{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:19:21Z","timestamp":1759191561632,"version":"3.44.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032076939","type":"print"},{"value":"9783032076946","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-07694-6_15","type":"book-chapter","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T18:04:49Z","timestamp":1759169089000},"page":"152-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Prediction of\u00a0Recurrence After Prostate Cancer Radiotherapy Using in Silico digital twin model and\u00a0machine learning"],"prefix":"10.1007","author":[{"given":"Valentin","family":"Septiers","sequence":"first","affiliation":[]},{"given":"Carlos","family":"Sosa-Marrero","sequence":"additional","affiliation":[]},{"given":"Eleonora","family":"Poeta","sequence":"additional","affiliation":[]},{"given":"Hilda","family":"Chourak","sequence":"additional","affiliation":[]},{"given":"Aur\u00e9lien","family":"Briens","sequence":"additional","affiliation":[]},{"given":"Renaud","family":"De Crevoisier","sequence":"additional","affiliation":[]},{"given":"Maria A.","family":"Zuluaga","sequence":"additional","affiliation":[]},{"given":"Oscar","family":"Acosta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Abramowitz, M.C., et al.: The phoenix definition of biochemical failure predicts for overall survival in patients with prostate cancer. In: Cancer (2008)","DOI":"10.1002\/cncr.23139"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Beven, K.: A sensitivity analysis of the penman-monteith actual evapotranspiration estimates. In: J. Hydrol. (1979)","DOI":"10.1016\/0022-1694(79)90130-6"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Bray, F., et al.: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. In: CA. Cancer J. Clin. (2024)","DOI":"10.3322\/caac.21834"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Chanrion, M.-A., et al.: The influence of the local effect model parameters on the prediction of the tumor control probability for prostate cancer. In: Phys. Med. Biol. (2014)","DOI":"10.1088\/0031-9155\/59\/12\/3019"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. In: J. Artif. Intell. Res. (2002)","DOI":"10.1613\/jair.953"},{"key":"15_CR6","unstructured":"Duenweg, S.R., et al.: T2-weighted MRI radiomic features predict prostate cancer presence and eventual biochemical recurrence. In: Cancers"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Dutta, A., et al.: Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: impact on recurrence prediction after radiation therapy. In: Phys. Imaging Radiat. Oncol. (2024)","DOI":"10.1016\/j.phro.2023.100530"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Epstein, J.I., et al.: The 2014 international society of urological pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. In: Am. J. Surg. Pathol. (2016)","DOI":"10.1097\/PAS.0000000000000530"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Frey, B.J., Dueck, D.: Clustering by passing messages between data points. In: Science (2007)","DOI":"10.1126\/science.1136800"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Gnep, K.K., et al.: Haralick textural features on $$T_{2}$$-weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer: impact of MRI in prostate cancer. In: $$J$$. Magn. Reson. Imaging (2017)","DOI":"10.1002\/jmri.25335"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Hami, R., et al.: Predicting the Tumour response to radiation by modelling the five Rs of radiotherapy using PET images. In: J. Imaging (2023)","DOI":"10.3390\/jimaging9060124"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, A.I., et al.: A multiformalism and multiresolution modelling environment: application to the cardiovascular system and its regulation. In: Phil. Trans. R. Soc. A. (2009)","DOI":"10.1098\/rsta.2009.0163"},{"key":"15_CR13","unstructured":"Hooker, S., et al.: A benchmark for interpretability methods in deep neural networks. In: Adv. Neural Inf. Process. Syst. (2019)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Joiner, M.C., van der Kogel, A.J.: Basic Clinical Radiobiology (2018). ISBN: 978-0-429-95540-2","DOI":"10.1201\/9780429490606"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Kwak, J.T., et al.: Prostate cancer: a correlative study of multiparametric MR imaging and digital histopathology. In: Radiology (2017)","DOI":"10.1148\/radiol.2017160906"},{"key":"15_CR16","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (2017). ISBN: 978-1-5108-6096-4"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Marinkovic, M., et al.: Comparison of different machine learning models in prediction of postirradiation recurrence in prostate carcinoma patients. In: Biomed. Res. Int. (2022)","DOI":"10.1155\/2022\/7943609"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Nanekaran, N.P., et al.: Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information. In: Biomed. Phys. Eng. Express. (2024)","DOI":"10.1088\/2057-1976\/ad8201"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Nicol\u00f3, C., et al.: Machine learning and mechanistic modeling for prediction of metastatic relapse in early-stage breast cancer. In: JCO Clin. Cancer Inform. (2020)","DOI":"10.1101\/634428"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016). ISBN: 978-1-4503-4232-2","DOI":"10.1145\/2939672.2939778"},{"key":"15_CR21","unstructured":"Samek, W., Wiegand, T., M\u00fcller, K.-R.: Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models (2017). arXiv: 1708.08296"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Sosa-Marrero, C., et al.: Towards a reduced in silico model predicting biochemical recurrence after radiotherapy in prostate cancer. In: IEEE Trans. Biomed. Eng. (2021)","DOI":"10.1109\/TBME.2021.3052345"},{"key":"15_CR23","unstructured":"Joost J.M., Van G., et al.: Computational Radiomics System to Decode the Radiographic Phenotype. In: Cancer Res. (2017)"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer. In: Front. Oncol. (2024)","DOI":"10.3389\/fonc.2024.1342104"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Zumsteg, Z.S., et al.: Anatomic patterns of recurrence following biochemical relapse in the dose-escalation era for prostate patients undergoing external beam radiotherapy. In: Urol. J. (2015)","DOI":"10.1016\/j.juro.2015.06.100"}],"container-title":["Lecture Notes in Computer Science","Digital Twin for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07694-6_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T18:04:58Z","timestamp":1759169098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07694-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"ISBN":["9783032076939","9783032076946"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07694-6_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,30]]},"assertion":[{"value":"30 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"DT4H","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Digital Twin for Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dt4h2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/digitaltwinforhealthcare.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}