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Radiomics in Cancer
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Radiomics in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 14723

Special Issue Editor

Dr. Isabelle M. Germano

E-Mail Website
Guest Editor
Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York, NY, USA
Interests: machine learning; radiomics; brain cancer; neurosurgery; neuro

Special Issue Information

Dear Colleagues,

Radiomics is an innovative and rapidly expanding field that combines medical imaging, computer science, and data analysis to improve cancer care. By extracting detailed quantitative data from medical images, radiomics provides unique insights that can guide personalized treatment plans. This approach requires collaboration among radiologists, imaging scientists, and data scientists, each bringing a specialized expertise to navigate the complex workflow. The radiomics process typically follows a structured sequence: tumor segmentation, where the tumor is outlined; image preprocessing, which standardizes images for analysis; feature extraction, which identifies relevant characteristics; and finally, model development and validation, ensuring that results are reliable. Each step is essential to produce accurate, actionable insights.

One of the most promising aspects of radiomics is its potential to transform the diagnosis, staging, and management of cancer. Through advanced imaging analysis, radiomics can help in diagnosing and precisely staging different cancer types, enabling clinicians to make well-informed decisions. It can also predict the likelihood of metastasis, allowing doctors to consider more targeted treatments, and estimate patient survival, which can help with treatment planning and patient counseling. Additionally, radiomics offers a way to evaluate the effectiveness of therapies over time, potentially providing early indications of treatment response. These capabilities have significant implications for improving patient outcomes and tailoring interventions to individual patients’ needs.

Despite the enthusiasm surrounding radiomics, there are challenges and limitations that must be addressed to fully integrate it into routine clinical practice. Physicians and researchers need to be aware of potential pitfalls, such as variability in imaging techniques, data quality, and the reproducibility of results, which can impact the reliability of radiomic models. There is also the need for rigorous validation across diverse patient populations to ensure that models are generalizable. This Special Issue aims to explore these current limitations in radiomics for cancer, assess the state of the art, and discuss future directions for advancing the field, with the ultimate goal of refining and expanding radiomics to enhance cancer therapies and patient care.

Dr. Isabelle M. Germano
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 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

  • radiomic
  • medical imaging
  • personalized treatment
  • diagnosis
  • staging
  • management
  • targeted treatment
  • patient outcome
  • clinical practice
  • cancer therapies
  • patient care

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Published Papers (5 papers)

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Research

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17 pages, 1445 KB  
Article
A Deep Learning Model Integrating Clinical and MRI Features Improves Risk Stratification and Reduces Unnecessary Biopsies in Men with Suspected Prostate Cancer
by Emiliano Bacchetti, Axel De Nardin, Gianluca Giannarini, Lorenzo Cereser, Chiara Zuiani, Alessandro Crestani, Rossano Girometti and Gian Luca Foresti
Cancers 2025, 17(13), 2257; https://doi.org/10.3390/cancers17132257 - 7 Jul 2025
Cited by 6 | Viewed by 2518
Abstract
Background: Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. Methods: We retrospectively analysed 538 men who underwent MRI and biopsy between [...] Read more.
Background: Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. Methods: We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. Results: Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. Conclusions: Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions. Full article
(This article belongs to the Special Issue Radiomics in Cancer)
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Review

