Анализ результатов проектирования считывающей электроники кремниевых умножителей на основе базового матричного кристалла МН2ХА030

The aim of the work is analyzing the results of an experimental research of a charge-sensitive amplifier with an adjustable conversion coefficient and a base level recovery circuit fabricated on the master slice array MN2XA030 for silicon photomultiplier tubes. The amplifier is called ADPreampl3. The parameters were measured on a small batch of chips in the amount of 20 samples. In the process of measuring the main parameters of the amplifier, the signal from the SiPM Photonique equivalent circuit was fed to the amplifier input. In the course of measuring the parameters, it was revealed that the spread of the baseline level for the FOut output ranged from -24 to 276 mV with an average value of 85.6 mV. In this case, a voltage changing in the FOoutShift node from -3 to 3 V is sufficient to establish a base level value of FOut output close to zero. When the recovery scheme is disabled, the spread of the basic level for OutA output is from 300 to 800 mV. When the OutAShift output is connected to the zero-voltage bus the average base level for OutA output is 3.72 mV and for OutAinv output it is minus 2.42 mV. The base level at the outputs OutA and OutAinv smoothly changes in the range of ± 0.9 V. At maximum gain, the dynamic range of ADPreampl3 exceeds 20 dB, however, at the same time, the conversion coefficient depends on the value of the input charge. To register large input charges, it is recommended to reduce the output pulse by reducing the voltage at the Gain pin or process the signal from the FOut pin. The output parameters of the experimental samples are compared with the results of computer simulation. The discrepancy between the results of modeling and measurements, peak time and propagation delays of the amplifier signal was revealed. Based on this, a decision to adjust the SPICE parameters of the elements used in the simulation was made. Целью работы является анализ результатов экспериментального исследования зарядочувствительного усилителя с регулируемым коэффициентом преобразования и схемой восстановления базового уровня, изготовленного на базовом матричном кристалле МН2ХА030 для кремниевых фотоэлектронных умножителей. Усилитель получил название ADPreampl3. Измерение параметров проводилось на партии чипов в количестве 20 штук. В процессе измерения основных параметров усилителя на его вход подавался сигнал с эквивалентной схемы SiPM Photonique. В ходе измерения параметров выявлено, что разброс базового уровня по выходу FOut составил от -24 до 276 мВ при среднем значении 85,6 мВ. При этом изменение напряжения в узле FOoutShift от -3 до 3 В достаточно для установления близкого к нулю значения базового уровня по выходу FOut. При отключенной схеме восстановления разброс базового уровня по выходу OutA составил от 300 до 800 мВ. При соединении вывода OutAShift с шиной нулевого напряжения среднее значение базового уровня по выходу OutA составило 3,72 мВ, а по выходу OutAinv - минус 2,42 мВ. Базовый уровень на выходах OutA и OutAinv плавно изменяется в диапазоне ±0,9 В. При максимальном усилении динамический диапазон ADPreampl3 превышает 20 дБ, однако при этом наблюдается зависимость коэффициента преобразования от величины входного заряда. Для регистрации больших входных зарядов рекомендуется уменьшить величину выходного импульса уменьшением напряжения на выводе Gain либо обрабатывать сигнал с вывода FOut. Проведено сравнение выходных параметров экспериментальных образцов с результатами компьютерного моделирования. Выявлено несовпадение результатов моделирования и измерений, времени пика и задержек распространения сигнала усилителя. Исходя из этого, принято решение о корректировке SPICE-параметров элементов, использованных при моделировании

Contribution of Intravital Neuroimaging to Study Animal Models of Multiple Sclerosis

Multiple sclerosis (MS) is a complex and long-lasting neurodegenerative disease of the central nervous system (CNS), characterized by the loss of myelin within the white matter and cortical fbers, axonopathy, and infammatory responses leading to consequent sensory-motor and cognitive defcits of patients. While complete resolution of the disease is not yet a reality, partial tissue repair has been observed in patients which ofers hope for therapeutic strategies. To address the molecular and cellular events of the pathomechanisms, a variety of animal models have been developed to investigate distinct aspects of MS disease. Recent advances of multiscale intravital imaging facilitated the direct in vivo analysis of MS in the animal models with perspective of clinical transfer to patients. This review gives an overview of MS animal models, focusing on the current imaging modalities at the microscopic and macroscopic levels and emphasizing the importance of multimodal approaches to improve our understanding of the disease and minimize the use of animals

