Dataset Information

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

ABSTRACT:

Background

Multiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores.

Purpose

To develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores.

Study type

Retrospective.

Population

In all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study.

Field strength/sequence

Conventional and diffusion-weighted MR images (b values?=?0, 1000 sec/mm² ) were acquired on a 3.0T MR scanner.

Assessment

A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T₂ WI, respectively. A predictive model was constructed for differentiating PCa from non-PCa and high-grade from low-grade PCa. The diagnostic performance of each radiomics-based model was compared with that of the PI-RADS v2 scores.

Statistical tests

A radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.

Results

For PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T₂ WI, ADC, and T₂ WI&ADC features, respectively. For low-grade versus high-grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T₂ WI, ADC, and T₂ WI&ADC features, respectively. PI-RADS v2 had an AUC of 0.867 in differentiating PCa from non-PCa and an AUC of 0.763 in differentiating high-grade from low-grade PCa.

Data conclusion

Both the T₂ WI- and ADC-based radiomics models showed high diagnostic efficacy and outperformed the PI-RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high-grade vs. low-grade PCa.

Level of evidence

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.

SUBMITTER: Chen T

PROVIDER: S-EPMC6620601 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

Chen Tong T Li Mengjuan M Gu Yuefan Y Zhang Yueyue Y Yang Shuo S Wei Chaogang C Wu Jiangfen J Li Xin X Zhao Wenlu W Shen Junkang J

Journal of magnetic resonance imaging : JMRI 20180919 3

<h4>Background</h4>Multiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores.<h4>Purpose</h4>To develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores.<h4>Study type</h4>Retrospective.< ...[more]

PMID: 30230108

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Project description:CONTEXT:The Prostate Imaging-Reporting and Data System (PI-RADS) v2 analysis system for multiparametric magnetic resonance imaging (mpMRI) detection of prostate cancer (PCa) is based on PI-RADS v1, accumulated scientific evidence, and expert consensus opinion. OBJECTIVE:To summarize the accuracy, strengths and weaknesses of PI-RADS v2, discuss pathway implications of its use and outline opportunities for improvements and future developments. EVIDENCE ACQUISITION:For this consensus expert opinion from the PI-RADS steering committee, clinical studies, systematic reviews, and professional guidelines for mpMRI PCa detection were evaluated. We focused on the performance characteristics of PI-RADS v2, comparing data to systems based on clinicoradiologic Likert scales and non-PI-RADS v2 imaging only. Evidence selections were based on high-quality, prospective, histologically verified data, with minimal patient selection and verifications biases. EVIDENCE SYNTHESIS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice retains higher accuracy over systematic transrectal ultrasound (TRUS) biopsies for PCa diagnosis. PI-RADS v2 fails to detect all cancers but does detect the majority of tumors capable of causing patient harm, which should not be missed. Test performance depends on the definition and prevalence of clinically significant disease. Good performance can be attained in practice when the quality of the diagnostic process can be assured, together with joint working of robustly trained radiologists and urologists, conducting biopsy procedures within multidisciplinary teams. CONCLUSIONS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice is improved, retaining higher accuracy over systematic TRUS biopsies for PCa diagnosis. PATIENT SUMMARY:Multiparametric magnetic resonance imaging (MRI) and MRI-directed biopsies using the Prostate Imaging-Reporting and Data System improves the detection of prostate cancers likely to cause harm, and at the same time decreases the detection of disease that does not lead to harms if left untreated. The keys to success are high-quality imaging, reporting, and biopsies by radiologists and urologists working together in multidisciplinary teams.

Project description: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.

