LAS: a software platform to support oncological data management

The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data qualit

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine

NEOREG : design and implementation of an online neonatal registration system to access, follow and analyse data of newborns with congenital cytomegalovirus infection

Today's registration of newborns with congenital cytomegalovirus (cCMV) infection is still performed on paper-based forms in Flanders, Belgium. This process has a large administrative impact. It is imortant that all screening tests are registered to have a complete idea of the impact of cCMV. Although these registrations are usable in computerised data analysis, these data are not available in a format to perform electronic processing. An online Neonatal Registry (NEOREG) System was designed and developed to access, follow and analyse the data of newborns remotely. It allows patients' diagnostic registration and treatment follow-up through a web interface and uses document forms in Portable Document Format (PDF), which incorporate all the elements from the existing forms. Forms are automatically processed to structured EHRs. Modules are included to perform statistical analysis. The design was driven by extendibility, security and usability requirements. The website load time, throughput and execution time of data analysis were evaluated in detail. The NEOREG system is able to replace the existing paper-based CMV records

Previsão Inteligente das alterações metabólicas no cancro retal com base em modelos de machine e deep learning

Machine learning, broadly speaking, applies statistical methods to training data to automatically adjust the parameters of a model, rather than a programmer needing to set them manually. Deep Learning is a sub-area of Machine Learning that studies how to solve complex and intuitive problems. The methodologies adopted, using computational means, such as the machines learned and those understood in the world in specific contexts from previous experiences and based on the hierarchy of concepts, use the most used concepts for the form and efficient solution of more varied complex problems. The main objective in this work is to study various classification algorithms in the area of machine learning, and validate until these points can use a solution for choosing more accurate methods in the selection of tests and in new statistics to improve the therapeutic response. The data involved in the training of classification algorithms refer to all patients with metabolic diseases shredding between the years 2003-2021 and the retrospective part. The best classification algorithms to develop are used in the decision support system in the most effective way in choosing the appropriate therapy for each of the future patients who predicted an approximate rate of 20 patients per year.Machine Learning, em termos gerais, aplica métodos estatísticos aos dados de treino para ajustar automaticamente os parâmetros de um modelo, em vez de um programador necessitar de defini-los manualmente. Deep Learning é uma subárea de Machine Learning que estuda como solucionar problemas complexos e intuitivos. As metodologias propostas permitem, com recurso a meios computacionais, que as máquinas aprendam e compreendam o mundo em determinados contextos a partir de experiências anteriores e com base na hierarquia de conceitos possam compreender conceitos mais complexos de forma a solucionarem eficientemente A mais variadíssima gama de problemas. O principal objetivo neste trabalho consiste no estudo de vários algoritmos de classificação na área de machine learning de forma a validar até que ponto estes podem representar uma solução para a escolha de métodos mais precisos na selecção dos doentes e em novas estratégias para melhorar a resposta terapêutica. Os dados envolvidos para treino dos algoritmos de classificação referem-se a todos os doentes tratados com doenças metabólicas entre os anos 2003-2021 na parte retrospectiva. Os melhores algoritmos de classificação a desenvolver serão usados num sistema de apoio à decisão que ajude de forma mais efetiva na escolha da terapia adequada para cada um dos futuros pacientes que se prevê surgirem a uma taxa aproximada de 20 pacientes por ano

Guanylate cyclase C as a target for prevention, detection, and therapy in colorectal cancer.

INTRODUCTION: Colorectal cancer remains the second leading cause of cancer death in the United States, and new strategies to prevent, detect, and treat the disease are needed. The receptor, guanylate cyclase C (GUCY2C), a tumor suppressor expressed by the intestinal epithelium, has emerged as a promising target. Areas covered: This review outlines the role of GUCY2C in tumorigenesis, and steps to translate GUCY2C-targeting schemes to the clinic. Endogenous GUCY2C-activating ligands disappear early in tumorigenesis, silencing its signaling axis and enabling transformation. Pre-clinical models support GUCY2C ligand supplementation as a novel disease prevention paradigm. With the recent FDA approval of the GUCY2C ligand, linaclotide, and two more synthetic ligands in the pipeline, this strategy can be tested in human trials. In addition to primary tumor prevention, we also review immunotherapies targeting GUCY2C expressed by metastatic lesions, and platforms using GUCY2C as a biomarker for detection and patient staging. Expert commentary: Results of the first GUCY2C targeting schemes in patients will become available in the coming years. The identification of GUCY2C ligand loss as a requirement for colorectal tumorigenesis has the potential to change the treatment paradigm from an irreversible disease of genetic mutation, to a treatable disease of ligand insufficiency

