ArticleScientific reports2023
Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Radiomics Reproducibility in Prostate Cancer Diagnosis Based on PROSTATEx.International neurourology journal · 2025Article
- Beyond Radiomics Alone: Enhancing Prostate Cancer Classification with ADC Ratio in a Multicenter Benchmarking Study.Diagnostics (Basel, Switzerland) · 2025Article
- Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.BMC medical imaging · 2025Article
- Artificial Intelligence-Assisted Segmentation of Prostate Tumors and Neurovascular Bundles: Applications in Precision Surgery for Prostate Cancer.Annals of surgical oncology · 2025Article
- Article
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
- DWI-based Biologically Interpretable Radiomic Nomogram for Predicting 1-year Biochemical Recurrence after Radical Prostatectomy: A Deep Learning, Multicenter Study.Current medical imaging · 2025Article
- Reproducibility of MRI-derived radiomic features in prostate cancer detection: a methodological approach.Polish journal of radiology · 2025Article
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.BJR artificial intelligence · 2024Article
- Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer.Frontiers in oncology · 2024Article
- AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer.International urology and nephrology · 2023Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
There is a growing piece of evidence that artificial intelligence may be helpful in the entire prostate cancer disease continuum. However, building machine learning algorithms robust to inter- and intra-radiologist segmentation variability is still a challenge. With this goal in mind, several model training approaches were compared: removing unstable features according to the intraclass correlation coefficient (ICC); training independently with features extracted from each radiologist's mask; training with the feature average between both radiologists; extracting radiomic features from the intersection or union of masks; and creating a heterogeneous dataset by randomly selecting one of the radiologists' masks for each patient. The classifier trained with this last resampled dataset presented with the lowest generalization error, suggesting that training with heterogeneous data leads to the development of the most robust classifiers. On the contrary, removing features with low ICC resulted in the highest generalization error. The selected radiomics dataset, with the randomly chosen radiologists, was concatenated with deep features extracted from neural networks trained to segment the whole prostate. This new hybrid dataset was then used to train a classifier. The results revealed that, even though the hybrid classifier was less overfitted than the one trained with deep features, it still was unable to outperform the radiomics model.
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