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Bioengineering
Special Issues
Application of Artificial Intelligence in Oncologic PET Imaging
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Application of Artificial Intelligence in Oncologic PET Imaging

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 95

Special Issue Editors

Dr. Angelo Castello

E-Mail Website
Guest Editor
Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
Interests: PET/CT; nuclear medicine; neuro-oncology; lung cancer; prostate cancer; lymphoma; immunotherapy; radiomics; radioembolization
Special Issues, Collections and Topics in MDPI journals
Dr. Seyed Ali Mirshahvalad

E-Mail Website
Guest Editor
1. Department of Radiology, Health Sciences North, Northern Ontario School of Medicine, Sudbury, ON P3E 5J1, Canada
2. Joint Department of Medical Imaging, University Health Network, University Medical Imaging Toronto, Toronto, ON M5G 2N2, Canada
Interests: positron emission tomography; PET/CT; PET/MRI; radiomics; machine learning; deep learning
Dr. Adam Farag

E-Mail Website
Guest Editor
Joint Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, ON, Canada
Interests: magnetic resonance imaging; positron emission tomography; kinetic modelling; deep learning; image reconstruction

Special Issue Information

Dear Colleagues,

Positron emission tomography (PET) has long been a cornerstone in oncologic imaging, offering detailed metabolic and functional insights into cancer biology. The integration of artificial intelligence (AI) in PET imaging presents exciting new opportunities to enhance the precision, accuracy, and clinical relevance of this powerful modality. From improving image reconstruction and quantification to developing predictive models for treatment outcomes, AI is reshaping how we approach cancer diagnosis and management through PET imaging.

This Special Issue aims to bring together cutting-edge research that explores the application of AI in oncologic PET imaging. We welcome contributions that span the full spectrum of AI-enhanced PET applications, including (but not limited to) AI-driven image analysis, segmentation, quantification and detection algorithms, and AI-assisted decision support systems and prognostication models. Of particular interest are studies focusing on novel PET radiotracers and their role in advancing oncologic care, where AI plays a critical role in optimizing imaging protocols and improving diagnostic accuracy and patient prognostication.

By publishing this Special Issue, we hope to highlight the transformative impact of AI on oncologic PET imaging and provide a comprehensive view of how these advancements are paving the way for more personalized, efficient, and effective cancer care.

We encourage authors to submit original research, review articles, and meta-analytical studies that align with these themes and push the boundaries of what is possible in AI-driven oncologic PET imaging.

Dr. Angelo Castello
Dr. Seyed Ali Mirshahvalad
Dr. Adam Farag
Guest Editors

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. Bioengineering 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 2700 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.

Keywords

  • positron emission tomography (PET)
  • PET/CT
  • PET/MRI
  • artificial intelligence
  • radiomics
  • machine learning
  • deep learning
  • convolutional neural networks
  • auto-segmentation
  • quantification
  • theranostics
  • novel radiopharmaceuticals

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Published Papers (1 paper)

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Research

13 pages, 1152 KiB  
Article
Machine Learning Models Derived from [18F]FDG PET/CT for the Prediction of Recurrence in Patients with Thymomas
by Angelo Castello, Luigi Manco, Margherita Cattaneo, Riccardo Orlandi, Lorenzo Rosso, Giorgio Alberto Croci, Luigia Florimonte, Giovanni Scribano, Alessandro Turra, Stefano Ferrero, Mario Nosotti, Gianpaolo Carrafiello, Massimo Castellani and Paolo Mendogni
Bioengineering 2025, 12(7), 721; https://doi.org/10.3390/bioengineering12070721 - 30 Jun 2025
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Abstract
Background/Objectives: This study aimed to develop machine learning (ML) models to predict recurrence in thymoma patients using conventional and radiomic signatures extracted from preoperative [18F]FDG PET/CT. Methods: A total of 50 patients (25 males, 25 females; mean age 63.3 ± 14.2 [...] Read more.
Background/Objectives: This study aimed to develop machine learning (ML) models to predict recurrence in thymoma patients using conventional and radiomic signatures extracted from preoperative [18F]FDG PET/CT. Methods: A total of 50 patients (25 males, 25 females; mean age 63.3 ± 14.2 years) who underwent thymectomy and preoperative [18F]FDG PET/CT between 2012 and 2022 were retrospectively analyzed. Radiomic analysis was performed using free-from-recurrence (FFR) status as a reference. Clinico-metabolic PET parameters were collected, and thymoma lesions were manually segmented on [18F]FDG PET/CT. A total of 856 radiomic features (RFts) were extracted from PET and CT datasets following IBSI guidelines, and robust RFts were selected. The dataset was split into training (70%) and validation (30%) sets. Two ML models (PET- and CT-based, respectively), each with three classifiers—Random Forest (RF), Support-Vector-Machine, and Tree—were trained and internally validated using RFts and clinico-metabolic signatures. Results: A total of 50 ROIs were selected and segmented. FFR was observed in 84% of our cohort. Forty-three robust RFts were selected from the CT dataset and 16 from the PET dataset, predominantly wavelet-based RFts. Additionally, three metabolic PET parameters were selected and included in the PET Model. Both the CT and PET models successfully discriminated against FFR after surgery, with the CT Model slightly outperforming the PET Model across different classifiers. The performance metrics of the RF classifier for the CT and PET models were AUC = 0.970/0.949, CA = 0.880/0.840, Precision = 0.884/0.842, Recall = 0.880/0.846, Specificity = 0.887/0.839, Sensitivity = 0.920/0.844, TP = 81.8%/83.3%, and TN = 92.9%/84.6%, respectively. Conclusions: ML-models trained on PET/CT radiomic features show promising results for predicting recurrence in patients with thymomas, which could be potentially applied in clinical practice for a better personalized treatment strategy. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Oncologic PET Imaging)
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