Academic literature on the topic 'Imaging biomarker validation'

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Biomarkers are important tools in the medical field, once they allow better prediction, characterization, and treatment of diseases. In this scenario, it is essential that biomarkers are highly accurate. Thus, biomarker validation is an essential part of ensuring the effectiveness of a biomarker. Validation of biomarkers is the process by which biomarkers are evaluated for accuracy and consistency, as well as their ability to inform the condition of health or disease. Although, there is no unique measure that can be used to determine the validity for all biomarkers, there are general criteria that all biomarkers must meet to be useful. In this work, we review the definition of biomarkers and discuss the validity components. We then critically discuss the main methods used to validate biomarkers and consider some examples of biomarkers of the diseases which most killer in the world (cardiovascular diseases, cancer, and viral infections), highlighting the potential biochemical pathways of these biomarkers in the biological system. In addition, we also comment on the omic strategies used in the biomarker discovery process and conclude with information about perspectives in biomarker validation through imaging techniques.

Hepatocellular carcinoma is a major contributor to the global cancer burden. It affects millions of people in Pakistan on a yearly basis. Furthermore, HCC is linked to viral infections Hepatitis B and C, which account for roughly 87 percent of HCC cases in Pakistan. HCC is identified using imaging techniques such as MRI, Ultrasound, and histology, which have radiation hazards and frequently need expensive healthcare systems that are less available in most of the developing countries. Novel HCC biomarkers are being developed as part of a large research project aimed at detecting the disease early. These include the creation of biomarkers based on HCC patients' transcriptome and proteomic profiles. Circulating proteins, which are easily detected in body fluids, including blood serum, may thus provide an opportunity for the development of HCC biomarkers. Blood-based serum biomarkers must be developed for easy, non-invasive, and early detection of HCC. In conjunction with imaging techniques, alpha-fetoprotein (AFP) has been used to detect HCC, although it has little clinical usefulness. Also, the reported AFP negative results make its utility meager. Multiple circulating proteins have been studied as biomarker possibilities for HCC diagnosis in recent years. In this study, Blood serum was used to validate three novel protein biomarker candidates to detect HBV induced HCC that had previously been predicted using a bioinformatics methodology. Proteins named C6, C8A and C8B were measured in the serum of 22 HCC patients infected with HBV in Pakistani population and compared to AFP levels using quantitative ELISA. C8A possesses considerable biomarker potential, with 95.45 percent specificity and 77.27% sensitivity with 0.933 Area Under the Curve (AUC), whereas C6 and C8B showed poor biomarker potential. Hence, C8A demonstrated great promise as a circulating blood-based protein biomarker for HBV induced HCC diagnosis.

Objective.The Outcome Measures in Rheumatology (OMERACT) Filter provides a framework for the validation of outcome measures for use in rheumatology clinical research. However, imaging and biochemical measures may face additional validation challenges because of their technical nature. The Imaging and Soluble Biomarker Session at OMERACT 11 aimed to provide a guide for the iterative development of an imaging or biochemical measurement instrument so it can be used in therapeutic assessment.Methods.A hierarchical structure was proposed, reflecting 3 dimensions needed for validating an imaging or biochemical measurement instrument: outcome domain(s), study setting, and performance of the instrument. Movement along the axes in any dimension reflects increasing validation. For a given test instrument, the 3-axis structure assesses the extent to which the instrument is a validated measure for the chosen domain, whether it assesses a patient-centered or disease-centered variable, and whether its technical performance is adequate in the context of its application. Some currently used imaging and soluble biomarkers for rheumatoid arthritis, spondyloarthritis, and knee osteoarthritis were then evaluated using the original OMERACT Filter and the newly proposed structure. Breakout groups critically reviewed the extent to which the candidate biomarkers complied with the proposed stepwise approach, as a way of examining the utility of the proposed 3-dimensional structure.Results.Although there was a broad acceptance of the value of the proposed structure in general, some areas for improvement were suggested including clarification of criteria for achieving a certain level of validation and how to deal with extension of the structure to areas beyond clinical trials.Conclusion.General support was obtained for a proposed tri-axis structure to assess validation of imaging and soluble biomarkers; nevertheless, additional work is required to better evaluate its place within the OMERACT Filter 2.0.

