Diseño y desarrollo de sistemas automáticos de clasificación de tumores melanocíticos spitzoides.

[ES] El cáncer de piel representa el grupo más común de neoplasias malignas en la población blanca. De hecho, en la actualidad, uno de cada tres cánceres diagnosticados es cáncer de piel. Aunque dos de los cánceres cutáneos más comúnmente diagnosticados son el carcinoma de células basales y el de células escamosas, sin duda, el cáncer de piel más agresivo y peligroso es el melanoma. El melanoma cutáneo es una neoplasia agresiva, con un elevado riesgo metastásico y una extrema complejidad genética y biológica. En la mayoría de tumores melanocíticos, es posible una distinción patológica precisa entre benigno (nevus melanocítico) y maligno (melanoma). Sin embargo, existen neoplasias melanocíticas de difícil diagnóstico clínico debido a que poseen características morfológicas ambiguas. Un ejemplo de ello son los llamados ¿tumores spitzoides¿. Las neoplasias melanocíticas spitzoides son lesiones cutáneas poco frecuentes que constituyen problemas de diagnóstico para los dermotopatólogos. Estos tumores destacan por la proliferación de grandes melanocitos epitelioides o fusocelulares con grandes núcleos de cromatina vesicular y nucléolo prominente. La discrepancia entre su aspecto morfológico, histopatológico y su evolución supone un desafío en el diagnóstico. Esta discrepancia ha servido de estímulo para la realización de estudios moleculares que permitan comprender la biología y el comportamiento de las lesiones spitzoides. Entre estos estudios destaca la metilación del DNA, ya que es una marca distintiva en las lesiones melanocíticas y puede ser clave para su desarrollo y progresión metastásica, convirtiéndose en un marcador diagnóstico, pronóstico y terapéutico. En el presente Trabajo Fin de Master (TFM) se pretende abordar el desarrollo de algoritmos automáticos de clasificación basados en deep learning con los que poder identificar las neoplasitas melanocíticas spitzoides benignas y malignas y realizar el pronóstico de aquellas con potencial maligno incierto, utilizando para ello los datos de metilación del ADN y sus imágenes histopatológicas correspondientes. Para llevar a cabo la clasificación en base a los datos de metilación del DNA, se emplearán algoritmos de reducción de la dimensionalidad, así como técnicas de clasificación supervisadas y no supervisadas. Por otro lado, las imágenes histopatológicas serán analizadas con redes neuronales convolucionales y se combinarán con los datos de metilación para el desarrollo de algoritmos de clasificación de lesiones melanocíticas.[VL] El càncer de pell representa el grup més comú de neoplàsies malignes en la població blanca. De fet, en l’actualitat, un de cada tres càncers diagnosticats és càncer de pell. Encara que dos dels càncers cutanis més comunament diagnosticats són el carcinoma de cèl·lules basals i el de cèl·lules escatoses, sens dubte, el càncer de pell més agressiu i perillós és el melanoma. El melanoma cutani és una neoplàsia agressiva, amb un elevat risc metastásico i una extrema complexitat genètica i biològica. En la majoria de tumors melanocítics, és possible una distinció patològica precisa entre benigne (nevus melanocític) i maligne (melanoma) . No obstant això, hi ha neoplàsies melanocitiques de difícil diagnòstic clínic pel fet que posseïxen característiques morfològiques ambigües. Un exemple d’això són els cridats tumors spitzoides. Les neoplàsies melanocíticas spitzoides són lesions cutànies poc freqüents que constituïxen problemes de diagnòstic per als dermatopatólegs. Estos tumors destaquen per la proliferació de grans melanòcits epitelioides o fusocelulares amb grans nuclis de cromatina vesicular i nuclèol prominent. La discrepància