Big Data and machine learning in radiation oncology: State of the art and future prospects

Cancer Letters

Volumn 382, Issue 1, 2016, Pages 110-117

  Bibault, Jean Emmanuel ^a,b   Giraud, Philippe ^a   Burgun, Anita ^a,b  

^a HÔPITAL EUROPÉEN GEORGES POMPIDOU   (France)

^b CENTRE DE RECHERCHE DES CORDELIERS   (France)

Author keywords

Big Data; Machine learning; Predictive model; Radiation oncology

Indexed keywords

ANTINEOPLASTIC AGENT;

ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; BIOINFORMATICS; BLADDER DISEASE; CANCER RADIOTHERAPY; CHEMORADIOTHERAPY; DATA EXTRACTION; DECISION TREE; DEEP LEARNING; ESOPHAGITIS; HEAD AND NECK CANCER; INFORMATION PROCESSING; K NEAREST NEIGHBOR; LUNG CANCER; MACHINE LEARNING; OVERALL SURVIVAL; PREDICTION; PRIORITY JOURNAL; PROSTATE CANCER; RADIATION PNEUMONIA; RECTUM DISEASE; SENSITIVITY AND SPECIFICITY; SHORT SURVEY; STATISTICAL MODEL; STEREOTACTIC BODY RADIATION THERAPY; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME; XEROSTOMIA; ADVERSE EFFECTS; DATA MINING; DECISION SUPPORT SYSTEM; ELECTRONIC HEALTH RECORD; FACTUAL DATABASE; HUMAN; MEDICAL RESEARCH; NEOPLASMS; PATHOLOGY; PROCEDURES; RADIATION DOSE; RADIATION ONCOLOGY; RADIOTHERAPY PLANNING SYSTEM; RISK ASSESSMENT; RISK FACTOR; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; UNSUPERVISED MACHINE LEARNING;

BIOMEDICAL RESEARCH; DATA INTERPRETATION, STATISTICAL; DATA MINING; DATABASES, FACTUAL; DECISION SUPPORT TECHNIQUES; ELECTRONIC HEALTH RECORDS; HUMANS; MACHINE LEARNING; NEOPLASMS; NEURAL NETWORKS (COMPUTER); RADIATION DOSAGE; RADIATION ONCOLOGY; RADIOTHERAPY; RADIOTHERAPY PLANNING, COMPUTER-ASSISTED; RISK ASSESSMENT; RISK FACTORS; SUPPORT VECTOR MACHINE; UNSUPERVISED MACHINE LEARNING;

EID: 84976466056     PISSN: 03043835     EISSN: 18727980     Source Type: Journal    
DOI: 10.1016/j.canlet.2016.05.033     Document Type: Review

References (70)

