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Cancers
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Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours
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Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1053

Special Issue Editors

Dr. Francesco Bianconi

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Perugia, Perugia, Italy
Interests: artificial intelligence; computational imaging; computer vision; image processing; medical image analysis; radiomics
Special Issues, Collections and Topics in MDPI journals
Dr. Barbara Palumbo

E-Mail Website
Guest Editor
Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Italy
Interests: nuclear medicine; image-based diagnostics; artificial intelligence; PET/CT; SPECT; SPECT/CT; radiomics; oncology; neurodegenerative disorders
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radiomics is an approach to medical imaging that is aimed at extracting quantitative features that would otherwise go unnoticed to the human eye. By leveraging artificial intelligence and machine learning algorithms, radiomics can generate prediction models capable of assisting medical professionals in clinical decision-making. In this context, radiomics has attracted increasing worldwide interest as a potential tool for diagnosis, risk stratification, and treatment planning. Yet the translation of radiomics into clinical practise still faces some major hurdles, such as standardisation and reproducibility problems, lack of data for training the models, man-machine interaction issues (e.g., interpretability and willingness to accept the results of an algorithm), as well as legal and ethical issues. Radiomics is also a strongly multidisciplinary discipline, and its success depends a great deal on the cooperation of experts from different fields, including physicians, biologists, mathematicians, statisticians, computer scientists, and engineers.

This Special Issue wants to provide a forum to discuss challenges, discoveries, and opportunities of radiomics in the field of solid tumours. We welcome both methodological and application-oriented contributions. We encourage the submission of original research articles, reviews, and comparative evaluations.

Dr. Francesco Bianconi
Dr. Barbara Palumbo
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 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 for the diagnosis, prognostication and treatment planning of solid tumours
  • methods, translational research and clinical applications
  • conventional and deep learning radiomics
  • interpretability of radiomics features and prediction models
  • image processing (including acquisition, segmentation and feature extraction)
  • imaging methods (including CT, MRI, PET, SPECT and US)

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Published Papers (2 papers)

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Research

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17 pages, 7314 KiB  
Article
Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning
by Dimitrije Sarac, Milica Badza Atanasijevic, Milica Mitrovic Jovanovic, Jelena Kovac, Ljubica Lazic, Aleksandra Jankovic, Dusan J. Saponjski, Stefan Milosevic, Katarina Stosic, Dragan Masulovic, Dejan Radenkovic, Veljko Papic and Aleksandra Djuric-Stefanovic
Cancers 2025, 17(7), 1119; https://doi.org/10.3390/cancers17071119 - 27 Mar 2025
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Abstract
Background: This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images. Methods: We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma [...] Read more.
Background: This study analyzed different classifier models for differentiating pancreatic adenocarcinoma from surrounding healthy pancreatic tissue based on radiomic analysis of magnetic resonance (MR) images. Methods: We observed T2W-FS and ADC images obtained by 1.5T-MR of 87 patients with histologically proven pancreatic adenocarcinoma for training and validation purposes and then tested the most accurate predictive models that were obtained on another group of 58 patients. The tumor and surrounding pancreatic tissue were segmented on three consecutive slices, with the largest area of interest (ROI) of tumor marked using MaZda v4.6 software. This resulted in a total of 261 ROIs for each of the observed tissue classes in the training–validation group and 174 ROIs in the testing group. The software extracted a total of 304 radiomic features for each ROI, divided into six categories. The analysis was conducted through six different classifier models with six different feature reduction methods and five-fold subject-wise cross-validation. Results: In-depth analysis shows that the best results were obtained with the Random Forest (RF) classifier with feature reduction based on the Mutual Information score (all nine features are from the co-occurrence matrix): an accuracy of 0.94/0.98, sensitivity of 0.94/0.98, specificity of 0.94/0.98, and F1-score of 0.94/0.98 were achieved for the T2W-FS/ADC images from the validation group, retrospectively. In the testing group, an accuracy of 0.69/0.81, sensitivity of 0.86/0.82, specificity of 0.52/0.70, and F1-score of 0.74/0.83 were achieved for the T2W-FS/ADC images, retrospectively. Conclusions: The machine learning approach using radiomics features extracted from T2W-FS and ADC achieved a relatively high sensitivity in the differentiation of pancreatic adenocarcinoma from healthy pancreatic tissue, which could be especially applicable for screening purposes. Full article
(This article belongs to the Special Issue Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours)
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Review

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34 pages, 2945 KiB  
Review
Radiogenomic Landscape of Metastatic Endocrine-Positive Breast Cancer Resistant to Aromatase Inhibitors
by Richard Khanyile, Talent Chipiti, Rodney Hull and Zodwa Dlamini
Cancers 2025, 17(5), 808; https://doi.org/10.3390/cancers17050808 - 26 Feb 2025
Viewed by 528
Abstract
Breast cancer poses a significant global health challenge and includes various subtypes, such as endocrine-positive, HER2-positive, and triple-negative. Endocrine-positive breast cancer, characterized by estrogen and progesterone receptors, is commonly treated with aromatase inhibitors. However, resistance to these inhibitors can hinder patient outcomes due [...] Read more.
Breast cancer poses a significant global health challenge and includes various subtypes, such as endocrine-positive, HER2-positive, and triple-negative. Endocrine-positive breast cancer, characterized by estrogen and progesterone receptors, is commonly treated with aromatase inhibitors. However, resistance to these inhibitors can hinder patient outcomes due to genetic and epigenetic alterations, mutations in the estrogen receptor 1 gene, and changes in signaling pathways. Radiogenomics combines imaging techniques like MRI and CT scans with genomic profiling methods to identify radiographic biomarkers associated with resistance. This approach enhances our understanding of resistance mechanisms and metastasis patterns, linking them to specific genomic profiles and common metastasis sites like the bone and brain. By integrating radiogenomic data, personalized treatment strategies can be developed, improving predictive and prognostic capabilities. Advancements in imaging and genomic technologies offer promising avenues for enhancing radiogenomic research. A thorough understanding of resistance mechanisms is crucial for developing effective treatment strategies, making radiogenomics a valuable integrative approach in personalized medicine that aims to improve clinical outcomes for patients with metastatic endocrine-positive breast cancer. Full article
(This article belongs to the Special Issue Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours)
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