A major goal of current clinical research in Parkinson’s disease (PD) is the validation and standardization of biomarkers enabling early diagnosis, predicting outcomes, understanding PD pathophysiology, and demonstrating target engagement in clinical trials. Molecular imaging with specific dopamine-related tracers offers a practical indirect imaging biomarker of PD, serving as a powerful tool to assess the status of presynaptic nigrostriatal terminals. In this review we provide an update on the dopamine transporter (DAT) imaging in PD and translate recent findings to potentially valuable clinical practice applications. The role of DAT imaging as diagnostic, preclinical and predictive biomarker is discussed, especially in view of recent evidence questioning the incontrovertible correlation between striatal DAT binding and nigral cell or axon counts.

(1) Background: MALDI imaging is a technique that still largely depends on time of flight (TOF)-based instrument such as the Bruker UltrafleXtreme. While capable of performing targeted MS/MS, these instruments are unable to perform fragmentation while imaging a tissue section necessitating the reliance of MS1 values for peptide level identifications. With this premise in mind, we have developed a hybrid bioinformatic/image-based method for the identification and validation of viral biomarkers. (2) Methods: Formalin-Fixed Paraffin-Embedded (FFPE) mouse samples were sectioned, mounted and prepared for mass spectrometry imaging using our well-established methods. Peptide identification was achieved by first extracting confident images corresponding to theoretical viral peptides. Next, those masses were used to perform a Peptide Mmass Fingerprint (PMF) searched against known viral FASTA sequences against a background mouse FASTA database. Finally, a correlational analysis was performed with imaging data to confirm pixel-by-pixel colocalization and intensity of viral peptides. (3) Results: 14 viral peptides were successfully identified with significant PMF Scores and a correlational result of >0.79 confirming the presence of the virus and distinguishing it from the background mouse proteins. (4) Conclusions: this novel approach leverages the power of mass spectrometry imaging and provides confident identifications for viral proteins without requiring MS/MS using simple MALDI Time Of Flight/Time Of Flight (TOF/TOF) instrumentation.

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

Positron emission tomography (PET) allows noninvasive, quantitative studies of various biologic processes in the tumor tissue. By using PET, investigators can study the pharmacokinetics of anticancer drugs, identify various therapeutic targets and monitor the inhibition of these targets during therapy. Furthermore, PET provides various markers to assess tumor response early in the course of therapy. A significant number of studies have now shown that changes in tumor glucose utilization during the first weeks of chemotherapy are significantly correlated with patient outcome. These data suggest that PET may be used as a sensitive test to assess the activity of new cytotoxic agents in phase II studies. Furthermore, early identification of nonresponding tumors provides the opportunity to adjust treatment regimens according to the individual chemosensitivity of the tumor tissue. However, further prospective and randomized validation of PET is still required before PET controlled chemotherapy can be used in clinical practice.