PSI-PSYCHO SCHIZOFRENI IMAGING : Hjärnavbildning vid Schizofreni. Del 2. Hjärnavbildning med radioaktiva spårämnen

Denna andra delen av Hjärnavbildning vid Schizofreni beskriver användningen av radioaktiva ämnen som startade i Lund med professor David Ingvars pionjär studier av att avbilda cerebralt blodflöde med Xenon-133 hos patienter med Schizofreni. De nukelar medicinska metoderna utvecklades till Single Photon Emission Computed Tomography, SPECT, använder gammastrålning som emitteras direkt från internt distribuerade radiofarmaka i patienten som detekteras av en enda eller en uppsättning kollimerade strålnings-detektorer eller gammakameror. Nya radiofarmaka utvecklades såsom 99mTc-HMPAO d.v.s. 99mTc-Hexa-Methyl-Propylen-Amine-Oxim kräver ingen flödesstudie utan visar hur perfusionsmönstret såg ut vid tidpunkten för injektionen. Cerebral SPECT och 99mTc-HMPAO kan användas för att skilja mellan patienter som diagnostiserats med Schizofreni och friska kontrollpersoner. Även dopaminhypotesen som förklaringsmodell till Schizofreni har sitt ursprung i Lund med Arvid Carlssons upptäckt (Carlsson and Lindqvist, 1963). Med hjärnavbildnings-tekniken Positron-Emissions-Tomografi(PET) blev det möjligt att studera receptorbindning direkt i den levande mänskliga hjärnan. PET studier av frisättning och transport av dopamin, samt bindning av dopamin till Dopaminreceptorn D2R, visar att schizofrena patienter har en ökad tillgänglighet av dopamin i den striato-thalamo-kortikala transportvägen vägen och en minskad tillgänglighet i den mesolimbocortical vägen. Under 1980 talet visade det sig att regionalt blodflöde och metabolism med användningen av PET med 18F-fluordeoxyglucos (18FDG) var ett kraftfullt verktyg i psykiatrisk forskning om Schizofreni Under 1990 talet ökade omfattningen av 19FDG-PET studier på patienter med Schizofreni som ofta resulterar i dysfunktionella avvikelser vilka indikerar störd kommunikation i hjärnans kretslopp snarare än fokala skador i hjärnan. Hos patienter med Schizofreni visar 19FDG-PET studier förändringar främst i prefrontala, striatala, talamiska och temporala strukturer

Folate receptor beta as an imaging target in myocardial infarction

For decades, myocardial infarction (MI) has been one of the leading causes of mortality worldwide. MI is characterized by different stages of inflammation of myocardium up to subsequent fibrosis. During the inflammatory process, macrophage population increases and the expression of FR-β on their surfaces are extensive. We studied utility of positron emission tomography/computed tomography (PET/CT) tracer aluminium [18F]fluoride-labelled 1,4,7-triazacyclononane-1,4,7-triacetic acid conjugated folate (Al[18F]F–NOTA–Folate) targeting the FR-β receptor on activated macrophages for the assessment of inflammation in rats with MI. Methods: Surgical ligation of the left anterior descending (LAD) coronary artery irreversibly induced MI, while the sham model was prepared with similar procedures except ligation. Al[18F]F–NOTA–Folate tracer was administered intravenously to perform PET studies at three different time points (3, 7 and 90 days post-MI) using in vivo PET/CT imaging and ex vivo digital autoradiography for both MI and sham groups. Additionally, rat heart cryosections were prepared for histological (hematoxylin-eosin) and immunohistochemical staining with anti-CD68 antibody detecting activated macrophages. Uptake of Al[18F]F–NOTA–Folate was evaluated in the MI region and the remote area in both groups by image analysis. Results: In the MI model, infarcted cardiac areas had higher tracer uptake in ex vivo autoradiography compared to the remote areas or corresponding regions in sham-operated rats. Anti-CD68 immunohistochemistry demonstrated increased macrophage activity in the infarcted areas. The Al[18F]F–NOTA–Folate uptake in the infarcted area and amount of CD68-positive cells correlated positively and significantly. Conclusion: The study suggests that the novel Al[18F]F–NOTA–Folate PET tracer targeting FR-β expressed on activated macrophages is a promising tool for non-invasive imaging of inflammation associated with MI. Further research is required to clarify if this tracer is useful in the diagnostic and prognostic evaluation after MI

Correlação entre a magnitude de janelas de transparência em tecidos e a osmolaridade do agente usado

