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Surgical site infection after posterior cervical decompression: the role of adiposity-related factors
No full textBackground
Local subcutaneous fat thickness has been identified as a significant predictor of surgical site infection (SSI) risk in lumbar spine procedures. This study aims to further investigate this association by comparing the impact of Body Mass Index (BMI) and localized fat thickness at the C5 level on SSI risk in patients undergoing posterior cervical decompression without fusion
Methods
Retrospective analysis of patients treated with posterior cervical decompression without fusion or stabilization for cervical spondylotic myelopathy. A combination of univariate and multivariate analysis was employed to identify significant predictors of SSIs.
Results
From the 346 patients, 20 (5.8%) experienced SSIs. Those with SSIs generally had higher BMIs (median 29 ± 4.6 vs. 27 ± 5.2, p=0.032), greater fat thickness at C5 level (median 27 mm vs. 23mm, p=0.012), higher ratio of fat to muscle thickness (median 0.98 vs. 0.75, p=0.003), and more extensive surgeries (75% had multiple levels operated compared to 55% in the non-SSI cohort, p=0.001). In multivariable analysis restricted to one surgical and one patient-related factor, BMI (OR = 1.10, 95% CI 1.01–1.23; p = 0.038) and the number of operated levels (OR = 2.25, 95% CI 1.35–3.74; p = 0.002) remained independent predictors of SSI, whereas fat thickness and fat-to-muscle ratio did not provide additional predictive value beyond BMI.
Conclusion
This study indicates that, localized fat thickness at the C5 level was not shown to be an independent predictive factor for SSI following posterior cervical decompression. Instead, it highlights BMI and the number of operated levels as significant and quantifiable risk factors. Prompt surgical debridement should be considered the first-line treatment for deep or organ space SSI
Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response
No full textOver recent years, several deep learning (DL) models have been presented to predict colorectal cancer (CRC) patient survival directly from haematoxylin and eosin (H&E)-stained routine whole-slide images (WSIs). Unlike traditional studies that rely on manually defined histopathological features, weakly supervised DL allows training directly on clinical endpoints without prior specification of the model's focus. This offers a unique opportunity to study the tissue morphology underlying these predictions, improving our understanding of disease biology. Here, we present a comprehensive analysis of the clinicopathological features, tumour morphology and biology, as well as gene expression-based predicted drug response of over 4,000 CRC patients derived from four different international cohorts with available H&E-inferred DL-based risk scores (low- versus high-risk as well as absolute risk scores). The results from our study suggest that conventional clinicopathological risk factors, such as grade of differentiation, presence of lymph node metastasis, tumour budding, and percentage of tumour necrosis, are positively associated with DL-based risk scores. Moreover, CRCs with direct tumour-adipocyte interactions are enriched in the DL-based high-risk group. Through detailed morphologic review, we provide comprehensive evidence that direct tumour-adipocyte interaction, a high degree of tumour budding, and poorly differentiated morphology are linked to high DL-based risk scores. Transcriptomic and genetic subgroups show only limited association with H&E-derived DL-based risk scores. Moreover, we present data suggesting that DL-based low- versus high-risk CRCs may be characterised by differential drug sensitivity. Our study highlights that DL-based risk scores derived from H&E WSIs not only align with established clinicopathological features but also highlight morphological features, such as tumour-adipocyte interaction, that are not routinely captured by established clinicopathological scoring systems. Moreover, DL-based risk groups may be associated with a differential treatment response, underlining their potential to guide patient stratification in routine clinical practice
Nutzung von KI-Technologien für Diagnostik und Gesprächsanalyse in der Psychotherapie: Potential und Herausforderungen
No full textImproving genetic operators of Cartesian Genetic Programming
No full textCartesian Genetic Programming (CGP) belongs to the family of Evolutionary Algorithms and can be used for a wide range of supervised machine learning applications.
It has been extensively used to automatically generate digital circuits, shows great potential in the automated design of deep neural networks, and has also been successfully utilized for (interpretable medical) image processing tasks, to name a few examples.
Additionally, CGP profits from generating relatively small programs, further increasing its potential as this characteristic improves the interpretability of its solutions.
