[en] Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.
Disciplines :
Oncology Computer science
Author, co-author :
Manzo, Gaetano ; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland, National Institutes of Health (NIH), Bethesda, MD, USA. Electronic address: gaetano.manzo@nih.gov
Pannatier, Yvan; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Duflot, Patrick ; Centre Hospitalier Universitaire de Liège - CHU > > Secteur Appui méthodologique aux Projets GSI et Planification (APP)
Kolh, Philippe ; Centre Hospitalier Universitaire de Liège - CHU > > Service des informations médico économiques (SIME)
Chavez, Marcela; CHU of Liege, Department of Information System Management, Belgium
Bleret, Valerie ; Centre Hospitalier Universitaire de Liège - CHU > > Service de sénologie
Calvaresi, Davide; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Jimenez-Del-Toro, Oscar; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Schumacher, Michael; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Calbimonte, Jean-Paul; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland, The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
Language :
English
Title :
Breast cancer survival analysis agents for clinical decision support.
H2020 - 875406 - PERSIST - Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies
Funders :
EC - European Commission University of Applied Sciences and Arts Western Switzerland NIH - National Institutes of Health
Funding text :
This work was partially supported by the H2020 Project PERSIST under grant agreement No. 875406. The authors gratefully acknowledge the helpful remarks from Dina Demner-Fushman (National Institute of Health).
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Bibliography
Miller, K.D., Nogueira, L., Mariotto, A.B., Rowland, J.H., Yabroff, K.R., Alfano, C.M., Jemal, A., Kramer, J.L., Siegel, R.L., Cancer treatment and survivorship statistics, 2019. CA Cancer J. Clin. 69:5 (2019), 363–385.
Runowicz, C.D., Leach, C.R., Henry, N.L., Henry, K.S., Mackey, H.T., Cowens-Alvarado, R.L., Cannady, R.S., Pratt-Chapman, M.L., Edge, S.B., Jacobs, L.A., Hurria, A., Marks, L.B., LaMonte, S.J., Warner, E., Lyman, G.H., Ganz, P.A., American cancer society/American society of clinical oncology breast cancer survivorship care guideline. CA Cancer J. Clin. 66:1 (2016), 43–73.
Cheville, A., Lee, M., Moynihan, T., Schmitz, K.H., Lynch, M., De Choudens, F.R., Dean, L., Basford, J., Therneau, T., The impact of arm lymphedema on healthcare utilization during long-term breast cancer survivorship: a population-based cohort study. J. Cancer Surviv. 14:3 (2020), 347–355.
Sabaté, E., Sabaté, E., et al. Adherence to Long-Term Therapies: Evidence for Action. 2003, World Health Organization.
Louis, D.N., Perry, A., Reifenberger, G., Von Deimling, A., Figarella-Branger, D., Cavenee, W.K., Ohgaki, H., Wiestler, O.D., Kleihues, P., Ellison, D.W., The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol., 2016.
Kim, D.W., Lee, S., Kwon, S., Nam, W., Cha, I.-H., Kim, H.J., Deep learning-based survival prediction of oral cancer patients. Sci. Rep., 2019.
Curtis, C., Shah, S.P., Chinriadis, The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 2012.
Mlakar, I., Lin, S., Aleksandraviča, I., Arcimoviča, K., Eglītis, J., Leja, M., Salgado Barreira, Á., Gómez, J.G., Salgado, M., Mata, J.G., Batorek, D., Horvat, M., Molan, M., Ravnik, M., Kaux, J.-F., Bleret, V., Loly, C., Maquet, D., Sartini, E., Smrke, U., Patients-centered survivorship care plan after cancer treatments based on big data and artificial intelligence technologies (persist): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors. BMC Med. Inform. Decis. Mak., 21(1), 2021, 243, 10.1186/s12911-021-01603-w.
Györffy, B., Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput. Struct. Biotechnol. J. 19 (2021), 4101–4109, 10.1016/j.csbj.2021.07.014.
Cox, D.R., Regression models and life-tables. J. R. Stat. Soc. Ser. B (Methodological), 1972.
Xiang, A., Lapuerta, P., Ryutov, A., Buckley, J., Azen, S., Comparison of the performance of neural network methods and Cox regression for censored survival data. Comput. Stat. Data Anal., 2000.
Nagy, Á., Munkácsy, G., Győrffy, B., Pancancer survival analysis of cancer hallmark genes. Sci. Rep., 11(1), 2021, 6047, 10.1038/s41598-021-84787-5.