Introduction: Pancreatic cancer is the 10th most common cause of cancer in the United Kingdom (UK) yet the 5th most common cause of cancer related death. Although excess adiposity, measured as body mass index (BMI), is a risk factor for the development of pancreatic cancer the increase in relative risk is modest. Animal models suggest that the intra-organ deposition of lipids may be more specific to disease risk than anthropometric measurements. There is therefore a need to develop non-invasive methods to quantify intra-pancreatic fat deposition as a potential biomarker for pancreatic cancer predisposition. Cancer Research UK (CRUK) sets out clear guidelines for biomarker discovery and development. Potential biomarkers must go through a process of discovery and assay development followed by qualification. Methods Three streams of research: (i) Stage-one of the PanORAMA project. Assessment of accuracy through comparison of CS-MR and MRS quantified intra-pancreatic fat with histologically quantified intra-pancreatic fat in 12 patients undergoing pancreatic surgery. (ii) Stage-two of the PanORAMA study. Assessment of precision (reproducibility) and comparison with other anthropometric markers of excess adiposity in healthy volunteers (n=15). Refinement of MRS protocols and repeated assessment of precision in healthy volunteers (n=10). (iii) The Breast Risk Reduction Intermittent Dietary Evaluation 2 (BRRIDE-2) trial. Comparison of the effects of Intermittent Energy Restriction (IER) with Daily Energy Restriction (DER) on intra-pancreatic and intra-hepatic fat stores and metabolic markers of disease risk (n=26). Results (i) CS-MR and MRS had agreement with histological assessment of intra-pancreatic fat, but correlations were only moderate to good (rho 0.672 and 0.781 respectively). (ii) CS-MR, and after refinement, MRS, have clinically acceptable precision. This study tested this principle in intra-pancreatic fat in healthy volunteers with a range of intra-pancreatic fat consistent with the literature on the healthy population. (iii) I found no differences in reduction in intra-hepatic or intra-pancreatic fat when comparing IER with DER. Overall, I found that significant reductions (mean: 6.5%) in both of these ectopic fat stores could be achieved with eight-weeks of dietary intervention. Discussion More recent hypotheses on the link between excess adiposity and cancer have focused on the importance of within organ local ectopic fat as an abnormal micro-environment favouring cancer development and progression. Importantly, this hypothesis explains the specificity of epidemiological associations between excess adiposity and cancer risk. The observations that within a given individual, in the presence of short-term weight reduction, there are differential changes in local within organ fats – hepatic fat and pancreatic fat – support the specificity hypothesis. This thesis has put us in position to scale-up and explore the importance of intra-organ fats using non-invasive imaging techniques.

L’imagerie par résonance magnétique (IRM) est une technique d’imagerie médicale largement utilisée pour son caractère non-invasif et pour sa capacité à explorer les tissus mous. Des techniques IRM avancées et innovantes ont été développées de manière à améliorer la spécificité du signal des techniques conventionnelles et ainsi accéder à de nouvelles informations. Un axe de recherche particulièrement important en IRM concerne la possibilité d’accéder in vivo à des informations sur la myéline. Cette dernière est un constituant majeur du système nerveux central qui assure la bonne conduction nerveuse. Sa dégradation est l’une des caractéristiques de la sclérose en plaques, qui est la première cause de handicap sévère non traumatique chez le jeune adulte. Imager la myéline par IRM demeure néanmoins un challenge du fait du temps de relaxation T2 très court des protons la constituant. Le transfert d’aimantation inhomogène (ihMT) est une technique récemment découverte qui permet d’isoler le signal de composantes macromoléculaires grâce à leurs propriétés de relaxation dipolaire, caractérisées par la constante T1D. L’objectif de ce travail de thèse a concerné la validation de la technique ihMT comme biomarqueur de la myéline et l’évaluation de la spécificité du signal pour la myéline, sur des modèles murins (souris)
Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique, widely used to explore soft tissues. Advanced and innovated MRI techniques have been developed to improve the specificity of conventional MR sequences thus allowing accessing new information. A particularly important research topic concerns the ability to in vivo access myelin information. Myelin is a major component of the central nervous system responsible for a good nerve conduction. Myelin alteration occurs in multiple sclerosis, one of the main cause for young adult permanent disability. However, myelin MRI is challenged by the very short relaxation time, T2, of myelin protons. Inhomogeneous magnetization transfer (ihMT) is a recent technique, which allows assessing macromolecular tissue component by exploiting their dipolar order relaxation properties, characterized by the time constant T1D. The objective of this thesis concerned the validation of ihMT as a myelin biomarker and the evaluation of the specificity of ihMT for myelin on mouse models

