PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer

Giambelluca, Dario; Cannella, Roberto; Vernuccio, Federica; Comelli, Albert; Pavone, A.; Salvaggio, L; Galia, Massimo; Midiri, Massimo; Lagalla, Roberto; Salvaggio, Giuseppe

doi:10.1067/j.cpradiol.2019.10.009

Cited by 49 publications

(57 citation statements)

References 39 publications

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“…It must be noted that PI-RADS v2.1 still showed a high false positive rate (moderate specificity) for PCa diagnosis, similar to that with PI-RADS v2. Moreover, PI-RADS 3 lesions are frequently encountered (22–32%), and carry a moderate malignant potential (up to 20–30%), the stratification of these lesions is still challenging when using PI-RADS ( 19 ). Therefore, quantitative parameters, such as radiomics, may help to prevent misdiagnoses and improve performance of PI-RADS v2.1.…”

Section: Discussionmentioning

confidence: 99%

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

Yang

Yue

et al. 2021

Front. Oncol.

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ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P < 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

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Section: Discussionmentioning

confidence: 99%

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

Yang

Yue

et al. 2021

Front. Oncol.

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“…Interestingly, for both classification tasks, specificity was found to be lower than the other performance parameters across each model order, highlighting that our models may be prone to false positive errors. Previous studies aimed at stratifying PI-RADS 3 lesions 9,11,[13][14][15] , but only one investigated the power of radiomic features for this purpose 23 . Our results on PI-RADS 3 lesions are in line with those obtained in this work, in which authors also reported that T2 and ADC texture features could help in stratification of PI-RADS 3 lesions.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomic tool has been widely explored in the field of PCa and led to promising results, but especially in studies aiming at differentiating between normal and cancerous prostatic tissue, characterizing PCa lesions in terms of aggressiveness according to Gleason Score (GS), and also comparing diagnostic power of radiomic features with that of PI-RADS scoring 16 – 22 . However, to our knowledge, only Giambelluca et al 23 applied radiomic approach to stratify PI-RADS 3 lesions, and there are any studies aiming at investigating the power of radiomics in stratify PI-RADS 3 upgraded to PI-RADS 4.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Brancato

Aiello

Basso

et al. 2021

Sci Rep

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Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve—AUC— = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

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“…In addition, automatic computer-based assessment may further improve the objectivity, sensitivity, and repeatability of such quantitative analysis (e.g., histogram analysis and texture patterns, see Figure 1). In this context, 'radiomics' has shown high potential for disease detection and treatment response prediction [18][19][20][21]. In contrast with traditional approaches, where HRCT images are inspected and subjectively interpreted, radiomics extracts a large set of quantitative features and analyses their statistical correlation with observable aspects of the disease (e.g., physiological parameters) to identify those of In this context, 'radiomics' has shown high potential for disease detection and treatment response prediction [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, 'radiomics' has shown high potential for disease detection and treatment response prediction [18][19][20][21]. In contrast with traditional approaches, where HRCT images are inspected and subjectively interpreted, radiomics extracts a large set of quantitative features and analyses their statistical correlation with observable aspects of the disease (e.g., physiological parameters) to identify those of In this context, 'radiomics' has shown high potential for disease detection and treatment response prediction [18][19][20][21]. In contrast with traditional approaches, where HRCT images are inspected and subjectively interpreted, radiomics extracts a large set of quantitative features and analyses their statistical correlation with observable aspects of the disease (e.g., physiological parameters) to identify those of most relevance [22,23].…”

Section: Introductionmentioning

confidence: 99%

Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT

et al. 2020

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Background: Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. Methods: We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis features. Next, we evaluate the relationship between pulmonary function and the HRCT features at selected HU thresholds, namely −200 HU, 0 HU, and +200 HU. We model the relationship using a Poisson approximation to identify the measure with the highest log-likelihood. Results: Our Poisson models reveal no difference at the −200 and 0 HU thresholds. However, inferential conclusions change at the +200 HU threshold. Among the HRCT features considered, the percentage of normally attenuated lung at −200 HU shows the most significant diagnostic utility. Conclusions: The percentage of normally attenuated lung can be used together with qualitative HRCT assessment and pulmonary function tests to enhance the idiopathic pulmonary fibrosis (IPF) diagnostic process.

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PI-RADS 3 Lesions: Role of Prostate MRI Texture Analysis in the Identification of Prostate Cancer

Cited by 49 publications

References 39 publications

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT

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