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Oligodendroglioma, IDH-mutant and 1p/19q-codeleted has highly variable outcomes that are strongly influenced by patient age. The distribution of oligodendroglioma age is non-Gaussian and reportedly bimodal, which motivated our investigation of age-associated molecular alterations that may drive poorer outcomes. We found that elevated HOXD12 expression was associated with both older patient age and shorter survival in the TCGA (FDR < 0.01, FDR = 1e-5) and the CGGA (p = 0.03, p < 1e-3). HOXD12 gene body hypermethylation was associated with older age, higher WHO grade, and shorter survival in the TCGA (p < 1e-6, p < 0.001, p < 1e-3) and with older age and higher WHO grade in Capper et al. (p < 0.002, p = 0.014). In the TCGA, HOXD12 gene body hypermethylation and elevated expression were independently prognostic of NOTCH1 and PIK3CA mutations, loss of 15q, MYC activation, and standard histopathological features. Single-nucleus RNA and ATAC sequencing data showed that HOXD12 activity was elevated in neoplastic tissue, particularly within cycling and OPC-like cells, and was associated with a stem-like phenotype. A pan-HOX DNA methylation analysis revealed an age and survival-associated HOX-high signature that was tightly associated with HOXD12 gene body methylation. Overall, HOXD12 expression and gene body hypermethylation were associated with an older, atypically aggressive subtype of oligodendroglioma., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model's effectiveness in replicating pathologists' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists., (© 2024. The Author(s).)