Development of a measure for evaluating lesion‐wise performance of CAD algorithms in the context of mpMRI detection of prostate cancer

Leng, Ethan; Spilseth, Benjamin; Zhang, Lin; Jin, Jin; Koopmeiners, Joseph S.; Metzger, Gregory J.

doi:10.1002/mp.12861

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Another future direction is cancer lesion identification using voxel-wise mpMRI data, which is challenging and has so far received limited attention. 29,30 We are currently discussing voxel-wise PCa classification, which is the focus of the majority of the mpMRI classifiers. However, clinical practice requires that the results are eventually translated into detection of cancer lesions, which can be a better guidance for clinical decision, and determining how best to identify lesions using voxel-wise data is worthy of investigation.…”

Section: Discussionmentioning

confidence: 99%

Bayesian spatial models for voxel‐wise prostate cancer classification using multi‐parametric magnetic resonance imaging data

Jin

Zhang

Leng

et al. 2021

Statistics in Medicine

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Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.

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Section: Discussionmentioning

confidence: 99%

Bayesian spatial models for voxel‐wise prostate cancer classification using multi‐parametric magnetic resonance imaging data

Jin

Zhang

Leng

et al. 2021

Statistics in Medicine

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“…The average sensitivity for cancer detection for all relevant studies was 86.8% [14 -31]. For all studies, peak sensitivity was 98% and lowest was 46.8% (25,30). The average specificity of CAD was 79.8% [14 -31].…”

Section: Cad Sensitivity and Specificity Versus Radiologistsmentioning

confidence: 99%

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Nelson¹,

Ekberg²,

Fridell³

2020

TOAIJ

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Background: Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer. Aim: The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening. Methods: This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results. Results: CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist. Conclusion: When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

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“…Quantitative T2 mapping provides a more objective and potentially sensitive imaging biomarker for diagnosis and grading of prostate cancer compared to qualitative T2-weighted imaging [1][2][3][4] .…”

Section: Introductionmentioning

confidence: 99%

Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks

Bolan

Saunders

Kay

et al. 2023

Preprint

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Purpose This work seeks to evaluate multiple methods for quantitative parameter estimation from standard T2 mapping acquisitions in the prostate. The T2 estimation performance of methods based on neural networks (NN) was quantitatively compared to that of conventional curve fitting techniques. Methods Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Ten combinations of different NN architectures, training strategies, and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. Results In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst all the methods. On in vivo data, this best-performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. Conclusion This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

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Development of a measure for evaluating lesion‐wise performance of CAD algorithms in the context of mpMRI detection of prostate cancer

Abstract: The proposed measures allow for the assessment of lesion detection performance, which is most relevant in a clinical setting and would not be possible to do with voxel-wise measures alone.

Cited by 9 publications

References 28 publications

Bayesian spatial models for voxel‐wise prostate cancer classification using multi‐parametric magnetic resonance imaging data

Bayesian spatial models for voxel‐wise prostate cancer classification using multi‐parametric magnetic resonance imaging data

Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence

Improved Quantitative Parameter Estimation for Prostate T2 Relaxometry using Convolutional Neural Networks

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