PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone

Wen, Jing; Tang, Tingting; Ji, Yugang; Zhang, Yilan

doi:10.3389/fonc.2022.861928

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…Compared with conventional methods that depend on digital rectal examination (DRE) or transrectal ultrasonography (TRUS)–guided biopsy alone, mpMRI demonstrated higher accuracy and thus could substantially reduce unnecessary biopsies ( 6 – 8 ). In recent years, various novel models and nomograms were developed to improve the diagnostic performance of PCa, which utilizes one or more of following tools or biomarkers: serum prostate-specific antigen density (PSA), PSAD, age, history of prior prostate biopsy, DRE, radiomics, genomics, and molecular imaging ( 9 , 10 )…”

Section: Introductionmentioning

confidence: 99%

Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis

Wen

Han

et al. 2022

Front. Oncol.

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ObjectivesWe aimed to systematically assess the inter-reader agreement of the Prostate Imaging Reporting and Data System Version (PI-RADS) v2.1 for the detection of prostate cancer (PCa).MethodsWe included studies reporting inter-reader agreement of different radiologists that applied PI-RADS v2.1 for the detection of PCa. Quality assessment of the included studies was performed with the Guidelines for Reporting Reliability and Agreement Studies. The summary estimates of the inter-reader agreement were pooled with the random-effect model and categorized (from slight to almost perfect) according to the kappa (κ) value. Multiple subgroup analyses and meta-regression were performed to explore various clinical settings.ResultsA total of 12 studies comprising 2475 patients were included. The pooled inter-reader agreement for whole gland was κ=0.65 (95% CI 0.56-0.73), and for transitional zone (TZ) lesions was κ=0.62 (95% CI 0.51-0.72). There was substantial heterogeneity presented throughout the studies (I2= 95.6%), and meta-regression analyses revealed that only readers’ experience (<5 years vs. ≥5 years) was the significant factor associated with heterogeneity (P<0.01). In studies providing head-to-head comparison, there was no significant difference in inter-reader agreement between PI-RADS v2.1 and v2.0 for both the whole gland (0.64 vs. 0.57, p=0.37), and TZ (0.61 vs. 0.59, p=0.81).ConclusionsPI-RADS v2.1 demonstrated substantial inter-reader agreement among radiologists for whole gland and TZ lesions. However, the difference in agreement between PI-RADS v2.0 and v2.1 was not significant for the whole gland or the TZ.

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Section: Introductionmentioning

confidence: 99%

Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis

Wen

Han

et al. 2022

Front. Oncol.

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“…According to recent literatures, PSAd is an important predictive indicator for csPCa compared to traditional tPSA, and has a broader prospect. The strategy of mpMRI combined with PSAd can effectively detect PI-RADS 1–3 cases and tumors in peripheral zone lesions with extremely high negative predictive values ( 15 - 17 ). Therefore, we subsequently compared two schemes of the prediction model, PI-RADS + SUVmax + PSAd and PI-RADS + SUVmax.…”

Section: Resultsmentioning

confidence: 99%

“…Considering the complementary effect of PSAd and mpMRI, Wei et al incorporated PSAd into the predictive model based on mpMRI, which significantly improved the performance in diagnosing csPCa in the transitional zone ( 27 ). Similarly, Wen et al constructed a clinical predictive model based on PSAd and mpMRI, and the results showed that it could significantly improve the diagnostic performance of tumors in peripheral zone ( 15 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of clinically significant prostate cancer using a novel 68Ga-PSMA PET-CT and multiparametric MRI-based model

