{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T01:48:47Z","timestamp":1779241727859,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"BMBF, S\u00e4chsisches Staatsministerium f\u00fcr Wissenschaft, Kultur und Tourismus","award":["SCADS24B"],"award-info":[{"award-number":["SCADS24B"]}]},{"name":"BMBF, S\u00e4chsisches Staatsministerium f\u00fcr Wissenschaft, Kultur und Tourismus","award":["SCADS24B"],"award-info":[{"award-number":["SCADS24B"]}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["031L0261"],"award-info":[{"award-number":["031L0261"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["031L0261"],"award-info":[{"award-number":["031L0261"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008678","name":"Universit\u00e4t Leipzig","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008678","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cancer Res Clin Oncol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Thrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual\u2019s risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin\u2019s lymphoma to learn the respective models on an individual scale and to compare prediction performances with that of the semi-mechanistic model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the examined network models, the NARX with GRU architecture performs best. In comparison to the semi-mechanistic model, the network model can result in a substantial improvement of prediction accuracy for patients with irregular dynamics, given well-spaced measurements. TL improves individual prediction performances.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>NARX networks can be utilized to predict an individual\u2019s thrombotoxic response to cytotoxic chemotherapy treatment. For reasonable model learning, we recommend at least three well-spaced measurements per cycle: at baseline, during the nadir phase and during the recovery phase. We aim at generalizing our approach to other treatment scenarios and blood lineages in the future.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00432-024-05985-y","type":"journal-article","created":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T20:01:53Z","timestamp":1728849713000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting chemotherapy-induced thrombotoxicity by NARX neural networks and transfer learning"],"prefix":"10.1007","volume":"150","author":[{"given":"Marie","family":"Steinacker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuri","family":"Kheifetz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Scholz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"5985_CR1","doi-asserted-by":"publisher","unstructured":"An G (1996) The Effects of Adding Noise During Backpropagation Training on a Generalization Performance. Neural Computation 8(3):643\u2013674. https:\/\/doi.org\/10.1162\/neco.1996.8.3.643, https:\/\/direct.mit.edu\/neco\/article\/8\/3\/643-674\/5975. Accessed 26 Apr 2024","DOI":"10.1162\/neco.1996.8.3.643"},{"key":"5985_CR2","unstructured":"Brown R (1956) EXPONENTIAL SMOOTHING FOR PREDICTING DEMAND. Cambridge, Massachusetts: Arthur D Little Inc p p. 15. http:\/\/legacy.library.ucsf.edu\/tid\/dae94e00. Accessed 9 Apr 2024"},{"issue":"5","key":"5985_CR3","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1287\/opre.9.5.673","volume":"9","author":"RG Brown","year":"1961","unstructured":"Brown RG, Meyer RF (1961) The fundamental theorem of exponential smoothing. Oper Res 9(5):673\u2013685. https:\/\/doi.org\/10.1287\/opre.9.5.673","journal-title":"Oper Res"},{"key":"5985_CR4","unstructured":"Chen RTQ, Rubanova Y, Bettencourt J, et\u00a0al (2018) Neural Ordinary Differential Equations. In: Bengio S, Wallach H, Larochelle H, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a031. Curran Associates, Inc., https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2018\/file\/69386f6bb1dfed68692a24c8686939b9-Paper.pdf. Accessed 29 Jul 