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

Vignati, A.; Mazzetti, Simone; Giannini, Valentina; Russo, Filippo; Bollito, Enrico; Porpiglia, Francesco; Stasi, M.; Regge, Daniele

doi:10.1088/0031-9155/60/7/2685

Cited by 111 publications

(106 citation statements)

References 43 publications

(92 reference statements)

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“…We believe that textural analysis of each multiparametric image is analogous to the manner by which radiologists visually localise TZ cancer on mpMRI [4]. Other workers [e.g., 28, 29] have examined first and second order (e.g., two-dimensional grey-level co-occurrence matrix) textural features from one or more mpMRI sequences from ROIs drawn around individual TZ tumours. Such approaches, while informative, have less clinical application, since they are more computationally intensive and require the radiologist to first identify areas of concern.…”

Section: Discussionmentioning

confidence: 99%

“Textural analysis of multiparametric MRI detects transition zone prostate cancer”

et al. 2016

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ObjectivesTo evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour.MethodsSixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had ‘significant’ TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis.ResultsADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83).ConclusionTextural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion.Key Points• MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.

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Section: Discussionmentioning

confidence: 99%

“Textural analysis of multiparametric MRI detects transition zone prostate cancer”

et al. 2016

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“…(46) was to use contrast and homogeneity from the computation of texture features on T2w and ADC to predict prostate cancer aggressiveness and to compare to traditional ADC metrics (mean, median, 10 th and 25 th percentile). A cohort of 93 patients who underwent mpMRI before prostatectomy were used and clinically significant tumors (≥0.5 mL) were outlined on histological sections and transferred to contours in T2w and ADC (ROIs).…”

Section: Review Of Applications Of Radiomics To Prostate Cancermentioning

confidence: 99%

Prostate cancer radiomics and the promise of radiogenomics

Stoyanova

Takhar

Tschudi

et al. 2016

Transl. Cancer Res.

113

104

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Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinicalpathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing Author Manuscript aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. HHS Public Access

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“…25,27 For example, some works have treated the GLCMs as separated in each direction, [29][30][31] whereas others have preferred to average the matrices to reduce the number of parameters used in the subsequent statistical analysis. 28,32 • Specific parameters for texture matrix formulation: the choice of the distance between pixels for the computation of GLCM is another factor that can affect the value and number of the features. However, in this case, it is general practice to consider a distance of 1 pixel, 9 even if some works preferred to take into account more than one distance.…”

mentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

133

108

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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

Cited by 111 publications

References 43 publications

“Textural analysis of multiparametric MRI detects transition zone prostate cancer”

“Textural analysis of multiparametric MRI detects transition zone prostate cancer”

Prostate cancer radiomics and the promise of radiogenomics

Texture analysis of medical images for radiotherapy applications

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