entre el seu aspecte morfològic histopatológic i la seua evolució suposa un desafiament en el diagnòstic. Esta discrepància ha servit d’estímul per a la realització d’estudis moleculars que permeten comprendre la biologia i el comportament de les lesions spitzoides. Entre estos estudis destaca la metilación del ADN, ja que és una marca distintiva en les lesions melanocíticas i pot ser clau per al seu desenrotllament i progressió metastásica, convertint-se en un marcador diagnòstic, pronòstic i terapèutic. En el present Treball Fi de Màster (TFM) es pretén abordar el desenrotllament d’algoritmes automàtics de classificació basats en deep learning amb els que poder identificar les neoplasitas melanocítics spitzoides benignes i malignes i realitzar el pronòstic d’aquelles amb potencial maligne incert utilitzant les dades de metilación de l’ADN i les seues imatges histopatológques corresponents. Per a dur a terme la classificació basant-se en les dades de metilación del ADN s’empraran algoritmes de reducció de la dimensionalitat, així com tecniques de classificació supervisades i no supervisades. D’altra banda, les imatges histopatológicas seran analitzades amb xarxes neuronals convolucionals i es combinaran amb les dades de metilació per al desenrotllament d’algoritmes de classificació de lesions melanocítics.[EN] Skin cancer represents the most common group of malignant neoplasms in the white population. In fact, one in three cancers diagnosed today is skin cancer. Although two of the most commonly diagnosed skin cancers are basal cell carcinoma and squamous cell carcinoma, without a doubt, the most aggressive and dangerous skin cancer is melanoma. Skin melanoma is an aggressive neoplasm with a high metastatic risk and extreme genetic and biological complexity. In most melanocytic tumors, a precise pathological distinction between benign (melanocytic nevus) and malignant (melanoma) is possible. However, there are melanocytic neoplasms that are difficult to clinically diagnose because of their ambiguous morphological characteristics. An example of this is the so-called "spitzoid tumours". Spitzoid melanocytic neoplasms are rare skin lesions that are diagnostic problems for dermatologists. These tumors are characterized by the proliferation of large epithelioid or fusocellular melanocytes with large nuclei of vesicular chromatin and prominent nucleolus. The discrepancy between their histopathological morphology and their evolution poses a diagnostic challenge. This discrepancy has stimulated molecular studies to understand the biology and behaviour of spitzoid lesions. Among these studies, DNA methylation stands out, since it is a distinctive mark in melanocytic lesions and it can be key to their development and metastatic progression, becoming a diagnostic, prognostic and therapeutic marker. In the present Master’s Degree thesis (TFM), I intend to address the development of automatic classification algorithms based on deep learning to identify benign and malignant spitzoid melanocytic neoplasms and to make the prognosis of those with uncertain malignant potential using DNA methylation data and its corresponding histopathological images. To carry out the classification based on the DNA methylation data, dimensionality reduction algorithms as well as supervised and unsupervised classification techniques will be used. Additionally, the histopathological images will be analyzed with convolutional neural networks and combined with the methylation data to develop classification algorithms for melanocytic lesions.Del Amor Del Amor, MR. (2020). Diseño y desarrollo de sistemas automáticos de clasificación de tumores melanocíticos spitzoides. Universitat Politècnica de València. http://hdl.handle.net/10251/159881TFG

