Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography

Tesis por compendio[ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT.[CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT.[EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data.García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400Compendi

Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies

[EN] Background and objective:Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans. Methods:The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM). Results:The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes. Conclusions:The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used here.This work has been funded by GALAHAD project [H2020-ICT-2016-2017, 732613], SICAP project (DPI2016-77869-C2-1-R) and GVA through project PROMETEO/2019/109. The work of Gabriel García has been supported by the State Research Spanish Agency PTA2017-14610-I.García-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V. (2021). Glaucoma Detection from Raw SD-OCT Volumes: a Novel Approach Focused on Spatial Dependencies. Computer Methods and Programs in Biomedicine. 200:1-16. https://doi.org/10.1016/j.cmpb.2020.105855S116200Weinreb, R. N., & Khaw, P. T. (2004). Primary open-angle glaucoma. The Lancet, 363(9422), 1711-1720. doi:10.1016/s0140-6736(04)16257-0Jonas, J. B., Aung, T., Bourne, R. R., Bron, A. M., Ritch, R., & Panda-Jonas, S. (2018). Glaucoma – Authors’ reply. The Lancet, 391(10122), 740. doi:10.1016/s0140-6736(18)30305-2Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Huang, D., Swanson, E. A., Lin, C. P., Schuman, J. S., Stinson, W. G., Chang, W., … Fujimoto, J. G. (1991). Optical Coherence Tomography. Science, 254(5035), 1178-1181. doi:10.1126/science.1957169Medeiros, F. A., Zangwill, L. M., Alencar, L. M., Bowd, C., Sample, P. A., Susanna, R., & Weinreb, R. N. (2009). Detection of Glaucoma Progression with Stratus OCT Retinal Nerve Fiber Layer, Optic Nerve Head, and Macular Thickness Measurements. Investigative Opthalmology & Visual Science, 50(12), 5741. doi:10.1167/iovs.09-3715Sinthanayothin, C., Boyce, J. F., Williamson, T. H., Cook, H. L., Mensah, E., Lal, S., & Usher, D. (2002). Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine, 19(2), 105-112. doi:10.1046/j.1464-5491.2002.00613.xWalter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., & Klein, J.-C. (2007). Automatic detection of microaneurysms in color fundus images. Medical Image Analysis, 11(6), 555-566. doi:10.1016/j.media.2007.05.001Diaz-Pinto, A., Colomer, A., Naranjo, V., Morales, S., Xu, Y., & Frangi, A. F. (2019). Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 38(9), 2211-2218. doi:10.1109/tmi.2019.2903434Bussel, I. I., Wollstein, G., & Schuman, J. S. (2013). OCT for glaucoma diagnosis, screening and detection of glaucoma progression. British Journal of Ophthalmology, 98(Suppl 2), ii15-ii19. doi:10.1136/bjophthalmol-2013-304326Varma, R., Steinmann, W. C., & Scott, I. U. (1992). Expert Agreement in Evaluating the Optic Disc for Glaucoma. Ophthalmology, 99(2), 215-221. doi:10.1016/s0161-6420(92)31990-6Jaffe, G. J., & Caprioli, J. (2004). Optical coherence tomography to detect and manage retinal disease and glaucoma. American Journal of Ophthalmology, 137(1), 156-169. doi:10.1016/s0002-9394(03)00792-xHood, D. C., & Raza, A. S. (2014). On improving the use of OCT imaging for detecting glaucomatous damage. British Journal of Ophthalmology, 98(Suppl 2), ii1-ii9. doi:10.1136/bjophthalmol-2014-305156Bizios, D., Heijl, A., Hougaard, J. L., & Bengtsson, B. (2010). Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT. Acta Ophthalmologica, 88(1), 44-52. doi:10.1111/j.1755-3768.2009.01784.xKim, S. J., Cho, K. J., & Oh, S. (2017). Development of machine learning models for diagnosis of glaucoma. PLOS ONE, 12(5), e0177726. doi:10.1371/journal.pone.0177726Medeiros, F. A., Jammal, A. A., & Thompson, A. C. (2019). From Machine to Machine. Ophthalmology, 126(4), 513-521. doi:10.1016/j.ophtha.2018.12.033An, G., Omodaka, K., Hashimoto, K., Tsuda, S., Shiga, Y., Takada, N., … Nakazawa, T. (2019). Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images. Journal of Healthcare Engineering, 2019, 1-9. doi:10.1155/2019/4061313Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., & Farsiu, S. (2017). Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical Optics Express, 8(5), 2732. doi:10.1364/boe.8.002732Pekala, M., Joshi, N., Liu, T. Y. A., Bressler, N. M., DeBuc, D. C., & Burlina, P. (2019). Deep learning based retinal OCT segmentation. Computers in Biology and Medicine, 114, 103445. doi:10.1016/j.compbiomed.2019.103445Barella, K. A., Costa, V. P., Gonçalves Vidotti, V., Silva, F. R., Dias, M., & Gomi, E. S. (2013). Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT. Journal of Ophthalmology, 2013, 1-7. doi:10.1155/2013/789129Vidotti, V. G., Costa, V. P., Silva, F. R., Resende, G. M., Cremasco, F., Dias, M., & Gomi, E. S. (2013). Sensitivity and Specificity of Machine Learning Classifiers and Spectral Domain OCT for the Diagnosis of Glaucoma. European Journal of Ophthalmology, 23(1), 61-69. doi:10.5301/ejo.5000183Xu, J., Ishikawa, H., Wollstein, G., Bilonick, R. A., Folio, L. S., Nadler, Z., … Schuman, J. S. (2013). Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection. PLoS ONE, 8(2), e55476. doi:10.1371/journal.pone.0055476Maetschke, S., Antony, B., Ishikawa, H., Wollstein, G., Schuman, J., & Garnavi, R. (2019). A feature agnostic approach for glaucoma detection in OCT volumes. PLOS ONE, 14(7), e0219126. doi:10.1371/journal.pone.0219126Ran, A. R., Cheung, C. Y., Wang, X., Chen, H., Luo, L., Chan, P. P., … Tham, C. C. (2019). Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. The Lancet Digital Health, 1(4), e172-e182. doi:10.1016/s2589-7500(19)30085-8De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6Wang, X., Chen, H., Ran, A.-R., Luo, L., Chan, P. P., Tham, C. C., … Heng, P.-A. (2020). Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning. Medical Image Analysis, 63, 101695. doi:10.1016/j.media.2020.101695Ran, A. R., Shi, J., Ngai, A. K., Chan, W.-Y., Chan, P. P., Young, A. L., … Cheung, C. Y. (2019). Artificial intelligence deep learning algorithm for discriminating ungradable optical coherence tomography three-dimensional volumetric optic disc scans. Neurophotonics, 6(04), 1. doi:10.1117/1.nph.6.4.041110Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Jiang, J., Liu, X., Liu, L., Wang, S., Long, E., Yang, H., … Lin, H. (2018). 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A Self-Training Framework for Glaucoma Grading In OCT B-Scans

