{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:47:49Z","timestamp":1761709669854},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Curr Colorectal Cancer Rep"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s11888-019-00446-y","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T05:02:53Z","timestamp":1574830973000},"page":"175-180","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Challenges and Promises of Radiomics for Rectal Cancer"],"prefix":"10.1007","volume":"15","author":[{"given":"Jos\u00e9 Maria","family":"Moreira","sequence":"first","affiliation":[]},{"given":"In\u00eas","family":"Santiago","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Santinha","sequence":"additional","affiliation":[]},{"given":"Nuno","family":"Figueiredo","sequence":"additional","affiliation":[]},{"given":"Kostas","family":"Marias","sequence":"additional","affiliation":[]},{"given":"M\u00e1rio","family":"Figueiredo","sequence":"additional","affiliation":[]},{"given":"Leonardo","family":"Vanneschi","sequence":"additional","affiliation":[]},{"given":"Nickolas","family":"Papanikolaou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"446_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"\u2022 Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre LA, Jemal A. Global Cancer Statistics 2018 : GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2018;68:394\u2013424 A global epidemiological platform that presents global cancer statistics to inform cancer control and cancer research.","journal-title":"CA Cancer J Clin"},{"issue":"4","key":"446_CR2","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1136\/gutjnl-2015-310912","volume":"66","author":"M Arnold","year":"2017","unstructured":"Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66(4):683\u201391.","journal-title":"Gut"},{"key":"446_CR3","doi-asserted-by":"crossref","unstructured":"Dinapoli N, Cas\u00e0 C, Barbaro B, Chiloiro GV, Damiani A, Di Matteo M, Farchione A, Gambacorta MA, Gatta R, Lanzotti V, Masciocchi C, Valentini V. Radiomics for rectal cancer 2016;5(1):424\u201331.","DOI":"10.21037\/tcr.2016.06.08"},{"issue":"9","key":"446_CR4","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1056\/NEJMoa010580","volume":"345","author":"Ellen Kapiteijn","year":"2001","unstructured":"Kapiteijn E, Marijnen CAM, Nagtegaal I, Putter H, Steup WH, Wiggers T, Rutten HJ, Pahlman L, Glimelius B, van Krieken J, Leer JW, van de Velde C, Dutch Colorectal Cancer Group. Preoperative radiotherapy combned with total mesorectal excision for resectable rectal cancer. N Engl J Med 2001;345(9):638\u2013646.","journal-title":"New England Journal of Medicine"},{"issue":"17","key":"446_CR5","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1056\/NEJMoa040694","volume":"351","author":"R Sauer","year":"2004","unstructured":"Sauer R, Becker H, Hohenberger W, R\u00f6del C, Wittekind C, Fietkau R, et al. Preoperative versus postoperative chemoradiotherapy for rectal Cancer. N Engl J Med. 2004;351(17):1731\u201340. Available from:. https:\/\/doi.org\/10.1056\/NEJMoa040694.","journal-title":"N Engl J Med"},{"issue":"1","key":"446_CR6","doi-asserted-by":"publisher","first-page":"1.e1","DOI":"10.1016\/j.ejca.2013.06.048","volume":"50","author":"CJH van de Velde","year":"2014","unstructured":"van de Velde CJH, Boelens PG, Borras JM, Coebergh J-W, Cervantes A, Blomqvist L, et al. EURECCA colorectal: multidisciplinary management: European consensus conference colon & rectum. Eur J Cancer. 2014;50(1):1.e1\u20131.e34 Available from:\nhttp:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0959804913007806.","journal-title":"Eur J Cancer"},{"issue":"12","key":"446_CR7","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1007\/s00330-013-2936-5","volume":"23","author":"HIL Ha","year":"2013","unstructured":"Ha HIL, Kim AY, Yu CS, Park SH, Ha HK. Locally advanced rectal cancer: diffusion-weighted MR tumour volumetry and the apparent diffusion coefficient for evaluating complete remission after preoperative chemoradiation therapy. Eur Radiol. 2013;23(12):3345\u201353.","journal-title":"Eur Radiol"},{"key":"446_CR8","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1016\/S0140-6736(09)60484-0","volume":"373","author":"D Sebag-montefi","year":"2009","unstructured":"Sebag-montefi D, Stephens RJ, Steele R, Monson J, Grieve R, Khanna S, et al. Preoperative radiotherapy versus selective postoperative chemoradiotherapy in patients with rectal cancer ( MRC CR07 and NCIC-CTG C016 ): a multicentre , randomised trial. Lancet. 