Diseño y desarrollo de sistemas automáticos de segmentación aplicados a imágenes OCT de retina y piel

[ES] La tomografía por coherencia óptica (OCT) es una técnica de diagnóstico por imagen basada en el principio de interferometría de baja coherencia con la que se obtienen cortes tomográficos a escala micrométrica. La OCT es ampliamente utilizada como una modalidad de imagenología retiniana ya que es una técnica no invasiva capaz de capturar la estructura interior de la retina permitiendo monitorizar su posible daño. Una forma de monitorizarlo es a partir del análisis del espesor de las capas retinianas. Para obtener el grosor de las diferentes capas, es esencial realizar una segmentación previa. Por otro lado, actualmente, se está aumentando la aplicabilidad de la OCT a estudios dermatológicos. Un parámetro clave a inferir en estas imágenes es el espesor epidérmico. El análisis del grosor de esta capa permite realizar un diagnóstico temprano en algunas enfermedades como la psoriasis. Es por ello por lo que, la segmentación de las capas de la piel que permita delimitar la epidermis resulta fundamental. Sin embargo, la segmentación manual de imágenes tanto de retina como de piel por parte de los especialistas resulta tedioso y consume gran cantidad de tiempo. Por ello, es necesario el desarrollo de sistemas de segmentación automáticos que permitan reducir tanto la carga de trabajo como el nivel de subjetividad a la hora de realizar una segmentación manual. El principal objetivo de este TFG reside en el desarrollo de algoritmos automáticos de segmentación basados en Deep learning con los que poder identificar tanto las diferentes capas retinianas como las principales capas de la piel. Para ello, se partirá de una base de datos de imágenes de piel y otra de retina que serán segmentadas manualmente con el objetivo de disponer del ground truth. Para llevar a cabo el algoritmo de segmentación se emplearán redes convoluciones autoencoders. Por otro lado, las imágenes OCT sufren de un tipo de ruido denominado speckle, que dificulta la extracción de características de la imagen. Debido a dicho fenómeno, se diseñarán diferentes algoritmos para la reducción automática de este tipo de ruido. Gracias a ello, se podrá comprobar como éste afecta a los resultados de la segmentación automática.[EN] Optical coherence tomography (OCT) is a diagnostic imaging technique based on the principle of low coherence interferometry with which tomographic slices are obtained on a micrometric scale. OCT is widely used as a modality of retinal imaging since it is a non-invasive technique capable of capturing the inner structure of the retina allowing to monitor its possible damage. One way to monitor it is from the analysis of the thickness of the retinal layers. To obtain the thickness of the different layers, it is essential to perform a previous segmentation. On the other hand, currently, the applicability of OCT to dermatological studies is increasing. A key parameter to infer in these images is the epidermal thickness. The analysis of the thickness of this layer allows an early diagnosis in some diseases such as psoriasis. That is why the segmentation of the layers of the skin that allows delimiting the epidermis is essential. However, manual segmentation of both retina and skin images by specialists is tedious and time-consuming. Therefore, it is necessary to develop automatic segmentation systems that reduce both the workload and the level of subjectivity when performing a manual segmentation. The main objective of this TFG is the development of automatic segmentation algorithms based on Deep Learning with which to identify both the different retinal layers and the main layers of the skin. To do this, It will be used a database of skin images and another one of the retina that will be segmented manually in order to have the ground truth. In order to carry out the segmentation algorithm, convolution networks autoencoders will be used.Amor Del Amor, MRD. (2019). Diseño y desarrollo de sistemas automáticos de segmentación aplicados a imágenes OCT de retina y piel. http://hdl.handle.net/10251/124036TFG

Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks

Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis

Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks

[EN] Background and Objective: Optical coherence tomography (OCT) is a useful technique to monitor retinal layer state both in humans and animal models. Automated OCT analysis in rats is of great relevance to study possible toxic effect of drugs and other treatments before human trials. In this paper, two different approaches to detect the most significant retinal layers in a rat OCT image are presented. Methods: One approach is based on a combination of local horizontal intensity profiles along with a new proposed variant of watershed transformation and the other is built upon an encoder-decoder convolutional network architecture. Results: After a wide validation, an averaged absolute distance error of 3.77 +/- 2.59 and 1.90 +/- 0.91 mu m is achieved by both approaches, respectively, on a batch of the rat OCT database. After a second test of the deep-learning-based method using an unseen batch of the database, an averaged absolute distance error of 2.67 +/- 1.25 mu m is obtained. The rat OCT database used in this paper is made publicly available to facilitate further comparisons. Conclusions: Based on the obtained results, it was demonstrated the competitiveness of the first approach since outperforms the commercial Insight image segmentation software (Phoenix Research Labs) as well as its utility to generate labelled images for validation purposes speeding significantly up the ground truth generation process. Regarding the second approach, the deep-learning-based method improves the results achieved by the more conventional method and also by other state-of-the-art techniques. In addition, it was verified that the results of the proposed network can be generalized to new rat OCT images.Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-020101213). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. This work has received funding from Horizon 2020, the European Union's Framework Programme for Research and Innovation, under grant agreement No. 732613 (GALAHAD Project), the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869 and GVA through project PROMETEO/2019/109.Morales, S.; Colomer, A.; Mossi García, JM.; Del Amor, R.; Woldbye, D.; Klemp, K.; Larsen, M.... (2021). Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks. Computer Methods and Programs in Biomedicine. 198:1-14. https://doi.org/10.1016/j.cmpb.2020.105788S11419

