The advent of artificial intelligence in medical imaging has paved the way for significant advancements in the diagnosis of brain tumors. This study presents a novel ensemble approach that uses magnetic resonance imaging (MRI) to identify and categorize common brain cancers, such as pituitary, meningioma, and glioma. The proposed workflow is composed of a two-fold approach: firstly, it employs non-trivial image enhancement techniques in data preprocessing, low-rank Tucker decomposition for dimensionality reduction, and machine learning (ML) classifiers to detect and predict the type of brain tumor. Secondly, persistent homology (PH), a topological data analysis (TDA) technique, is exploited to extract potential critical areas in MRI scans. When paired with the ML classifier output, this additional information can help domain experts to identify areas of interest that might contain tumor signatures, improving the interpretability of ML predictions. When compared to automated diagnoses, this transparency adds another level of confidence and is essential for clinical acceptance. The performance of the system was quantitatively evaluated on a well-known MRI dataset, with an overall classification accuracy of 97.28% using an extremely randomized trees model. The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.

The Lymph2Cx assay has played a key role in the clinical translation of the diffuse large B‐cell lymphoma (DLBCL) molecular classification for cell‐of‐origin (COO) determination, as mandated by the 2017 Revised WHO Classification. Applicable to routine formalin‐fixed paraffin‐embedded biopsies, Lymph2Cx has shown high concordance with gold‐standard gene expression profiling (GS) performed on fresh/frozen samples. However, conventional concordance metrics may overlook systematic bias. We first reassessed agreement between Lymph2Cx and GS using Bland–Altman analysis and Cohen's kappa. Subsequently, the prognostic impact of the cell‐of‐origin of diffuse large B‐cell lymphoma as determined by Lymph2Cx was compared with that as determined by GS. This was done by assembling large cohorts of publicly available, clinically annotated cases, analyzed by Lymph2Cx or GS, comprising a total of 2321 cases. Lymph2Cx consistently underestimated the frequency of the activated B‐cell‐like (ABC) subtype, misclassifying a subset of ABC cases as germinal center B‐cell‐like (GCB) or unclassified (UNC). While ABC DLBCL retained its adverse prognosis across cohorts, GCB and UNC groups defined by Lymph2Cx showed higher hazard ratios than their GS‐defined counterparts, consistent with redistribution of higher‐risk ABC cases. Overall, although Lymph2Cx remains a valuable tool for routine COO determination, our findings indicate the need for further refinement to improve categorical accuracy, particularly for the ABC subtype, to support optimal clinical risk stratification.