Quantitative Analysis of Multiparametric Prostate MR Images: Differentiation between Prostate Cancer and Normal Tissue and Correlation with Gleason Score—A Computer-aided Diagnosis Development Study

Peng, Yahui; Jiang, Yulei; Yang, Cheng; Brown, Jeremy Bancroft; Antic, Tatjana; Sethi, Ila; Schmid-Tannwald, Christine; Giger, Maryellen L.; Eggener, Scott E.; Oto, Aytekin

doi:10.1148/radiol.13121454

Cited by 233 publications

(237 citation statements)

References 52 publications

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“…AUC values for ADC parameters in discriminating low-from intermediateand high-risk lesions (AUC = 0.821-0.854) also lie within the range of previously reported values for peripheral prostate lesions (Donati et al 2014, Kobus et al 2012, Vargas et al 2011, Turkbey et al 2011, Oto et al 2011, Hambrock et al 2011, Peng et al 2013. One recent study reported a higher value of AUC (0.90) for the median ADC in peripheral zone PCas (Hambrock et al 2011).…”

Section: Discussionsupporting

confidence: 83%

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness

Vignati¹,

Mazzetti²,

Giannini³

et al. 2015

Phys. Med. Biol.

111

100

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To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low-from intermediate/high-grade PCas.The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume ≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS ≥ 7.The

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Section: Discussionsupporting

confidence: 83%

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness

Vignati¹,

Mazzetti²,

Giannini³

et al. 2015

Phys. Med. Biol.

111

100

View full text Add to dashboard Cite

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“…However, studies differ in the specific ADC value used to distinguish between the cancers. The features used have included ADC mean computed from a single slice region of interest (ROI) (15,16,18), ADC mean computed from the entire volume using different sets of diffusion b-values (all vs. fast vs. slow) (19), 10th percentile of the ADC computed from the entire lesion (20), 10th percentile and ADC mean (21), and ADC mean computed over the entire lesion (22). Furthermore, none of the aforementioned studies used more than five imaging features for the analysis.…”

mentioning

confidence: 99%

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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“…The final approach to analyzing DCE images aims to estimate physiologically interpretable, kinetic parameters by fitting pharmacokinetic models to the enhancement curves [88,113,114]. The most common is the two-compartment model.…”

Section: Dynamic Contrast-enhanced Imagingmentioning

confidence: 99%

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

et al. 2015

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The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

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Quantitative Analysis of Multiparametric Prostate MR Images: Differentiation between Prostate Cancer and Normal Tissue and Correlation with Gleason Score—A Computer-aided Diagnosis Development Study

Abstract: The combination of 10th percentile ADC, average ADC, and T2-weighted skewness with CAD is promising in the differentiation of prostate cancer from normal tissue. ADC image features and K(trans) moderately correlate with GS.

Cited by 233 publications

References 52 publications

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness

Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness

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

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

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