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

What am I capable? Self-Assessment of Basic Competences

Los objetivos de esta investigación se centran en evaluar las competencias en comunicación lingüística y matemática del alumnado de sexto de educación primaria desde su reflexión personal y elaborar un cuestionario válido y fiable que pueda medir dicha autoevaluación. La metodología que se siguió es de carácter cuantitativo, descriptivo y correlacional. En el estudio participaron 1,424 alumnos de 46 centros educativos de Córdoba (España) y su provincia. Los resultados obtenidos muestran que variables como el género, la edad, el número de hermanos y la titularidad del centro influyen en una mejor o peor autoevaluación del alumnado, pero son las actividades extraescolares realizadas –y el mayor tiempo semanal dedicado a las mismas– las que provocan una mayor valoración de cada competencia. En contrapartida, un menor uso diario de la televisión, la computadora y los dispositivos portátiles de videojuegos posibilita que el alumnado se autoevalúe más capaz para diferentes aspectos de ambas competencias.The aims of this research are focus in evaluate linguistic communication and mathematics competences of students in sixth grade of primary education from their perception, and to develop a valid and reliable questionnaire in order to perform a self-assessment. The methodology has quantitative, descriptive and correlational character. In this research 1424 students from 46 schools in Cordova and its province participated. The results show that variables such as gender, age, number of siblings and type of center have influence for a better or worse self-assessment of students; but extracurricular activities undertaken by students and the increased weekly time devoted are those which cause a greater appreciation of each competence. On the other hand, a lower daily use of television, computer and games console allows that students make a self-assess more capeble to different aspects of both competences

Liver injury in non-alcoholic fatty liver disease is associated with urea cycle enzyme dysregulation

The main aim was to evaluate changes in urea cycle enzymes in NAFLD patients and in two preclinical animal models mimicking this entity. Seventeen liver specimens from NAFLD patients were included for immunohistochemistry and gene expression analyses. Three-hundred-and-eighty-two biopsy-proven NAFLD patients were genotyped for rs1047891, a functional variant located in carbamoyl phosphate synthetase-1 (CPS1) gene. Two preclinical models were employed to analyse CPS1 by immunohistochemistry, a choline deficient high-fat diet model (CDA-HFD) and a high fat diet LDLr knockout model (LDLr −/−). A significant downregulation in mRNA was observed in CPS1 and ornithine transcarbamylase (OTC1) in simple steatosis and NASH-fibrosis patients versus controls. Further, age, obesity (BMI > 30 kg/m), diabetes mellitus and ALT werefound to be risk factors whereas A-allele from CPS1 was a protective factor from liver fibrosis. CPS1 hepatic expression was diminished in parallel with the increase of fibrosis, and its levels reverted up to normality after changing diet in CDA-HFD mice. In conclusion, liver fibrosis and steatosis were associated with a reduction in both gene and protein expression patterns of mitochondrial urea cycle enzymes. A-allele from a variant on CPS1 may protect from fibrosis development. CPS1 expression is restored in a preclinical model when the main trigger of the liver damage disappears.The research leading to these results has received funding from the Consejería de Salud de la Junta de Andalucía under grant agreement PC-0148-2016-0148 and PE-0451-2018 and Instituto de Salud Carlos III under grant agreements CD21/00095, PI16/01842, PI19/01404, PI19/00589, IFI18/00041, FI20/00201, CD18/00126 and EHD18PI04/2021. Rocío Gallego-Durán has received the Andrew K Burroughs Fellowship from European Association for the Study of the Liver (EASL), Aprendizaje de Nuevas Tecnologías fellowship from Asociación Española para el Estudio del Hígado (AEEH) and CIBERehd Grant to support researcher’s mobility

Usos del e-learning en las Universidades Andaluzas: Estado de la situación y análisis de buenas prácticas. (Versión completa)

Secretaría General de Universidades, Investigación y Tecnología P07-SE-J.0267

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio