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Life
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Advanced Technologies and Clinical Practice of Cancer Radiotherapy
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Advanced Technologies and Clinical Practice of Cancer Radiotherapy

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: 26 June 2026 | Viewed by 768

Special Issue Editor

Prof. Dr. Marcin Balcerzyk

E-Mail Website
Guest Editor
1. Department of Medical Physiology and Biophysics, Faculty of Medicine, University of Seville, 41004 Sevilla, Spain
2. National Accelerators' Center, University of Seville, 41004 Sevilla, Spain
Interests: molecular imaging; positron emission tomography – PET; computed tomography – CT; medical imaging; PET detectors; boron neutron capture therapy – BNCT; artificial intelligence applied to medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have witnessed notable advancements in cancer radiotherapy, including in vivo dose verification for gamma and proton therapies, the development of novel proton accelerators for proton, boron, and other elements in neutron capture therapy (BNCT and NCT), the introduction of arc technology in proton therapy, and the design of new boron-containing compounds for BNCT, among others. These technologies are increasingly being integrated into clinical radiation therapy. Concurrently, several innovative approaches remain in preliminary stages, encompassing basic research, simulation, development, or certification prior to clinical implementation.

This Special Issue aims to showcase publications addressing this dynamic field. We particularly welcome submissions from collaborative teams comprising both basic researchers and clinicians. Original research articles are preferred over review papers.

Prof. Dr. Marcin Balcerzyk
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 250 words) can be sent to the Editorial Office for assessment.

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. Life 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 2600 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

  • proton therapy
  • BNCT
  • in vivo dose verification in radiotherapy
  • accelerators for radiation therapy
  • simulation of radiotherapies

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Published Papers (1 paper)

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Research

23 pages, 5744 KB  
Article
Improving the Prediction of Radiation Pneumonitis: Leveraging Radiomics and Dosiomics Within IDLSS Lung Subregions
by Tsair-Fwu Lee, Wen-Ping Yun, Ling-Chuan Chang-Chien, Hung-Yu Chang, Yi-Lun Liao, Ya-Shin Kuan, Chiu-Feng Chiu, Cheng-Shie Wuu, Yang-Wei Hsieh, Liyun Chang, Yu-Chang Hu, Yu-Wei Lin and Pei-Ju Chao
Life 2026, 16(2), 328; https://doi.org/10.3390/life16020328 - 13 Feb 2026
Viewed by 519
Abstract
Purpose: This study develops a predictive model for radiation pneumonitis (RP) risk in lung cancer patients after volume-modulated arc therapy (VMAT) that leverages high-dimensional dosiomics and dose–volume histogram (DVH) features within IDLSS (incremental-dose interval-based lung subregion) lung subregions. Methods: We retrospectively analyzed data [...] Read more.
Purpose: This study develops a predictive model for radiation pneumonitis (RP) risk in lung cancer patients after volume-modulated arc therapy (VMAT) that leverages high-dimensional dosiomics and dose–volume histogram (DVH) features within IDLSS (incremental-dose interval-based lung subregion) lung subregions. Methods: We retrospectively analyzed data from 136 lung cancer patients treated with VMAT between 2015 and 2022, including 39 patients who developed RP greater than Grade 2. Using the IDLSS method, seven regions of interest (ROIs), including the Planning Target Volume (PTV), normal lung, and five subdivided lung areas, were delineated on pretreatment Computed Tomography (CT) images. DVH, radiomics, and dosiomics features were extracted from these ROIs and organized into nine distinct feature sets. A comprehensive pipeline was applied, integrating IDLSS-defined lung subregions, high-dimensional dosiomics features, LASSO-based feature selection, and SMOTE oversampling to address class imbalance in the training data. Logistic regression, random forest, and feedforward neural networks were constructed and optimized via tenfold cross-validation. Model performance across different feature sets was evaluated via the average AUC, F1 score, and other performance metrics. Results: LASSO regression revealed that BMI and volume within the 5–10 Gy and 10–20 Gy lung subregions were significant predictors of RP. The performance evaluation demonstrated that the dosiomics features consistently outperformed the DVH features across the models. Combining radiomics and dosiomics achieved the highest predictive accuracy (AUC = 0.91, ACC = 0.89, NPV = 0.95, PPV = 0.78, F1 score = 0.82, sensitivity = 0.88, specificity = 0.90). Applying SMOTE during training significantly improved sensitivity without compromising specificity, confirming the value of balancing strategies in enhancing model performance. Incorporating all the features together did not provide additional performance gains. Conclusions: Integrating radiomics and dosiomics features extracted from IDLSS-defined lung subregions significantly enhances the ability to predict RP after VMAT, surpassing traditional DVH metrics. The substantial contribution of dosiomics features highlights the importance of spatial dose heterogeneity in RP risk assessment. Full article
(This article belongs to the Special Issue Advanced Technologies and Clinical Practice of Cancer Radiotherapy)
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