2009;373:811\u201320.","journal-title":"Lancet."},{"issue":"6","key":"446_CR9","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/S1470-2045(11)70097-3","volume":"12","author":"W Van Gijn","year":"2011","unstructured":"Van Gijn W, CAM M, Nagtegaal ID, Kranenbarg EM, Putter H, Wiggers T, et al. Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer : 12-year follow-up of the multicentre , randomised controlled TME trial. Lancet Oncol. 2011;12(6):575\u201382. Elsevier Ltd; Available from:. https:\/\/doi.org\/10.1016\/S1470-2045(11)70097-3.","journal-title":"Lancet Oncol"},{"issue":"34","key":"446_CR10","doi-asserted-by":"publisher","first-page":"8697","DOI":"10.1200\/JCO.2005.02.9017","volume":"23","author":"H Birgisson","year":"2005","unstructured":"Birgisson H, P\u00e5hlman L, Gunnarsson U, Glimelius B. Adverse effects of preoperative radiation therapy for rectal cancer : long-term follow-up of the Swedish rectal cancer trial. J Clin Oncol. 2005;23(34):8697\u2013705.","journal-title":"J Clin Oncol"},{"issue":"5","key":"446_CR11","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1200\/JCO.2009.25.8541","volume":"28","author":"C Fern\u00e1ndez-Martos","year":"2010","unstructured":"Fern\u00e1ndez-Martos C, Pericay C, Aparicio J, Salud A, Safont M, Massuti B, et al. Phase II , Randomized study of concomitant chemoradiotherapy followed by surgery and adjuvant capecitabine plus oxaliplatin ( CAPOX ) compared with induction CAPOX followed by concomitant chemoradiotherapy and surgery in magnetic resonance imaging \u2013 Defin. J Clin Oncol. 2010;28(5):859\u201365.","journal-title":"J Clin Oncol"},{"issue":"27","key":"446_CR12","doi-asserted-by":"publisher","first-page":"4233","DOI":"10.1200\/JCO.2009.26.5264","volume":"28","author":"RJ Stephens","year":"2010","unstructured":"Stephens RJ, Thompson LC, Quirke P, Steele R, Grieve R, Couture J, et al. Impact of short-course preoperative radiotherapy for rectal cancer on patients\u2019 quality of life: data from the Medical Research Council CR07 \/ National Cancer Institute of Canada clinical trials group C016 randomized clinical trial. J Clin Oncol. 2010;28(27):4233\u20139.","journal-title":"J Clin Oncol"},{"key":"446_CR13","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1016\/S0140-6736(18)31078-X","volume":"391","author":"MJM Valk","year":"2018","unstructured":"\u2022 Valk MJM, Van Der HDE, Bastiaannet E, Kranenbarg EM, Beets GL, Figueiredo NL, et al. Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): an international multicentre registry study. Lancet. 2018;391:2537\u201345 The first large registry-based study on international watch-and-wait strategies for patients with rectal cancer.","journal-title":"Lancet"},{"key":"446_CR14","doi-asserted-by":"crossref","unstructured":"\u2022\u2022 Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, et al. Rectal cancer : assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. 2016;22(15):5256\u201364 First study to integrate anatomical, perfusion, and diffusion MRI using both volume-averaged and voxel-based quantitative analysis to predict pCR in rectal cancer.","DOI":"10.1158\/1078-0432.CCR-15-2997"},{"issue":"1","key":"446_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-05728-9","volume":"7","author":"S Trebeschi","year":"2017","unstructured":"Trebeschi S, Van Griethuysen JJM, Lambregts DMJ, Lahaye MJ, Parmer C, Bakers FCH, et al. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci Rep Springer US. 2017;7(1):1\u20139.","journal-title":"Sci Rep"},{"key":"446_CR16","doi-asserted-by":"publisher","first-page":"3873","DOI":"10.1245\/s10434-015-4687-9","volume":"22","author":"M Maas","year":"2015","unstructured":"Maas M, Lambregts DMJ, Nelemans PJ, Heijnen LA, Martens MH, Leijtens JWA, et al. Assessment of clinical complete response after chemoradiation for rectal cancer with digital rectal examination, endoscopy, and MRI: selection for organ-saving treatment. Ann Surg Oncol. 2015;22:3873\u201380.","journal-title":"Ann Surg Oncol"},{"key":"446_CR17","first-page":"1","volume-title":"Imaging Biomarkers","author":"Siegfried Trattnig","year":"2016","unstructured":"Trattnig S. The shift in paradigm to precision medicine in imaging: international initiatives for the promotion of imaging biomarkers. In: Mart\u00ed-Bonmat\u00ed L, Alberich-Bayarri A, editors. Imaging Biomarkers. Cham: Springer International Publishing; 2017. p. 1\u20137. Available from: http:\/\/link.springer.com\/10.1007\/978-3-319-43504-6_1."