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Bioengineering
Special Issues
Diagnostic Imaging and Radiation Therapy in Biomedical Engineering
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Diagnostic Imaging and Radiation Therapy in Biomedical Engineering

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1200

Special Issue Editor

Dr. Carlo A. Mallio

E-Mail Website
Guest Editor
Research Unit of Radiology, Department of Medicine and Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
Interests: neuroradiology; oncologic imaging; contrast media; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Diagnostic imaging and radiation therapy are essential tools in the field of biomedical engineering. Diagnostic imaging techniques such as X-ray, MRI, CT, ultrasound, and nuclear imaging play a crucial role in the early detection and diagnosis of various diseases and abnormalities. These techniques provide detailed images of the internal structures of the body, allowing healthcare professionals to accurately identify and treat health conditions. On the other hand, radiation therapy is a common treatment option for cancer patients, where high-energy radiation is used to destroy cancerous cells while minimizing damage to surrounding healthy tissue.

The intersection of diagnostic imaging and radiation therapy with biomedical engineering has led to numerous advancements in the design, development, and implementation of imaging devices and treatment modalities. Researchers and practitioners in this field are constantly innovating to improve the accuracy, efficiency, and safety of diagnostic imaging procedures and radiation therapy treatments. This Special Issue aims to showcase the latest research and developments in the field of diagnostic imaging and radiation therapy within the context of biomedical engineering, with a focus on advancements in imaging technology, treatment planning, and image-guided therapy.

Dr. Carlo A. Mallio
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. 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

  • diseases diagnostic
  • radiation therapy
  • biomedical engineering
  • imaging technology

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Published Papers (2 papers)

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Research

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16 pages, 2439 KiB  
Article
Ultrasound-Based Deep Learning Radiomics Models for Predicting Primary and Secondary Salivary Gland Malignancies: A Multicenter Retrospective Study
by Zhen Xia, Xiao-Chen Huang, Xin-Yu Xu, Qing Miao, Ming Wang, Meng-Jie Wu, Hao Zhang, Qi Jiang, Jing Zhuang, Qiang Wei and Wei Zhang
Bioengineering 2025, 12(4), 391; https://doi.org/10.3390/bioengineering12040391 - 5 Apr 2025
Viewed by 269
Abstract
Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, [...] Read more.
Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, and deep learning—independently or in combination—for distinguishing between primary and secondary salivary gland malignancies. Methods: This retrospective study included a total of 140 patients, comprising 68 with primary and 72 with secondary salivary gland malignancies, all pathologically confirmed, from four medical centers. Ultrasound features of salivary gland tumors were analyzed, and a radiomics model was established. Transfer learning with multiple pre-trained models was used to create deep learning (DL) models from which features were extracted and combined with radiomics features to construct a radiomics-deep learning (RadiomicsDL) model. A combined model was further developed by integrating ultrasound features. Least absolute shrinkage and selection operator (LASSO) regression and various machine learning algorithms were employed for feature selection and modeling. The optimal model was determined based on the area under the receiver operating characteristic curve (AUC), and interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The RadiomicsDL model, which combines radiomics and deep learning features using the Multi-Layer Perceptron (MLP), demonstrated the best performance on the test set with an AUC of 0.807. This surpassed the performances of the ultrasound (US), radiomics, DL, and combined models, which achieved AUCs of 0.421, 0.636, 0.763, and 0.711, respectively. SHAP analysis revealed that the radiomic feature Wavelet_LHH_glcm_SumEntropy contributed most significantly to the mode. Conclusions: The RadiomicsDL model based on ultrasound images provides an efficient and non-invasive method to differentiate between primary and secondary salivary gland malignancies. Full article
(This article belongs to the Special Issue Diagnostic Imaging and Radiation Therapy in Biomedical Engineering)
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Review

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14 pages, 657 KiB  
Review
The Role of Neck Imaging Reporting and Data System (NI-RADS) in the Management of Head and Neck Cancers
by Daniele Vertulli, Marco Parillo and Carlo Augusto Mallio
Bioengineering 2025, 12(4), 398; https://doi.org/10.3390/bioengineering12040398 - 8 Apr 2025
Viewed by 316
Abstract
This review evaluates the current evidence on the use of the Neck Imaging Reporting and Data System (NI-RADS) for the surveillance and early detection of recurrent head and neck cancers. NI-RADS offers a standardized, structured framework specifically tailored for post-treatment imaging, aiding radiologists [...] Read more.
This review evaluates the current evidence on the use of the Neck Imaging Reporting and Data System (NI-RADS) for the surveillance and early detection of recurrent head and neck cancers. NI-RADS offers a standardized, structured framework specifically tailored for post-treatment imaging, aiding radiologists in differentiating between residual tumors, scar tissue, and post-surgical changes. NI-RADS demonstrated a strong diagnostic performance across multiple studies, with high sensitivity and specificity reported in detecting recurrent tumors at primary and neck sites. Despite these strengths, limitations persist, including a high frequency of indeterminate results and variability in di-agnostic concordance across imaging modalities (computed tomography, magnetic resonance imaging (MRI), positron emission tomography(PET)). The review also highlights the NI-RADS’s reproducibility, showing high inter- and intra-reader agreements across different imaging techniques, although some modality-specific differences were observed. While it demonstrates strong diagnostic performance and good reproducibility across imaging modalities, attention is required to address indeterminate imaging findings and the limitations of modality-specific variations. Future studies should focus on integrating advanced imaging characteristics, such as diffusion-weighted imaging and PET/MRI fusion techniques, to further enhance NI-RADS’s diagnostic accuracy. Continuous efforts to refine NI-RADS protocols and imaging interpretations will ensure more consistent detection of recurrences, ultimately improving clinical outcomes in head and neck cancer management. Full article
(This article belongs to the Special Issue Diagnostic Imaging and Radiation Therapy in Biomedical Engineering)
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