Cheng¹,

Liu²,

Yi³

et al. 2023

Transl Androl Urol

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Background There are some limitations in the commonly used methods for the detection of prostate cancer. There is a lack of nomograms based on multiparametric magnetic resonance imaging (mpMRI) and 68 Ga-prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography (PET-CT) for the prediction of prostate cancer. The study seeks to compare the performance of mpMRI and 68 Ga-PSMA PET-CT, and design a novel predictive model capable of predicting clinically significant prostate cancer (csPCa) before biopsy based on a combination of 68 Ga-PSMA PET-CT, mpMRI, and patient clinical parameters. Methods From September 2020 to June 2021, we prospectively enrolled 112 consecutive patients with no prior history of prostate cancer who underwent both 68 Ga-PSMA PET-CT and mpMRI prior to biopsy at our clinical center. Univariate and multivariate regression analyses were used to identify predictors of csPCa, with a predictive model and its nomogram incorporating 68 Ga-PSMA PET-CT, mpMRI, and the clinical predictors then being generated. The constructed model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, and further validated with the internal and external cohorts. Results The model incorporated prostate-specific antigen density (PSAd), Prostate Imaging Reporting and Data System (PI-RADS) category, and maximum standardized uptake value (SUVmax), and it exhibited excellent predictive efficacy when applying to evaluate both training and validation cohorts [area under the curve (AUC): 0.936 and 0.940, respectively]. Compared with SUVmax alone, the model demonstrated excellent diagnostic performance with improved specificity (0.910, 95% CI: 0.824–0.963) and positive predictive values (0.811, 95% CI: 0.648–0.920). Calibration curve and decision curve analysis further confirmed that the model exhibited a high degree of clinical net benefit and low error rate. Conclusions The constructed model in this study was capable of accurately predicting csPCa prior to biopsy with excellent discriminative ability. As such, this model has the potential to be an effective non-invasive approach for the diagnosis of csPCa.

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“…Although the PSAD and f/tPSA have good differential diagnostic efficacy and sensitivity for csPCa and non-csPCa, their specificity is relatively low. Wen et al [ 25 ] showed that PSAD is an independent predictor of PCa (PZ: OR = 37.66, 95% CI = 3.3–429.1, p = 0.002; TZ: OR = 14.57, 95% CI = 4.64–45.76, p < 0.001). However, PSA and PGV were not investigated thoroughly because they are strongly correlated with PSAD.…”

Section: Discussionmentioning

confidence: 99%

PI-RADS v2.1 evaluation of prostate “nodule in nodule” variants: clinical, imaging, and pathological features

Sun,

Xu,

Zhang

et al. 2024

Insights Imaging

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Objectives To analyze the correlation among the imaging features of prostate “nodule in nodule,” clinical prostate indices, and pathology results. Methods We retrospectively analyzed the prostate images from 47 male patients who underwent MRI scans and pathological biopsy from January 2022 to July 2023. Two radiologists (R1/R2) evaluated the morphology and signal intensity of the “nodule in nodule” in a double-blind manner and calculated the PI-RADS v2.1 score, which was compared with clinical prostate indices and pathological results. Results 34.04% (16/47) of patients were pathologically diagnosed with clinically significant prostate cancer (csPCa). Total prostate-specific antigen (tPSA), free/t PSA, PSA density (PSAD), and prostate gland volume (PGV) were significantly different between csPCa patients and benign prostatic hyperplasia (BPH) patients with prostate “nodule in nodule”. R1/R2 detected 17/17 prostate “nodule in nodule” pathologically confirmed as csPCa on MRI; 10.60% (16/151) (R1) and 11.11% (17/153) (R2) had diffusion-weighted imaging (DWI) PI-RADS v2.1 score of 4, and 0.66% (1/151) (R1) had a score of 3. The percentages of encapsulated, circumscribed, and atypical nodules and obscured margins were 0.00% (0/151), 0.00% (0/151), 5.96% (9/151), and 5.30% (8/151), respectively, for R1, and 0.00% (0/153), 0.00% (0/153), 5.88% (9/153), and 4.58% (7/153) for R2. Conclusion When the inner nodules of “nodule in nodule” lesions in PI-RADS v2.1 category 1 in the TZ show incomplete capsulation or obscured margins, they are considered atypical nodules and might be upgraded to PI-RADS v2.1 category 3 if they exhibit marked diffusion restriction. However, further validation is needed. Critical relevance statement This study first analyzed the relationship between clinical and pathological findings and the size, margin, and multimodal MRI manifestations of the prostate “nodule in nodule.” These findings could improve the diagnostic accuracy of PI-RADS v2.1 for prostate lesions. Key points • The margin of the prostate inner nodules affects the PI-RADS v2.1 score. • The morphology of prostate “nodule in nodule” is related to their pathology. • The PI-RADS v2.1 principle requires consideration of prostate “nodule in nodule” variants. Graphical Abstract

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PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone

Cited by 7 publications

References 35 publications

Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis

Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis

Prediction of clinically significant prostate cancer using a novel 68Ga-PSMA PET-CT and multiparametric MRI-based model

PI-RADS v2.1 evaluation of prostate “nodule in nodule” variants: clinical, imaging, and pathological features

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