A criação de efeitos de transparência em tecidos biológicos tem sido fortemente explorada nos últimos anos, com o objetivo de reduzir o espalhamento da luz, que é uma caraterística natural destes materiais, e descobrir novos procedimentos clínicos de diagnóstico ou de tratamento com recurso à utilização da luz. Uma descoberta recente mostrou que na zona do ultravioleta se verifica uma eficiência na criação de transparência superior à que é verificada na zona desde o visível até ao infravermelho. Dado que tal descoberta está associada à criação de janelas de transparência localizadas no ultravioleta, para comprimentos de onda entre os 200 e os 400 nm, torna-se importante caracterizar tais janelas, nomeadamente avaliar a sua magnitude em função da concentração do agente de transparência usado em solução para tratar o tecido. Escolhendo músculo esquelético de coelho e soluções aquosas com concentrações volúmicas de sacarose entre os 20 % e 60 %, realizou-se um estudo que permitiu verificar a ocorrência de duas janelas de transparência, uma centrada a 230 nm e outra centrada a 300 nm. Tais comprimentos de onda centrais das janelas de transparência observadas foram associados às bandas de absorção das ligações de aminoácidos nas proteínas, nomeadamente a fenilalanina, e tirosina e triptofano. O estudo realizado mostrou que as magnitudes destas duas janelas crescem com a concentração da sacarose na solução de acordo com uma equação polinomial de segundo grau, que é particular para cada janela. Uma análise de tais dependências mostrou que para concentrações de sacarose entre 20 % e 40 %, as ligações associadas à fenilalanina são as que são dissociadas em maior número e que para concentrações superiores, a dissociação das ligações associadas à tirosina e ao triptofano passam a dominar no tecido muscular. Embora se tenha verificado uma dependência polinomial entre as magnitudes das janelas e a concentração de sacarose na solução, a proporção entre tais magnitudes depende linearmente da concentração da sacarose, o que mostra que os dois mecanismos de dissociação estão diretamente relacionados entre si. Estes resultados são uma mais-valia para se entender o mecanismo de transparência designado por dissociação de proteínas e como tal mecanismo depende da concentração do agente de transparência na solução de tratamento.The creation of transparency effects in biological tissues has been strongly explored in the recent years with the objective of reducing the native light scattering that these materials present, so that new clinical procedures that use light for diagnosis or treatment can be developed. One recent discovery in this field showed that a higher efficiency of transparency is observed in the ultraviolet range of spectrum than in the visible to infrared range. Since such discovery is associated to the creation of transparency windows in the ultraviolet, for wavelengths between 200 and 400 nm, it becomes necessary to characterize such windows, namely by relating their magnitude with the concentration of the transparency agent in the solution used to treat the tissue. By selecting the skeletal muscle from rabbit and aqueous solutions containing volume concentrations of sucrose between 20 % and 60 %, the present study allowed to confirm the creation of two transparency windows, one centered at 230 nm and the other centered at 300 nm. An association between those central wavelengths of the transparency windows with the absorption bands of the amino acid connections in proteins, the one of phenylalanine, and the one of tyrosine and tryptophan, was made. The present study showed that the magnitudes of these two windows grow with the sucrose concentration in the treating solution according to a second-degree polynomial equation, which is different for the two windows. An analysis of such dependencies showed that for sucrose concentrations in the treating solution between 20 % and 40 %, the phenylalanine connections in proteins are the ones that are dissociated in greater numbers and that for higher sucrose concentrations, the dissociation of the connections associated with the tyrosine and tryptophan connections become dominant in the muscle tissue. Although it was verified that a polynomial relation is observed between the magnitude of each of the two windows and the sucrose concentration in the treating solution, the proportion between those magnitudes depends linearly on the sucrose concentration in the treating solution. This shows that the two dissociation mechanisms are directly related. These results are valuable for the explanation of the transparency mechanism designated as protein dissociation and how such mechanism depends on the agent concentration in the treating solution

Монте-Карло симуляция ПЭТ мелких животных

Целью научной работы является разработка симуляция установки ПЭТ для мелких животных в инструментарии Geant4. Результатом работы является гибкая модель, позволяющая качественную и количественную оценку характеристик томографа.The aim of the scientific work is to develop a simulation of a PET setup for small animals in the Geant4 instrumentation. The result of the work is a flexible model that allows a qualitative and quantitative assessment of the characteristics of the tomograph

Avaliação do potencial de técnicas de machine learning no diagnóstico diferencial da doença de Parkinson com base em imagem molecular

