Trial reportPloS one2019
Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis.World journal of radiology · 2026Article
- Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.European journal of nuclear medicine and molecular imaging · 2025Article
- Combining MRI radiomics, hypoxia gene signature score and clinical variables for prediction of biochemical recurrence-free survival after radiotherapy in prostate cancer.La Radiologia medica · 2025Article
- Dosiomic predictors of biochemical failure in patients with localized prostate cancer treated with Iodine-125 low-dose-rate brachytherapy.Radiation oncology (London, England) · 2025Article
- Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer.Cancers · 2025Article
- An interpretable clinical-radiomics-deep learning model based on magnetic resonance imaging for predicting postoperative Gleason grading in prostate cancer: a dual-center study.Frontiers in oncology · 2025Article
- Lesion detection using artificial intelligence models in MR images of prostate cancer and prostatitis patients and comparison of model performance.Frontiers in urology · 2025Article
- Computational analysis of variability and uncertainty in the clinical reference on magnetic resonance imaging radiomics: modelling and performance.Visual computing for industry, biomedicine, and art · 2024Article
- Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.Insights into imaging · 2024Review
- Evaluation of Machine Learning Classification Models for False-Positive Reduction in Prostate Cancer Detection Using MRI Data.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Article
- Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.Abdominal radiology (New York) · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- Role of Artificial Intelligence in Surgical Decision-Making: A Comprehensive Review.Galen medical journal · 2024Review
- Evaluation of the Reliability and the Performance of Magnetic Resonance Imaging Radiomics in the Presence of Randomly Generated Irrelevant Features for Prostate Cancer.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2).
methodsT2w, DWI (12 b values, 0-2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.
resultsIn total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82-0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.
conclusionTexture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.
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