scholarly journals Voxel‐based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

2020 ◽  
Vol 21 (10) ◽  
pp. 179-191
Author(s):  
Neda Gholizadeh ◽  
John Simpson ◽  
Saadallah Ramadan ◽  
Jim Denham ◽  
Peter Lau ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Peripheral Zone ◽  
Multiparametric Mri ◽  
Supervised Machine Learning

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.

scholarly journals Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6065
Author(s):  
Ana Rodrigues ◽  
João Santinha ◽  
Bernardo Galvão ◽  
Celso Matos ◽  
Francisco M. Couto ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Gleason Score ◽  
Feature Selection Method ◽  
Supervised Machine Learning ◽  
Lesion Volume ◽  
Clinical Images ◽  
Gland Volume ◽  
Volume Of Interest ◽  
Cancer Aggressiveness

Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.

scholarly journals Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

2020 ◽  
Author(s):  
L. Papp ◽  
C. P. Spielvogel ◽  
B. Grubmüller ◽  
M. Grahovac ◽  
D. Krajnc ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cross Validation ◽  
Prospective Trial ◽  
Specific Antigen ◽  
Tnm Staging ◽  
Risk Classification ◽  
Supervised Machine Learning ◽  
In Vivo Models ◽  
Primary Prostate Cancer

Abstract Purpose Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. Methods Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. Results The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. Conclusion Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

scholarly journals Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1785
Author(s):  
Yongkai Liu ◽  
Haoxin Zheng ◽  
Zhengrong Liang ◽  
Qi Miao ◽  
Wayne G. Brisbane ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Peripheral Zone ◽  
Significant Degree ◽  
Classification Performance ◽  
Multiparametric Mri ◽  
Superior Performance ◽  
Study Cohort

The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar’s test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment.

TU-AB-BRA-01: Classification of Prostate Cancer Gleason Scores Through Machine Learning From Multiparametric MRI

2015 ◽  
Vol 42 (6Part31) ◽  
pp. 3586-3586
Author(s):  
D Fehr ◽  
A Wibmer ◽  
T Gondo ◽  
K Matsumoto ◽  
H Vargas ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Multiparametric Mri

Genomic alterations associated with the progression from castration-sensitive to castration-resistant metastatic prostate cancer based on machine learning analysis of cell-free DNA genomic profile.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17596-e17596
Author(s):  
Edwin Lin ◽  
Andrew W. Hahn ◽  
Roberto Nussenzveig ◽  
Sergiusz Wesolowski ◽  
Benjamin Louis Maughan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Supervised Machine Learning ◽  
Pi3k Signaling ◽  
Genomic Alterations ◽  
Cell Free Dna ◽  
Associated Gas ◽  
Castration Resistant ◽  
Free Dna ◽  
Pathway Level

e17596 Background: Metastatic castration-sensitive prostate cancer (mCSPC) eventually progresses to metastatic castration-resistant prostate cancer (mCRPC), which has few treatment options and carries a poor prognosis. We hypothesize that there are specific genomic alterations (GAs) associated with the progression from mCSPC to mCRPC. Methods: Patients (Pts) with mCSPC and mCRPC undergoing next-generation sequencing of cell-free DNA by a CLIA certified lab (G360, Guardant Health Inc., Redwood City, CA) as a part of routine care were retrospectively identified. Principal components analysis, an unsupervised ML algorithm, was used for data exploration and visualization. A combination of feature selection and supervised machine learning classification algorithms were used to identify genes associated with mCRPC. Gene Ontology enrichment analysis was used to identify pathways enriched for mCRPC-associated GAs. Patterns of mCRPC-associated GAs at a gene- and pathway-level were identified by Bayesian networks fitted using an exact structure learning algorithm. Results: 154 Pts with mCSPC and 187 Pts with mCRPC were included. A set of 16 GAs that robustly distinguished mCRPC from mCSPC (PPV = 94%, specificity = 91%) using supervised machine learning algorithms. These GAs, primarily amplifications, corresponded to AR, MAPK signaling, PI3K signaling, G1/S cell cycle, and receptor tyrosine kinases (RTKs). Positive statistical dependencies were observed between genes in these pathways. At a pathway-level, the presence of G1/S GAs in mCRPC samples increased the likelihood of harboring GAs in RTK, MAPK, and PI3K signaling. Limitations: The retrospective nature of our study means that unknown exposures could act as confounding variables, however this is representative of real-world clinical settings. Although the strength of this study is inclusion of clinically annotated patient samples, the limitation is that patients with mCSPC and mCRPC were unmatched. Conclusions: These results provide evidence that progression from mCSPC to mCRPC is associated with stereotyped concomitant gain-of-function in the RTK, PI3K, MAPK, and G1/S pathways in addition to AR. Upon external validation, these hypothesis generating data may warrant further investigation into combinatorial therapies that target these pathways.

scholarly journals Visibility of significant prostate cancer on multiparametric magnetic resonance imaging (MRI)—do we still need contrast media?

2020 ◽  
Author(s):  
Nicolai Alexander Huebner ◽  
Stephan Korn ◽  
Irene Resch ◽  
Bernhard Grubmüller ◽  
Tobias Gross ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Linear Regression ◽  
Clinical Decision Making ◽  
Peripheral Zone ◽  
Clinical Decision ◽  
Lesion Size ◽  
Multiparametric Mri ◽  
Characteristic Analysis ◽  
Visual Grading ◽  
Clinically Significant

Abstract Objectives To assess the visibility of clinically significant prostate cancer (PCA) lesions on the sequences multiparametric MRI of the prostate (mpMRI) and to evaluate whether the addition of dynamic contrast–enhanced imaging (DCE) improves the overall visibility. Methods We retrospectively evaluated multiparametric MRI images of 119 lesions in 111 patients with biopsy-proven clinically significant PCA. Three readers assigned visual grading scores for visibility on each sequence, and a visual grading characteristic analysis was performed. Linear regression was used to explore which factors contributed to visibility in individual sequences. Results The visibility of lesions was significantly better with mpMRI when compared to biparametric MRI in visual grading characteristic (VGC) analysis, with an AUCVGC of 0.62 (95% CI 0.55–0.69; p < 0.001). This benefit was seen across all readers. Multivariable linear regression revealed that a location in the peripheral zone was associated with better visibility on T2-weighted imaging (T2w). A higher Prostate Imaging-Reporting and Data System (PI-RADS) score was associated with better visibility on both diffusion-weighted imaging (DWI) and DCE. Increased lesion size was associated with better visibility on all sequences. Conclusions Visibility of clinically significant PCA is improved by using mpMRI. DCE and DWI images independently improve lesion visibility compared to T2w images alone. Further research into the potential of DCE to impact on clinical decision-making is suggested. Key Points • DCE and DWI images independently improve clinically significant prostate cancer lesion visibility compared to T2w images alone. • Multiparametric MRI (DCE, DWI, T2w) achieved significantly higher visibility scores than biparametric MRI (DWI, T2w). • Location in the transition zone is associated with poor visibility on T2w, while it did not affect visibility on DWI or DCE.

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