BioMedInformatics

17 pages, 1887 KB

Open AccessArticle

AlphaGlue: A Novel Conceptual Delivery Method for α Therapy

by Lujin Abu Sabah, Laura Ballisat, Chiara De Sio, Magdalena Dobrowolska, Adam Chambers, Jinyan Duan, Susanna Guatelli, Dousatsu Sakata, Yuyao Shi, Jaap Velthuis and Anatoly Rosenfeld

BioMedInformatics 2025, 5(4), 58; https://doi.org/10.3390/biomedinformatics5040058 - 13 Oct 2025

Abstract

Extensive research is being carried out on the application of

α

particles for cancer treatment. A key challenge in

α

therapy is how to deliver the

α

emitters to the tumour. In AlphaGlue, a novel treatment delivery concept, the

α

emitters are suspended [...] Read more.

Extensive research is being carried out on the application of

α

particles for cancer treatment. A key challenge in

α

therapy is how to deliver the

α

emitters to the tumour. In AlphaGlue, a novel treatment delivery concept, the

α

emitters are suspended in a thin layer of glue that is put on top of the tumour. In principle, this should be an easy and safe way to apply

α

therapy. In this study, the effectiveness of AlphaGlue is evaluated using GEANT4 and GEANT4-DNA simulations to calculate the DNA damage as a function of depth. Two radionuclides are considered in this work, ²¹¹At and ²²⁴Ra. The results indicate that, as a concept, the method offers a promising hypothesis for treating superficial tumours, such as skin cancer, when ²²⁴Ra is applied directly on the tissue and stabilized with a glue layer. This results in 2

\times 10^{- 5}

complex double strand breaks and 5

\times 10^{- 5}

double strand breaks at 5 mm depth per applied ²²⁴Ra atom. When applying a ²²⁴Ra atom concentration of

(4.35 \pm 0.2) \times 10^{11} /

cm² corresponding to an activity of

(21.8 \pm 1) μ

Ci/cm² on the skin surface, the RBE weighted dose exceeds 20 Gy at 5 mm depth. Hence, there is significant cell death at 5 mm into the tissue; a depth matching clinical requirements for skin cancer treatment. Given the rapidly falling weighted dose versus depth curve, the treatment depth can be tuned with good precision. The results of this study show that AlphaGlue is a promosing treatment and open the pathway towards the next stage of the research, which includes in-vitro studies. Full article

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24 pages, 4173 KB

Open AccessArticle

Development of a Machine Learning-Based Predictive Model for Urinary Tract Infection Risk in Patients with Vitamin D Deficiency: A Multidimensional Clinical Data Analysis

by Krittin Naravejsakul, Watcharaporn Cholamjiak, Watcharapon Yajai, Jakkaphong Inpun and Waragunt Waratamrongpatai

BioMedInformatics 2025, 5(4), 57; https://doi.org/10.3390/biomedinformatics5040057 - 10 Oct 2025

Viewed by 104

Abstract

Background: Urinary tract infections (UTIs) remain among the most common bacterial infections, yet reliable risk stratification remains challenging. Serum vitamin D has been linked to immune regulation, but its predictive role in UTI subtypes is unclear. Methods: We analyzed 332 de-identified clinical records using six machine learning algorithms: Extra Trees, Gradient Boosting, XGBoost, Logistic Regression, Random Forest, and LightGBM. Two preprocessing strategies were applied: (i) removing rows with missing fasting blood sugar (FBs) and HbA1c, and (ii) dropping columns with Null FBs and HbA1c values. Model performance was evaluated using 10-fold cross-validation. Results: Serum vitamin D showed weak correlations with UTI subtypes but modest importance in tree-based models. The highest predictive accuracy was obtained with Extra Trees (0.9510) under the row-removal strategy and Random Forest (0.9525) under the column-dropping strategy. Models excluding vitamin D maintained comparable accuracy, suggesting minimal impact on overall predictive performance. Conclusions: Machine learning models demonstrated high accuracy and robustness in predicting UTI subtypes across preprocessing strategies. While vitamin D contributes as a supportive feature, it is not essential for reliable prediction. These findings highlight the adaptability and clinical utility of both vitamin D-inclusive and vitamin D-exclusive models, supporting deployment in diverse healthcare settings. Full article

(This article belongs to the Special Issue Editor's Choices Series for Clinical Informatics Section)

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20 pages, 1853 KB

Open AccessArticle

Enhanced U-Net for Spleen Segmentation in CT Scans: Integrating Multi-Slice Context and Grad-CAM Interpretability

by Sowad Rahman, Md Azad Hossain Raju, Abdullah Evna Jafar, Muslima Akter, Israt Jahan Suma and Jia Uddin

BioMedInformatics 2025, 5(4), 56; https://doi.org/10.3390/biomedinformatics5040056 - 8 Oct 2025

Viewed by 329

Abstract

Accurate spleen segmentation in abdominal CT scans remains a critical challenge in medical image analysis due to variable morphology, low tissue contrast, and proximity to similar anatomical structures. This paper presents an enhanced U-Net architecture that addresses these challenges through multi-slice contextual integration and interpretable deep learning. Our approach incorporates three-channel inputs from adjacent CT slices, implements a hybrid loss function combining Dice and binary cross-entropy terms, and integrates Grad-CAM visualization for enhanced model interpretability. Comprehensive evaluation on the Medical Decathlon dataset demonstrates superior performance, with a Dice similarity coefficient of 0.923 ± 0.04, outperforming standard 2D approaches by 3.2%. The model exhibits robust performance across varying slice thicknesses, contrast phases, and pathological conditions. Grad-CAM analysis reveals focused attention on spleen–tissue interfaces and internal vascular structures, providing clinical insight into model decision-making. The system demonstrates practical applicability for automated splenic volumetry, trauma assessment, and surgical planning, with processing times suitable for clinical workflow integration. Full article

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BioMedInformatics, Volume 5, Issue 4 (December 2025) – 3 articles

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