Prognostische Bedeutung der EMMPRIN-Expression bei operablen nicht-kleinzelligen Bronchialkarzinomen

Das komplexe Wechselspiel von Krebszellen und anderen Zellen des Körpers ist weitgehend unverstanden. Erste Einsichten brachte die Erforschung der Tumorangiogenese und der Tumorinvasion. Hier zeigte sich, dass Tumorzellen selbst einerseits proteolytische Systeme wie die Matrix-Metalloproteasen (MMP) aktivieren, um die Extrazelluläre Matrix (ECM) abzubauen und zu migrieren, sich andererseits aber auch andere Zellen des Körpers bei diesen Prozessen zunutze machen. So wurde gefunden, dass sie durch das Protein EMMPRIN in der Lage sind, die Expression von MMP in Stromazellen zu induzieren. EMMPRIN erwies sich in der Folge als ein Molekül mit weiteren Funktionen über die Induktion von MMP hinaus. Ziel der vorliegenden Arbeit war zu untersuchen, ob die Expression von EMMPRIN in nicht-kleinzelligen Bronchialkarzinomen (NSCLC) einen Einfluss auf das Überleben der Patienten hat. Zu diesem Zweck wurden in Paraffin-eingebetteten Gewebeproben von 150 Patienten mit einem Anti-EMMPRIN Antikörper (HIM6) immunhistologisch gefärbt. Bei der Auswertung wurde für jeden Tumor zunächst ein Färbewert ermittelt, der aus dem Produkt der Färbeintensität und dem Anteil der gefärbten Tumorzellen generiert wurde. Ebenso wurde festgehalten, ob die EMMPRIN-Färbung überwiegend membranständig oder zytoplasmatisch lokalisiert war. Die Färbeergebnisse wurden mit klinischen Parametern korreliert, um die Bedeutung von EMMPRIN auf den Verlauf der Erkrankung und das Überleben der Patienten zu überprüfen. Um den Einfluss von EMMPRIN auf MMP zu untersuchen, wurde zusätzlich die Expression von MMP-2 und MMP-9 mit der EMMPRIN-Expression in den Primärtumoren verglichen. Im untersuchten Kollektiv zeigte sich eine spezifische EMMPRIN-Färbung in 95% aller Primärtumoren. Die ermittelten Färbewerte konnten mit keinem klinischen Faktor und auch nicht mit der Expression von MMP-2 oder MMP-9 in Zusammenhang gebracht werden. Allerdings fand sich ein signifikanter Zusammenhang zwischen membranständiger Lokalisation von EMMPRIN und der Entwicklung eines Rezidivs. In univariaten Analysen der Subgruppe der Patienten mit geringem Lymphknotenbefall (pN0-pN1) ergab sich, dass Patienten über 60 Jahren und mit einem membranständigen EMMPRIN-Färbemuster ein schlechteres Überleben hatten. Die multivariate Cox-Regressionsanalyse zeigte, dass Patienten mit geringem Lymphknotenbefall mit einer membranständigen EMMPRIN-Expression ein mehr als doppelt so hohes Mortalitätsrisiko hatten als Patienten mit zytoplasmatisch gefärbten Tumoren. Die vorliegende Arbeit zeigt erstmals, dass eine membranständige EMMPRIN-Lokalisation einen unabhängigen Vorhersagewert für ungünstige Krankheitsverläufe bei frühen nicht–kleinzelligen Bronchialkarzinomen darstellt. Da sich kein Zusammenhang zwischen der membranständigen EMMPRIN Expression und der Expression von MMP-2 oder MMP-9 fand, ist momentan offen, durch welche Funktion von EMMPRIN dieser Effekt ausgelöst wird

AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks

Nowadays, machine learning is playing a crucial role in harnessing the power of the massive amounts of data that we are currently producing every day in our digital world. With the booming demand for machine learning applications, it has been recognized that the number of knowledgeable data scientists can not scale with the growing data volumes and application needs in our digital world. In response to this demand, several automated machine learning (AutoML) techniques and frameworks have been developed to fill the gap of human expertise by automating the process of building machine learning pipelines. In this study, we present a comprehensive evaluation and comparison of the performance characteristics of six popular AutoML frameworks, namely, Auto-Weka, AutoSKlearn, TPOT, Recipe, ATM, and SmartML across 100 data sets from established AutoML benchmark suites. Our experimental evaluation considers different aspects for its comparison including the performance impact of several design decisions including time budget, size of search space, meta-learning, and ensemble construction. The results of our study reveal various interesting insights that can significantly guide and impact the design of AutoML frameworks

FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data

Abstract Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertension, mortality, and diabetes risks, utilizing only nine patient features. They achieved an average AUC of 0.90 ± 0.001 on the hypertension dataset, 0.90 ± 0.002 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset through tenfold cross-validation. Additionally, the models demonstrated strong performance with an average AUC of 0.89 ± 0.001 on the hypertension dataset, 0.90 ± 0.001 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset using bootstrap evaluation with 1000 resamples

Comparison of machine learning techniques to predict all-cause mortality using fitness data: The Henry Ford exercIse testing (FIT) project

© 2017 The Author(s). Background: Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). Methods: We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Results: Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. Conclusions: The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data

Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project

This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ

Using machine learning to define the association between cardiorespiratory fitness and all-cause mortality: The fit (henry ford exercise testing) project

Background: Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification technique that classifies the data into predetermined categories. Theaim of the analysis is to assess the relation between CRF and all-cause mortality (ACM) using ML approaches. Methods: We included 34,212 patients (55% males, mean age 54±13years) not known to have coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had complete 10-year follow-up. The primary outcome of this analysis was ACM at 10 years. The probability of 10 year ACM was calculated usinglogistic regression (LR) and ML and the accuracy of these methods were calculated and compared. Results: A total of 3,921 patients experienced ACM at ten years. Using LR, thesensitivity to predict ACM was 44.9% (95%CI 43.3%- 46.5%) while the specificity was 93.4% (95%CI 93.1%-93.7%). Thesensitivity of ML to predict ACM was 87.40% (86.32%-88.42%) while the specificity was 97.21% (97.02%-97.39%). ML approach was associated with improved model discrimination, (area under the curve for ML (0.923 (95%CI 0.917-0.928)) compared to LR (0.836 (95%CI 0.829-0.846)),