Project description:Rationale and objectiveThe Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) published a set of minimum technical standards (MTS) to improve image quality and reduce variability in multiparametric prostate MRI. The effect of PIRADSv2 MTS on image quality has not been validated. We aimed to determine whether adherence to PI-RADSv2 MTS improves study adequacy and perceived quality.Materials and methodsSixty-two prostate MRI examinations including T2 weighted (T2W) and diffusion weighted image (DWI) consecutively referred to our center from 62 different institutions within a 12-month period (September 2017 to September 2018) were included. Six readers assessed images as adequate or inadequate for use in PCa detection and a numerical image quality ranking was given using a 1-5 scale. The PI-RADSv2 MTS were synthesized into sets of seven and 10 rules for T2W and DWI, respectively. Image adherence was assessed using Digital Imaging and Communications in Medicine (DICOM) metadata. Statistical analysis of survey results and image adherence was performed based on reader quality scoring (Kendall Rank tau-b) and reader adequate scoring (Wilcoxon test for association) for T2 and DWI quality assessment.ResultsOut of 62 images, 52 (83%) T2W and 38 (61%) DWIs were rated to be adequate by a majority of readers. Reader adequacy scores showed no significant association with adherence to PI-RADSv2. There was a weak (tau-b = 0.22) but significant (p value = 0.01) correlation between adherence to PIRADSv2 MTS and image quality for T2W. Studies following all PI-RADSv2 T2W rules achieved a higher median average quality score (3.58 for 7/7 vs. 3.0 for <7/7, p = 0.012). No statistical relationship with PI-RADSv2 MTS adherence and DWI quality was found.ConclusionAmong 62 sites performing prostate MRI, few were considered of high quality, but the majority were considered adequate. DWI showed considerably lower rates of adequate studies in the sample. Adherence to PI-RADSv2 MTS did not increase the likelihood of having a qualitatively adequate T2W or DWI.

Project description:Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist's evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.

Project description:ObjectivesTo evaluate whether low PI-RADS v2 assessment categories are effective at excluding extraprostatic extension (EPE) of prostate cancer (?pT3a PCa).MethodsThe local institutional ethics committee approved this retrospective analysis of 301 consecutive PCa patients. Patients were classified as low- or intermediate/high-risk based on clinical parameters and underwent pre-surgical multiparametric magnetic resonance imaging. A PI-RADS v2 assessment category and ESUR EPE score were assigned for each lesion by two readers working in consensus. Histopathologic analysis of the whole-mount radical prostatectomy specimen was the reference standard. Univariate and multivariate analyses were performed to evaluate the association of PI-RADS v2 assessment category with final histology ?pT3a PCa.ResultsFor a PI-RADS v2 assessment category threshold of 3, the overall performance for ruling out (sensitivity, negative predictive value, negative likelihood ratio) ?pT3a PCa was 99%/98%/0.04 and was similar in both the low-risk (96%/97%/0.12; N?=?137) and the intermediate/high-risk groups (100%/100%/0.0; N?=?164). In univariate analysis, all clinical and tumor characteristics except age were significantly associated with ?pT3a PCa. In multivariate analysis, PI-RADS v2 assessment categories ??3 had a protective effect relative to categories 4 and 5. The inclusion of ESUR EPE score improved the AUC of ?pT3a PCa prediction (from 0.73 to 0.86, p?=?0.04 in the overall cohort). The impact of PI-RADS v2 assessment category is reflected in a nomogram derived on the basis of our cohort.ConclusionsIn our cohort, low PI-RADS v2 assessment categories of 3 or less confidently ruled out the presence of ?pT3a PCa irrespective of clinical risk group.Key points• Our analysis of 301 mp-MRI and RARP specimens showed that the addition of PI-RADS v2 assessment categories to clinical parameters improves the exclusion of ?pT3a (extraprostatic) prostate cancer. • PI-RADS v2 assessment categories of 1 to 3 are useful for excluding ?pT3a prostate cancer with a NPV of 98%; such patients can be considered as candidates for less invasive approaches. • The ability to exclude ?pT3a prostate cancer may improve confidence in choosing nerve-sparing surgery or in avoiding pelvic nodal dissections, and similarly for patients undergoing radiotherapy, in adopting short-course adjuvant hormonal therapy or foregoing prophylactic nodal irradiation.

Dataset Information

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

Background

Purpose

Study type

Population

Field strength/sequence

Assessment

Statistical tests

Results

Data conclusion

Level of evidence

Publications

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.

Similar Datasets

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