Multiparametric MRI maps for detection and grading of dominant prostate tumors

Moradi, Mehdi; Salcudean, Septimiu E.; Chang, Silvia D.; Jones, Edward C.; Buchan, Nicholas; Casey, Rowan G.; Goldenberg, S. Larry; Kozłowski, Piotr

doi:10.1002/jmri.23540

Cited by 53 publications

(40 citation statements)

References 37 publications

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“…Our work also incorporated sample augmentation methods to tackle the problem of highly unbalanced data and achieved a much higher accuracy of 0.93 for cancers in the PZ and TZ and 0.92 for cancers occurring only in the PZ following reduction by the hyperparameter selection bias. The majority of the prior works have been focused on classifying cancerous regions from benign structures (24,25,43,44). Our results for classifying noncancerous prostate from malignant cancers ADC mean has been shown to be a viable biomarker for differentiating cancers by their aggressiveness with reasonable accuracy of 0.75 (22).…”

Section: Discussion and Future Workmentioning

confidence: 72%

“…We analyzed the efficacy of classifying GS 6ð3 + 3Þ (n = 34) vs. GS ≥7 (n = 159) cancers and GS 7ð3 + 4Þ (n = 114) vs. 7ð4 + 3Þ (n = 26) cancers using (i) t test SVM, (ii) RFE-SVM, and (iii) AdaBoost trained without and with sample augmentation using (i) Gibbs oversampling and (ii) synthetic minority oversampling technique (SMOTE) oversampling. To compare our results with those of previous works (23)(24)(25), we also applied the same methods for distinguishing between noncancerous structures and prostate cancers. The number of samples for noncancerous structures (n = 158) and cancer (n = 198, the GS for five tumors were not provided) were balanced, and hence, did not require sample augmentation.…”

Section: Resultsmentioning

confidence: 99%

“…Texture-based imaging features in conjunction with machine learning-based classification have predominantly been applied for classifying malignant from noncancerous prostate tissues (23)(24)(25) with one exception (26). Linear discriminant analysis (LDA)-based classification of various histogram-based ADC measures, namely,…”

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confidence: 99%

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Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

Fehr

Veeraraghavan

Wibmer

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

342

315

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Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93% accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92% for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92% accuracy for cancers occurring in both the PZ and TZ and with 93% for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58% for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63% for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59% for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60% for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.

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Section: Discussion and Future Workmentioning

confidence: 72%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

Fehr

Veeraraghavan

Wibmer

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

342

315

View full text Add to dashboard Cite

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“…It should be emphasized that the most accurate methods of MRI-based prostate cancer detection are typically multiparametric and include diffusion imaging, which is a very effective modality in prostate cancer detection [2,40]. In our previous work we have shown that diffusion imaging outperforms DCE in detection of cancer from biopsy cases and that the combination of the two methods is the most effective solution [16]. Therefore, we expect a significantly improved performance resulting from adding the diffusion parameters to our feature vector in the current wholemount prostatectomy study as well.…”

Section: Discussionmentioning

confidence: 99%

“…The pharmacokinetic parameters extracted from DCE MR images provide physiological information about tissue microvasculature and may be linked to active cancer growth [13,14]. These parameters, when used in a supervised machine learning framework with diffusion tensor features [15,16], or with T2-weighted and diffusion weighted features [17], have shown great potential in prostate cancer detection.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI

Haq

Kozłowski

Jones

et al. 2015

Computerized Medical Imaging and Graphics

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Magnetic resonance imaging (MRI), particularly dynamic contrast enhanced (DCE) imaging, has shown great potential in prostate cancer diagnosis and staging. In the current practice of DCE-MRI, diagnosis is based on quantitative parameters extracted from the series of T1-weighted images acquired after the injection of a contrast agent. To calculate these parameters, a pharmacokinetic model is fitted to the T1-weighted intensities. Most models make simplistic assumptions about the perfusion process. Moreover, these models require accurate estimation of the arterial input function, which is challenging. In this work we propose a data-driven approach to characterization of the prostate tissue that uses the time series of DCE T1-weighted images without pharmacokinetic modeling. This approach uses a number of model-free empirical parameters and also the principal component analysis (PCA) of the normalized T1-weighted intensities, as features for cancer detection from DCE MRI. The optimal set of principal components is extracted with sparse regularized regression through Least Absolute Shrinkage and Selection Operator (LASSO). A support vector machine classifier was used with leave-onepatient-out cross validation to determine the ability of this set of features in cancer detection. Our data is obtained from patients prior to radical prostatectomy and the results are validated based on histological evaluation of the extracted specimens. Our results, obtained on 449 tissue regions from 64 images, show that the proposed data-driven features outperform the traditional pharmacokinetic parameters with an area under ROC of 0.86 for LASSO-isolated PCA parameters, compared to 0.78 for pharmacokinetic parameters. This shows that our novel approach to the analysis of DCE data has the potential to improve the multiparametric MRI protocol for prostate cancer detection.

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

Gholizadeh

Simpson

Ramadan

et al. 2020

J Applied Clin Med Phys

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Purpose The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp‐MRI), comprised of T2‐weighted imaging (T2WI), diffusion‐weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. Materials and methods In this work, 191 radiomic features were extracted from mp‐MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp‐MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave‐one‐patient‐out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. Results The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC , sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. Conclusions Combination of noncontrast mp‐MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.

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Multiparametric MRI maps for detection and grading of dominant prostate tumors

Cited by 53 publications

References 37 publications

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI

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

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