1. [1] Chen, C., He, M., Zhu, Y., Shi, L., Wang, X., Five critical elements to ensure the precision medicine. Cancer Metastasis Rev 34 (2015), 313–318, 10.1007/s10555-015-9555-3.
2. [2] Paez, J.G., Jänne, P.A., Lee, J.C., Tracy, S., Greulich, H., Gabriel, S., et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304 (2004), 1497–1500, 10.1126/science.1099314.
3. [3] Hodi, F.S., O'Day, S.J., McDermott, D.F., Weber, R.W., Sosman, J.A., Haanen, J.B., et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med 363 (2010), 711–723, 10.1056/NEJMoa1003466.
4. [4] Georgakilas, A.G., Pavlopoulou, A., Louka, M., Nikitaki, Z., Vorgias, C.E., Bagos, P.G., et al. Emerging molecular networks common in ionizing radiation, immune and inflammatory responses by employing bioinformatics approaches. Cancer Lett 368 (2015), 164–172, 10.1016/j.canlet.2015.03.021.
5. [5] Abernethy, A.P., Etheredge, L.M., Ganz, P.A., Wallace, P., German, R.R., Neti, C., et al. Rapid-learning system for cancer care. J. Clin. Oncol 28 (2010), 4268–4274, 10.1200/JCO.2010.28.5478.
6. [6] Mardis, E.R., A decade's perspective on DNA sequencing technology. Nature 470 (2011), 198–203, 10.1038/nature09796.
7. [7] Metzker, M.L., Sequencing technologies – the next generation. Nat. Rev. Genet 11 (2010), 31–46, 10.1038/nrg2626.
8. [8] DNA sequencing costs. n.d. http://www.genome.gov/sequencingcosts/ accessed 12.03.16.
9. [9] Geifman, N., Butte, A.J., Do cancer clinical trial populations truly represent cancer patients? A comparison of open clinical trials to the cancer genome atlas. Pac. Symp. Biocomput 21 (2016), 309–320.
10. [10] Kohane, I.S., Drazen, J.M., Campion, E.W., A glimpse of the next 100 years in medicine. N. Engl. J. Med 367 (2012), 2538–2539, 10.1056/NEJMe1213371.
11. [11] Lambin, P., van Stiphout, R.G.P.M., Starmans, M.H.W., Rios-Velazquez, E., Nalbantov, G., Aerts, H.J.W.L., et al. Predicting outcomes in radiation oncology–multifactorial decision support systems. Nat. Rev. Clin. Oncol 10 (2013), 27–40, 10.1038/nrclinonc.2012.196.
12. [12] Bibault, J.-E., Fumagalli, I., Ferté, C., Chargari, C., Soria, J.-C., Deutsch, E., Personalized radiation therapy and biomarker-driven treatment strategies: a systematic review. Cancer Metastasis Rev 32 (2013), 479–492, 10.1007/s10555-013-9419-7.
13. [13] Le, Q.-T., Courter, D., Clinical biomarkers for hypoxia targeting. Cancer Metastasis Rev 27 (2008), 351–362, 10.1007/s10555-008-9144-9.
14. [14] Okunieff, P., Chen, Y., Maguire, D.J., Huser, A.K., Molecular markers of radiation-related normal tissue toxicity. Cancer Metastasis Rev 27 (2008), 363–374, 10.1007/s10555-008-9138-7.
15. [15] Kang, J., Schwartz, R., Flickinger, J., Beriwal, S., Machine learning approaches for predicting radiation therapy outcomes: a clinician's perspective. Int. J. Radiat. Oncol. Biol. Phys 93 (2015), 1127–1135, 10.1016/j.ijrobp.2015.07.2286.
16. [16] Denis, F., Yossi, S., Septans, A.-L., Charron, A., Voog, E., Dupuis, O., et al. Improving survival in patients treated for a lung cancer using self-evaluated symptoms reported through a web application. Am. J. Clin. Oncol, 2015, 10.1097/COC.0000000000000189.
17. [17] Falchook, A.D., Tracton, G., Stravers, L., Fleming, M.E., Snavely, A.C., Noe, J.F., et al. Use of mobile device technology to continuously collect patient-reported symptoms during radiotherapy for head and neck cancer: a prospective feasibility study, Adv. Radiat. Oncol, 2016, 10.1016/j.adro.2016.02.001.
18. [18] Li, M., Yu, S., Ren, K., Lou, W., Securing personal health records in cloud computing: patient-centric and fine-grained data access control in multi-owner settings. Jajodia, S., Zhou, J., (eds.) Security and Privacy in Communication Networks, 2010, Springer Berlin, Heidelberg, 89–106 http://link.springer.com/chapter/10.1007/978-3-642-16161-2_6 accessed 21.05.16.
19. [19] Canuel, V., Rance, B., Avillach, P., Degoulet, P., Burgun, A., Translational research platforms integrating clinical and omics data: a review of publicly available solutions. Brief. Bioinform 16 (2015), 280–290, 10.1093/bib/bbu006.
20. [20] Huser, V., Cimino, J.J., Impending challenges for the use of Big Data. Int. J. Radiat. Oncol. Biol. Phys, 2015, 10.1016/j.ijrobp.2015.10.060.
21. [21] Systematized nomenclature of medicine – clinical terms – summary | NCBO BioPortal. n.d. https://bioportal.bioontology.org/ontologies/SNOMEDCT accessed 07.03.16.
22. [22] National cancer institute thesaurus – summary | NCBO BioPortal. n.d. https://bioportal.bioontology.org/ontologies/NCIT accessed 07.03.16.
23. [23] Common terminology criteria for adverse events – summary | NCBO BioPortal. n.d. https://bioportal.bioontology.org/ontologies/CTCAE accessed 07.03.16.
24. [24] Fact SheetUMLS® Metathesaurus®. n.d. https://www.nlm.nih.gov/pubs/factsheets/umlsmeta.html accessed 07.03.16.
25. [25] Radiation oncology ontology – summary | NCBO BioPortal. n.d. http://bioportal.bioontology.org/ontologies/ROO accessed 07.03.16.
26. [26] El Naqa, I., Bradley, J., Blanco, A.I., Lindsay, P.E., Vicic, M., Hope, A., et al. Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors. Int. J. Radiat. Oncol. Biol. Phys 64 (2006), 1275–1286, 10.1016/j.ijrobp.2005.11.022.
27. [27] Lee, T.-F., Chao, P.-J., Ting, H.-M., Chang, L., Huang, Y.-J., Wu, J.-M., et al. Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer. PLoS ONE, 9, 2014, e89700, 10.1371/journal.pone.0089700.
28. [28] Lee, T.-F., Liou, M.-H., Huang, Y.-J., Chao, P.-J., Ting, H.-M., Lee, H.-Y., et al. LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma. Sci. Rep, 4, 2014, 6217, 10.1038/srep06217.
29. [29] Quinlan, J.R., Induction of decision trees. Mach. Learn 1 (1986), 81–106.
30. [30] Langley, P., Iba, W., Thompson, K., An analysis of Bayesian classifiers. AAAI, 1992, 223–228.
31. [31] Langley, P., Sage, S., Induction of selective Bayesian classifiers. Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence, 1994, Morgan Kaufmann Publishers Inc., 399–406 http://dl.acm.org/citation.cfm?id=2074445 accessed 12.03.16.
32. [32] Patrick, E.A., Fischer, F.P. III, A generalized k-nearest neighbor rule. Inf. Control 16 (1970), 128–152, 10.1016/S0019-9958(70)90081-1.
33. [33] Vapnik, V., Estimation of Dependences Based on Empirical Data. 1982, Springer Verlag, New York.
34. [34] Rumelhart, D.E., McClelland, J., Parallel Distributed Processing: Explorations in the Microstructure of Cognition. 1986, MIT Press, Cambridge.
35. [35] LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444, 10.1038/nature14539.
36. [36] Chen, S., Zhou, S., Yin, F.-F., Marks, L.B., Das, S.K., Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis. Med. Phys 34 (2007), 3808–3814.
37. [37] Klement, R.J., Allgäuer, M., Appold, S., Dieckmann, K., Ernst, I., Ganswindt, U., et al. Support vector machine-based prediction of local tumor control after stereotactic body radiation therapy for early-stage non-small cell lung cancer. Int. J. Radiat. Oncol. Biol. Phys 88 (2014), 732–738, 10.1016/j.ijrobp.2013.11.216.
38. [38] Hayashida, Y., Honda, K., Osaka, Y., Hara, T., Umaki, T., Tsuchida, A., et al. Possible prediction of chemoradiosensitivity of esophageal cancer by serum protein profiling. Clin. Cancer Res 11 (2005), 8042–8047, 10.1158/1078-0432.CCR-05-0656.
39. [39] Bryce, T.J., Dewhirst, M.W., Floyd, C.E., Hars, V., Brizel, D.M., Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. Int. J. Radiat. Oncol. Biol. Phys 41 (1998), 339–345.
40. [40] Gulliford, S.L., Webb, S., Rowbottom, C.G., Corne, D.W., Dearnaley, D.P., Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. Radiother. Oncol 71 (2004), 3–12, 10.1016/j.radonc.2003.03.001.
41. [41] Pella, A., Cambria, R., Riboldi, M., Jereczek-Fossa, B.A., Fodor, C., Zerini, D., et al. Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy. Med. Phys 38 (2011), 2859–2867.
42. [42] Tomatis, S., Rancati, T., Fiorino, C., Vavassori, V., Fellin, G., Cagna, E., et al. Late rectal bleeding after 3D-CRT for prostate cancer: development of a neural-network-based predictive model. Phys. Med. Biol 57 (2012), 1399–1412, 10.1088/0031-9155/57/5/1399.
43. [43] Chen, S., Zhou, S., Zhang, J., Yin, F.-F., Marks, L.B., Das, S.K., A neural network model to predict lung radiation-induced pneumonitis. Med. Phys 34 (2007), 3420–3427.
44. [44] Su, M., Miften, M., Whiddon, C., Sun, X., Light, K., Marks, L., An artificial neural network for predicting the incidence of radiation pneumonitis. Med. Phys 32 (2005), 318–325.
45. [45] Ochi, T., Murase, K., Fujii, T., Kawamura, M., Ikezoe, J., Survival prediction using artificial neural networks in patients with uterine cervical cancer treated by radiation therapy alone. Int. J. Clin. Oncol 7 (2002), 294–300, 10.1007/s101470200043.
46. [46] Hua, K.-L., Hsu, C.-H., Hidayati, S.C., Cheng, W.-H., Chen, Y.-J., Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8 (2015), 2015–2022, 10.2147/OTT.S80733.
47. [47] Guo, Y., Gao, Y., Shen, D., Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging, 2015, 10.1109/TMI.2015.2508280.
