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Cancers
Special Issues
Artificial Intelligence-Assisted Radiomics in Cancer
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Artificial Intelligence-Assisted Radiomics in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3320

Special Issue Editor

Prof. Dr. Seong K. Mun

E-Mail Website
Guest Editor
Arlington Innovation Center, Health Research, Virginia Tech, Arlington, VA 22203, USA
Interests: operationalizing AI in healthcare; digital transformation of medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical images contain a massive amount of information that no human can fully appreciate and quantify. The images are formed by computing complex biological signals into visible shapes and forms, discarding potentially rich biological information that is not visible. Radiomics aims to develop the next-generation quantitative decision support system by extracting new quantitative information from multidimensional imaging data by applying high-order statistics and artificial intelligence (AI). The recent explosive growth of artificial intelligence research and development for computer-aided detection (CADe) and diagnosis (CADx) is based on three-dimensional pattern recognition; thus, perhaps we have just “scratched the surface of imaging AI”. Radiomics research will help us explore precise cancer diagnosis and the progression of disease at a deeper individual biological level. Much progress has been made in radiomics, and the rate of advances is accelerating. Undoubtedly, various AI tools and concepts in data science will play significant roles in accelerating advances in radiomics to reach the goal of precision medicine. 

This Special Issue presents a unique opportunity to bring together diverse perspectives from academics, industry professionals, and policymakers. We are open to a wide range of contributions, including original research articles, comprehensive reviews, and other related publications. By facilitating the timely communication of multidisciplinary research results, we aim to help form a global community of experts in AI radiomics. The topical areas may include (but are not limited to) the following:

  • Artificial intelligence tools and deep learning;
  • Generative artificial intelligence;
  • Segmentation;
  • Image labeling;
  • Image denoising;
  • Image normalization;
  • Feature extraction;
  • Strategy for standardization;
  • High-order statistics;
  • Integration of EHR data; 
  • Integration with proteomics and genomics;
  • Clinical adoption strategy and workflow optimization;
  • Training and education.

I look forward to receiving your contributions.

Prof. Dr. Seong K. Mun
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 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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • radiomics
  • learning tools
  • data normalization
  • segmentation
  • artificial intelligence
  • quantitative imaging
  • multi-dimensional feature analysis
  • statistical models
  • precision medicine

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Published Papers (2 papers)

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Research

17 pages, 3222 KiB  
Article
Radiomic Fingerprinting of the Peritumoral Edema in Brain Tumors
by Ghasem Azemi and Antonio Di Ieva
Cancers 2025, 17(3), 478; https://doi.org/10.3390/cancers17030478 - 1 Feb 2025
Cited by 1 | Viewed by 817
Abstract
Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role in tumor behavior and progression. While most studies focus on the radiomic features of the tumor core, this work investigates whether peritumoral edema exhibits distinct [...] Read more.
Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role in tumor behavior and progression. While most studies focus on the radiomic features of the tumor core, this work investigates whether peritumoral edema exhibits distinct radiomic fingerprints specific to glioma (GLI), meningioma (MEN), and metastasis (MET). By analyzing these patterns, we aim to deepen our understanding of the tumor microenvironment’s role in tumor development and progression. Methods: Radiomic features were extracted from peritumoral edema regions in T1-weighted (T1), post-gadolinium T1-weighted (T1-c), T2-weighted (T2), and T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) sequences. Three classification tasks using those features were then conducted: differentiating between Low-Grade Glioma (LGG) and High-Grade Glioma (HGG), distinguishing GLI from MET and MEN, and examining all four tumor types, i.e., LGG, HGG, MET, and MEN, to observe how tumor-specific signatures manifest in peritumoral edema. Model performance was assessed using balanced accuracy derived from 10-fold cross-validation. Results: The radiomic fingerprints specific to tumor types were more distinct in the peritumoral regions of T1-c images compared to other modalities. The best models, utilizing all features extracted from the peritumoral regions of T1-c images, achieved balanced accuracies of 0.86, 0.81, and 0.76 for the LGG-HGG, GLI-MET-MEN, and LGG-HGG-MET-MEN tasks, respectively. Conclusions: This study demonstrates that peritumoral edema, as characterized by radiomic features extracted from MRIs, contains fingerprints specific to tumor type, providing a non-invasive approach to understanding tumor-brain interactions. The results of this study hold the potential for predicting recurrence, distinguishing progression from pseudo-progression, and assessing treatment-induced changes, particularly in gliomas. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Radiomics in Cancer)
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20 pages, 3672 KiB  
Article
Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights
by Fatma M. Talaat, Samah A. Gamel, Rana Mohamed El-Balka, Mohamed Shehata and Hanaa ZainEldin
Cancers 2024, 16(21), 3668; https://doi.org/10.3390/cancers16213668 - 30 Oct 2024
Cited by 6 | Viewed by 2066
Abstract
Breast cancer (BCa) poses a severe threat to women’s health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of [...] Read more.
Breast cancer (BCa) poses a severe threat to women’s health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the radiologist’s experience. Therefore, we propose a novel, mammogram image-based BCa explainable AI (BCaXAI) model with a deep learning-based framework for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet V2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model’s decision-making process, fostering trust and confidence in the AI-based system. Based on using the DDSM and CBIS-DDSM mammogram datasets, BCaXAI achieved exceptional performance, surpassing traditional models such as ResNet50 and VGG16. The model demonstrated superior accuracy (98.53%), recall (98.53%), precision (98.40%), F1-score (98.43%), and AUROC (0.9933), highlighting its effectiveness in distinguishing between benign and malignant cases. These promising results could alleviate the diagnostic subjectivity that might arise as a result of the experience-variability between different radiologists, as well as minimize the need for repetitive biopsy procedures. Full article
(This article belongs to the Special Issue Artificial Intelligence-Assisted Radiomics in Cancer)
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