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia Biomédica.A doença de Parkinson (DP) é uma doença neurodegenerativa que resulta da perda de neurónios dopaminérgicos na substância negra. É um grave problema de saúde pública que afeta 1-1,5% da população idosa a nível mundial. A perda dos neurónios dopaminérgicos devido à DP é um processo lento e que, de uma forma geral, pode demorar mais de uma década até que os primeiros sintomas sejam detetáveis, realçando a importância de um diagnóstico precoce para iniciar a terapêutica mais adequada o mais rapidamente possível [1]. O diagnóstico da DP é baseado na observação de sinais clínicos, nomeadamente a caracterização de uma variedade de sintomas motores, a resposta aos fármacos dopaminérgicos e a avaliação do padrão de captação (imagens) de radiofármacos específicos para avaliar a integridade do sistema dopaminérgico, usando equipamentos de SPECT (do inglês single-photon emission computed tomography) ou PET (do inglês positron emission tomography) [2]. Em grande parte dos casos, a avaliação visual destas imagens é suficiente para a caracterização do sistema dopaminérgico. No entanto, noutros casos, esta avaliação tem de ser complementada com uma análise quantitativa. Mesmo assim, por vezes ainda surgem dúvidas, que podem ser clarificadas com a utilização de técnicas de classificação baseadas em machinelearning [3]. As redes neuronais convolucionais (CNN, do inglês convolutional neural network) têm vindo a mostrar potencial na classificação de diversos tipos de imagens médicas, especialmente na área da oncologia [4],[5],[6] mas também existem exemplos de aplicação na área da neuroimagem [7],[8],[9]. Deste modo, pretendeu-se com este estudo avaliar o potencial das CNN, em comparação com outras técnicas muito populares, no diagnóstico diferencial da DP com base em imagens moleculares do cérebro obtidas com [123I] FP-CIT SPECT. Este trabalho incluiu um conjunto de 806 imagens cerebrais volumétricas obtidas com [123I]FP-CIT SPECT (208 controlos saudáveis e 598 doentes com DP). Os dados foram obtidos a partir da base de dados da Parkinson's Progression Markers Initiative (PPMI) (www.ppmi-info.org/data). Para cada sujeito, apenas foi considerado o primeiro exame [123I]FP-CIT SPECT (baseline ou screening). O protocolo de aquisição e pré-processamento de imagens encontra-se disponível em http://www.ppmi- info.org/study-design/research-documents-and-sops/. A técnica de classificação baseada em CNN foi comparada com os classificadores: k-vizinhos mais próximos (kNN, do inglês k-nearest neighbor), regressão logística (RL), árvores de decisão (AD), support vector machine (SVM) e redes neuronais artificiais (ANN, do inglês artificial neural networks). O classificador baseado em CNN foi treinado com imagens bidimensionais (dimensões: 88 mm × 82 mm) contendo a região do estriado, nomeadamente a projeção de intensidade máxima superior-inferior da cabeça. Os restantes classificadores foram treinados com cinco características extraídas da região do estriado tridimensional: potencial de ligação do caudato, potencial de ligação do putamen, rácio putamen para caudato, volume da região do estriado com "captação normal" e comprimento do eixo maior dessa região. Foram utilizados apenas os valores mínimos inter-hemisférios cerebral. Os dados foram divididos na razão 75:25 (75% para treino e 25% para teste). Cada uma das cinco características foi também estudada individualmente para avaliar o seu potencial de classificação em termos de desempenho (precisão, sensibilidade e especificidade). No conjunto de dados do teste, a precisão, sensibilidade, e especificidade da CNN para diferenciar imagens de doentes com DP das imagens de controlos saudáveis foi 96%, 98%, e 91%, respetivamente. Estes resultados foram muito semelhantes aos obtidos com os outros classificadores (kNN: 95%, 99%, 85%; RL: 94%, 97%, 86%; AD: 94%, 97%, 84%; SVM: 94%, 98%, 88%; e ANN: 94%, 97%, 86%). II. As diferenças de precisão não são estatisticamente significativas (teste Q de Cochran, p = 0,592). Individualmente, a característica que melhor diferenciou as imagens de doentes com DP das imagens dos controlos saudáveis foi o potencial de ligação do putamen com 93% de precisão, 93% de sensibilidade e 94% de especificidade no conjunto de dados do teste, usando o valor de corte que maximizou o coeficiente de Younden obtido do conjunto de dados de treino (valor de corte de 1,716). O classificador baseado em CNN provou ser tão robusto e preciso como os outros classificadores utilizados