Since the invention of CGP, many advantages and shortcomings have been researched by various scientists. Moreover, new operators and extensions have been proposed to mitigate its disadvantages and/or improve its performance. Although there is still a lot of foundational research to be done, as some CGP specific mechanisms and behaviours are not fully understood.By further enhancing its evolutionary operators, CGP's performance can also be improved upon. Hence, this thesis aims to broaden the general understanding of CGP and to improve its performance by introducing new operators and extensions.
All findings of this dissertation are of empirical nature, which is why a fast and reliable framework is of utmost importance. Thus, the first contribution of this thesis is a new CGP specific toolbox written in the Rust programming language. This framework is highly modular, which enables the simple implementation of different configurations and combinations of operators and/or extensions. In addition, its fast execution time allows for the extensive study of numerous training runs in a short amount of time.
The second contribution of this thesis is the further development of an existing extension called Reorder. CGP suffers from a bias called positional bias. It occurs due to its specific representation of solutions, resulting in potential performance degradations. The original extension should remove this bias; however, this work finds a flaw in Reorder. It is able to mitigate positional bias but is not able to completely eliminate it. By introducing several extensions adjacent to Reorder in this dissertation, this bias can be completely removed. There is also the option to purposefully introduce new biases, which might aid CGP's evolutionary search process. Both options have the potential to improve CGP's performance, resulting in faster convergence times and/or better fitness values.
Afterwards, two new mutation operators for CGP are introduced, improving CGP's performance on real-world datasets.In addition, new insights can be gained by closely examining the outcomes of both operators.
The following chapter introduces weights to influence CGP's mutation mechanism. This allows for small performance gains and mitigates the aforementioned positional bias. Further insights into CGP's behaviour and workings are discussed. The most important finding is: Positional bias is lessened but this does not automatically lead to an increased performance. This goes against the current understanding of CGP, and more research should be done in this direction.
Crossover is an important evolutionary operator for Evolutionary Algorithms. CGP, however, generally does not use standard crossover operators as it generally leads to no improvements or even worse performances. The next chapter investigates if positional bias is at fault for this phenomenon. While this hypothesis could not be verified, the chapter still has two major findings. At first, CGP combined with tournament selection degrades the performance on Boolean benchmarks. Secondly, if correctly configured and tuned, CGP combined with crossover converges faster and/or improves its final fitness values. Especially the last point goes against the general research trend. Potential reasons regarding this opposite conclusion are also given.
The last contribution of this dissertation discusses CGP's robustness against redundant attributes in datasets. Especially real-world datasets might contain attributes that are of redundant nature or even regarded as noise. Data preprocessing and data mining techniques are normally used to remove most of these unwanted attributes. However, some redundant attributes might not be found, or this process might lead to subpar results of the learning algorithm. By design, CGP is able to choose its own input attributes. Through evolutionary mechanisms, CGP should be able to ignore redundant attributes. The last chapter of this thesis confirms this hypothesis for different types and quantities of artificially inserted redundant attributes
Depression and risk of gastro-oesophageal reflux disease (GERD): results from the UK Biobank study
No full textBackground
This study investigated the association between depression and the incidence of gastro-oesophageal reflux disease (GERD) and examined whether the association interacts with age. The analysis was based on 457,958 participants aged 37–73 years from the UK-Biobank prospective cohort study.
Methods
The baseline examination started 2006 and the participants were followed up until 2019–2023 (median follow-up time 13.52 years [interquartile range12.62–14.27]). Depression at baseline and incident GERD at follow-up were defined through sources of the British health system (ICD-codes) and self-report. Multivariable adjusted Cox regression models were used for analysis. Formal tests for interaction with sex and age were conducted.
Results
Participants who developed GERD during follow-up were characterized by an unhealthier lifestyle and more comorbidities than individuals without GERD. In multivariable analysis, depression was associated with incident GERD (Hazard ratio 1.51 [1.46,1.55]; P < 0.001). The association decreased with increasing age. There was no interaction with sex.
Conclusion
Depression and its psycho-physiological consequences may be associated with the development of GERD, in particular in middle-aged people. Consequently, increased attention of the treating physicians regarding an increased risk of GERD in depressed persons is important
