Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

Alessandrino, Francesco; Taghipour, Mehdi; Hassanzadeh, Elmira; Ziaei, Alireza; Vangel, Mark; Fedorov, Andriy; Tempany, Clare M.; Fennessy, Fiona M.

doi:10.1007/s00261-018-1718-6

Cited by 28 publications

(28 citation statements)

References 38 publications

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“…The ADC has shown an increased accuracy in addition to the DWI analysis in detection and localization of PCa (31). ADC map is a model that expresses the signal decay with increased b-value.…”

Section: Apparent Diffusion Coefficient (Adc)mentioning

confidence: 99%

Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

et al. 2019

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BackgroundProstate cancer (PCa) is one of the most common cancer in the male population, with bone metastases in up to 70% of cases, which often require a more aggressive treatment (1-4). The incidence of PCa increases with age, ranging from 34% in the fifth decade, to 70% over the 80 years (5). The increase in survival (about 99%) (5) and the development of minimally invasive procedures in PCa treatment (6) paid attention to improving PCa detection.Diagnostic imaging has achieved high accuracy in detect tumor and defining their characteristic and aggressiveness (7).

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Section: Apparent Diffusion Coefficient (Adc)mentioning

confidence: 99%

Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

et al. 2019

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“…Apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) sequences are useful imaging markers to evaluate the aggressiveness of PCa non-invasively, and have repeatedly proven to be correlated with GS ( 19 – 21 ). Furthermore, ADC maps could differentiate GG2 from GG3 ( 22 , 23 ). Moreover, studies showed that ADC maps had the value of predicting GS upgrading ( 24 , 25 ).…”

Section: Introductionmentioning

confidence: 97%

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Xie

Min

et al. 2021

Front. Oncol.

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ObjectiveTo evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).Materials and methodsFifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed.ResultsSix texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort.ConclusionA combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

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“…Many authors affirm that the ADC ratio seems to be a more useful and reproducible method because it is independent of the diffusion parameters as selected b values and vector strength [ 21 - 24 ]. Nevertheless, a number of factors still limit ADCr’s efficacy.…”

Section: Discussionmentioning

confidence: 99%

The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging

Kus¹

2021

Pol J Radiol

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IntroductionThis study has focused on investigating the relationship between the exponential apparent diffusion coefficient (exp-ADC), selective apparent diffusion coefficient (sel-ADC) values, the ADC ratio (ADCr), and prostate cancer aggressiveness with transrectal ultrasound-guided prostate biopsy in patients with prostate cancer.Material and methodsAll patients underwent a multiparametric magnetic resonance imaging (mpMRI) including tri-planar T2-weighted (T2W), dynamic contrast-enhanced (DCE), diffusion-weighted sequences using a 3.0-Tesla MR scanner (Skyra, Siemens Medical Systems, Germany) with a dedicated 18-channel body coil and a spine coil underneath the pelvis, with the patient in the supine position. Exp-ADC, sel-ADC, and ADCr of defined lesions were evaluated using region-of-interest-based measurements. Exp-ADC, sel-ADC, and ADCr were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy.ResultsPatients were divided into 2 groups. Group I is Gleason score ≥ 3 + 4, group II is Gleason score = 6. Sel-ADC and exp-ADC were statistically significant between 2 groups (0.014 and 0.012, respectively). However, the ADCr difference between nonclinical significant prostate cancer from clinically significant prostate cancer was not significant (p = 0.09).ConclusionsThis study is the first to evaluate exp-ADC and sel-ADC values of prostate carcinoma with ADCr. One limitation of this study might be the limited number of patients. Exp-ADC and sel-ADC values in prostate MRI imaging improved the specificity, accuracy, and area under the curve (AUC) for detecting clinically relevant prostate carcinoma. Adding exp-ADC and sel-ADC values to ADCr can be used to increase the diagnostic accuracy of DWI.

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Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

Abstract: ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

Cited by 28 publications

References 38 publications

Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging

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