Abstract
Abstract. In the domain of Artificial Intelligence, deep learning is part of a broader family of machine learning methods based on deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks that have been applied to fields including computer vision, medical image analysis, histopathological diagnosis, with results comparable to and in some cases superior to human experts. This study shows that these methods applied to medical imaging can assist pathologists in the detection of cancer subtype, gene mutations and/or metastases for applying appropriate therapies. Results show that trajectories of AI technology applied in cancer imaging seems to be driven by high rates of mortality of some types of cancer in order to improve detection and characterization of cancer to apply efficiently anticancer therapies. This new technology can generate a technological paradigm shift for diagnostic assessment of any cancer type. However, application of these methods to medical imaging requires further assessment and validation to support the efficiency of the workflow of pathologists in clinical practice and improve overall healthcare sector.
Keywords. Artificial intelligence, Diagnostic assessment, Histopathology images, Deep learning algorithms, Cancer, Clinical challenges.
JEL. O32, O33.
References
Arthur, B.W. (2009). The Nature of Technology. What it is and How it Evolves, Allen Lane–Penguin Books: London.
Bi, W.L., Hosny, A., Schabath, M.B., Giger, M. L., Birkbak, N.J., et al., (2019). Artificial intelligence in cancer imaging: Clinical challenges and applications, CA Cancer J Clin, 0, 1-31, doi. 10.3322/caac.21552
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. Nov, 68(6), 394-424. doi. 10.3322/caac.21492
Calabrese, G., Coccia, M., Rolfo, S. (2005). Strategy and market management of new product development: evidence from Italian SMEs., International Journal of Product Development, 2(1-2) 170-189. doi. 10.1504/IJPD.2005.006675
Coccia, M. (2005a). A Scientometric model for the assessment of scientific research performance within public institutes, Scientometrics, 65(3), 307-321. doi. 10.1007/s11192-005-0276-1
Coccia, M. (2005b). Metrics to measure the technology transfer absorption: analysis of the relationship between institutes and adopters in northern Italy. International Journal of Technology Transfer and Commercialization, 4(4), 462-486. doi. 10.1504/IJTTC.2005.006699
Coccia, M. (2009). What is the optimal rate of R&D investment to maximize productivity growth?, Technological Forecasting & Social Change, 76(3), 433-446. doi. 10.1016/j.techfore.2008.02.008
Coccia, M. (2010). Democratization is the driving force for technological and economic change, Technological Forecasting & Social Change, 77(2), 248-264. doi. 10.1016/j.techfore.2009.06.007
Coccia, M. (2010a). The asymmetric path of economic long waves, Technological Forecasting & Social Change, 77(5), 730-738. doi. 10.1016/j.techfore.2010.02.003
Coccia, M. (2010b). Spatial patterns of technology transfer and measurement of its friction in the geo-economic space, International Journal of Technology Transfer and Commercialisation, 9(3), 255-267. doi. 10.1504/IJTTC.2010.030214
Coccia, M. (2010c). Public and private investment in R&D: complementary effects and interaction with productivity growth, European Review of Industrial Economics and Policy, 1, 1-21.
