author":"R Bristow","year":"2014","unstructured":"Bristow R, Berlin A, Dal Pra A (2014) An arranged marriage for precision medicine: hypoxia and genomic assays in localized prostate cancer radiotherapy. Br J Radiol 87(1035):20130753","journal-title":"Br J Radiol"},{"issue":"2\u20133","key":"1644_CR28","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s10555-013-9470-4","volume":"33","author":"A Amaro","year":"2014","unstructured":"Amaro A, Esposito AI, Gallina A, Nees M, Angelini G, Albini A et al (2014) Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel. Cancer Metastasis Rev 33(2\u20133):657\u2013671","journal-title":"Cancer Metastasis Rev"},{"issue":"15","key":"1644_CR29","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1056\/NEJM198710083171501","volume":"317","author":"TA Stamey","year":"1987","unstructured":"Stamey TA, Yang N, Hay AR, McNeal JE, Freiha FS, Redwine E (1987) Prostate-Specific Antigen as a Serum Marker for Adenocarcinoma of the Prostate. N Engl J Med 317(15):909\u2013916","journal-title":"N Engl J Med"},{"issue":"3","key":"1644_CR30","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/radiol.2021202553","volume":"298","author":"MR Tomaszewski","year":"2021","unstructured":"Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 298(3):505\u2013516","journal-title":"Radiology"},{"key":"1644_CR31","doi-asserted-by":"crossref","unstructured":"Gao J, Ye F, Han F, Wang X, Jiang H, Zhang J. 2021 A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma. Front Oncol, 11","DOI":"10.3389\/fonc.2021.739815"},{"issue":"8","key":"1644_CR32","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1158\/1078-0432.CCR-19-2556","volume":"26","author":"N Beig","year":"2020","unstructured":"Beig N, Bera K, Prasanna P, Antunes J, Correa R, Singh S et al (2020) Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res [Internet] 26(8):1866\u20131876","journal-title":"Clin Cancer Res [Internet]"},{"issue":"18","key":"1644_CR33","doi-asserted-by":"publisher","first-page":"9971","DOI":"10.3390\/ijms22189971","volume":"22","author":"M Ferro","year":"2021","unstructured":"Ferro M, de Cobelli O, Vartolomei MD, Lucarelli G, Crocetto F, Barone B et al (2021) Prostate cancer radiogenomics\u2014from imaging to molecular characterization. Int J Mol Sci 22(18):9971","journal-title":"Int J Mol Sci"},{"issue":"1104","key":"1644_CR34","doi-asserted-by":"publisher","first-page":"20190373","DOI":"10.1259\/bjr.20190373","volume":"92","author":"Y Sun","year":"2019","unstructured":"Sun Y, Williams S, Byrne D, Keam S, Reynolds HM, Mitchell C et al (2019) Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer: a pilot study. Br J Radiol 92(1104):20190373","journal-title":"Br J Radiol"},{"issue":"3","key":"1644_CR35","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1038\/nrclinonc.2016.162","volume":"14","author":"JPB O\u2019Connor","year":"2017","unstructured":"O\u2019Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14(3):169\u2013186","journal-title":"Nat Rev Clin Oncol"},{"issue":"1","key":"1644_CR36","doi-asserted-by":"publisher","first-page":"351","DOI":"10.21873\/anticanres.16170","volume":"43","author":"M Leech","year":"2023","unstructured":"Leech M, Leijenaar RTH, Hompland T, Gaffney J, Lyng H, Marignol L (2023) Exploring hypoxia in prostate cancer with T2-weighted magnetic resonance imaging radiomics and pimonidazole scoring. Anticancer Res 43(1):351\u2013357","journal-title":"Anticancer Res"},{"issue":"4","key":"1644_CR37","doi-asserted-by":"publisher","first-page":"432","DOI":"10.21037\/tcr.2016.06.20","volume":"5","author":"R Stoyanova","year":"2016","unstructured":"Stoyanova R, Takhar M, Tschudi Y, Ford JC, Sol\u00f3rzano G, Erho N et al (2016) Prostate cancer radiomics and the promise of radiogenomics. Transl Cancer Res [Internet] 5(4):432\u2013447","journal-title":"Transl Cancer Res [Internet]"},{"issue":"1","key":"1644_CR38","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1186\/s12859-022-04807-7","volume":"23","author":"B Hanczar","year":"2022","unstructured":"Hanczar B, Bourgeais V, Zehraoui F (2022) Assessment of deep learning and transfer learning for cancer prediction based on gene expression data. BMC Bioinformatics 23(1):262","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"1644_CR39","doi-asserted-by":"publisher","first-page":"2272","DOI":"10.1007\/s00330-020-07284-9","volume":"31","author":"F Orlhac","year":"2021","unstructured":"Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F et al (2021) How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 31(4):2272\u20132280","journal-title":"Eur Radiol"},{"issue":"1","key":"1644_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"R Da-ano","year":"2020","unstructured":"Da-ano R, Masson I, Lucia F, Dor\u00e9 M, Robin P, Alfieri J et al (2020) Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep [Internet] 10(1):1\u201312","journal-title":"Sci Rep [Internet]"},{"issue":"16","key":"1644_CR41","doi-asserted-by":"publisher","first-page":"4774","DOI":"10.1158\/0008-5472.CAN-17-3806","volume":"78","author":"T Hompland","year":"2018","unstructured":"Hompland T, Hole KH, kon, Ragnum HB, Aarnes EK, Vlatkovic L, Kathrine Lie A, et al (2018) Combined mr imaging of oxygen consumption and supply reveals tumor hypoxia and aggressiveness in prostate cancer patients. Cancer Res 78(16):4774\u20134785","journal-title":"Cancer Res"},{"issue":"1","key":"1644_CR42","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1038\/s41467-020-14415-9","volume":"11","author":"X Cao","year":"2020","unstructured":"Cao X, Rao Allu S, Jiang S, Jia M, Gunn JR, Yao C et al (2020) Tissue pO2 distributions in xenograft tumors dynamically imaged by Cherenkov-excited phosphorescence during fractionated radiation therapy. Nat Commun 11(1):573","journal-title":"Nat Commun"},{"issue":"9","key":"1644_CR43","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/S1470-2045(18)30413-3","volume":"19","author":"R Sun","year":"2018","unstructured":"Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19(9):1180\u20131191","journal-title":"Lancet Oncol"}],"container-title":["La radiologia medica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11547-023-01644-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11547-023-01644-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11547-023-01644-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T17:09:54Z","timestamp":1686676194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11547-023-01644-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,17]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["1644"],"URL":"https:\/\/doi.org\/10.1007\/s11547-023-01644-3","relation":{},"ISSN":["1826-6983"],"issn-type":[{"value":"1826-6983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,17]]},"assertion":[{"value":"15 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2023","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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. The study was approved by the United Kingdom North West Research Ethics Committee (Validation and qualification of a multiplex hypoxia biomarker for radiotherapy individualisation in prostate cancer study (IRAS 15\/NW\/0559)).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}