Histone deacetylase (HDAC) inhibitors (HDACIs) have long been studied and shown promises in the treatment of various neurodegenerative disorders including Huntington’s disease (HD). Based on many demonstrated potentials of HDACIs in mitigating various diseases, we evaluated the utility of [18F]FAHA, a radiolabeled derivative of suberoylanilide hydroxamic acid (SAHA), as a PET imaging agent for characterizing HDAC activity in a non-human primate model and a R6/2 transgenic mouse model of HD. We were aiming at HD as a potential first application, and therefore also examined the expression of HDAC and acetyl histone (AH) in brains of HD patients. This thesis describes that [18F]FAHA was metabolized rapidly to [18F]FACE in both blood plasma and brain. Kinetic analysis indicated that peripherally generated [18F]FACE contributed to the total brain activity. We therefore used a dual-input function model to analyze the kinetics of tracer accumulation and inhibition by SAHA in rhesus monkeys. Parametric images demonstrated the inhibition of HDAC activity in the brain by SAHA in a dose-dependent manner. Huntington’s mice (R6/2) showed a gradual increase of [18F]FAHA accumulation in all organs including the brain with age. In human tissue we found significant losses of acetyl histons expression from cells in the caudate nucleus and Purkinje cells of the cerebellum in HD, while the level of HDAC 5 was increased in these cells. The data obtained in rhesus monkeys indicated that PET imaging with [18F]FAHA could be used as a pharmacodynamic biomarker of the inhibition of class IIa HDACs by HDACIs in the brain and facilitate the development and clinical translation of novel class-IIa HDACIs.

La recherche et développement (R&D) au sein de l’industrie pharmaceutique est une étape cruciale pour la découverte de nouveaux médicaments ou biomarqueurs. Le développement de nouveaux traitements innovants est un moteur essentiel du progrès dans la prise en charge de nombreuses maladies, comme la parodontite, ou encore en immuno-oncologie. Ce travail de thèse s’intéresse dans un premier temps à la place de l’imagerie par spectrométrie de masse utilisant une source désorption laser assistée par matrice (MALDI-MSI) lors du développement de nouveaux médicaments, suivie de ses évolutions passées en revue (matrices, instruments et logiciels), avant d’illustrer plusieurs développements réalisés avec cette technologie pour améliorer la sensibilité de détection de composés d’intérêt (médicaments/biomarqueurs), mais aussi la qualité des résultats (qualité des échantillons ou qualité de l’analyse) en vue d’une standardisation. Nos résultats montrent que la mise en place d’une stratégie de dérivation (pour une drogue ou des biomarqueurs), ainsi que l’optimisation des étapes de préparation des échantillons (stabilisation des métabolites, optimisation du dépôt automatisé de matrice) permettent d’améliorer la sensibilité de détection au sein des échantillons. Aussi, la mise en place de contrôles qualité et la validation de méthode de quantification permet d’améliorer la qualité des résultats. Ces développements permettent alors d’aider les industries pharmaceutiques dans les étapes de R&D, en leur permettant de combiner cette technologie à leur arsenal, dans le but de gagner du temps et de l’argent lors des étapes de développement de nouveaux candidats médicaments
Research and development (R&D) in the pharmaceutical industry is a crucial step for the discovery of new drugs or biomarkers. The development of new innovative treatments is a key driver of progress in the management of many diseases, such as periodontitis or immuno-oncology. This thesis work is initially interested in the place of mass spectrometry imaging using a matrix-assisted laser desorption ionization source (MALDI-MSI) during the development of new drugs, followed by its evolution reviewed (matrices, instruments and software) before illustrating several developments made with this technology to improve the detection of the compounds of interest (drugs / biomarkers), but also the quality of the results (quality of the samples or the quality of the analyze) with a view to standardization. Also, the implementation of quality controls and the validation of the quantification method improves the quality of the results. These developments then help pharmaceutical industries in the R&D stages, allowing them to combine this technology with their arsenal, in order to save time and money during the development stages of new drug candidates

