Tomography

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Dr. Pascal N. Tyrrell

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Guest Editor

1. Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
2. Department of Statistical Sciences, University of Toronto, Toronto, ON M5T 1W7, Canada
3. Institute of Medical Science, University of Toronto, Toronto, ON M5T 1W7, Canada
Interests: artificial Intelligence; biostatistics; machine learning; medical images
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Special Issue Information

Dear Colleagues,

In 2025, we proudly mark the 10th Anniversary of Tomography (ISSN: 2379-139X), a journal committed to advancing the frontiers of biomedical imaging. Over the past decade, Tomography has become a leading platform for disseminating groundbreaking discoveries and fostering innovation in imaging technologies, bridging basic research, clinical applications, and cutting-edge developments in imaging data science and artificial intelligence.

To celebrate this milestone, we are curating a Special Issue that reflects on the progress and transformative impact of biomedical imaging over the past 10 years. This Special Issue will feature research that exemplifies excellence in imaging science, showcasing advancements in imaging technologies, novel contrast mechanisms, AI-driven innovations, and interdisciplinary approaches that have shaped the field.

We warmly invite researchers to contribute original research articles, comprehensive reviews, and perspective pieces. Submissions should align with the journal’s mission to advance biomedical imaging across all scales and modalities, including but not limited to the following topics:

Cross-sectional imaging (e.g., US, CT, MRI, PET);
Optical and electronic microscopy;
Photoacoustic and bioluminescence imaging;
Cryo-electron microscopy and optical computed tomography;
Innovations in imaging hardware, software, and informatics;
Novel imaging contrast agents and chemical probes;
Artificial intelligence applications in biomedical imaging.

This Special Issue serves as both a tribute to the extraordinary achievements of the imaging community and a forward-looking exploration of the limitless possibilities in tomography. By contributing, you join us in celebrating a decade of innovation and charting the course for future advancements in imaging science.

We look forward to receiving your submissions and collaborating to highlight the remarkable progress in biomedical imaging. Let us commemorate this anniversary by continuing to push the boundaries of knowledge and technology in healthcare and life sciences.

Dr. Pascal N. Tyrrell
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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38 pages, 751 KiB

Open AccessArticle

Machine Learning and Feature Selection in Pediatric Appendicitis

by John Kendall, Gabriel Gaspar, Derek Berger and Jacob Levman

Tomography 2025, 11(8), 90; https://doi.org/10.3390/tomography11080090 - 13 Aug 2025

Abstract

Background/Objectives: Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular focus was placed on the role of ultrasound (US) image-descriptive features in model performance and explainability. Methods: We conducted a retrospective cohort study on a dataset of 781 pediatric patients aged 0–18 presenting to Children’s Hospital St. Hedwig in Regensburg, Germany, between January 2016 and February 2023. We developed and validated predictive models; machine learning algorithms included the random forest, logistic regression, stochastic gradient descent, and the light gradient boosting machine (LGBM). These were paired exhaustively with feature selection methods spanning filter-based (association and prediction), embedded (LGBM and linear), and a novel redundancy-aware step-up wrapper approach. We employed a machine learning benchmarking study design where AI models were trained to predict diagnosis, management, and severity outcomes, both with and without US image-descriptive features, and evaluated on held-out testing samples. Model performance was assessed using overall accuracy and area under the receiver operating characteristic curve (AUROC). A deep learner optimized for tabular data, GANDALF, was also evaluated in these applications. Results: US features significantly improved diagnostic accuracy, supporting their use in reducing model bias. However, they were not essential for maximizing accuracy in predicting management or severity. In summary, our best-performing models were, for diagnosis, the random forest with embedded LGBM feature selection (98.1% accuracy, AUROC: 0.993), for management, the random forest without feature selection (93.9% accuracy, AUROC: 0.980), and for severity, the LGBM with filter-based association feature selection (90.1% accuracy, AUROC: 0.931). Conclusions: Our results demonstrate that high-performing, interpretable machine learning models can predict key clinical outcomes in pediatric appendicitis. US image features improve diagnostic accuracy but are not critical for predicting management or severity. Full article

(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)

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