A Radiomics Machine Learning Based Redefining Score Robustly Identifies Clinically Significant Prostate Cancer in Equivocal PI-RADS Score 3 Lesions

2020 ◽  
Author(s):  
Ying Hou ◽  
Mei-Ling Bao ◽  
Chen-Jiang Wu ◽  
Jing Zhang ◽  
Yu-Dong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Clinically Significant

Development of a machine learning algorithm to accurately detect clinically significant prostate cancer on MRI

2019 ◽  
Vol 18 (11) ◽  
pp. e3493
Author(s):  
J.W.M. Dillon ◽  
T.I. Whish-Wilson ◽  
S.J. Riddell ◽  
L-M. Wong ◽  
P. Brotchie ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Clinically Significant

A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions

2020 ◽  
Vol 45 (12) ◽  
pp. 4223-4234
Author(s):  
Ying Hou ◽  
Mei-Ling Bao ◽  
Chen-Jiang Wu ◽  
Jing Zhang ◽  
Yu-Dong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Clinically Significant

scholarly journals Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

2020 ◽  
Vol 30 (12) ◽  
pp. 6757-6769 ◽  
Author(s):  
Simon Bernatz ◽  
Jörg Ackermann ◽  
Philipp Mandel ◽  
Benjamin Kaltenbach ◽  
Yauheniya Zhdanovich ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Clinical Significance ◽  
Clinical Assessment ◽  
Diagnostic Performance ◽  
Learning Algorithms ◽  
Peripheral Zone ◽  
Multiparametric Mri ◽  
Machine Learning Algorithms ◽  
Clinically Significant

Abstract Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis

2020 ◽  
Vol 30 (12) ◽  
pp. 6877-6887 ◽  
Author(s):  
Renato Cuocolo ◽  
Maria Brunella Cipullo ◽  
Arnaldo Stanzione ◽  
Valeria Romeo ◽  
Roberta Green ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Meta Analysis ◽  
Clinically Significant

scholarly journals Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

2021 ◽  
Author(s):  
Tao Peng ◽  
JianMing Xiao ◽  
Lin Li ◽  
BingJie Pu ◽  
XiangKe Niu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Machine Learning ◽  
Clinical Parameters ◽  
Resonance Imaging ◽  
Internal Validation ◽  
Dce Mri ◽  
Machine Learning Model ◽  
Temporal Validation ◽  
Clinically Significant

Abstract Purpose To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation. Methods The dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong’s method. Results Eight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05). Conclusions Each machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance.

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