[ES] El aumento de la esperanza de vida en la población con edad por encima de 50 años está generando un mayor número de casos detectados de cáncer de próstata (CaP). Por este motivo, los recursos se destinan al diagnóstico en etapas tempranas y al tratamiento efectivo. A pesar de la multitud de estudios basados en biomarcadores y discriminación histológica, es difícil diferenciar con efectividad los casos de CaP con baja agresividad de aquellos que progresarán y acabarán produciendo mortalidad o una disminución en la esperanza de vida del paciente. Con el objetivo de mejorar el diagnostico, localización y gradación de los tumores malignos, las técnicas de imagen por Resonancia Magnética (MRI) son las más adecuadas para el estudio del cáncer, proporcionando métodos de diagnóstico no-invasivos, sensibles y específicos, basados en secuencias morfológicas (T2w) y funcionales (perfusión de la sangre y difusión del agua). Las diferentes características y parámetros extraídos de estas secuencias, conocidos como biomarcadores de imagen, pueden evaluar las diferencias asociadas al desarrollo de los procesos tumorales, como los modelos farmacocinéticos para estudiar angiogénesis (perfusión) y los modelos mono- y bi-exponenciales para estudiar la caída de la señal en difusión con el objetivo de estudiar la celularización. Normalmente, estos biomarcadores de imagen se analizan de forma "univariante", sin aprovechar la información de las estructuras de correlación interna que existen entre ellos. Una manera de mejorar este análisis es mediante la aplicación de las técnicas estadísticas que ofrece el Análisis Multivariante de Imágenes (MIA), obteniendo estructuras (latentes) simplificadas que ayudan a entender la relación entre los parámetros (variables) y sus propios procesos fisiológicos, además de reducir la incertidumbre en la estimación de los biomarcadores. En esta tesis, se han desarrollado nuevos biomarcadores de imagen para perfusión y difusión con la aplicación de alguna de las herramientas de MIA como la Resolución Multivariante de Curvas con Mínimos Cuadrados Alternos (MCR-ALS), obteniendo parámetros que tienen interpretación clínica directa. A continuación, los métodos basados en mínimos cuadrados parciales (PLS) se aplicaron para estudiar la capacidad de clasificación de estos biomarcadores. En primer lugar, los biomarcadores de perfusión se utilizaron para la detección de tumores (control vs lesión). Posteriormente, la combinación de perfusión + difusión + T2 se empleó para estudiar agresividad tumoral con la aplicación de métodos PLS multibloque, en concreto (secuencial) SMB-PLS. Los resultados mostrados indican que los biomarcadores de perfusión obtenidos mediante MCR son mejores que los parámetros farmacocinéticos en la diferenciación de la lesión. Con lo que respecta al estudio de la agresividad tumoral, la combinación de los biomarcadores de difusión (empleando ambos métodos: modelos paramétricos y MCR) y los valores de T2w normalizados proporcionaron los mejores resultados. En conclusión, MIA se puede aplicar a las secuencias morfológicas y funcionales de resonancia magnética para mejorar el diagnóstico y el estudio de la agresividad de los tumores en próstata. Obteniendo nuevos parámetros cuantitativos y combinándolos con los biomarcadores más ampliamente utilizados en el ambiente clínico.
[CAT] El increment de la esperança de vida en la població per damunt dels 50 anys està generant un major nombre de casos detectats de càncer de pròstata (CaP). Per aquest motiu, els recursos es destinen al diagnòstic en etapes primerenques i al tractament efectiu. Tot i la multitud de estudis basats en biomarcadors y discriminació histològica, es difícil diferenciar amb efectivitat els casos de CaP que tenen baixa agressivitat dels que progressaran y acabaran produint mortalitat o una disminució en la esperança de vida del pacient. Amb el objectiu de millorar el diagnòstic, localització y gradació dels tumors malignes, les tècniques de imatge per Ressonància Magnètica (MRI) son els mètodes més adequats per al estudi del càncer, proporcionant metodologies de diagnòstic no-invasius, sensibles y específiques basades en seqüències morfològiques (T2w) y funcionals (perfusió de la sang y difusió del aigua). Les diferents característiques i paràmetres extrets de aquestes seqüències, coneguts com biomarcadors d'imatge, poden avaluar les diferències associades al desenvolupament dels processos tumorals. Primer, amb els models farmacocinétics per a estudiar angiogènesis (perfusió) y segon, amb els models mono- i bi-exponencials per a estudiar la caiguda de la senyal en difusió amb el objectiu de estudiar la cel·lularització. Normalment, aquests biomarcadors d'imatge s'analitzen de forma "univariant", sense aprofitar la informació de las estructures de correlació interna que existeixen entre ells. Una forma de millorar aquest anàlisis es mitjançant