The expanding landscape of diffusion-weighted MRI in prostate cancer

Wibmer, Andreas G.; Sala, Evis; Hricak, Hedvig; Vargas, Hebert Alberto

doi:10.1007/s00261-016-0646-6

Cited by 9 publications

(4 citation statements)

References 74 publications

(70 reference statements)

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“…The role of qMRI in clinical oncology settings has been elegantly reviewed in the past. 1,10,[133][134][135][136][137][138] This review is focused on the need for more test-retest studies, defining repeatability and reproducibility, and determining the extent of repeatability and reproducibility determinations that have been performed in phantoms and patients and reported in the literature with adequate technical data and details to allow a statistically robust meta-analysis. Although there are about 1000 publications for human subject DWI and DCE studies in oncology in the literature, the test-retest pool of articles is still quite limited.…”

Section: Pearls and Pitfalls As Qmri Precision Is Improvedmentioning

confidence: 99%

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Shukla–Dave

Obuchowski

Chenevert

et al. 2018

Magnetic Resonance Imaging

270

232

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Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion‐weighted imaging and dynamic contrast‐enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion‐weighted imaging and dynamic contrast‐enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101–e121.

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Section: Pearls and Pitfalls As Qmri Precision Is Improvedmentioning

confidence: 99%

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Shukla–Dave

Obuchowski

Chenevert

et al. 2018

Magnetic Resonance Imaging

270

232

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“…Cancer detection in the TZ is much more challenging compared with in the PZ because adjacent benign prostatic hyperplasia can obscure cancers. 38 DWI and ADC have recently been shown to have sufficient ability for healthy prostate tissue and tumour characterisation, 39 so it is important to further develop a CAD system while exploiting the imaging features of DWI and ADC for the grading of PCa.…”

Section: Discussionmentioning

confidence: 99%

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring

et al. 2021

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Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusionweighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlationbased feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.apparent diffusion coefficient, diffusion-weighted imaging, machine learning, prostate cancer, Prostate Imaging-Reporting and Data System version 2, texture analysis | INTRODUCTIONEarly diagnosis and grading of prostate cancer (PCa) are essential for the clinical management of patients, and can directly impact planning management strategy and clinical outcomes. One challenge in the diagnosis of PCa is to correctly identify patients with moderate and high-risk

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“…[14][15][16][17] However, there is a considerable overlap in ADC values between cancers of various grades. 8 In addition, although ADC values are based on free diffusion, diffusion is restricted by the presence of various barriers in biologic tissues. Thus, a new diffusion metric that may allow for improved discrimination between various grades of prostate cancer is desirable.…”

Section: Discussionmentioning

confidence: 99%

“…7 However, there is considerable overlap in ADCs between clinically insignificant and significant prostate cancer. 8 Thus, further research to identify additional diffusion metrics is warranted to improve the clinical performance of diffusion measurements. In addition, recently, Epstein et al conducted a large cohort study to verify whether a new grading system, which was approved at the consensus meeting of the 2014 International Society of Urological Pathology and included Grade 1 (GS ≤ 6), Grade 2 (GS 3 + 4 = 7), Grade 3 (GS 4 + 3 = 7), Grade 4 (GS 8), and Grade 5 (GS 9-10), accurately uses a smaller number of grades with the most significant prognostic differences.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Diffusion-weighted MR Images Based on a Gamma Distribution Model to Differentiate Prostate Cancers with Different Gleason Score

et al. 2020

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Prostate cancer management includes identification of clinically significant cancers that may require curative treatment. Statistical models based on gamma distribution can describe diffusion signal decay curves of prostate cancer. The purpose of this study was to evaluate the ability of parameters obtained with the gamma model in differentiating prostate cancers with different Gleason score values. Methods: This study included 155 patients with prostate cancer who underwent multiparametric magnetic resonance imaging prior to prostate biopsy (127 patients) or radical prostatectomy (28 patients) between January 2015 and June 2017; 159 foci of prostate cancer were included in our study. We compared cases scored as Gleason score (GS) 3 + 3 and GS ≥ 3 + 4, and analyzed cases scored as GS ≤ 3+ 4 and GS ≥ 4 + 3 based on the gamma model (Frac < 1.0, Frac < 0.8, Frac < 0.5, Frac < 0.3, and Frac > 3.0), and apparent diffusion coefficient (ADC). Results: Among 159 cancerous lesions in 155 patients, 13 (8.2%) were GS 3 + 3 prostate cancers, 51 (32.0%) were GS 3 + 4 prostate cancers, 30 (18.2%) were GS 4 + 3 cancers, and 65 (40.9%) were GS ≥ 4 + 4 cancers. Frac < 0.3, Frac < 0.5, Frac < 0.8, and Frac < 1.0 were significantly higher and ADC values were significantly lower in GS ≥ 4 + 3 cancers than in GS ≤ 3 + 4 cancers (P < 0.01, P < 0.01, P < 0.01, P = 0.01, and P < 0.01, respectively). With receiver operating characteristic (ROC) analysis, Frac < 0.3 and Frac < 0.5 had significantly greater area under the ROC curve for discriminating GS ≥ 4 + 3 cancers from GS ≤ 3 + 4 cancers than ADC (P = 0.03, P < 0.01, respectively). Conclusion: Frac < 0.3 and Frac < 0.5 showed higher diagnostic performance than ADC for differentiating GS ≥ 4 + 3 from GS ≤ 3 + 4 cancers. The gamma model may add additional value in discrimination of tumor grades.

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The expanding landscape of diffusion-weighted MRI in prostate cancer

Cited by 9 publications

References 74 publications

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring

Analysis of Diffusion-weighted MR Images Based on a Gamma Distribution Model to Differentiate Prostate Cancers with Different Gleason Score

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