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Life
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Advancing Cancer Radiodiagnostics Through Artificial Intelligence: Techniques, Applications, and Challenges
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Advancing Cancer Radiodiagnostics Through Artificial Intelligence: Techniques, Applications, and Challenges

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1505

Special Issue Editors

Dr. Pei-Ju Chao

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Guest Editor
Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
Interests: machine learning; deep learning; artificial intelligence; health; medical informatics; radiotheapy; cancer radiodiagnostics
Prof. Dr. Tsair-Fwu Lee

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Guest Editor
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Interests: biomedical engineering; artificial intelligence; medical informatics; radiation applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peter Elkin

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Guest Editor
Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA
Interests: biomedical Informatics; health data representation; medical informatics
Dr. Roberto Iezzi

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Guest Editor
Department of Emergency & Interventional Radiology, IRCCS Fondazione Policlinico Universitario A. Gemelli, 00168 Roma, Italy
Interests: radiology; interventional radiology; interventional oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative tool in cancer radiodiagnostics, offering novel approaches for improving the accuracy, efficiency, and accessibility of cancer detection and treatment planning. This Special Issue, titled “Advancing Cancer Radiodiagnostics Through Artificial Intelligence: Techniques, Applications, and Challenges”, invites researchers, clinicians, and industry experts to contribute their work in this rapidly evolving field.

This Special Issue focuses on AI-driven techniques that leverage advanced algorithms, data analysis, and machine learning methodologies to optimize cancer diagnostics. Emphasis will be placed on real-world applications, novel AI methodologies, and overcoming challenges in clinical implementation. Topics of interest include, but are not limited to, the following:

  • The development and application of AI algorithms for tumor detection and diagnosis;
  • AI integration with imaging modalities such as MRI, CT, PET, and ultrasound;
  • Radiomics-based machine learning and deep learning models for cancer prediction;
  • Multimodal data integration, including radiomics, genomics, and clinical data;
  • Addressing challenges in AI model interpretability, validation, and generalization;
  • Ethical and regulatory considerations for implementing AI in radiodiagnostics;
  • Case studies showcasing AI implementation in clinical radiology.

This Special Issue aims to advance the knowledge and application of AI in radiodiagnostics, fostering interdisciplinary collaboration and accelerating the adoption of AI in oncology to enhance patient outcomes.

Dr. Pei-Ju Chao
Prof. Dr. Tsair-Fwu Lee
Prof. Dr. Peter Elkin
Dr. Roberto Iezzi
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 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

  • artificial intelligence
  • cancer radiodiagnostics
  • machine learning
  • tumor detection
  • radiomics integration

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Published Papers (1 paper)

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Research

19 pages, 3717 KB  
Article
Using Radiomics and Explainable Ensemble Learning to Predict Radiation Pneumonitis and Survival in NSCLC Patients Post-VMAT
by Tsair-Fwu Lee, Lawrence Tsai, Po-Shun Tseng, Chia-Chi Hsu, Ling-Chuan Chang-Chien, Jun-Ping Shiau, Yang-Wei Hsieh, Shyh-An Yeh, Cheng-Shie Wuu, Yu-Wei Lin and Pei-Ju Chao
Life 2025, 15(11), 1753; https://doi.org/10.3390/life15111753 - 14 Nov 2025
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Abstract
Purpose: This study aimed to develop a precise predictive model to assess the risk of radiation pneumonitis (RP) and three-year survival in patients with non-small cell lung cancer (NSCLC) following volumetric modulated arc therapy (VMAT). Radiomics features, ensemble stacking, and explainable artificial [...] Read more.
Purpose: This study aimed to develop a precise predictive model to assess the risk of radiation pneumonitis (RP) and three-year survival in patients with non-small cell lung cancer (NSCLC) following volumetric modulated arc therapy (VMAT). Radiomics features, ensemble stacking, and explainable artificial intelligence (XAI) were integrated to enhance predictive performance and clinical interpretability. Materials and Methods: A retrospective cohort of 221 NSCLC patients treated with VMAT at Kaohsiung Veterans General Hospital between 2013 and 2023 was analyzed, including 168 patients for RP prediction (47 with ≥grade 2 RP) and 118 patients for survival prediction (34 deaths). Clinical variables, dose–volume histogram (DVH) parameters, and radiomic features (original, Laplacian of Gaussian [LoG], and wavelet filtered) were extracted. ANOVA was used for initial feature reduction, followed by LASSO and Boruta-SHAP for feature selection, which formed 10 feature subsets. The data were divided at an 8:2 ratio into training and testing sets, with SMOTE balancing and 10-fold cross-validation for parameter optimization. Six models—logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), XGBoost, and Ensemble Stacking—were evaluated in terms of the AUC, accuracy (ACC), negative predictive value (NPV), precision, and F1 score. SHAP analysis was applied to interpret feature contributions. Results: For RP prediction, the LASSO-selected radiomic subset (FR) combined with Ensemble Stacking achieved optimal performance (AUC 0.91, ACC 0.89), with SHAP identifying V40 Firstorder_Min as the most influential feature. For survival prediction, the FR subset yielded an AUC of 0.97, an ACC of 0.92, and an NPV of 1.00, with V10 Wavelet Firstorder_Min as the top contributor. The multimodal subset (FC+R) also performed strongly, achieving an AUC of 0.91 for RP and 0.96 for survival. Conclusions: This study demonstrated the superior performance of radiomics combined with Ensemble Stacking and XAI for the prediction of RP and survival following VMAT in patients with NSCLC. SHAP-based interpretation enhances transparency and clinical trust, offering a robust foundation for personalized radiotherapy and precision medicine. Full article
(This article belongs to the Special Issue Advancing Cancer Radiodiagnostics Through Artificial Intelligence: Techniques, Applications, and Challenges)
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