Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2

Türkbey, Barış; Rosenkrantz, Andrew B.; Haider, Masoom A.; Padhani, Anwar R.; Villeirs, Geert; Macura, Katarzyna J.; Tempany, Clare M.; Choyke, Peter L.; Cornud, F.; Margolis, Daniel; Thoeny, Harriet C.; Verma, Sadhna; Barentsz, Jelle O.; Weinreb, Jeffrey C.

doi:10.1016/j.eururo.2019.02.033

Cited by 1,412 publications

(1,439 citation statements)

References 39 publications

Supporting

Mentioning

1,356

Contrasting

Unclassified

Order By: Relevance

“…5 Most important, the interobserver agreement for subjective evaluation of prostate multiparametric MRI (mp-MRI) using the Prostate Imaging and Data Reporting System (PI-RADS) v. 2, which is the current clinical standard, is at best moderate and has been acknowledged as an area of potential improvement in future iterations of PI-RADS. 6,7 The use of more quantitative or automated methods for PCa detection from MRI is therefore clinically desirable. The addition of quantitative metrics to subjective PI-RADS improves accuracy 8 with improved interobserver agreement.…”

mentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

show abstract

mentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…In clinical practice, invasive biopsy procedures and histological analysis are routinely used to evaluate the malignancy of prostate tissues. Although the noninvasive detection of PCa and characterization of its aggressiveness by mp‐MRI using T 2 WI, standard DWI, and DCE MRI has progressed substantially, it remains a challenging task in the central gland of the prostate . A recent study demonstrated that high b‐value DWI only improved the sensitivity in the detection of central gland PCa .…”

Section: Discussionmentioning

confidence: 99%

“…Although the noninvasive detection of PCa and characterization of its aggressiveness by mp-MRI using T 2 WI, standard DWI, and DCE MRI has progressed substantially, it remains a challenging task in the central gland of the prostate. 5,24 A recent study demonstrated that high b-value DWI only improved the sensitivity in the detection of central gland PCa. 25 It is well established that 1 H MRSI can provide valuable metabolic information for the non-invasive assessment of PCa, but the robust application of this technique in clinical practice is still demanding.…”

Section: Discussionmentioning

confidence: 99%

Supervised risk predictor of central gland lesions in prostate cancer using ¹H MR spectroscopic imaging with gradient offset‐independent adiabaticity pulses

Gholizadeh

Greer

Simpson

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge. Purpose To evaluate the efficacy of in vivo 3D 1H MR spectroscopic imaging (3D 1H MRSI) with a semi‐localized adiabatic selective refocusing (sLASER) sequence and gradient‐modulated offset‐independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low‐ vs. high‐risk cancer, 3) low‐ vs. intermediate‐risk cancer, and 4) intermediate‐ vs. high‐risk cancer voxels. Study Type Prospective. Subjects Thirty‐six patients with biopsy‐proven central gland prostate cancer. Field Strength/Sequence 3T MRI / 3D 1H MRSI using GOIA‐sLASER. Assessment Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing. Statistical Tests Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset. Results High‐quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low‐ vs. high‐risk cancer and low‐ vs. intermediate‐risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate‐ vs. high‐risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%. Data Conclusion The GOIA‐sLASER sequence with an external phased‐array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low‐ vs. high‐risk cancer, and low‐ vs. intermediate‐risk cancer. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1926–1936.

show abstract

“…Another major limitation of mpMRI is interobserver variability. The current Prostate Imaging Reporting and Data System version 2.1 guidelines provide no clear definition of what constitutes mild, moderate, or marked restriction on DWI or hypointensity on T 2 ‐weighted imaging . Therefore, prostate MRE has the potential to improve diagnostic imaging and radiologic reporting as a quantitative imaging method.…”

Section: Discussionmentioning

confidence: 99%

Prostate cancer assessment using MR elastography of fresh prostatectomy specimens at 9.4 T

Reiter

Majumdar

Kearney

et al. 2019

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose Despite its success in the assessment of prostate cancer (PCa), in vivo multiparametric MRI has limitations such as interobserver variability and low specificity. Several MRI methods, among them MR elastography, are currently being discussed as candidates for supplementing conventional multiparametric MRI. This study aims to investigate the detection of PCa in fresh ex vivo human prostatectomy specimens using MR elastography. Methods Fourteen fresh prostate specimens from men with clinically significant PCa without formalin fixation or prior radiation therapy were examined by MR elastography at 500 Hz immediately after radical prostatectomy in a 9.4T preclinical scanner. Specimens were divided into 12 segments for both calculation of storage modulus (G′ in kilopascals) and pathology (Gleason score) as reference standard. Sensitivity, specificity, and area under the receiver operating characteristic curve were calculated to assess PCa detection. Results The mean G′ and SD were as follows: all segments, 8.74 ± 5.26 kPa; healthy segments, 5.44 ± 4.40 kPa; and cancerous segments, 10.84 ± 4.65 kPa. The difference between healthy and cancerous segments was significant with P ≤ .001. Diagnostic performance assessed with the Youden index was as follows: sensitivity, 69%; specificity, 79%; area under the curve, 0.81; and cutoff, 10.67 kPa. Conclusion Our results suggest that prostate MR elastography has the potential to improve diagnostic performance of multiparametric MRI, especially regarding its 2 major limitations: interobserver variability and low specificity. Particularly the high value for specificity in PCa detection is a stimulating result and encourages further investigation of this method.

show abstract

Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2

Cited by 1,412 publications

References 39 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Supervised risk predictor of central gland lesions in prostate cancer using ¹H MR spectroscopic imaging with gradient offset‐independent adiabaticity pulses

Prostate cancer assessment using MR elastography of fresh prostatectomy specimens at 9.4 T

Contact Info

Product

Resources

About

Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2

Cited by 1,412 publications

References 39 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Supervised risk predictor of central gland lesions in prostate cancer using 1H MR spectroscopic imaging with gradient offset‐independent adiabaticity pulses

Prostate cancer assessment using MR elastography of fresh prostatectomy specimens at 9.4 T

Contact Info

Product

Resources

About

Supervised risk predictor of central gland lesions in prostate cancer using ¹H MR spectroscopic imaging with gradient offset‐independent adiabaticity pulses