A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset
Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available d...
Main Authors: | Leandro Donisi, Giuseppe Cesarelli, Anna Castaldo, Davide Raffaele De Lucia, Francesca Nessuno, Gaia Spadarella, Carlo Ricciardi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-10-01
|
Series: | Journal of Imaging |
Subjects: | |
Online Access: | https://www.mdpi.com/2313-433X/7/10/215 |
Similar Items
-
Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation
by: Sithin Thulasi Seetha, et al.
Published: (2023-07-01) -
MRI-Based Radiomics Analysis of Levator Ani Muscle for Predicting Urine Incontinence after Robot-Assisted Radical Prostatectomy
by: Mohammed Shahait, et al.
Published: (2023-09-01) -
Quality of Multicenter Studies Using MRI Radiomics for Diagnosing Clinically Significant Prostate Cancer: A Systematic Review
by: Jeroen Bleker, et al.
Published: (2022-06-01) -
The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature
by: Linda My Huynh, et al.
Published: (2023-03-01) -
Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization
by: Lorenzo Ugga, et al.
Published: (2022-05-01)