scholarly journals Prediction of Gleason Grade Group of Prostate Cancer on Multiparametric MRI using Deep Machine Learning Models

2020 ◽  
Vol 108 (2) ◽  
pp. E9-E10
Author(s):  
Weiwei Zong ◽  
Joon Lee ◽  
Milan Pantelic ◽  
Ning Wen
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Multiparametric Mri ◽  
Gleason Grade ◽  
Learning Models ◽  
Grade Group ◽  
Machine Learning Models

Machine-Learning Models for Multicenter Prostate Cancer Treatment Plans

2020 ◽  
Author(s):  
Khajamoinuddin Syed ◽  
William Sleeman ◽  
Payal Soni ◽  
Michael Hagan ◽  
Jatinder Palta ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Treatment ◽  
Learning Models ◽  
Prostate Cancer Treatment ◽  
Treatment Plans ◽  
Machine Learning Models

scholarly journals Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models 

2020 ◽  
Author(s):  
Hailang Liu ◽  
Kun Tang ◽  
Ejun Peng ◽  
Liang Wang ◽  
Ding Xia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Support Vector ◽  
Gleason Grade ◽  
Good Discrimination ◽  
Grade Group ◽  
Testing Dataset

Abstract Background: This study aimed to develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions.Methods: We retrospectively collected data from prostate cancer (PCa) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), random forest (RF) and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730 and 0.695, respectively, followed by SVM (AUC 0.740, 95% confidence interval [CI]: 0.690–0.790), LR (AUC 0.725, 95% CI: 0.674–0.776) and RF (AUC 0.666, 95% CI: 0.618–0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC 0.735, 95% CI: 0.656–0.813), followed by SVM (AUC 0.723, 95% CI: 0.644–0.802), LR (AUC 0.697, 95% CI: 0.615–0.778) and RF (AUC 0.607, 95% CI: 0.531–0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. Conclusion: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.

scholarly journals A Hierarchical Machine Learning Model to Discover Gleason Grade Group-specific Biomarkers in Prostate Cancer

2019 ◽  
Author(s):  
Osama Hamzeh ◽  
Abedalrhman Alkhateeb ◽  
Julia Zheng ◽  
Srinath Kandalam ◽  
Crystal Lueng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Gleason Score ◽  
Cancer Progression ◽  
Class Imbalance ◽  
Gleason Grade ◽  
Gene Expressions ◽  
Grade Group ◽  
Potential Biomarkers

1) Background: One of the deadliest cancers that affect men worldwide and North American men is prostate cancer. This disease motivates parts of the cells in the prostate to lose control of their growth and division. 2) Methods: We are proposing a machine learning method used to analyze gene expressions of prostate tumors with different Gleason scores, and to identify potential genetic biomarkers for each group. A publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients have been retrieved from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository. We categorize patients by their Gleason scores into different groups to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups (hereinafter called nodes) to identify and predict nodes based on their mRNA or gene expressions. At each node, patient samples are analyzed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome of each node is a set of genes that can separate the Gleason group from the remaining groups. To validate the proposed method, the set of identified genes are used to classify a second dataset of 499 prostate cancer patients that have been collected from cBioportal.. 3) Results: Two genes have been found to be potential biomarkers of specific Gleason groups; PIAS3 has been identifed for Gleason score 4+3=7, while UBE2 could be a poteintial biomarker for Gleason score 6. Other proposed genes that were not found in the literature might be potential biomarkers. 4) Conclusion: The latest literature supports that the genes predicted by the proposed method are strongly correlated with prostate cancer progression and tumour development processes. Furthermore, pathway analysis shows that both PIAS3 and UBE2 share the same protein interaction pathway, the JAK/STAT signaling process.

scholarly journals Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1767 ◽  
Author(s):  
Piotr Woźnicki ◽  
Niklas Westhoff ◽  
Thomas Huber ◽  
Philipp Riffel ◽  
Matthias F. Froelich ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Prostate Gland ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Predictive Ability ◽  
Image Data ◽  
Learning Models ◽  
Potential Biomarker ◽  
Machine Learning Models

Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist’s evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

scholarly journals Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-support Models

2020 ◽  
Author(s):  
Hailang Liu ◽  
Kun Tang ◽  
Ejun Peng ◽  
Liang Wang ◽  
Ding Xia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Support Vector ◽  
Gleason Grade ◽  
Good Discrimination ◽  
Grade Group ◽  
Testing Dataset

Abstract Objective: To develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions by integrating multiple clinical characteristics.Materials and Methods: We retrospectively collected data from PCa (prostate cancer) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by Lasso regularization (Lasso-LR), random forest (RF) and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730 and 0.695, respectively, followed by SVM (AUC 0.740, 95%CI: 0.690–0.790), LR (AUC 0.725, 95%CI: 0.674–0.776) and RF (AUC 0.666, 95%CI: 0.618–0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC 0.735, 95%CI: 0.656–0.813), followed by SVM (AUC 0.723, 95%CI: 0.644–0.802), LR (AUC 0.697, 95%CI: 0.615–0.778) and RF (AUC 0.607, 95%CI: 0.531–0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. Conclusions: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.

scholarly journals A Hierarchical Machine Learning Model to Discover Gleason Grade Group-specific Biomarkers in Prostate Cancer

2019 ◽  
Author(s):  
Osama Hamzeh ◽  
Abedalrhman Alkhateeb ◽  
Julia Zheng ◽  
Srinath Kandalam ◽  
Crystal Lueng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Gleason Score ◽  
Class Imbalance ◽  
Gleason Grade ◽  
Grade Group ◽  
Next Generation Sequencing Technology ◽  
Potential Biomarker

1) Background: One of the most common cancer that affects men worldwide and North American men is prostate cancer. Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. The advancement in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason score more accurately and effectively. 2) Methods: In this study, we used a novel machine learning method to analyze gene expression of prostate tumors with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) repository, and categorized patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analyzed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node is a set of genes that can differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes are used to classify a second dataset of 499 prostate cancer patients collected from cBioportal [1]. 3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%, and further validated to 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4+3=7, and UBE2V2 for Gleason score 6. 4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.

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