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Bioengineering
Special Issues
Engineering the Future of Radiotherapy: Innovations and Challenges
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Engineering the Future of Radiotherapy: Innovations and Challenges

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 676

Special Issue Editors

Dr. Kalliopi Platoni

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Guest Editor
2nd Department of Radiology, Medical Physics Unit (Attikon Hospital), School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
Interests: medical physics in radiation oncology; stereotactic radiotherapy; TSEB; dosimetry; modern techniques and quality assurance in radiotherapy
Special Issues, Collections and Topics in MDPI journals
Dr. Vassilis Kouloulias

E-Mail Website
Guest Editor
2nd Department of Radiology, Radiation Oncology Unit (Attikon Hospital), School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
Interests: radiation oncology; bioengineering; stereotactic radiotherapy; radiobiology; quality assurance in radiotherapy; modern techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, radiation therapy (RT) has shown tremendous evolution in terms of technological achievements and novel techniques. Stereotactic radiosurgery and proton therapy have demonstrated excellent results by minimizing toxicity, while new image-guided techniques (IGRTs), such as surface-guided RT (SGRT) or MRI-based IGRT through MR-Linacs, have already been adopted worldwide. The new promising treatment of “flash” RT seems quite promising for minimizing toxicity.

The combination of nanoparticles (NPs) and RT opens up a new frontier in cancer treatment.

NPs can be used as contrast enhancement in IGRT and may lead to an increased local radiation dose by using particles with higher atomic numbers (Z). The various roles of NPs as radiosensitizers in radiotherapy may provide new directions for optimization and radiotherapy efficacy.

The introduction of AI in the routine clinical practice of radiation oncology focuses on two main goals. The first goal is to improve efficiency, mainly through automation, while the second is to provide predictive tools for the better personalization of treatment. Deep learning models are used for automatic delineation and the segmentation of tumors and organs at risk. AI has also been utilized in treatment planning and optimization. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in adaptive radiotherapy, in the quality control and assurance of treatment plans, the optimization of image-guided RT, and the monitoring of mobile tumors during treatment.

All innovations are driving the RT in the “era of excellence” in anticancer treatment, undertaking the challenge of developing more sophisticated and tailored RT.

Dr. Kalliopi Platoni
Dr. Vassilis Kouloulias
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

  • radiosurgery
  • stereotactic radiotherapy
  • proton therapy
  • artificial intelligence (AI)
  • flash therapy
  • image/surface-guided radiotherapy
  • nanoparticles

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Published Papers (1 paper)

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Research

16 pages, 589 KiB  
Article
CT-Based Radiomics Enhance Respiratory Function Analysis for Lung SBRT
by Alice Porazzi, Mattia Zaffaroni, Vanessa Eleonora Pierini, Maria Giulia Vincini, Aurora Gaeta, Sara Raimondi, Lucrezia Berton, Lars Johannes Isaksson, Federico Mastroleo, Sara Gandini, Monica Casiraghi, Gaia Piperno, Lorenzo Spaggiari, Juliana Guarize, Stefano Maria Donghi, Łukasz Kuncman, Roberto Orecchia, Stefania Volpe and Barbara Alicja Jereczek-Fossa
Bioengineering 2025, 12(8), 800; https://doi.org/10.3390/bioengineering12080800 - 25 Jul 2025
Viewed by 499
Abstract
Introduction: Radiomics is the extraction of non-invasive and reproducible quantitative imaging features, which may yield mineable information for clinical practice implementation. Quantification of lung function through radiomics could play a role in the management of patients with pulmonary lesions. The aim of this [...] Read more.
Introduction: Radiomics is the extraction of non-invasive and reproducible quantitative imaging features, which may yield mineable information for clinical practice implementation. Quantification of lung function through radiomics could play a role in the management of patients with pulmonary lesions. The aim of this study is to test the capability of radiomic features to predict pulmonary function parameters, focusing on the diffusing capacity of lungs to carbon monoxide (DLCO). Methods: Retrospective data were retrieved from electronical medical records of patients treated with Stereotactic Body Radiation Therapy (SBRT) at a single institution. Inclusion criteria were as follows: (1) SBRT treatment performed for primary early-stage non-small cell lung cancer (ES-NSCLC) or oligometastatic lung nodules, (2) availability of simulation four-dimensional computed tomography (4DCT) scan, (3) baseline spirometry data availability, (4) availability of baseline clinical data, and (5) written informed consent for the anonymized use of data. The gross tumor volume (GTV) was segmented on 4DCT reconstructed phases representing the moment of maximum inhalation and maximum exhalation (Phase 0 and Phase 50, respectively), and radiomic features were extracted from the lung parenchyma subtracting the lesion/s. An iterative algorithm was clustered based on correlation, while keeping only those most associated with baseline and post-treatment DLCO. Three models were built to predict DLCO abnormality: the clinical model—containing clinical information; the radiomic model—containing the radiomic score; the clinical-radiomic model—containing clinical information and the radiomic score. For the models just described, the following were constructed: Model 1 based on the features in Phase 0; Model 2 based on the features in Phase 50; Model 3 based on the difference between the two phases. The AUC was used to compare their performances. Results: A total of 98 patients met the inclusion criteria. The Charlson Comorbidity Index (CCI) scored as the clinical variable most associated with baseline DLCO (p = 0.014), while the most associated features were mainly texture features and similar among the two phases. Clinical-radiomic models were the best at predicting both baseline and post-treatment abnormal DLCO. In particular, the performances for the three clinical-radiomic models at predicting baseline abnormal DLCO were AUC1 = 0.72, AUC2 = 0.72, and AUC3 = 0.75, for Model 1, Model 2, and Model 3, respectively. Regarding the prediction of post-treatment abnormal DLCO, the performances of the three clinical-radiomic models were AUC1 = 0.91, AUC2 = 0.91, and AUC3 = 0.95, for Model 1, Model 2, and Model 3, respectively. Conclusions: This study demonstrates that radiomic features extracted from healthy lung parenchyma on a 4DCT scan are associated with baseline pulmonary function parameters, showing that radiomics can add a layer of information in surrogate models for lung function assessment. Preliminary results suggest the potential applicability of these models for predicting post-SBRT lung function, warranting validation in larger, prospective cohorts. Full article
(This article belongs to the Special Issue Engineering the Future of Radiotherapy: Innovations and Challenges)
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