},{"key":"446_CR18","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3462-1","volume-title":"Regression Modeling Strategies","author":"Frank E. Harrell","year":"2001","unstructured":"Harrell F. Regression modeling strategies. New York, NY: Springer New York; 2001. (Springer Series in Statistics). Available from: http:\/\/link.springer.com\/10.1007\/978-1-4757-3462-1."},{"issue":"13","key":"446_CR19","doi-asserted-by":"publisher","first-page":"5213","DOI":"10.1073\/pnas.0801279105","volume":"105","author":"M Diehn","year":"2008","unstructured":"Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci. 2008;105(13):5213\u20138. Available from:. https:\/\/doi.org\/10.1073\/pnas.0801279105.","journal-title":"Proc Natl Acad Sci"},{"issue":"4","key":"446_CR20","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1007\/s00330-011-2319-8","volume":"22","author":"B Ganeshan","year":"2012","unstructured":"Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol. 2012;22(4):796\u2013802.","journal-title":"Eur Radiol"},{"issue":"1","key":"446_CR21","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/nrclinonc.2012.196","volume":"10","author":"P Lambin","year":"2013","unstructured":"Lambin P, Van Stiphout RGPM, Starmans MHW, Rios-Velazquez E, Nalbantov G, Aerts HJWL, et al. Predicting outcomes in radiation oncology-multifactorial decision support systems. Nat Rev Clin Oncol. 2013;10(1):27\u201340.","journal-title":"Nat Rev Clin Oncol"},{"issue":"12","key":"446_CR22","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1001\/jamaoncol.2016.2631","volume":"2","author":"HJWL Aerts","year":"2016","unstructured":"\u2022 Aerts HJWL. The potential of radiomic-based phenotyping in precision medicine a review. JAMA Oncol. 2016;2(12):1636\u201342 Important review that highlights the promise ofradiomicsfor precision medicine.","journal-title":"JAMA Oncol"},{"key":"446_CR23","doi-asserted-by":"crossref","unstructured":"Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Cavalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5.","DOI":"10.1038\/ncomms5644"},{"key":"446_CR24","doi-asserted-by":"crossref","unstructured":"\u2022\u2022 Sun Y, Hu P, Wang J, Shen L, Xia F, Qing G, Hu W, Zhang Z. Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer : preliminary findings. J Magn Reson Imaging. 2018; Study determining if radiomic features extracted from T2-weighted imaging (T2WI) can identify pathological features in rectal cancer.","DOI":"10.1002\/jmri.26309"},{"key":"446_CR25","doi-asserted-by":"crossref","unstructured":"Liu L, Liu Y, Xu L, Li Z, Lv H, Dong N, Li W, Yang Z, Wang Z, Jin E. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging 2016.","DOI":"10.1002\/jmri.25460"},{"key":"446_CR26","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1007\/s00330-011-2182-7","volume":"21","author":"C Cui","year":"2011","unstructured":"Cui C, Cai H, Liu L, Li L, Tian H, Li L. Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging. Eur Soc Radiol. 2011;21:2318\u201325.","journal-title":"Eur Soc Radiol"},{"key":"446_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-17765-5","volume":"8","author":"J Bibault","year":"2018","unstructured":"\u2022\u2022 Bibault J, Giraud P, Housset M, Durdux C, Taieb J, Berger A, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep. 2018;8:1\u20138 A proof-of-concept study, stating that combining clinical and radiomics features is feasible and can accurately predict patients who will have a complete pathological response a\u017fter neo-adjuvant chemoradiotherapy.","journal-title":"Sci Rep"},{"key":"446_CR28","doi-asserted-by":"crossref","unstructured":"Chee CG, Kim YH, Lee KH, Lee YJ, Park JH, Lee HS, Ahn S, Kim B. CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy : a potential imaging biomarker for treatment response and prognosis. PLoS One 2017;1\u201312.","DOI":"10.1371\/journal.pone.0182883"},{"issue":"23","key":"446_CR29","doi-asserted-by":"publisher","first-page":"7253","DOI":"10.1158\/1078-0432.CCR-17-1038","volume":"23","author":"Zhenyu Liu","year":"2017","unstructured":"\u2022\u2022 Liu Z, Zhang X, Shi Y, Wang L, Zhu H, Tang Z, et al. Radiomics analysis for evaluation of pathological complete response to Neoadjuvant Chemoradiotherapy in locally advanced rectal Cancer. Clin Cancer Res. 2017;(16):7253\u201363 