ArticleSensors (Basel, Switzerland)2020
Radiomics for Gleason Score Detection through Deep Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- 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
- MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.Journal of Zhejiang University. Science. B · 2023Review
- Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer.Journal of personalized medicine · 2022Article
- Article
- Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer.The British journal of radiology · 2022Article
- The added value of PSMA PET/MR radiomics for prostate cancer staging.European journal of nuclear medicine and molecular imaging · 2022Article
- Artificial intelligence assists precision medicine in cancer treatment.Frontiers in oncology · 2022Review
- Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study.Journal of clinical medicine · 2021Article
- Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization.International journal of molecular sciences · 2021Review
- Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift.Journal of clinical medicine · 2021Review
- Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI.Frontiers in oncology · 2021Article
- MRI Radiomics in Prostate Cancer: A Reliability Study.Frontiers in oncology · 2021Article
- Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion.Computational intelligence and neuroscience · 2021Article
- Radiomics in prostate cancer: an up-to-date review.Therapeutic advances in urologyReview
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction.
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Registered trials
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.