Evaluating Autoencoders for Dimensionality Reduction of MRI-derived Radiomics and Classification of Malignant Brain Tumors.

Malignant brain tumors including parenchymal metastatic (MET) lesions, glioblastomas (GBM), and lymphomas (LYM) account for 29.7% of brain cancers. However, the characterization of these tumors from MRI imaging is difficult due to the similarity of their radiologically observed image features. Radiomics is the extraction of quantitative imaging features to characterize tumor intensity, shape, and texture. Applying machine learning over radiomic features could aid diagnostics by improving the classification of these common brain tumors. However, since the number of radiomic features is typically larger than the number of patients in the study, dimensionality reduction is needed to balance feature dimensionality and model complexity. Autoencoders are a form of unsupervised representation learning that can be used for dimensionality reduction. It is similar to PCA but uses a more complex and non-linear model to learn a compact latent space. In this work, we examine the effectiveness of autoencoders for dimensionality reduction on the radiomic feature space of multiparametric MRI images and the classification of malignant brain tumors: GBM, LYM, and MET. We further aim to address the class imbalances imposed by the rarity of lymphomas by examining different approaches to increase overall predictive performance through multiclass decomposition strategies.

Predicting EGFR mutational status from pathology images using a real-world dataset.

Treatment of non-small cell lung cancer is increasingly biomarker driven with multiple genomic alterations, including those in the epidermal growth factor receptor (EGFR) gene, that benefit from targeted therapies. We developed a set of algorithms to assess EGFR status and morphology using a real-world advanced lung adenocarcinoma cohort of 2099 patients with hematoxylin and eosin (H&E) images exhibiting high morphological diversity and low tumor content relative to public datasets. The best performing EGFR algorithm was attention-based and achieved an area under the curve (AUC) of 0.870, a negative predictive value (NPV) of 0.954 and a positive predictive value (PPV) of 0.410 in a validation cohort reflecting the 15% prevalence of EGFR mutations in lung adenocarcinoma. The attention model outperformed a heuristic-based model focused exclusively on tumor regions, and we show that although the attention model also extracts signal primarily from tumor morphology, it extracts additional signal from non-tumor tissue regions. Further analysis of high-attention regions by pathologists showed associations of predicted EGFR negativity with solid growth patterns and higher peritumoral immune presence. This algorithm highlights the potential of deep learning tools to provide instantaneous rule-out screening for biomarker alterations and may help prioritize the use of scarce tissue for biomarker testing.

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.

Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.

Identifying Symptom Clusters Through Association Rule Mining.

Cancer patients experience many symptoms throughout their cancer treatment and sometimes suffer from lasting effects post-treatment. Patient-Reported Outcome (PRO) surveys provide a means for monitoring the patient's symptoms during and after treatment. Symptom cluster (SC) research seeks to understand these symptoms and their relationships to define new treatment and disease management methods to improve patient's quality of life. This paper introduces association rule mining (ARM) as a novel alternative for identifying symptom clusters. We compare the results to prior research and find that while some of the SCs are similar, ARM uncovers more nuanced relationships between symptoms such as anchor symptoms that serve as connections between interference and cancer-specific symptoms.

Loss of RHBDF2 results in an early-onset spontaneous murine colitis.

Inflammatory bowel disease (IBD) is a heterogeneous group of inflammation-mediated pathologies that include Crohn's disease and ulcerative colitis and primarily affects the colon and small intestine. Previous studies have shown that a disintegrin and metalloprotease (ADAM) 17, a membrane-bound sheddase, capable of cleaving the proinflammatory cytokine TNF and epidermal growth factor receptor ligands, plays a critical role in maintaining gut homeostasis and modulating intestinal inflammation during IBD. Rhomboid 5 homolog 2 (RHBDF2), a catalytically inactive member of the rhomboid family of intramembrane serine proteases, was recently identified as a crucial regulator of ADAM17. Here, we assessed the role of RHBDF2 in the development of colitis in the context of IL10 deficiency. Il10-/- /Rhbdf2-/- mice developed spontaneous colitis and experienced severe weight loss starting at 8 wk of age, without the need for exogenous triggers. Severity of disease pathology in Il10-/- /Rhbdf2-/- mice correlated with a dysbiotic gut microbiota and elevated Th1-associated immune responses with increased interferon gamma and IL2 production. In addition, Il10-/- /Rhbdf2-/- mice failed to maintain their epithelial cell homeostasis, although the intestinal epithelial barrier of Rhbdf2-/- mice is intact and loss of Rhbdf2 did not significantly exacerbate sensitivity to dextran sulfate sodium-induced colitis, suggesting differences in the underlying disease pathway of intestinal inflammation in this model. Taken together, our results demonstrate a critical regulatory role for RHBDF2 in the maintenance of the unique homeostasis between intestinal microbiota and host immune responses in the gut that is dysregulated during the pathogenesis of IBD.