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21 pages, 584 KB  
Review
Beyond Imaging: Integrating Radiomics, Genomics, and Multi-Omics for Precision Breast Cancer Management
by Xiaorong Wu and Wei Dai
Cancers 2025, 17(21), 3408; https://doi.org/10.3390/cancers17213408 - 23 Oct 2025
Cited by 3 | Viewed by 3610
Abstract
Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct [...] Read more.
Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct biological interpretability. Combining information provided by radiomics with genomics or other multi-omics data can be important to personalise diagnostic and therapeutic work up in breast cancer management. This review aims to explore the current progress in integrating radiomics with multi-omics data—genomics and transcriptomics—to establish biologically grounded, multidimensional models for precision management of breast cancer. We will review recent advances in integrative radiomics and radiogenomics, highlight the synergy between imaging and molecular profiling, and discuss emerging machine learning methodologies that facilitate the integration of high-dimensional data. Applications of radiogenomics, including breast cancer subtype and molecular mutation prediction, radiogenomic mapping of the tumour immune microenvironment, and response forecasting to immunotherapy and targeted therapies, as well as lymph nodes involvement, will be evaluated. Challenges in technical limitations including imaging modalities harmonization, interpretability, and advancing machine learning methodologies will be addressed. This review positions integrative radiogenomics as a driving force for next-generation breast cancer care. Full article
(This article belongs to the Special Issue Radiomics in Cancer)
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14 pages, 721 KB  
Review
Role of Artificial Intelligence in Musculoskeletal Interventions
by Anuja Dubey, Hasaam Uldin, Zeeshan Khan, Hiten Panchal, Karthikeyan P. Iyengar and Rajesh Botchu
Cancers 2025, 17(10), 1615; https://doi.org/10.3390/cancers17101615 - 10 May 2025
Cited by 12 | Viewed by 4006
Abstract
Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods—including machine learning (ML) and deep learning (DL)—streamline diagnostic processes, guide interventions, and improve patient outcomes. Key applications discussed include ultrasound-guided procedures [...] Read more.
Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods—including machine learning (ML) and deep learning (DL)—streamline diagnostic processes, guide interventions, and improve patient outcomes. Key applications discussed include ultrasound-guided procedures for joints, nerves, and tumor-targeted interventions, along with CT-guided biopsies and ablations, and fluoroscopy-guided facet joint and nerve block injections. AI-powered segmentation algorithms, real-time feedback systems, and dose-optimization protocols collectively enable greater precision, operator consistency, and patient safety. In rehabilitation, AI-driven wearables and predictive models facilitate personalized exercise programs that can accelerate recovery and enhance long-term function. While challenges persist—such as data standardization, regulatory hurdles, and clinical adoption—ongoing interdisciplinary collaboration, federated learning models, and the integration of genomic and environmental data hold promise for expanding AI’s capabilities. As personalized medicine continues to advance, AI is poised to refine risk stratification, reduce radiation exposure, and support minimally invasive, patient-specific interventions, ultimately reshaping musculoskeletal care from early detection and diagnosis to individualized treatment and rehabilitation. Full article
(This article belongs to the Special Issue Radiomics in Cancer)
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18 pages, 1647 KB  
Review
The Evolving Landscape of Radiomics in Gliomas: Insights into Diagnosis, Prognosis, and Research Trends
by Mehek Dedhia and Isabelle M. Germano
Cancers 2025, 17(9), 1582; https://doi.org/10.3390/cancers17091582 - 6 May 2025
Cited by 8 | Viewed by 3553
Abstract
Gliomas are the most prevalent and aggressive form of primary brain tumors. The clinical challenge in managing patients with this disease revolves around the difficulty of diagnosis, both at onset and during treatment, and the scarcity of prognostic outcome indicators. Radiomics involves the [...] Read more.
Gliomas are the most prevalent and aggressive form of primary brain tumors. The clinical challenge in managing patients with this disease revolves around the difficulty of diagnosis, both at onset and during treatment, and the scarcity of prognostic outcome indicators. Radiomics involves the extraction of quantitative features from medical images with the help of artificial intelligence, positioning it as a promising tool to be integrated into the care of glioma patients. Using data from 52 studies and 12,482 patients over two years, this review explores how radiomics can enhance the initial diagnosis of gliomas, especially helping to differentiate treatment stages that may be difficult for the human eye to do otherwise. Radiomics has also been able to identify patient-specific tumor molecular signatures for targeted treatments without the need for invasive surgical biopsy. Such an approach could lead to earlier interventions and more precise individualized therapies that are tailored to each patient. Additionally, it could be integrated into clinical practice to improve longitudinal diagnosis during treatment and predict tumor recurrence. Finally, radiomics has the potential to predict clinical outcomes, helping both patients and providers set realistic expectations. While this field is continuously evolving, future research should conduct such studies in larger, multi-institutional cohorts to enhance generalizability and applicability in clinical practice and focus on combining radiomics with other modalities to improve its predictive accuracy and clinical utility. Full article
(This article belongs to the Special Issue Radiomics in Cancer)
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Other

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25 pages, 1313 KB  
Systematic Review
Radiomics’ Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review
by Huda Mohammed, Hadeel Mohamed, Momoh Fofana, Samreen Jawaid, Mohamed Hersi, Omneya Alwani, Roshith Nair and Jayesh Sagar
Cancers 2026, 18(9), 1440; https://doi.org/10.3390/cancers18091440 - 30 Apr 2026
Viewed by 418
Abstract
Radiomics involves the extraction of quantitative information from medical images and can offer valuable insights into the management of rectal cancer (RC). We aim to systematically review the current literature on radiomics’ role in the prediction of lymph nodes and distance metastases, local [...] Read more.
Radiomics involves the extraction of quantitative information from medical images and can offer valuable insights into the management of rectal cancer (RC). We aim to systematically review the current literature on radiomics’ role in the prediction of lymph nodes and distance metastases, local recurrence and survival outcomes in rectal cancer. Method: This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and principles. We systematically searched PubMed, Cochrane, Google Scholar, and ResearchGate databases to identify the relevant articles. Result: After searching 3327 articles, only 50 articles were included after assessing the quality using a radiomics quality score (QRS) and finding a mean score of 14.74. Only eight studies used CT radiomics, with the rest based on MRI radiomics. Only three of the included studies are prospective, with the others being retrospective cohort studies and 14 having external validation. Most of the included studies concluded that radiomic models alone or combined models achieve better outcome predictions when compared with clinical and subjective analysis. Conclusions: Radiomics offers significant promise as a non-invasive tool for predicting lymph node status, distant metastasis (DM), recurrence, and survival outcomes in rectal cancer. There is a need for more robust prospective studies with cost benefit analysis for implementation into clinical practice. Full article
(This article belongs to the Special Issue Radiomics in Cancer)
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