A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.

Purpose: Recent papers suggested a correlation between the risk of distant metastasis (DM) and dose outside the PTV, though conclusions in different publications conflicted. This study resolves these conflicts and provides a compelling explanation of prognostic factors.

Materials And Methods: A dataset of 478 NSCLC patients treated with SBRT (IMRT or VMAT) was analyzed.

Phylogenetic insights into the genetic legacies of Hungarian-speaking communities in the Carpathian Basin.

This study focuses on exploring the uniparental genetic lineages of Hungarian-speaking minorities residing in rural villages of Baranja (Croatia) and the Zobor region (Slovakia). We aimed to identify ancestral lineages by examining genetic markers distributed across the entire mitogenome and on the Y-chromosome. This allowed us to discern disparities in regional genetic structures within these communities.

Improved outcome models with denoising diffusion.

Purpose: Radiotherapy outcome modelling often suffers from class imbalance in the modelled endpoints. One of the main options to address this issue is by introducing new synthetically generated datapoints, using generative models, such as Denoising Diffusion Probabilistic Models (DDPM). In this study, we implemented DDPM to improve performance of a tumor local control model, trained on imbalanced dataset, and compare this approach with other common techniques.

Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.

Purpose: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the nominal biologically effective dose (BED) of 100 Gy is felt by some to be insufficient for long-term local control of some lesions. In this study, we analyzed such patients using explainable deep learning techniques and consequently proposed appropriate treatment planning criteria. These novel criteria could help planners achieve optimized treatment plans for maximal local control.

Improved insights into the SABIO-RK database via visualization.

SABIO-RK is a database for biochemical reactions and their kinetics. Data in SABIO-RK are inherently multidimensional and complex. The complex relationships between the data are often difficult to follow or even not represented when using standard tabular views.