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Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours

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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 2026 | Viewed by 9204

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Special Issue Editors

Dr. Francesco Bianconi

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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

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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 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. 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 (5 papers)

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Research

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15 pages, 1100 KB

Open AccessArticle

18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study

by Fabiana Esposito, Luigi Manco, Luca Urso, Sara Adamantiadis, Giovanni Scribano, Lucrezia De Marchi, Adriano Venditti, Massimiliano Postorino, Nicoletta Urbano, Roberta Gafà, Antonio Cuneo, Agostino Chiaravalloti, Mirco Bartolomei and Luca Filippi

Cancers 2025, 17(11), 1827; https://doi.org/10.3390/cancers17111827 - 30 May 2025

Cited by 2 | Viewed by 2831

Abstract

Purpose: This bi-centric pilot study investigates the predictive value of pre-treatment [¹⁸F]FDG PET/CT radiomics for assessing therapy response in primary mediastinal B-cell lymphoma (PMBCL). Methods: All PMBCL patients underwent PET/CT with [¹⁸F]FDG between January 2011 and January 2022 at Policlinico Tor Vergata University Hospital of Rome (70% training and 30% internal validation cohort) and Sant’Anna University Hospital of Ferrara (external validation cohort). The Deauville score (DS) was used as a predictor of therapy response (DS1-DS3 vs. DS4/DS5). A total of 121 quantitative radiomics features (RFts) were extracted from manually segmented volumes of interest (VOIs) in PET and CT images, according to IBSI. ComBat harmonization was applied to correct the center variability of features, followed by class balancing with SMOTE. Two machine learning (ML) prediction models, the PET model and the CT model, were independently developed using robust RFts. For each ML model, two different algorithms were trained (i.e., Random Forest, RF, and Support Vector Machine, SVM) using 10-fold cross validation, tested on the internal/external validation set. Receiver operating characteristic (ROC) curves, area under the curve (AUC), classification accuracy (CA), precision (Prec), sensitivity (Sen), specificity (Spec), true positive (TP) scores, and true negative (TN) scores were computed. Results: The entire dataset was composed of 29 samples for the Rome cohort (23 from D1–D3 and 6 from D4/D5) and 9 samples for the Ferrara cohort (4 from D1–D3 and 5 from D4/D5). A total of 27 RFts were identified as robust for each imaging modality. Both the CT and PET models effectively predicted the Deauville score. The performance metrics of the best classifier (SVM) for the CT and PET models in external validation were AUC = 0.75/0.80, CA = 0.85/0.77, Prec = 0.97/0.67, Sen = 0.60/0.80, Spec = 0.98/0.75, TP = 75.0%/66.7%, and TN = 77.8%/85.7%, respectively. Conclusions: ML models trained on [¹⁸F]FDG PET/CT radiomic features in PMBLC patients could predict the Deauville score. Full article

(This article belongs to the Special Issue Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours)

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17 pages, 7314 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1798

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 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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36 pages, 2413 KB

Open AccessReview

Advances in Functional and Metabolic Imaging for Early Tumor Treatment Response and Resistance Evaluation: A Review

by Dengwei Gan, Wenhui Ma, Huan Jie, Cong Huang and Fang Xu

Cancers 2026, 18(5), 858; https://doi.org/10.3390/cancers18050858 - 7 Mar 2026

Viewed by 485

Abstract

Early assessment of tumor treatment response and elucidation of resistance mechanisms are critical for optimizing therapeutic strategies and improving patient outcomes. Functional and metabolic imaging technologies, particularly positron emission tomography (PET) combined with specific tracers, enable dynamic monitoring of tumor cell metabolism and microenvironmental changes during the initial phases of therapy. This capability facilitates early prediction of treatment efficacy and investigation into mechanisms underlying drug resistance. This review synthesizes recent advances in the application of functional and metabolic imaging for early tumor treatment response evaluation and resistance assessment. Emphasis is placed on integrating multimodal imaging techniques with molecular biology approaches to comprehensively analyze the relationships among imaging biomarkers, tumor heterogeneity, immune microenvironment, and molecular pathways. The article further explores the clinical translational potential of these imaging modalities while addressing current challenges and limitations. By providing an updated overview of this rapidly evolving field, this review aims to guide future research and clinical application toward more precise and personalized oncology care. Full article

(This article belongs to the Special Issue Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours)

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18 pages, 8697 KB

Open AccessReview

Radiomics-Based Characterization of Aggressive Prostate Cancer Variants: Diagnostic Challenges and Opportunities

by Katarzyna Sklinda, Martyna Rajca, Marek Kasprowicz, Łukasz Michałowski, Michał Małek, Bartłomiej Olczak and Jerzy Walecki

Cancers 2026, 18(5), 780; https://doi.org/10.3390/cancers18050780 - 28 Feb 2026

Viewed by 465

Abstract

Background/Objectives: Aggressive variants of prostate cancer pose significant diagnostic and prognostic challenges due to atypical imaging appearances, variable prostate-specific antigen behavior, and distinct molecular features. Conventional imaging may underestimate their biological aggressiveness. This review aimed to synthesize current evidence on imaging characteristics, biomarker dynamics, tumor localization, histology, and radiomic features of aggressive prostate cancer variants, and to evaluate the potential role of radiomics in early recognition and risk stratification. Methods: A structured narrative review was performed of studies reporting imaging, clinical, and molecular features of aggressive prostate cancer variants. Imaging modalities included multiparametric magnetic resonance imaging, positron emission tomography with prostate-specific membrane antigen or fluorodeoxyglucose, bone scintigraphy, and transrectal ultrasound. Data on prostate-specific antigen levels and kinetics, intraprostatic tumor location, tumor size, metastatic patterns, and molecular alterations were extracted. Evidence for rare entities such as basaloid and primary squamous carcinomas was derived from published case reports and series, while selected variants were complemented by institutional imaging and histopathologic observations. Results: Neuroendocrine and small cell carcinomas frequently showed low prostate-specific antigen levels, high fluorodeoxyglucose uptake, low prostate-specific membrane antigen expression, and central or transitional zone involvement with large tumor size at diagnosis. Ductal adenocarcinoma demonstrated marked diffusion restriction and elevated prostate-specific antigen, whereas basal cell carcinoma often appeared inconspicuous on conventional imaging. Radiomic analysis consistently captured tumor heterogeneity and spatial complexity beyond standard qualitative metrics. Conclusions: Aggressive prostate cancer variants represent a diagnostic blind spot in routine imaging. Radiomics offers complementary quantitative information that may improve early detection, subtype differentiation, and risk stratification when integrated into multimodal imaging workflows. Further prospective and radiogenomic studies are warranted to validate these findings. Full article

(This article belongs to the Special Issue Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours)

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34 pages, 2945 KB

Open AccessReview

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

Cited by 6 | Viewed by 2820

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 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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