ArticleCurrent oncology (Toronto, Ont.)2023
Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features.
Article in Current oncology (Toronto, Ont.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 27 citations in OpenAlex.
- Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [Prostate cancer and prostatic diseases · 2026Article
- Development and Validation of an AI-Assisted Predictive Model Integrating R2* Mapping and Clinical Indicators for Clinically Significant Prostate Cancer.Cancer medicine · 2026Article
- Biparametric magnetic resonance imaging-based radiomics model can improve the detection of dense and sparse prostate cancers.Quantitative imaging in medicine and surgery · 2025Article
- Radiologic Assessment of Periprostatic Fat as an Indicator of Prostate Cancer Risk on Multiparametric MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Transperineal Free-Hand Prostate Fusion Biopsy with AI-Driven Auto-Contouring: First Results of a Prospective Study.Cancers · 2025Article
- A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach.Journal of medical radiation sciences · 2025Article
- Promising Results About the Possibility to Identify Prostate Cancer Patients Employing a Random Forest Classifier: A Preliminary Study Preoperative Patients Selection.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer.Frontiers in oncology · 2025Article
- Tumour habitat-based radiomics analysis enhances the ability to predict prostate cancer aggressiveness with biparametric MRI-derived features.Frontiers in oncology · 2025Article
- Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models.Frontiers in oncology · 2025Article
- Development and Validation of an MRI-Based Radiomics Nomogram to Predict the Prognosis of De Novo Oligometastatic Prostate Cancer Patients.Cancer medicine · 2024Article
- Article
- Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.Scientific reports · 2024Article
- Review
- Prevalence and potential predictors of incidental prostate Cancer in patients undergoing surgery for Benign Prostatic obstruction: a retrospective study in the MRI era.World journal of urology · 2024Article
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).Quantitative imaging in medicine and surgery · 2024Article
- Can a nomogram predict apical prostate cancer pathology upgrade from fusion biopsy to final pathology? A multicenter study.Cancer medicine · 2024Article
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Authors and funding
17 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer.
methodsFrom September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation.
resultsThe feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%.
conclusionsRadiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.
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