Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer

Wang, Qing; Li, Hai; Xu, Yan; Wu, Chen‐Jiang; Liu, Xisheng; Shi, Haibin; Zhang, Yudong

doi:10.1016/j.urolonc.2015.05.005

Cited by 63 publications

(83 citation statements)

References 39 publications

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“…Additionally, we found that, among all imaging markers, only tumor ADCs was independently related to 3-years BCR, suggesting its prognostic value in the prediction of clinical outcome in PCa. The overall ACC for ADCs to differentiate low/intermediate- from high-grade PCa is 64.9%, partly consistent with previous reports [16, 18, 36]. And our mp-MRI produced high accuracy for detecting surgical ECE (81.9%) and SVI (95.1%).…”

Section: Discussionsupporting

confidence: 91%

“…The imaging contribution in PCa BCR predicting can be explained that, the newly proposed imaging markers, i.e., ADCs, DCE type, MR T-stage, might indicate the postoperative histopathological features of PCa more accurately than conventional clinical variables. This is true as the quantitative MR imaging parameters, e.g., DCE-derived Ktrans, DWI-derived ADCs and/or Dapp, had been identified to be highly associated with the localized cancer aggressiveness and prognosis in recent clinical studies [16-18, 33, 35]. Additionally, we found that, among all imaging markers, only tumor ADCs was independently related to 3-years BCR, suggesting its prognostic value in the prediction of clinical outcome in PCa.…”

Section: Discussionmentioning

confidence: 99%

“…The total number of cores obtained, the number of cores containing cancer, and the percent of biopsy cores involved with cancer (positive for cancer) were recorded. The prostatic specimens after RP were prepared as previously described [16] and examined by the institutional pathology department. The location and extent of invasive tumors were identified and precisely mapped in each section.…”

Section: Methodsmentioning

confidence: 99%

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An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Zhang

Wang

et al. 2016

Oncotarget

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Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Zhang

Wang

et al. 2016

Oncotarget

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“…This deviation from a uniform diffusion pattern can be mathematically described as kurtosis and visualized by DKI, which applies diffusion gradients with multiple b-values in multiple spatial directions [38]. The DKI-derived metric K has been shown to be significantly different in cancer than in non-cancerous prostate tissue [39] and has also been used to differentiate high-grade from low-grade prostate cancer [40–42]. Others, however, found that the K-values of prostate cancer and benign prostatic hyperplasia showed marked overlap [43] and that DKI-derived metrics did not provide a significant benefit for detection and grading of prostate cancer when compared to standard ADC-maps [44].…”

Section: Dw-mri As a Tool For Risk-assessmentmentioning

confidence: 99%

The expanding landscape of diffusion-weighted MRI in prostate cancer

et al. 2016

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The added value of diffusion-weighted magnetic resonance imaging (DW-MRI) for the detection, localization, and staging of primary prostate cancer has been extensively reported in original studies and meta-analyses. More recently, DW-MRI and related techniques have been used to non-invasively assess prostate cancer aggressiveness and estimate its biological behavior. The present article aims to summarize the potential applications of DW-MRI for non-invasive optimization of pretherapeutic risk assessment, patient management decisions, and evaluation of treatment response.

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“…52 There have been several approaches described to address this problem, including histogram and texture analysis 29,31,53 -55 and more advanced DWI-and post-processing techniques. [56][57][58]61 Although there has been some progress in reducing the overlap with some of these approaches, the problem is far from solved, and additional refinements or new approaches are warranted. An alternative use of the ADC-value is as a biological marker to predict the prognosis of PCa patients.…”

Section: Risk-assessment Of Mri-visible Prostate Lesionsmentioning

confidence: 99%

Role of MRI in the Risk Assessment of Primary Prostate Cancer

Wibmer

Verma²,

Vargas³

2016

Topics in Magnetic Resonance Imaging

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A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.

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Histogram analysis of diffusion kurtosis magnetic resonance imaging in differentiation of pathologic Gleason grade of prostate cancer

Cited by 63 publications

References 39 publications

An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

The expanding landscape of diffusion-weighted MRI in prostate cancer

Role of MRI in the Risk Assessment of Primary Prostate Cancer

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