neste trabalho, com a vantagem de utilizar imagens como entrada direta, minimizando os passos iniciais de pré-processamento. Todos os classificadores aqui utilizados atingiram valores de precisão de classificação superiores aos frequentemente reportados na literatura para avaliação visual qualitativa. Assim, sugere-se a sua utilização como complemento à avaliação visual qualitativa e como ferramenta de treino para médicos especialista com reduzida experiência.Parkinson's disease (PD) is a neurodegenerative disease that results from the loss of dopaminergic neurons in the substantia nigra. It is a serious public health problem that affects 1 to 1.5% of the elderly population worldwide. The loss of dopaminergic neurons is a slow process that takes decades to happen, highlighting the importance of an early diagnosis to start the most adequate therapeutic regimen as soon as possible [1]. The diagnosis of PD is based on the observation of clinical signs, namely the characterization of a variety of motor symptoms, the response to dopaminergic drugs and evaluation of the uptake pattern (images) of specific radiopharmaceuticals to assess the integrity of the dopaminergic system [2]. In most cases, a visual assessment of these images is sufficient to characterize the dopaminergic system. However, in other cases this assessment must be complemented with a quantitative analysis. Even so, sometimes doubts still arise, which can be clarified with the use of classification techniques based on artificial intelligence, being machine learning the most frequently used [3]. In the context of artificial intelligence, convolutional neural networks (CNN) have been showing potential in various types of medical images, especially in the field of oncology [4],[5],[6], but there are also examples of application in the field of neuroimaging [7],[8],[9]. Thus, the aim of this study is to evaluatethe potential of CNN, in comparison to other popular techniques, in the differential diagnosis of PD based on [123I]FP-CIT SPECT images of the central nervous system, in particular the basal ganglia. This work included 806 [123I]FP-CIT SPECT brain images (208 health controls and 598 with PD). Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi- info.org/data). For each subject, only the first scan [123I]FP-CIT SPECT was considered (baseline or screening). The protocol of image acquisition and pre-processing is available at http://www.ppmi- info.org/study-design/research-documents-and-sops/. CNN was compared against k-nearest neighbour (kNN), logistic regression (LR), decision trees (DT), support vector machines (SVM) and artificial neural networks (ANN) classifiers. The CNN classifier was trained with 2-dimensional image patches (dimensions: 88 mm × 82 mm) containing the striatal region, extracted from the head superior-inferior maximum intensity projection. The remaining classifiers were trained with five features extracted from 3-dimensional striatal region: caudate binding potential, putamen binding potential, putamen to caudate ratio, volume of the striatal region with “normal uptake”, and the length of major axis of that region. Only the inter-hemisphere minimum was used. The split ratio of the dataset was 75:25 (75% for training and 25% for testing). Each of the five features was also considered individually to assess its potential for classification in terms of performance (accuracy, sensitivity, and specificity). In the test dataset, accuracy, sensitivity, and specificity of the CNN were 96%, 98%, and 91%, respectively. This finding was very similar to what we obtained with the other classifiers (kNN: 95%, 99%, 85%; LR: 94%, 97%, 86%, DT: 94%, 97%, 84%, SVM: 94%, 98%, 88% and ANN: 94%, 97%, 86%). The accuracy differences were not statistically significant (Cochran Q test, p = 0.592). Individually, the feature that best differentiated PD from normal scans was the putamen binding potential with 93% accuracy, 93% sensitivity and 94% specificity in the test dataset, based on the optimal cut-off (1.716) that maximizes Younden’s coefficient in the training dataset. IV CNN classifier proved to be as robust and accurate as the other classifiers frequently used in the type of problems, with the great advantage of using images as direct input. All machine learning-based classifiers tested are robust and very accurate in the classification of brain [123I]FP-CIT SPECT scans. Standard visual clinical evaluation should be complemented with quantification classification, and also used as a training tool.N/