Coccia, M. (2011). The interaction between public and private R&D expenditure and national productivity. Prometheus-Critical Studies in Innovation, 29(2), 121-130. doi. 10.1080/08109028.2011.601079
Coccia, M. (2014). Religious culture, democratisation and patterns of technological innovation. International Journal of Sustainable Society, 6(4), 397-418. doi. 10.1504/IJSSOC.2014.066771
Coccia, M. (2014a). Structure and organisational behaviour of public research institutions under unstable growth of human resources, Int. J. Services Technology and Management, 20(4/5/6), 251–266. doi. 10.1504/IJSTM.2014.068857
Coccia, M. (2014b). Driving forces of technological change: The relation between population growth and technological innovation-Analysis of the optimal interaction across countries, Technological Forecasting & Social Change, 82(2), 52-65. doi. 10.1016/j.techfore.2013.06.001
Coccia, M. (2014a). Emerging technological trajectories of tissue engineering and the critical directions in cartilage regenerative medicine. Int. J. Healthcare Technology and Management, 14(3), 194-208. doi. 10.1504/IJHTM.2014.064247
Coccia, M. (2014). Socio-cultural origins of the patterns of technological innovation: What is the likely interaction among religious culture, religious plurality and innovation? Towards a theory of socio-cultural drivers of the patterns of technological innovation, Technology in Society, 36(1), 13-25. doi. 10.23760/2421-7158.2017.004
Coccia, M. (2015). The Nexus between technological performances of countries and incidence of cancers in society. Technology in Society, 42, 61-70. doi. 10.1016/j.techsoc.2015.02.003
Coccia, M. (2015a). Patterns of innovative outputs across climate zones: the geography of innovation, Prometheus. Critical Studies in Innovation, 33(2), 165-186. doi. 10.1080/08109028.2015.1095979
Coccia, M. (2015b). Technological paradigms and trajectories as determinants of the R&D corporate change in drug discovery industry. International Journal Knowledge and Learning, 10(1), 29-43. doi. 10.1504/IJKL.2015.071052
Coccia, M. (2016). Problem-driven innovations in drug discovery: co-evolution of radical innovation with the evolution of problems, Health Policy and Technology, 5(2), 143-155. doi. 10.1016/j.hlpt.2016.02.003
Coccia, M. (2017). Sources of technological innovation: Radical and incremental innovation problem-driven to support competitive advantage of firms. Technology Analysis & Strategic Management, 29(9), 1048-1061. doi. 10.1080/09537325.2016.1268682
Coccia, M. (2017a). The source and nature of general purpose technologies for supporting next K-waves: Global leadership and the case study of the U.S. Navy's Mobile User Objective System, Technological Forecasting & Social Change, 116, 331-339. doi. 10.1016/j.techfore.2016.05.019
Coccia, M. (2017b). Asymmetric paths of public debts and of general government deficits across countries within and outside the European monetary unification and economic policy of debt dissolution, The Journal of Economic Asymmetries, 15, 17-31. doi. 10.1016/j.jeca.2016.10.003
Coccia, M. (2018). A theory of the general causes of long waves: War, general purpose technologies, and economic change. Technological Forecasting & Social Change, 128, 287-295. 10.1016/j.techfore.2017.11.013
Coccia, M. (2018a). The relation between terrorism and high population growth, Journal of Economics and Political Economy, 5(1), 84-104.
Coccia, M. (2018c). Violent crime driven by income Inequality between countries, Turkish Economic Review, 5(1), 33-55.
Coccia, M. (2018d). The origins of the economics of innovation, Journal of Economic and Social Thought, 5(1), 9-28.
Coccia, M. (2018e). Theorem of not independence of any technological innovation, Journal of Economics Bibliography, 5(1), 29-35.
Coccia, M. (2018e). Theorem of not independence of any technological innovation, Journal of Social and Administrative Sciences, 5(1), 15-33.
Coccia, M. (2018f). Classification of innovation considering technological interaction, Journal of Economics Bibliography, 5(2), 76-93.
Coccia, M. (2018g). An introduction to the methods od inquiry in social sciences, Journal of Social and Administrative Sciences, 5(2), 116-126.
Coccia, M. (2018h). Growth rate of population associated with high terrorism incidents in society, Journal of Economics Bibliography, 5(3), 142-158.
Coccia, M. (2018i). Measurement and assessment of the evolution of technology with a simple biological model, Turkish Economic Review, 5(3), 263-284.
Coccia, M. (2018j). Functionality development of product innovation: An empirical analysis of the technological trajectories of smartphone, Journal of Economics Library, 5(3), 241-258.
Coccia, M. (2018k). World-System Theory: A socio political approach to explain World economic development in a capitalistic, Journal of Economics and Political Economy, 5(4), 459-465.
Coccia, M. (2018l). An introduction to the theories of institutional change, Journal of Economics Library, 5(4), 337-344.
Coccia, M. (2018m). An introduction to the theories of national and regional economic development, Turkish Economic Review, 5(4), 241-255.
Coccia, M. (2018n). What are the characteristics of revolution and evolution?, Journal of Economic and Social Thought, 5(4), 288-294.
Coccia, M. (2018o). Motivation and theory of self-determination: Some management implications in organizations, Growth rate of population associated with high terrorism incidents in society, Journal of Economics Bibliography, 5(4), 223-230.
Coccia, M. (2018p). Superpowers and conflict development: Is it a possible relation for supporting human progress?, Journal of Social and Administrative Sciences, 5(4), 274-281.