Machine Learning na previsão de Cancro Colorretal em função de alterações metabólicas

No mundo atual, a quantidade de informação disponível nos mais variados setores é cada vez maior. É o caso da área da saúde, onde a recolha e tratamento de dados biomédicos procuram melhorar a tomada de decisão no tratamento a aplicar a um doente, recorrendo a ferramentas baseadas em Machine Learning. Machine Learning é uma área da Inteligência Artificial em que através da aplicação de algoritmos a um conjunto de dados é possível prever resultados ou até descobrir relações entre estes que seriam impercetíveis à primeira vista. Com este projeto pretende-se realizar um estudo em que o objetivo é investigar diversos algoritmos e técnicas de Machine Learning, de modo a identificar se o perfil de acilcarnitinas pode constituir um novo marcador bioquímico para a predição e prognóstico do Cancro Colorretal. No decurso do trabalho, foram testados diferentes algoritmos e técnicas de pré-processamento de dados. Foram realizadas três experiências distintas com o objetivo de validar as previsões dos modelos construídos para diferentes cenários, nomeadamente: prever se o paciente tem Cancro Colorretal, prever qual a doença que o paciente tem (Cancro Colorretal e outras doenças metabólicas) e prever se este tem ou não alguma doença. Numa primeira análise, os modelos desenvolvidos apresentam bons resultados na triagem de Cancro Colorretal. Os melhores resultados foram obtidos pelos algoritmos Random Forest e Gradient Boosting, em conjunto com técnicas de balanceamento dos dados e Feature Selection, nomeadamente Random Oversampling, Synthetic Oversampling e Recursive Feature SelectionIn today´s world, the amount of information available in various sectors is increasing. That is the case in the healthcare area, where the collection and treatment of biochemical data seek to improve the decision-making in the treatment to be applied to a patient, using Machine Learning-based tools. Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. This project’s main objective is to study several algorithms and techniques of Machine Learning to identify if the acylcarnitine profile may constitute a new biochemical marker for the prediction and prognosis of rectal cancer. In the course of the work, different algorithms and data preprocessing techniques were tested. Three different experiments were carried out to validate the predictions of the models built for different scenarios, namely: predicting whether the patient has Colorectal Cancer, predicting which disease the patient has (Colorectal Cancer and other metabolic diseases) and predicting whether he has any disease. As a first analysis, the developed models showed good results in Colorectal Cancer screening. The best results were obtained by the Random Forest and Gradient Boosting algorithms, together with data balancing and feature selection techniques, namely Random Oversampling, Synthetic Oversampling and Recursive Feature Selectio

Machine learning models in decision support systems for diagnosing colorectal cancer based on metabolic profiles

In today’s ever-evolving technological landscape, the volume of data across sectors is grow ing, particularly in healthcare. Here, the gathering and processing of biochemical data aim to refine decision-making for patient treatments, especially using tools based on Machine Learning (ML). As a subset of Artificial Intelligence, ML harnesses algorithms to predict outcomes or unearth patterns that might otherwise remain concealed. The interpretability of ML models is pivotal, enabling healthcare professionals to place con fidence in and decipher the model’s predictions. This assumes particular significance when decisions could directly affect patient lives. This research embarked on an in-depth exploration of various ML algorithms and techniques to discern whether the combined metabolic profiles of amino acids and acylcarnitines might serve as new biochemical indicators for predicting colo-rectal cancer prognosis. Throughout this study, several algorithms and data preprocessing techniques were evaluated. Four distinct experiments validated the predictions of the models in different scenarios. These scenarios involved predicting Colorectal Cancer using amino acids with and without the age parameter, and similarly, using acylcarnitine with and without the age parameter. Each scenario’s predictions were elucidated using SHAP, both for overarching feature significance and individual instances. Preliminary analyses indicated that the constructed models demonstrated promising predic tive power, with notable variations for the different scenarios. Amongst the algorithms tested, Random Forest, Support Vector Machine, Gaussian Naive Bayes, and Gradient Boosting emerged as the top performers.No atual panorama tecnológico em constante evolução, o volume de dados em diversos setores está a aumentar, particularmente na saúde. Aqui, a recolha e processamento de dados bioquímicos visam aprimorar a tomada de decisão para tratamentos de pacientes, especialmente utilizando ferramentas baseadas em Aprendizagem Automática. Como um subconjunto da Inteligência Artificial, a Aprendizagem Automática utiliza algoritmos para prever resultados ou descobrir padrões que de outra forma poderiam permanecer ocultos. A interpretabilidade dos modelos de Aprendizagem Automática é fundamental, permitindo que os profissionais de saúde confiem e decifrem as previsões do modelo. Isto assume uma importância particular quando as decisões podem afetar diretamente a vida dos pacientes. Esta investigação levou a cabo uma exploração aprofundada de vários algoritmos e téc nicas de Aprendizagem Automática para determinar se os perfis metabólicos combinados de aminoácidos e acilcarnitinas poderiam servir como novos indicadores bioquímicos para a previsão e prognóstico do cancro colo-retal. Ao longo deste estudo, vários algoritmos e técnicas de pré-processamento de dados foram avaliados. Quatro experiências distintas validaram as previsões dos modelos em diferentes cenários. Estes cenários envolveram a previsão de Cancro Colorretal usando aminoácidos com e sem o atributo idade, e de forma semelhante, usando acilcarnitinas. As previsões de cada cenário foram elucidadas usando o SHAP, tanto para a importância geral dos atributos como para amostras individuais. Análises preliminares indicaram que os modelos construídos mostraram um poder preditivo promissor, com variações notáveis nos diferentes cenários. Entre os algoritmos testados, Random Forest, Support Vector Machines, Naive Bayes e Gradient Boosting destacaram-se com melhor desempenho