[EN] In this paper, we present a self-training-based framework for glaucoma grading using OCT B-scans under the presence of domain shift. Particularly, the proposed two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain, which is then used to train the final target model. This allows transferring knowledge-domain from the unlabeled data. Additionally, we propose a novel glaucoma-specific backbone which introduces residual and attention modules via skip-connections to refine the embedding features of the latent space. By doing this, our model is capable of improving state-of-the-art from a quantitative and interpretability perspective. The reported results demonstrate that the proposed learning strategy can boost the performance of the model on the target dataset without incurring in additional annotation steps, by using only labels from the source examples. Our model consistently outperforms the baseline by 1¿3% across different metrics and bridges the gap with respect to training the model on the labeled target data.We gratefully acknowledge the support of the Generalitat Valenciana (GVA) for the donation of the DGX A100 used for this work, action co-financed by the European Union through the Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) de la Comunitat Valenciana 2014-2020 (IDIFEDER/2020/030).García-Pardo, JG.; Colomer, A.; Verdú-Monedero, R.; Dolz, J.; Naranjo Ornedo, V. (2021). A Self-Training Framework for Glaucoma Grading In OCT B-Scans. IEEE. 1281-1285. https://doi.org/10.23919/EUSIPCO54536.2021.9616159S1281128

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

[EN] Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.This work was partially funded by Spanish National projects AES2017-PI17/00771 and AES2017-PI17/00821 (Instituto de Salud Carlos III), PID2019-105142RB-C21 (AI4SKIN) (Spanish Ministry of Economy and Competitiveness), PTA2017-14610-I (State Research Spanish Agency), regional project 20901/PI/18 (Fundacion Seneca) and Polytechnic University of Valencia (PAID-01-20).Berenguer-Vidal, R.; Verdú-Monedero, R.; Morales-Sánchez, J.; Sellés-Navarro, I.; Del Amor, R.; García-Pardo, JG.; Naranjo Ornedo, V. (2021). Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging. Sensors. 21(23):1-30. https://doi.org/10.3390/s21238027S130212

Direct transformation of crystalline MoO3_3 into few-layers MoS2_2

We fabricate large-area atomically thin MoS$_2$ layers through the direct transformation of crystalline molybdenum MoS$_2$ (MoO$_3$) by sulfurization at relatively low temperatures. The obtained MoS2 sheets are polycrystalline (~10-20 nm single-crystal domain size) with areas of up to 300x300 um$^2$ with 2-4 layers in thickness and show a marked p-type behaviour. The synthesized films are characterized by a combination of complementary techniques: Raman spectroscopy, X-ray diffraction, transmission electron microscopy and electronic transport measurements.Comment: 6 figures in main text, 2 figures in supp. inf

Riego de un cultivo de cítricos con agua marina desalinizada. Resultados preliminares en suelo y planta

La escasez de agua y la creciente presión sobre los recursos hídricos en las regiones semiáridas ha extendido la utilización para el riego de recursos hídricos no convencionales, como el agua marina desalinizada (AMD). Debido a su composición en Cl-, Na+ y B3+, el riego con AMD podría causar problemas agronómicos y afectar al suelo y a los cultivos a medio y largo plazo. En este estudio, se regó una parcela de mandarinos durante 20 meses con (i) agua proporcionada por la Comunidad de Regantes del Campo de Cartagena (CR), (ii) agua marina desalinizada (AMD) y (iii) mezcla de agua 50% CR y 50% AMD (AM). Se evaluó el efecto sobre la dinámica y acumulación de los iones tóxicos Cl-, Na+ y B3+ en el suelo y en la planta. La [B3+] del agua AMD fue superior a la de CR, acumulándose en el suelo, con una concentración un 25% superior a la encontrada con CR al final del ensayo. La [B3+] en la capa superficial del suelo se correlacionó con la [B3+] en el agua y con la [B3+] en la hoja. Aunque tras 20 meses los árboles regados con AMD tuvieron una [B3+] foliar un 25% superior a la de árboles regados con CR, no presentaron síntomas de toxicidad. Las [Cl-] y [Na+] del agua fueron similares en los tres tipos de agua, superando los umbrales a partir de los cuales pueden producir toxicidad en cítricos. Las concentraciones de Cl- y Na+ en hoja permanecieron por debajo del umbral de toxicidad establecido para cítricos. Los resultados obtenidos son preliminares ya que este estudio debería extenderse durante un periodo más largo para obtener datos más concluyentes acerca de los efectos a largo plazo de la utilización de AMD tanto en el suelo como en la planta.Este trabajo ha sido financiado por el Fondo Europeo de Desarrollo Regional (FEDER) y el Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación – a través de los proyectos SEARRISOST (RTC-2017-6192-2) y RIDESOST (AGL2017-85857)

Pancreatic metastases from renal cell carcinoma. Postoperative outcome after surgical treatment in a Spanish multicenter study (PANMEKID)