la aplicació de las tècniques estadístiques aportades pel Anàlisis Multivariant de Imatges (MIA), obtenint estructures (latents) simplificades què ajuden a entendre la relació entre els paràmetres (variables) i els seus processos fisiològics, a més de reduir la incertesa en la estimació dels biomarcadors. En aquesta tesis, s'han desenvolupat nous biomarcadors d'imatge per a perfusió i difusió amb la aplicació de alguna de las ferramentes de MIA com la Resolució Multivariant de Corbes i Mínims Quadrats Alterns (MCR-ALS), obtenint paràmetres què tenen interpretació clínica directa. A continuació, els mètodes basats en mínims quadrats parcials (PLS) s'han aplicat per a estudiar la capacitat de classificació d'aquests biomarcadors. En primer lloc, els biomarcadors de perfusió s'han utilitzat per a la detecció de tumors (control contra lesió). Posteriorment, la combinació de perfusió + difusió + T2 s'ha utilitzat per a estudiar agressivitat tumoral amb la aplicació de mètodes PLS multi-bloc, en concret (seqüencial) SMB-PLS. Els resultats mostren què els biomarcadors de perfusió obtinguts mitjançant MCR són millors què els paràmetres farmacocinètics en la diferenciació de la lesió. En lo què es refereix al estudi de la agressivitat tumoral, la combinació dels biomarcadors de difusió (utilitzant els dos mètodes: models paramètrics i MCR) i els valors de T2w normalitzats proporcionaren els millors resultats. En conclusió, MIA es pot aplicar a les seqüències morfològiques i funcionals de ressonància magnètica per a millorar el diagnòstic i el estudi de l'agressivitat dels tumors en pròstata. Obtenint nous paràmetres quantitatius y combinant-los amb els biomarcadors més utilitzats en el ambient clínic.
[EN] The increase in life expectancy and population with age higher than 50 years is producing a major number of detected cases of prostate cancer (PCa). For this reason, the resources are focused in the early diagnosis and effective treatment. In spite of multiple studies with histologic discriminant biomarkers, it is hard to clearly differentiate the low aggressiveness PCa cases from those that will progress and produce mortality or rather a decrease in the life expectancy. With the objective of improving the diagnosis, location and gradation of the malignant tumors, Magnetic Resonance Imaging (MRI) has come up as the most appropriate image acquisition technique for cancer studies, which provides a non-invasive, sensitive and specific diagnosis, based on morphological and functional (blood perfusion and water diffusion) sequences. The different characteristics and parameters extracted from these sequences, known as imaging biomarkers, can evaluate the different processes associated to tumor development, like pharmacokinetic modeling for angiogenesis assessment (perfusion) or mono- and bi-exponential signal decay modeling for cellularization (diffusion). Normally, these imaging biomarkers are analyzed in a "univariate" way, without taking advantage of the internal correlation structures among them. One way to improve this analysis is by applying Multivariate Image Analysis (MIA) statistical techniques, obtaining simplified (latent) structures that help to understand the relation between parameters (variables) and the inner physiological processes, moreover reducing the uncertainty in the estimation of the biomarkers. In this thesis, new imaging biomarkers are developed for perfusion and diffusion by applying MIA tools like Multivariate Curve Resolution Alternating Least Squares (MCR-ALS), obtaining parameters with direct clinical interpretation. Partial Least Squares (PLS) based methods are then used for studying the classification capability of these biomarkers. First, perfusion imaging biomarkers have been tested for tumor detection (control vs lesion). Then, diffusion + perfusion have been combined to study tumor aggressiveness by applying PLS-multiblock methods (SMB-PLS). The results showed that MCR-based perfusion biomarkers performed better than state-of-the-art pharmacokinetic parameters for lesion differentiation. Regarding the assessment of tumor aggressiveness, the combination of diffusion-based imaging biomarkers (using both the parametric models and MCR) and normalized T2-weighted measurements provided the best discriminating outcome, while perfusion was not needed as it did not supply additional information. In conclusion, MIA can be applied to morphologic and functional MRI to improve the diagnosis and aggressiveness assessment of prostate tumors by obtaining new quantitative parameters and combining them with state-of-the-art imaging biomarkers.
Aguado Sarrió, E. (2019). Application of multivariate image analysis to prostate cancer for improving the comprehension of the related physiological phenomena and the development and validation of new imaging biomarkers [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134023
TESIS