Coccia, M. (2018r). A theory of classification and evolution of technologies within a generalized Darwinism, Technology Analysis & Strategic Management, doi. 10.1080/09537325.2018.1523385
Coccia, M. (2018s). Optimization in R&D intensity and tax on corporate profits for supporting labor productivity of nations, The Journal of Technology Transfer, 43(3), 792-814. doi. 10.1007/s10961-017-9572-1
Coccia, M., & Bellitto, M. (2018). Human progress and its socioeconomic effects in society, Journal of Economic and Social Thought, 5(2), 160-178.
Coccia, M., & Igor, M. (2018). Rewards in public administration: a proposed classification, Journal of Social and Administrative Sciences, 5(2), 68-80.
Coccia, M., & Cadario, E. (2014). Organisational (un)learning of public research labs in turbulent context, International Journal of Innovation and Learning, 15(2), 115-129. doi. 10.1504/IJIL.2014.059756
Coccia, M., Falavigna, G., & Manello, A. (2015). The impact of hybrid public and market-oriented financing mechanisms on scientific portfolio and performances of public research labs: a scientometric analysis, Scientometrics, 102(1), 151-168. doi. 10.1007/s11192-014-1427-z
Coccia, M., & Rolfo, S. (2010). New entrepreneurial behaviour of public research organizations: opportunities and threats of technological services supply, International Journal of Services Technology and Management, 13(1/2), 134-151. doi. 10.1504/IJSTM.2010.029674
Coudray N., Ocampo P. S., Sakellaropoulos T., Narula N., Snuderl M., Fenyö D., Moreira A. L., Razavian N., Tsirigos A. 2018. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, vol. 24, October, pp.1559–1567.
Dempke, W.C.M., Sutob, T., Reck, M. (2010). Targeted therapies for non-small cell lung cancer. Lung Cancer, 67(3), 257–274.
Digital pathology images. EBioMedicine 27, 317–328.
Ehteshami-Bejnordi, B., Veta, M., et al., (2016). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 316(22), 2402-2410. doi. 10.1001/jama.2016.17216
Esteva, A., Kuprel, B., Novoa, R.A., et al., (2016). Dermatologist-level classification of skin cancer Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 6:26286.
Geuna, A., Guerzoni, M., Nuccio, M., Pammolli, F., & Rungi, A. (2017). Digital disruption and the transformation of Italian manufacturing. [Retrieved from].
Goodfellow, I., Yoshua, B., & Courville A. (2018). Deep Learning, MIT Press.
Gulshan, V. et al., (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316, 2402–2410..
Iafrate, F. (2018). Artificial Intelligence and Big Data- The Birth of a New Intelligence, ISTE Ltd and John Wiley & Sons,
Kantarjian, H., & Yu, P.P. (2015). Artificial intelligence, big data, and cancer. JAMA Oncology, 1(5), doi. 10.1001/jamaoncol.2015.1203
Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O. & Hajirasouliha, I. (2018). Deep convolutional neural networks enable discrimination of heterogeneous
Lamy, J.B., Sekar, B., Guezennec, G., Bouaud, J., & Séroussi, B. (2019). Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach, Artificial Intelligence in Medicine, 94, 42-53.
Madabhushi, A., & Lee, G. (2016). Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal, 33, 170-175.
McNerney, J., Farmer, J.D., Redner, S., & Trancik, J.E. (2011). Role of design complexity in technology improvement, Proceedings of the National Academy of Sciences, 108(22), 9008-9013. doi. 10.1073/pnas.1017298108
Parkin, D.M., Bray, F., Ferlay, J., & Pisani, P. (2005). Global cancer statistics 2002. CA Cancer Journal of Clinic Oncology, 55(2), 74-108.
Prager, G.W., Braga, S., Bystricky, B., et al., (2018). Global cancer control: responding to the growing burden, rising costs and inequalities in access. ESMO Open. 3(2), e000285. doi. 10.1136/esmoopen-2017-000285
Rosenberg, N. (1969). The direction of technological change: inducement mechanisms and focusing devices. Econ. Dev. Cult. Change, 18(1), 1–24.
Russakovsky, O., Deng, J., Su, H., et al., (2015). ImageNet large scale visual recognition challenge, Journal of International Journal of Computer Vision, 115(3), 211-252. doi. 10.1007/s11263-015-0816-y
ScienceDirect (2019). Advanced Research, Accessed May 2019. [Retrieved from].
World Health Organization, (2019). International Agency for research on cancer, May 2019. [Retrieved from].
Wright G. (1997). Towards a more historical approach to technological change, The Economic Journal, 107, 1560-1566. doi. 10.1098/rsif.2013.1190
