ArticleCancers2020
Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach.Journal of medical radiation sciences · 2025Article
- Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024Review
- Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.Cancers · 2023Article
- Article
- Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review.Journal of personalized medicine · 2023Review
- Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models.Magma (New York, N.Y.) · 2023Article
- Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer.Frontiers in endocrinology · 2023Article
- Precision Medicine in Radiomics and Radiogenomics.Journal of personalized medicine · 2022Article
- MiR-145 suppresses the motility of prostate cancer cells by targeting cadherin-2.Molecular and cellular biochemistry · 2021Article
- Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions.Scientific reports · 2021Article
- MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI.Frontiers in oncology · 2021Article
- MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis.Frontiers in medicine · 2021Article
- A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions.Frontiers in oncology · 2021Article
- Article
- What Is the Role of Imaging in Cancers?Cancers · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.
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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.