2024"},{"key":"5985_CR5","first-page":"301","volume":"56","author":"E Choi","year":"2016","unstructured":"Choi E, Bahadori MT, Schuetz A et al (2016) Doctor AI: predicting clinical events via recurrent neural networks. JMLR Workshop Conf Proc 56:301\u2013318","journal-title":"JMLR Workshop Conf Proc"},{"key":"5985_CR6","doi-asserted-by":"publisher","unstructured":"Cho K, Van\u00a0Merrienboer B, Gulcehre C, et\u00a0al (2014) Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, pp 1724\u20131734, https:\/\/doi.org\/10.3115\/v1\/D14-1179, http:\/\/aclweb.org\/anthology\/D14-1179. Accessed 7 Nov 2023","DOI":"10.3115\/v1\/D14-1179"},{"issue":"4","key":"5985_CR7","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1200\/JCO.2001.19.4.1137","volume":"19","author":"LS Elting","year":"2001","unstructured":"Elting LS, Rubenstein EB, Martin CG et al (2001) Incidence, cost, and outcomes of bleeding and chemotherapy dose modification among solid tumor patients with chemotherapy-induced thrombocytopenia. JCO 19(4):1137\u20131146. https:\/\/doi.org\/10.1200\/JCO.2001.19.4.1137","journal-title":"JCO"},{"key":"5985_CR8","doi-asserted-by":"publisher","unstructured":"Esteban C, Staeck O, Baier S, et\u00a0al (2016) Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks. pp 93\u2013101, https:\/\/doi.org\/10.1109\/ICHI.2016.16","DOI":"10.1109\/ICHI.2016.16"},{"issue":"2","key":"5985_CR9","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1023\/A:1023573429626","volume":"21","author":"LE Friberg","year":"2003","unstructured":"Friberg LE, Karlsson MO (2003) Mechanistic models for myelosuppression. Invest New Drugs 21(2):183\u2013194. https:\/\/doi.org\/10.1023\/A:1023573429626","journal-title":"Invest New Drugs"},{"issue":"24","key":"5985_CR10","doi-asserted-by":"publisher","first-page":"4713","DOI":"10.1200\/JCO.2002.02.140","volume":"20","author":"LE Friberg","year":"2002","unstructured":"Friberg LE, Henningsson A, Maas H et al (2002) Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. JCO 20(24):4713\u20134721. https:\/\/doi.org\/10.1200\/JCO.2002.02.140","journal-title":"JCO"},{"key":"5985_CR11","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press, https:\/\/www.deeplearningbook.org\/. Accessed 9 Apr 2024"},{"issue":"2","key":"5985_CR12","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1124\/jpet.117.240309","volume":"362","author":"A Henrich","year":"2017","unstructured":"Henrich A, Joerger M, Kraff S et al (2017) Semimechanistic bone marrow exhaustion pharmacokinetic\/pharmacodynamic model for chemotherapy-induced cumulative neutropenia. J Pharmacol Exp Ther 362(2):347\u2013358. https:\/\/doi.org\/10.1124\/jpet.117.240309","journal-title":"J Pharmacol Exp Ther"},{"key":"5985_CR13","doi-asserted-by":"publisher","unstructured":"Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 20(1):5\u201310. https:\/\/doi.org\/10.1016\/j.ijforecast.2003.09.015, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169207003001134. Accessed 9 Apr 2024","DOI":"10.1016\/j.ijforecast.2003.09.015"},{"key":"5985_CR14","doi-asserted-by":"publisher","unstructured":"Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2(5):359\u2013366. https:\/\/doi.org\/10.1016\/0893-6080(89)90020-8, https:\/\/www.sciencedirect.com\/science\/article\/pii\/0893608089900208. Accessed 11 Jul 2022","DOI":"10.1016\/0893-6080(89)90020-8"},{"issue":"3","key":"5985_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006775","volume":"15","author":"Y Kheifetz","year":"2019","unstructured":"Kheifetz Y, Scholz M (2019) Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy. PLoS Comput Biol 15(3):e1006775. https:\/\/doi.org\/10.1371\/journal.pcbi.1006775","journal-title":"PLoS Comput Biol"},{"key":"5985_CR16","doi-asserted-by":"publisher","unstructured":"Kheifetz Y, Scholz M (2021) Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances. Br J Clin Pharmacol p bcp.14722. https:\/\/doi.org\/10.1111\/bcp.14722","DOI":"10.1111\/bcp.14722"},{"key":"5985_CR17","unstructured":"Kleissl