Background: Renal Cell Carcinoma (RCC) occasionally spreads to the pancreas. The purpose of our study is to evaluate the short and long-term results of a multicenter series in order to determine the effect of surgical treatment on the prognosis of these patients. Methods: Multicenter retrospective study of patients undergoing surgery for RCC pancreatic metastases, from January 2010 to May 2020. Variables related to the primary tumor, demographics, clinical characteristics of metastasis, location in the pancreas, type of pancreatic resection performed and data on short and long-term evolution after pancreatic resection were collected. Results: The study included 116 patients. The mean time between nephrectomy and pancreatic metastases' resection was 87.35 months (ICR: 1.51-332.55). Distal pancreatectomy was the most performed technique employed (50 %). Postoperative morbidity was observed in 60.9 % of cases (Clavien-Dindo greater than IIIa in 14 %). The median follow-up time was 43 months (13-78). Overall survival (OS) rates at 1, 3, and 5 years were 96 %, 88 %, and 83 %, respectively. The disease-free survival (DFS) rate at 1, 3, and 5 years was 73 %, 49 %, and 35 %, respectively. Significant prognostic factors of relapse were a disease free interval of less than 10 years (2.05 [1.13-3.72], p 0.02) and a history of previous extrapancreatic metastasis (2.44 [1.22-4.86], p 0.01). Conclusions: Pancreatic resection if metastatic RCC is found in the pancreas is warranted to achieve higher overall survival and disease-free survival, even if extrapancreatic metastases were previously removed. The existence of intrapancreatic multifocal compromise does not always warrant the performance of a total pancreatectomy in order to improve survival. (C) 2021 The Authors. Published by Elsevier Ltd

Whole genome sequencing of turbot (Scophthalmus maximus; Pleuronectiformes):a fish adapted to demersal life

12 páginas, 5 figuras.-- Antonio Figueras ... et al.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly citedThe turbot is a flatfish (Pleuronectiformes) with increasing commercial value, which has prompted active genomic research aimed at more efficient selection. Here we present the sequence and annotation of the turbot genome, which represents a milestone for both boosting breeding programmes and ascertaining the origin and diversification of flatfish. We compare the turbot genome with model fish genomes to investigate teleost chromosome evolution. We observe a conserved macrosyntenic pattern within Percomorpha and identify large syntenic blocks within the turbot genome related to the teleost genome duplication. We identify gene family expansions and positive selection of genes associated with vision and metabolism of membrane lipids, which suggests adaptation to demersal lifestyle and to cold temperatures, respectively. Our data indicate a quick evolution and diversification of flatfish to adapt to benthic life and provide clues for understanding their controversial origin. Moreover, we investigate the genomic architecture of growth, sex determination and disease resistance, key traits for understanding local adaptation and boosting turbot production, by mapping candidate genes and previously reported quantitative trait loci. The genomic architecture of these productive traits has allowed the identification of candidate genes and enriched pathways that may represent useful information for future marker-assisted selection in turbotThis work was funded by the Spanish Government: projects Consolider Ingenio: Aquagenomics (CSD2007-00002) and Metagenoma de la Península Ibérica (CSD2007-00005), Ministerio de Economía y Competitividad and European Regional Development Funds (AGL2012-35904), and Ministerio de Economía y Competitividad (AGL2014-51773 and AGL2014-57065-R); and Local Government Xunta de Galicia (GRC2014/010). P.P. and D.R. gratefully acknowledge the Spanish Ministerio de Educación for their FPU fellowships (AP2010-2408, AP2012-0254). Funding to pay the Open Access publication charges for this article was provided by the Ministerio de Economía y Competitividad (AGL2014-51773) and Xunta de Galicia (GRC2014/010)Peer reviewe

Lo glocal y el turismo. Nuevos paradigmas de interpretación.

El estudio del turismo se realiza desde múltiples escalas y enfoques, este libro aborda muchos temas que es necesario discutir desde diversas perspectivas; es el caso de la reflexión sobre la propia disciplina y sus conceptos, así como los asuntos específicos referidos al impacto territorial, los tipos de turismo, las cuestiones ambientales, el tema de la pobreza, la competitividad, las políticas públicas, el papel de las universidades, las áreas naturales protegidas, la sustentabilidad, la cultura, el desarrollo, la seguridad, todos temas centrales documentados y expuestos con originalidad y dominio del asunto. Lo multiescalar es básico para la comprensión del sistema turístico, sistema formado de procesos globales, regionales y locales. El eje de discusión del libro es lo glocal, esa interacción entre lo nacional y local con lo global