Due to the absence of a definitive pathological finding, objective biomarker, or imaging correlate, neuropathic pain syndromes may be graded as possible or probable depending on the results of neurological assessment. It is important to acknowledge the diagnostic uncertainty inherent in such a grading system based on probability in a condition for which there is no “gold standard” upon which to base validation studies. Neuropathic pain is a multidimensional entity, and specific syndromes may have distinct sensory profiles (i.e. different combinations of sensory signs and symptoms). Clinical suspicion for an underlying neuropathic mechanism increases when pain is characterized by features such as numbness, paresthesias, and allodynia and when the symptoms are generally resistant to standard over-the-counter and prescribed analgesics. In this chapter a variety pain scales are reviewed.

Due to their rarity, heterogeneity, and multispecialty nature, the myositis syndromes have limited data-driven consensus on appropriate outcome measures. Recently, two international, multispecialty consortia developed new tools and consensus on core set measures of myositis disease activity and damage, as well as response criteria that are now recommended for use as clinical trial endpoints but will also be useful in clinical practice. Magnetic resonance imaging, muscle ultrasound, selected laboratory tests, and immunological biomarkers—including cytokines, chemokines, lymphocyte flow cytometry, and endothelial activation markers—can all be helpful adjuncts to serum muscle enzyme levels in assessing disease activity and damage, but these have not yet been fully validated. Definitions of clinically inactive disease, complete clinical response, and remission have also been proposed but require further validation. These advances should enhance the development of therapies by standardizing our ability to demonstrate their efficacy in treating the idiopathic inflammatory myopathies.

AbstractRecent advances in quantitative imaging with handcrafted radiomics and unsupervised deep learning have resulted in a plethora of validated imaging biomarkers in the field of head and neck oncology. Generally speaking, these algorithms are trained for one specific task, e.g. to classify between two or multiple types of underlying tumor biology (e.g. hypoxia, HPV status), predict overall survival (OS) or progression free survival (PFS), automatically segment a region of interest e.g. an organ at risk for radiotherapy dose or the gross tumor volume (GTV). Despite relatively good performances in external validation cohorts these algorithms still have not found their way into routine clinical practice. The reason this has not happened yet is complex, multifactorial, and can be usually divided into three categories: technical (a part of the algorithm or pre-processing step is not technically sound), statistical (mainly related to selection of subset of relevant biomarkers), and translational (not enough understanding by clinicians, not easily implementable within clinical workflow). We currently foresee that the next artificial intelligence (AI)-driven technique to find its way into clinical practice beside existing techniques (e.g. automatic organ at risk segmentation) will be the automatic segmentation of head and neck gross tumor volumes.

AbstractAnalysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.

Diabetic retinopathy (DR) is one of the most frequent microvascular complications of diabetes. A large body of evidence supports the role of inflammation in the development and progression of DR. Currently, DR is diagnosed based on the presence of morphological lesions detected on fundus examination. Yet, there are other laboratory or imaging biomarker whose alteration precede DR lesions. This chapter will first briefly explain the role of inflammation in DR pathogenesis and will analyze the molecules involved. Further, it will discuss significant and recent studies that analyzed local laboratory or imaging inflammatory biomarkers in different DR stages. It will then focus on several potential inflammation-targeting therapies which proved to be effective in animal or human studies. Validation of these reviewed biomarkers would allow the identification of patients who do not respond to the current available treatment and could benefit from an adjunctive therapy.