M, Drews L, Heyder BB, et\u00a0al (2023) SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data. In: Maughan K, Liu R, Burns TF (eds) The First Tiny Papers Track at ICLR 2023, Tiny Papers @ ICLR 2023, Kigali, Rwanda, May 5, 2023. OpenReview.net, https:\/\/openreview.net\/pdf?id=1wtUadpmVzu. Accessed 13 May 2024"},{"key":"5985_CR18","unstructured":"Krogh A, Hertz J (1991) A Simple Weight Decay Can Improve Generalization. In: Moody J, Hanson S, Lippmann RP (eds) Advances in Neural Information Processing Systems, vol\u00a04. Morgan-Kaufmann, https:\/\/proceedings.neurips.cc\/paper\/1991\/file\/8eefcfdf5990e441f0fb6f3fad709e21-Paper.pdf. Accessed 22 Jul 2022"},{"key":"5985_CR19","unstructured":"LeCun Y, Denker JS, Solla SA (1990) Optimal brain damage. In: Advances in neural information processing systems. pp 598\u2013605"},{"key":"5985_CR20","doi-asserted-by":"publisher","unstructured":"Li K, Daniels J, Liu C et al (2020) Convolutional Recurrent Neural Networks for Glucose Prediction. IEEE J Biomed Health Inform 24(2):603\u2013613. https:\/\/doi.org\/10.1109\/JBHI.2019.2908488, https:\/\/ieeexplore.ieee.org\/document\/8678399\/. Accessed 16 Jul 2024","DOI":"10.1109\/JBHI.2019.2908488"},{"issue":"1","key":"5985_CR21","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1124\/jpet.115.223776","volume":"354","author":"V Mangas-Sanjuan","year":"2015","unstructured":"Mangas-Sanjuan V, Buil-Bruna N, Garrido MJ et al (2015) Semimechanistic cell-cycle type-based pharmacokinetic\/pharmacodynamic model of chemotherapy-induced neutropenic effects of diflomotecan under different dosing schedules. J Pharmacol Exp Ther 354(1):55\u201364. https:\/\/doi.org\/10.1124\/jpet.115.223776","journal-title":"J Pharmacol Exp Ther"},{"key":"5985_CR22","unstructured":"National Cancer Institute (NCI) (2009) Common Terminology Criteria for Adverse Events v3.0. https:\/\/ctep.cancer.gov\/protocolDevelopment\/electronic_applications\/docs\/ctcaev3.pdf. Accessed 13 May 2024"},{"key":"5985_CR23","doi-asserted-by":"publisher","unstructured":"Pan SJ, Yang Q (2010) A Survey on Transfer Learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191, http:\/\/ieeexplore.ieee.org\/document\/5288526\/. Accessed 21 Jul 2022","DOI":"10.1109\/TKDE.2009.191"},{"issue":"3","key":"5985_CR24","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1182\/blood-2003-06-2095","volume":"104","author":"M Pfreundschuh","year":"2004","unstructured":"Pfreundschuh M (2004) Two-weekly or 3-weekly CHOP chemotherapy with or without etoposide for the treatment of elderly patients with aggressive lymphomas: results of the NHL-B2 trial of the DSHNHL. Blood 104(3):634\u2013641. https:\/\/doi.org\/10.1182\/blood-2003-06-2095","journal-title":"Blood"},{"issue":"3","key":"5985_CR25","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1182\/blood-2003-06-2094","volume":"104","author":"M Pfreundschuh","year":"2004","unstructured":"Pfreundschuh M (2004) Two-weekly or 3-weekly CHOP chemotherapy with or without etoposide for the treatment of young patients with good-prognosis (normal LDH) aggressive lymphomas: results of the NHL-B1 trial of the DSHNHL. Blood 104(3):626\u2013633. https:\/\/doi.org\/10.1182\/blood-2003-06-2094","journal-title":"Blood"},{"key":"5985_CR26","doi-asserted-by":"publisher","unstructured":"Raissi M, Perdikaris P, Karniadakis G (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378:686\u2013707. https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0021999118307125. Accessed 29 Jul 2024","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"5985_CR27","doi-asserted-by":"publisher","unstructured":"Scholz M, Gross A, Loeffler M (2010) A biomathematical model of human thrombopoiesis under chemotherapy. Journal of Theoretical Biology 264(2):287\u2013300. https:\/\/doi.org\/10.1016\/j.jtbi.2009.12.032, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022519310000056. Accessed 