Nafenopin-induced rat liver peroxisome proliferation reduces DNA methylation by N-nitrosodimethylamine in vivo

The hypolipidaemic drug nafenopin (NAF) has been shown to enhance the hepatocarcinogenic effect of N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine in rats. We have investigated whether the NAF-induced peroxisome proliferation in hepatocytes interferes with NDMA's metabolism and interaction with DNA. Adult male Wistar rats received a single i.p. injection of [14C]NDMA (2 mg/kg) and were killed 4 h later. DNA was isolated from liver and kidney, hydrolysed in 0.1 N HCI and analysed by Sephasorb chromatography. In rats pre-treated with NAF (0.2% in the diet over a period of 3 weeks), the concentration of N7-methylguanine in hepatic DNA (μmol/mol guanine) was 46% below control values. This is probably due to the greater amount of target DNA, as NAF caused a marked hepatomegaly with a 50% increase in total liver DNA content. Concentrations of N7-methylguanine in kidney DNA were twice as high in NAF-pre-treated animals when compared to control rats. This is unlikely to result from a shift in the metabolism of NDMA from liver to other rat tissues since the time course and extent of the conversion of [14C]NDMA to 14CO2 and 14C-labelled urinary metabolites were identical in NAF-treated and control animals. There was no indication that NAF inhibits the activity of the hepatic O6-alkylguanine-DNA alkyltransferas

(Predictable) Performance Bias in Unsupervised Anomaly Detection

Background: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. While previous studies have thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far been unexplored. Methods: In this study, we evaluated how dataset composition regarding subgroups manifests in disparate performance of UAD models along multiple protected variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced a novel subgroup-AUROC (sAUROC) metric, which aids in quantifying fairness in machine learning. Findings: Our experiments revealed empirical "fairness laws" (similar to "scaling laws" for Transformers) for training-dataset composition: Linear relationships between anomaly detection performance within a subpopulation and its representation in the training data. Our study further revealed performance disparities, even in the case of balanced training data, and compound effects that exacerbate the drop in performance for subjects associated with multiple adversely affected groups. Interpretation: Our study quantified the disparate performance of UAD models against certain demographic subgroups. Importantly, we showed that this unfairness cannot be mitigated by balanced representation alone. Instead, the representation of some subgroups seems harder to learn by UAD models than that of others. The empirical fairness laws discovered in our study make disparate performance in UAD models easier to estimate and aid in determining the most desirable dataset composition.Comment: 11 pages, 5 Figures, 1 pane

Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images

Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is crucial for disease assessment and treatment. However, MS lesions can occur throughout the brain and vary in shape, size and total count among patients. The high variance in lesion load and locations makes it challenging for machine learning methods to learn a globally effective representation of whole-brain MRI scans to assess and predict disease. Technically it is non-trivial to incorporate essential biomarkers such as lesion load or spatial proximity. Our work represents the first attempt to utilize graph neural networks (GNN) to aggregate these biomarkers for a novel global representation. We propose a two-stage MS inflammatory disease activity prediction approach. First, a 3D segmentation network detects lesions, and a self-supervised algorithm extracts their image features. Second, the detected lesions are used to build a patient graph. The lesions act as nodes in the graph and are initialized with image features extracted in the first stage. Finally, the lesions are connected based on their spatial proximity and the inflammatory disease activity prediction is formulated as a graph classification task. Furthermore, we propose a self-pruning strategy to auto-select the most critical lesions for prediction. Our proposed method outperforms the existing baseline by a large margin (AUCs of 0.67 vs. 0.61 and 0.66 vs. 0.60 for one-year and two-year inflammatory disease activity, respectively). Finally, our proposed method enjoys inherent explainability by assigning an importance score to each lesion for the overall prediction. Code is available at https://github.com/chinmay5/ms_ida.gi

Primitive Simultaneous Optimization of Similarity Metrics for Image Registration

Even though simultaneous optimization of similarity metrics represents a standard procedure in the field of semantic segmentation, surprisingly, this does not hold true for image registration. To close this unexpected gap in the literature, we investigate in a complex multi-modal 3D setting whether simultaneous optimization of registration metrics, here implemented by means of primitive summation, can benefit image registration. We evaluate two challenging datasets containing collections of pre- to post-operative and pre- to intra-operative Magnetic Resonance Imaging (MRI) of glioma. Employing the proposed optimization we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) on expert neuroradiologists' landmark annotations