Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

The estrogen receptor-α (ERα) determines the phenotype of breast cancers where it serves as a positive prognostic indicator. ERα is a well-established target for breast cancer therapy, but strategies to target its function remain of interest to address therapeutic resistance and further improve treatment. Recent findings indicate that proteasome inhibition can regulate estrogen-induced transcription, but how ERα function might be regulated was uncertain. In this study, we investigated the transcriptome-wide effects of the proteasome inhibitor bortezomib on estrogen-regulated transcription in MCF7 human breast cancer cells and showed that bortezomib caused a specific global decrease in estrogen-induced gene expression. This effect was specific because gene expression induced by the glucocorticoid receptor was unaffected by bortezomib. Surprisingly, we observed no changes in ERα recruitment or assembly of its transcriptional activation complex on ERα target genes. Instead, we found that proteasome inhibition caused a global decrease in histone H2B monoubiquitination (H2Bub1), leading to transcriptional elongation defects on estrogen target genes and to decreased chromatin dynamics overall. In confirming the functional significance of this link, we showed that RNA interference–mediated knockdown of the H2B ubiquitin ligase RNF40 decreased ERα-induced gene transcription. Surprisingly, RNF40 knockdown also supported estrogen-independent cell proliferation and activation of cell survival signaling pathways. Most importantly, we found that H2Bub1 levels decrease during tumor progression. H2Bub1 was abundant in normal mammary epithelium and benign breast tumors but absent in most malignant and metastatic breast cancers. Taken together, our findings show how ERα activity is blunted by bortezomib treatment as a result of reducing the downstream ubiquitin-dependent function of H2Bub1. In supporting a tumor suppressor role for H2Bub1 in breast cancer, our findings offer a rational basis to pursue H2Bub1-based therapies for future management of breast cancer. Cancer Res; 71(17); 5739–53. ©2011 AACR.

Modeling and analyses of complex systems using network theory have been an object of study for a long time. They have caught attention in many disciplines such as sociology, epidemiology, ecology, psychology, biology, biomedicine, and other fields. Network theory is especially an efficient tool to model biological networks such as gene co-expression networks, protein-protein interaction networks, or pathways. Considering the enhanced resolutions of complex real-world systems, the interest has been directed to multilayered networks. However, despite this surge of recent attention, the use of the multilayer framework in the biological field is still in its youth. In this paper, we review the different aspects and terminologies of multilayered networks. We also briefly discuss the variant applications of the multilayer framework, and finally, we give an overview of various existing applications of the multilayer model in network biology.

Background: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn.Implementation: The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization.Results: Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model.Conclusions: With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository:https://github.com/frankkramer-lab/MIScnn.

Activation of Wnt/β-catenin signaling plays a central role in the development and progression of colorectal cancer. The Wnt-transcription factor, TCF7L2, is overexpressed in primary rectal cancers that are resistant to chemoradiotherapy and TCF7L2 mediates resistance to chemoradiotherapy. However, it is unclear whether the resistance is mediated by a TCF7L2 inherent mechanism or Wnt/β-catenin signaling in general. Here, inhibition of β-catenin by siRNAs or a small-molecule inhibitor (XAV-939) resulted in sensitization of colorectal cancer cells to chemoradiotherapy. To investigate the potential role of Wnt/β-catenin signaling in controlling therapeutic responsiveness, nontumorigenic RPE-1 cells were stimulated with Wnt-3a, a physiologic ligand of Frizzled receptors, which increased resistance to chemoradiotherapy. This effect could be recapitulated by overexpression of a degradation-resistant mutant of β-catenin (S33Y), also boosting resistance of RPE-1 cells to chemoradiotherapy, which was, conversely, abrogated by siRNA-mediated silencing of β-catenin. Consistent with these findings, higher expression levels of active β-catenin were observed as well as increased TCF/LEF reporter activity in SW1463 cells that evolved radiation resistance due to repeated radiation treatment. Global gene expression profiling identified several altered pathways, including PPAR signaling and other metabolic pathways, associated with cellular response to radiation. In summary, aberrant activation of Wnt/β-catenin signaling not only regulates the development and progression of colorectal cancer, but also mediates resistance of rectal cancers to chemoradiotherapy. Implications: Targeting Wnt/β-catenin signaling or one of the downstream pathways represents a promising strategy to increase response to chemoradiotherapy. Mol Cancer Res; 15(11); 1481–90. ©2017 AACR.