Moncada-Torres, A., van Maaren, M.C., Hendriks, M.P., Siesling, S., Geleijnse, G., Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci. Rep., 11(1), 2021, 6968, 10.1038/s41598-021-86327-7.
S. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, 2017. 1705.07874.
Yoo, K.D., Noh, J., Lee, H., Kim, D.K., Lim, C.S., Kim, Y.H., Lee, J.P., Kim, G., Kim, Y.S., A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study. Sci. Rep., 7(1), 2017, 8904, 10.1038/s41598-017-08008-8.
Sarkar, J.P., Saha, I., Sarkar, A., Maulik, U., Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers. Comput. Biol. Med., 131, 2021, 104244, 10.1016/j.compbiomed.2021.104244.
Todd, J., Gepp, A., Stern, S., Vanstone, B.J., Improving decision making in the management of hospital readmissions using modern survival analysis techniques. Decis. Support Syst., 2022, 113747, 10.1016/j.dss.2022.113747.
Manzo, G., Calvaresi, D., Jimenez-Del-Toro, O., Calbimonte, J.P., Schumacher, M., Cohort and trajectory analysis in multi-agent support systems for cancer survivors. J. Med. Syst., 45(12), 2021, 109.
Calvaresi, D., Calbimonte, J.-P., Siboni, E., Eggenschwiler, S., Manzo, G., Hilfiker, R., Schumacher, M., EREBOTS: privacy-compliant agent-based platform for multi-scenario personalized health-assistant chatbots. Electronics, 2021.
Ranganath, R., Perotte, A., Elhadad, N., Blei, D., Deep survival analysis. Doshi-Velez, F., Fackler, J., Kale, D., Wallace, B., Wiens, J., (eds.) Proceedings of the 1st Machine Learning for Healthcare Conference Proceedings of Machine Learning Research, Vol. 56, 2016, PMLR, Northeastern University, Boston, MA, USA, 101–114 https://proceedings.mlr.press/v56/Ranganath16.html.
Shimizu, H., Nakayama, K.I., A 23 gene-based molecular prognostic score precisely predicts overall survival of breast cancer patients. EBioMedicine, 2019.
Bussy, S., Veil, R., Looten, V., Burgun, A., Gaïffas, S., Guilloux, A., Ranque, B., Jannot, A.S., Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework. BMC Med. Res. Methodol., 2019.
Mariani, L., Coradini, D., Biganzoli, E., Boracchi, P., Marubini, E., Pilotti, S., Salvadori, B., Silvestrini, R., Veronesi, U., Zucali, R., Rilke, F., Prognostic factors for metachronous contralateral breast cancer: a comparison of the linear Cox regression model and its artificial neural network extension. Breast Cancer Res. Treat., 1997.
Chatzis, S.P., Kosmopoulos, D.I., Varvarigou, T.A., Signal modeling and classification using a robust latent space model based on distributions. IEEE Trans. Signal Process. 56:3 (2008), 949–963, 10.1109/TSP.2007.907912.
Kamiński, B., Jakubczyk, M., Szufel, P., A framework for sensitivity analysis of decision trees. Cent. Eur. J. Oper. Res. 26:1 (2018), 135–159, 10.1007/s10100-017-0479-6.
Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79:8 (1982), 2554–2558, 10.1073/pnas.79.8.2554.
U. Arioz, B. Yildiz, M. Yilmaz, V.M.C. Gonzalez, D. Caldy, S. Lin, Persist deliverable 5.1 CDSS requirements, 2020, ( https://projectpersist.com/wp-content/uploads/2022/05/D5.1.-CDSS-requirements_compressed.pdf).
Mlakar, I., S̆afran, V., Hari, D., Rojc, M., Alankuş, G., Pérez Luna, R., Ariöz, U., Multilingual conversational systems to drive the collection of patient-reported outcomes and integration into clinical workflows. Symmetry, 13(7), 2021, 10.3390/sym13071187.
González-Castro, L., Cal-González, V.M., Del Fiol, G., López-Nores, M., CASIDE: a data model for interoperable cancer survivorship information based on FHIR. J. Biomed. Inform., 124, 2021, 103953, 10.1016/j.jbi.2021.103953.
Lee, E.T., Go, O.T., Survival analysis in public health research. Annu. Rev. Public Health 18:1 (1997), 105–134, 10.1146/annurev.publhealth.18.1.105 PMID: 9143714.
Kaplan, E.L., Meier, P., Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc., 1958.
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