Addressing the Health Needs of an Aging America: New Opportunities for Evidence-Based Policy Solutions

This report systematically maps research findings to policy proposals intended to improve the health of the elderly. The study identified promising evidence-based policies, like those supporting prevention and care coordination, as well as areas where the research evidence is strong but policy activity is low, such as patient self-management and palliative care. Future work of the Stern Center will focus on these topics as well as long-term care financing, the health care workforce, and the role of family caregivers

The holistic perspective of the INCISIVE Project: artificial intelligence in screening mammography

Finding new ways to cost-effectively facilitate population screening and improve cancer diagnoses at an early stage supported by data-driven AI models provides unprecedented opportunities to reduce cancer related mortality. This work presents the INCISIVE project initiative towards enhancing AI solutions for health imaging by unifying, harmonizing, and securely sharing scattered cancer-related data to ensure large datasets which are critically needed to develop and evaluate trustworthy AI models. The adopted solutions of the INCISIVE project have been outlined in terms of data collection, harmonization, data sharing, and federated data storage in compliance with legal, ethical, and FAIR principles. Experiences and examples feature breast cancer data integration and mammography collection, indicating the current progress, challenges, and future directions.This research received funding mainly from the European Union’s Horizon 2020 research and innovation program under grant agreement no 952179. It was also partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under contracts PID2019-107255GB and 2017-SGR-1414.Peer ReviewedArticle signat per 30 autors/es: Ivan Lazic (1), Ferran Agullo (2), Susanna Ausso (3), Bruno Alves (4), Caroline Barelle (4), Josep Ll. Berral (2), Paschalis Bizopoulos (5), Oana Bunduc (6), Ioanna Chouvarda (7), Didier Dominguez (3), Dimitrios Filos (7), Alberto Gutierrez-Torre (2), Iman Hesso (8), Nikša Jakovljević (1), Reem Kayyali (8), Magdalena Kogut-Czarkowska (9), Alexandra Kosvyra (7), Antonios Lalas (5) , Maria Lavdaniti (10,11), Tatjana Loncar-Turukalo (1),Sara Martinez-Alabart (3), Nassos Michas (4,12), Shereen Nabhani-Gebara (8), Andreas Raptopoulos (6), Yiannis Roussakis (13), Evangelia Stalika (7,11), Chrysostomos Symvoulidis (6,14), Olga Tsave (7), Konstantinos Votis (5) Andreas Charalambous (15) / (1) Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; (2) Barcelona Supercomputing Center, 08034 Barcelona, Spain; (3) Fundació TIC Salut Social, Ministry of Health of Catalonia, 08005 Barcelona, Spain; (4) European Dynamics, 1466 Luxembourg, Luxembourg; (5) Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece; (6) Telesto IoT Solutions, London N7 7PX, UK: (7) School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (8) Department of Pharmacy, Kingston University London, London KT1 2EE, UK; (9) Timelex BV/SRL, 1000 Brussels, Belgium; (10) Nursing Department, International Hellenic University, 57400 Thessaloniki, Greece; (11) Hellenic Cancer Society, 11521 Athens, Greece; (12) European Dynamics, 15124 Athens, Greece; (13) German Oncology Center, Department of Medical Physics, Limassol 4108, Cyprus; (14) Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece; (15) Department of Nursing, Cyprus University of Technology, Limassol 3036, CyprusPostprint (published version