48. [48] Raina, R., Madhavan, A., Ng, A.Y., Large-scale deep unsupervised learning using graphics processors. Proceedings of the 26th Annual International Conference on Machine Learning, 2009, ACM, New York, NY, USA, 873–880, 10.1145/1553374.1553486.
49. [49] Cruz, J.A., Wishart, D.S., Applications of machine learning in cancer prediction and prognosis. Cancer Inform 2 (2006), 59–77.
50. [50] Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I., Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13 (2015), 8–17, 10.1016/j.csbj.2014.11.005.
51. 3. Editorial. Yearb. Med. Inform 9 (2014), 6–7, 10.15265/IY-2014-0030.
52. [52] Somorjai, R.L., Dolenko, B., Baumgartner, R., Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 19 (2003), 1484–1491.
53. [53] TensorFlow – an open source software library for machine intelligence. n.d. https://www.tensorflow.org/ accessed 12.03.16.
54. [54] Parodi, S., Riccardi, G., Castagnino, N., Tortolina, L., Maffei, M., Zoppoli, G., et al. Systems medicine in oncology: signaling network modeling and new-generation decision-support systems. Methods Mol. Biol 1386 (2016), 181–219, 10.1007/978-1-4939-3283-2_10.
55. [55] Memorial Sloan Kettering Cancer Center, Watson oncology. n.d. https://www.mskcc.org/about/innovative-collaborations/watson-oncology accessed 10.03.16.
56. [56] The coming era of human phenotyping. Nat. Biotechnol, 33, 2015, 567, 10.1038/nbt.3266.
57. [57] Savage, N., Mobile data: made to measure. Nature 527 (2015), S12–S13, 10.1038/527S12a.
58. [58] Servant, N., Roméjon, J., Gestraud, P., La Rosa, P., Lucotte, G., Lair, S., et al. Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial. Front. Genet, 5, 2014, 152, 10.3389/fgene.2014.00152.
59. [59] Chen, R.C., Gabriel, P.E., Kavanagh, B.D., McNutt, T.R., How will big data impact clinical decision making and precision medicine in radiation therapy. Int. J. Radiat. Oncol. Biol. Phys, 2015, 10.1016/j.ijrobp.2015.10.052.
60. [60] Berrington de Gonzalez, A., Curtis, R.E., Kry, S.F., Gilbert, E., Lamart, S., Berg, C.D., et al. Proportion of second cancers attributable to radiotherapy treatment in adults: a cohort study in the US SEER cancer registries. Lancet Oncol 12 (2011), 353–360, 10.1016/S1470-2045(11)70061-4.
61. [61] Virnig, B.A., Warren, J.L., Cooper, G.S., Klabunde, C.N., Schussler, N., Freeman, J., Studying radiation therapy using SEER-Medicare-linked data. Med. Care 40 (2002), IV–49–54, 10.1097/01.MLR.0000020940.90270.4D.
62. [62] Darby, S.C., McGale, P., Taylor, C.W., Peto, R., Long-term mortality from heart disease and lung cancer after radiotherapy for early breast cancer: prospective cohort study of about 300,000 women in US SEER cancer registries. Lancet Oncol 6 (2005), 557–565, 10.1016/S1470-2045(05)70251-5.
63. [63] Du, X., Freeman, J.L., Goodwin, J.S., Information on radiation treatment in patients with breast cancer: the advantages of the linked medicare and SEER data. Surveillance, epidemiology and end results. J. Clin. Epidemiol 52 (1999), 463–470.
64. [64] Song, Y., Wang, W., Tao, G., Zhu, W., Zhou, X., Pan, P., Survival benefit of radiotherapy to patients with small cell esophagus carcinoma – an analysis of Surveillance Epidemiology and End Results (SEER) data. Oncotarget, 2015, 10.18632/oncotarget.6764.
65. [65] Wu, B., McNutt, T., Zahurak, M., Simari, P., Pang, D., Taylor, R., et al. Fully automated simultaneous integrated boosted-intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer: a prospective clinical study. Int. J. Radiat. Oncol. Biol. Phys 84 (2012), e647–e653, 10.1016/j.ijrobp.2012.06.047.
66. [66] Wu, B., Ricchetti, F., Sanguineti, G., Kazhdan, M., Simari, P., Jacques, R., et al. Data-driven approach to generating achievable dose-volume histogram objectives in intensity-modulated radiotherapy planning. Int. J. Radiat. Oncol. Biol. Phys 79 (2011), 1241–1247, 10.1016/j.ijrobp.2010.05.026.
67. [67] Petit, S.F., Wu, B., Kazhdan, M., Dekker, A., Simari, P., Kumar, R., et al. Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma. Radiother. Oncol 102 (2012), 38–44, 10.1016/j.radonc.2011.05.025.
68. [68] Appenzoller, L.M., Michalski, J.M., Thorstad, W.L., Mutic, S., Moore, K.L., Predicting dose-volume histograms for organs-at-risk in IMRT planning. Med. Phys 39 (2012), 7446–7461, 10.1118/1.4761864.
69. [69] Zhu, X., Ge, Y., Li, T., Thongphiew, D., Yin, F.-F., Wu, Q.J., A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med. Phys 38 (2011), 719–726.
70. [70] Robertson, S.P., Quon, H., Kiess, A.P., Moore, J.A., Yang, W., Cheng, Z., et al. A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients. Med. Phys 42 (2015), 4329–4337, 10.1118/1.4922686.