Study that uses pre- and post-treatment MRI data to build aradiomicsmodel that predicts pCR in patients with LARC.","journal-title":"Clinical Cancer Research"},{"issue":"4","key":"446_CR30","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/s11547-017-0838-3","volume":"123","author":"D Cusumano","year":"2018","unstructured":"\u2022\u2022 Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C. Fractal - based radiomic approach to predict complete pathological response after chemo - radiotherapy in rectal cancer. Radiol Med. 2018;123(4):286\u201395. https:\/\/doi.org\/10.1007\/s11547-017-0838-3Relevant study that uses MR images to predict pCR after CRT, in which the model is validated by an independent dataset.","journal-title":"Radiol Med"},{"issue":"3","key":"446_CR31","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1148\/radiol.2018172300","volume":"287","author":"N Horvat","year":"2018","unstructured":"\u2022\u2022 Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after Neoadjuvant therapy. Radiology. 2018;287(3):833\u201343 Study shows thatradiomicmeasures shows better classification performance compared to qualitative assessment for diagnosing pCR in patients with locally advanced rectal cancer.","journal-title":"Radiology"},{"issue":"15","key":"446_CR32","doi-asserted-by":"publisher","first-page":"11999","DOI":"10.18632\/oncotarget.23813","volume":"9","author":"Y Meng","year":"2018","unstructured":"Meng Y, Zhang C, Zou S, Zhao X, Xu K, Zhang H, et al. MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget. 2018;9(15):11999\u20132008.","journal-title":"Oncotarget."},{"issue":"4","key":"446_CR33","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1016\/j.ijrobp.2015.12.017","volume":"94","author":"Miriam M. van Heeswijk","year":"2016","unstructured":"van Heeswijk MM, Lambregts DMJ, van Griethuysen JJM, Oei S, Rao S-X, de Graaff CAM, et al. Automated and semi-automated segmentation of rectal tumpr volumes on diffusion-weighted MRI: can it replace manual volumetry? Int J Radiat Oncol Biol Phys. 2016. Elsevier Ltd 2015; Available from:. https:\/\/doi.org\/10.1016\/j.ijrobp.2015.12.017.","journal-title":"International Journal of Radiation Oncology*Biology*Physics"},{"key":"446_CR34","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61\u201378. Elsevier B.V.; Available from:. https:\/\/doi.org\/10.1016\/j.media.2016.10.004.","journal-title":"Med Image Anal"},{"key":"446_CR35","first-page":"1","volume":"22","author":"MI Razzak","year":"2017","unstructured":"Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: overview, challenges and future. CoRR. 2017;22:1\u201330 Available from:\nhttp:\/\/arxiv.org\/abs\/1704.06825.","journal-title":"CoRR"},{"key":"446_CR36","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.artmed.2018.09.003","volume":"96","author":"Roberto Gatta","year":"2019","unstructured":"Gatta R, Vallati M, Dinapoli N, Masciocchi C, Lenkowicz J, Cusumano D, et al. Towards a modular decision support system for radiomics: a case study on rectal cancer. Artif Intell Med. 2018. Elsevier; Available from:. https:\/\/doi.org\/10.1016\/j.artmed.2018.09.003.","journal-title":"Artificial Intelligence in Medicine"},{"issue":"2","key":"446_CR37","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/s00261-016-0950-1","volume":"42","author":"RM Summers","year":"2017","unstructured":"Summers RM. Texture analysis in radiology: does the emperor have no clothes ? Abdom Radiol. 2017;42(2):342\u20135 Relevant review on texture analysis in radiology.","journal-title":"Abdom Radiol"}],"container-title":["Current Colorectal Cancer Reports"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11888-019-00446-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11888-019-00446-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11888-019-00446-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T01:30:25Z","timestamp":1606354225000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11888-019-00446-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,27]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["446"],"URL":"https:\/\/doi.org\/10.1007\/s11888-019-00446-y","relation":{},"ISSN":["1556-3790","1556-3804"],"issn-type":[{"value":"1556-3790","type":"print"},{"value":"1556-3804","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,27]]},"assertion":[{"value":"27 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights and Informed Consent"}}]}}