Prostate-cancer imaging using machine-learning classifiers: Potential value for guiding biopsies, targeting therapy, and monitoring treatment

2009 ◽  
Author(s):  
Ernest J. Feleppa ◽  
Mark J. Rondeau ◽  
Paul Lee ◽  
Christopher R. Porter
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Imaging ◽  
Targeting Therapy ◽  
Machine Learning Classifiers ◽  
Potential Value ◽  
Learning Classifiers ◽  
Monitoring Treatment

Identification of patients with low-risk for aneuploidy: Comparative discriminatory models using linear and machine-learning classifiers in prostate cancer

The Prostate ◽  
2007 ◽  
Vol 67 (14) ◽  
pp. 1524-1536 ◽  
Author(s):  
Girish Venkataraman ◽  
Georg Heinze ◽  
Earle W. Holmes ◽  
Vijayalakshmi Ananthanarayanan ◽  
David G. Bostwick ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Low Risk ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

2019 ◽  
Vol 29 (9) ◽  
pp. 4754-4764 ◽  
Author(s):  
Michela Antonelli ◽  
Edward W. Johnston ◽  
Nikolaos Dikaios ◽  
King K. Cheung ◽  
Harbir S. Sidhu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Transition Zone ◽  
Cross Validation ◽  
Diagnostic Sensitivity ◽  
Machine Learning Classifiers ◽  
Gleason Pattern ◽  
Learning Classifiers ◽  
The Mean ◽  
Mean Sensitivity

Abstract Objective The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. Methods A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. Results The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). Conclusions Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. Key Points • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

scholarly journals Correction to: Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

2019 ◽  
Vol 30 (2) ◽  
pp. 1295-1295
Author(s):  
Michela Antonelli ◽  
Edward W. Johnston ◽  
Nikolaos Dikaios ◽  
King K. Cheung ◽  
Harbir S. Sidhu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Gleason Pattern ◽  
Learning Classifiers

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

Sign in / Sign up

Export Citation Format

Share Document