ArticleTranslational cancer research2016
Prostate cancer radiomics and the promise of radiogenomics.
Article in Translational cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
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Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer, Dictionary Version PM1.0.Journal of imaging informatics in medicine · 2026Article
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis.World journal of radiology · 2026Article
- CT Radiomics-Based Machine Learning Model for Predicting Capsular and Neural Invasion in Thyroid Carcinoma: Diagnostic Accuracy Study.JMIR medical informatics · 2026Article
- Radiomics-Based Characterization of Aggressive Prostate Cancer Variants: Diagnostic Challenges and Opportunities.Cancers · 2026Review
- Machine learning-DeepSurv prediction model integrating mpMRI radiomics and genomic biomarkers for BCR-free survival and tumor response in prostate radiotherapy.Journal of radiation research · 2026Article
- Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study.EJNMMI reports · 2025Article
- Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.Insights into imaging · 2024Review
- Application of next-generation imaging in biochemically recurrent prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.BJR open · 2024Review
- Repeatability, reproducibility, and the effects of radiotherapy on radiomic features of lowfield MR-LINAC images of the prostate.Frontiers in oncology · 2024Article
- Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images.Journal of medical signals and sensors · 2024Article
- Article
- Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer.Gland surgery · 2023Review
- The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.Magma (New York, N.Y.) · 2023Article
22 more citing papers are in PubMed but not listed here.
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11 authors.
Funding
Abstract
Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated.
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