Deep learning for clinical decision support in oncology

In den letzten Jahrzehnten sind medizinische Bildgebungsverfahren wie die Computertomographie (CT) zu einem unersetzbaren Werkzeug moderner Medizin geworden, welche eine zeitnahe, nicht-invasive Begutachtung von Organen und Geweben ermöglichen. Die Menge an anfallenden Daten ist dabei rapide gestiegen, allein innerhalb der letzten Jahre um den Faktor 15, und aktuell verantwortlich für 30 % des weltweiten Datenvolumens. Die Anzahl ausgebildeter Radiologen ist weitestgehend stabil, wodurch die medizinische Bildanalyse, angesiedelt zwischen Medizin und Ingenieurwissenschaften, zu einem schnell wachsenden Feld geworden ist. Eine erfolgreiche Anwendung verspricht Zeitersparnisse, und kann zu einer höheren diagnostischen Qualität beitragen. Viele Arbeiten fokussieren sich auf „Radiomics“, die Extraktion und Analyse von manuell konstruierten Features. Diese sind jedoch anfällig gegenüber externen Faktoren wie dem Bildgebungsprotokoll, woraus Implikationen für Reproduzierbarkeit und klinische Anwendbarkeit resultieren. In jüngster Zeit sind Methoden des „Deep Learning“ zu einer häufig verwendeten Lösung algorithmischer Problemstellungen geworden. Durch Anwendungen in Bereichen wie Robotik, Physik, Mathematik und Wirtschaft, wurde die Forschung im Bereich maschinellen Lernens wesentlich verändert. Ein Kriterium für den Erfolg stellt die Verfügbarkeit großer Datenmengen dar. Diese sind im medizinischen Bereich rar, da die Bilddaten strengen Anforderungen bezüglich Datenschutz und Datensicherheit unterliegen, und oft heterogene Qualität, sowie ungleichmäßige oder fehlerhafte Annotationen aufweisen, wodurch ein bedeutender Teil der Methoden keine Anwendung finden kann. Angesiedelt im Bereich onkologischer Bildgebung zeigt diese Arbeit Wege zur erfolgreichen Nutzung von Deep Learning für medizinische Bilddaten auf. Mittels neuer Methoden für klinisch relevante Anwendungen wie die Schätzung von Läsionswachtum, Überleben, und Entscheidungkonfidenz, sowie Meta-Learning, Klassifikator-Ensembling, und Entscheidungsvisualisierung, werden Wege zur Verbesserungen gegenüber State-of-the-Art-Algorithmen aufgezeigt, welche ein breites Anwendungsfeld haben. Hierdurch leistet die Arbeit einen wesentlichen Beitrag in Richtung einer klinischen Anwendung von Deep Learning, zielt auf eine verbesserte Diagnose, und damit letztlich eine verbesserte Gesundheitsversorgung insgesamt.Over the last decades, medical imaging methods, such as computed tomography (CT), have become an indispensable tool of modern medicine, allowing for a fast, non-invasive inspection of organs and tissue. Thus, the amount of acquired healthcare data has rapidly grown, increased 15-fold within the last years, and accounts for more than 30 % of the world's generated data volume. In contrast, the number of trained radiologists remains largely stable. Thus, medical image analysis, settled between medicine and engineering, has become a rapidly growing research field. Its successful application may result in remarkable time savings and lead to a significantly improved diagnostic performance. Many of the work within medical image analysis focuses on radiomics, i. e. the extraction and analysis of hand-crafted imaging features. Radiomics, however, has been shown to be highly sensitive to external factors, such as the acquisition protocol, having major implications for reproducibility and clinical applicability. Lately, deep learning has become one of the most employed methods for solving computational problems. With successful applications in diverse fields, such as robotics, physics, mathematics, and economy, deep learning has revolutionized the process of machine learning research. Having large amounts of training data is a key criterion for its successful application. These data, however, are rare within medicine, as medical imaging is subject to a variety of data security and data privacy regulations. Moreover, medical imaging data often suffer from heterogeneous quality, label imbalance, and label noise, rendering a considerable fraction of deep learning-based algorithms inapplicable. Settled in the field of CT oncology, this work addresses these issues, showing up ways to successfully handle medical imaging data using deep learning. It proposes novel methods for clinically relevant tasks, such as lesion growth and patient survival prediction, confidence estimation, meta-learning and classifier ensembling, and finally deep decision explanation, yielding superior performance in comparison to state-of-the-art approaches, and being applicable to a wide variety of applications. With this, the work contributes towards a clinical translation of deep learning-based algorithms, aiming for an improved diagnosis, and ultimately overall improved patient healthcare