Heterogeneidad tumoral en imágenes PET-CT

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Estructura de la Materia, Física Térmica y Electrónica, leída el 28/01/2021Cancer is a leading cause of morbidity and mortality [1]. The most frequent cancers worldwide are non–small cell lung carcinoma (NSCLC) and breast cancer [2], being their management a challenging task [3]. Tumor diagnosis is usually made through biopsy [4]. However, medical imaging also plays an important role in diagnosis, staging, response to treatment, and recurrence assessment [5]. Tumor heterogeneity is recognized to be involved in cancer treatment failure, with worse clinical outcomes for highly heterogeneous tumors [6,7]. This leads to the existence of tumor sub-regions with different biological behavior (some more aggressive and treatment-resistant than others) [8-10]. Which are characterized by a different pattern of vascularization, vessel permeability, metabolism, cell proliferation, cell death, and other features, that can be measured by modern medical imaging techniques, including positron emission tomography/computed tomography (PET/CT) [10-12]. Thus, the assessment of tumor heterogeneity through medical images could allow the prediction of therapy response and long-term outcomes of patients with cancer [13]. PET/CT has become essential in oncology [14,15] and is usually evaluated through semiquantitative metabolic parameters, such as maximum/mean standard uptake value (SUVmax, SUVmean) or metabolic tumor volume (MTV), which are valuables as prognostic image-based biomarkers in several tumors [16-17], but these do not assess tumor heterogeneity. Likewise, fluorodeoxyglucose (18F-FDG) PET/CT is important to differentiate malignant from benign solitary pulmonary nodules (SPN), reducing so the number of patients who undergo unnecessary surgical biopsies. Several publications have shown that some quantitative image features, extracted from medical images, are suitable for diagnosis, tumor staging, the prognosis of treatment response, and long-term evolution of cancer patients [18-20]. The process of extracting and relating image features with clinical or biological variables is called “Radiomics” [9,20-24]. Radiomic parameters, such as textural features have been related directly to tumor heterogeneity [25]. This thesis investigated the relationships of the tumor heterogeneity, assessed by 18F-FDG-PET/CT texture analysis, with metabolic parameters and pathologic staging in patients with NSCLC, and explored the diagnostic performance of different metabolic, morphologic, and clinical criteria for classifying (malignant or not) of solitary pulmonary nodules (SPN). Furthermore, 18F-FDG-PET/CT radiomic features of patients with recurrent/metastatic breast cancer were used for constructing predictive models of response to the chemotherapy, based on an optimal combination of several feature selection and machine learning (ML) methods...El cáncer es una de las principales causas de morbilidad y mortalidad. Los más frecuentes son el carcinoma de pulmón de células no pequeñas (NSCLC) y el cáncer de mama, siendo su tratamiento un reto. El diagnóstico se suele realizar mediante biopsia. La heterogeneidad tumoral (HT) está implicada en el fracaso del tratamiento del cáncer, con peores resultados clínicos para tumores muy heterogéneos. Esta conduce a la existencia de subregiones tumorales con diferente comportamiento biológico (algunas más agresivas y resistentes al tratamiento); las cuales se caracterizan por diferentes patrones de vascularización, permeabilidad de los vasos sanguíneos, metabolismo, proliferación y muerte celular, que se pueden medir mediante imágenes médicas, incluida la tomografía por emisión de positrones/tomografía computarizada con fluorodesoxiglucosa (18F-FDG-PET/CT). La evaluación de la HT a través de imágenes médicas, podría mejorar la predicción de la respuesta al tratamiento y de los resultados a largo plazo, en pacientes con cáncer. La 18F-FDG-PET/CT es esencial en oncología, generalmente se evalúa con parámetros metabólicos semicuantitativos, como el valor de captación estándar máximo/medio (SUVmáx, SUVmedio) o el volumen tumoral metabólico (MTV), que tienen un gran valor pronóstico en varios tumores, pero no evalúan la HT. Asimismo, es importante para diferenciar los nódulos pulmonares solitarios (NPS) malignos de los benignos, reduciendo el número de pacientes que van a biopsias quirúrgicas innecesarias. Publicaciones recientes muestran que algunas características cuantitativas, extraídas de las imágenes médicas, son robustas para diagnóstico, estadificación, pronóstico de la respuesta al tratamiento y la evolución, de pacientes con cáncer. El proceso de extraer y relacionar estas características con variables clínicas o biológicas se denomina “Radiomica”. Algunos parámetros radiómicos, como la textura, se han relacionado directamente con la HT. Esta tesis investigó las relaciones entre HT, evaluada mediante análisis de textura (AT) de imágenes 18F-FDG-PET/CT, con parámetros metabólicos y estadificación patológica en pacientes con NSCLC, y exploró el rendimiento diagnóstico de diferentes criterios metabólicos, morfológicos y clínicos para la clasificación de NPS. Además, se usaron características radiómicas de imágenes 18F-FDG-PET/CT de pacientes con cáncer de mama recurrente/metastásico, para construir modelos predictivos de la respuesta a la quimioterapia, combinándose varios métodos de selección de características y aprendizaje automático (ML)...Fac. de Ciencias FísicasTRUEunpu