Trial reportEuropean radiology2021
MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218).
Trial report in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Diagnostic accuracy of MRI radiomics in predicting lymph node metastasis in prostate cancer: A systematic review.European journal of radiology open · 2025Review
- Predicting bone metastasis and high-grade Gleason scores in prostate cancer: a retrospective study integrating clinical features and magnetic resonance imaging radiomics.Translational andrology and urology · 2025Article
- Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.Military Medical Research · 2025Article
- Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.Clinical proteomics · 2025Article
- An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer.Frontiers in oncology · 2025Article
- Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use.Radiology. Artificial intelligence · 2024Review
- Investigation of radiomics models for predicting biochemical recurrence of advanced prostate cancer on pretreatment MR ADC maps based on automatic image segmentation.Journal of applied clinical medical physics · 2024Article
- Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.Insights into imaging · 2024Article
- Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer.Frontiers in oncology · 2024Article
- Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022.Quantitative imaging in medicine and surgery · 2023Article
- Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.Insights into imaging · 2023Review
- Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study.Discover oncology · 2023Article
- Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness.Scientific reports · 2023Article
- The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics.Frontiers in endocrinology · 2023Article
- Quality assurance for automatically generated contours with additional deep learning.Insights into imaging · 2022Article
- Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.European radiology · 2022Article
- Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target, and pathological detection.World journal of surgical oncology · 2022Article
- Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics.Cancers · 2021Article
Corrections and comments
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Authors and funding
25 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesRadiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa).
methodsThis study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis.
resultsForty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94.
conclusionsMRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. KEY POINTS: • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.