13 Dec 2022","DOI":"10.1016\/j.jtbi.2009.12.032"},{"key":"5985_CR28","doi-asserted-by":"publisher","unstructured":"Siegelmann H, Horne B, Giles C (1997) Computational capabilities of recurrent NARX neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 27(2):208\u2013215. https:\/\/doi.org\/10.1109\/3477.558801, https:\/\/ieeexplore.ieee.org\/document\/558801\/. Accessed 7 Jul 2021","DOI":"10.1109\/3477.558801"},{"key":"5985_CR29","unstructured":"Srivastava N, Hinton G, Krizhevsky A, et\u00a0al (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15(56):1929\u20131958. http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html. Accessed 11 Apr 2024"},{"key":"5985_CR30","doi-asserted-by":"publisher","unstructured":"Steinacker M, Kheifetz Y, Scholz M (2023) Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach. Heliyon 9(7):e17890. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e17890, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2405844023050983. Accessed 25 Mar 2024","DOI":"10.1016\/j.heliyon.2023.e17890"},{"key":"5985_CR31","doi-asserted-by":"publisher","unstructured":"Wunderlich A, Kloess M, Reiser M et al (2003) Practicability and acute haematological toxicity of 2- and 3-weekly CHOP and CHOEP chemotherapy for aggressive non-Hodgkin\u2019s lymphoma: results from the NHL-B trial of the GermanHigh-Grade Non-Hodgkin\u2019s Lymphoma Study Group (DSHNHL). Annals of Oncology 14(6):881\u2013893. https:\/\/doi.org\/10.1093\/annonc\/mdg249, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0923753419635507. Accessed 25 May 2023","DOI":"10.1093\/annonc\/mdg249"},{"key":"5985_CR32","doi-asserted-by":"publisher","DOI":"10.1101\/2024.03.25.586390","author":"J Zabbarov","year":"2024","unstructured":"Zabbarov J, Witzke S, Kleissl M et al (2024). Optimizing ODE-derived synthetic data for transfer learning in dynamical biological systems. https:\/\/doi.org\/10.1101\/2024.03.25.586390","journal-title":"Optimizing ODE-derived synthetic data for transfer learning in dynamical biological systems."}],"container-title":["Journal of Cancer Research and Clinical Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00432-024-05985-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00432-024-05985-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00432-024-05985-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T08:04:42Z","timestamp":1730793882000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00432-024-05985-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,14]]},"references-count":32,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["5985"],"URL":"https:\/\/doi.org\/10.1007\/s00432-024-05985-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.08.06.606816","asserted-by":"object"}]},"ISSN":["1432-1335"],"issn-type":[{"value":"1432-1335","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,14]]},"assertion":[{"value":"6 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Markus Scholz received funding from Pfizer Inc. and has an ongoing cooperation with Owkin for projects not related to this research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data were obtained from studies of the German High-Grade Non-Hodgkin\u2019s Lymphoma Study Group and were collected in the framework of the NHL-B study (Pfreundschuh\n                      \n                      ,\n                      \n                      ).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All patients had given informed consent and studies were approved by responsible ethics committees and were carried out in accordance with the principles of good clinical practice and the declaration of Helsinki. Details on ethics committees and reference numbers can be found in the respective publications of the studies used (Pfreundschuh\n                      \n                      ,\n                      \n                      ).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"457"}}