Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement

Palumbo, Pierpaolo; Manetta, Rosa; Izzo, Antonio; Bruno, Federico; Arrigoni, Francesco; Filippo, Massimo De; Splendiani, Alessandra; Cesare, Ernesto Di; Masciocchi, Carlo

doi:10.21037/gs-20-547

Cited by 10 publications

(12 citation statements)

References 86 publications

(91 reference statements)

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“…As recommended by the PIRADS committee, the current role of DCE is limited to type 3 lesions to determine the nature of equivocal lesions ( 7 ). Although the sensitivity of DCE is high, but its specificity may be low.…”

Section: Discussionmentioning

confidence: 99%

“…Since the diagnostic performance of the bpMRI method is not inferior to that of the mpMRI, the application of the bpMRI method requires high image quality and reader expertise ( 7 ). In this study, we retrospectively analyzed the clinical data of 699 patients who underwent prostate biopsy from January 2018 to March 2021.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, a number of studies have shown the positive effect of biparametric magnetic resonance imaging (bpMRI) on improving the detection rate of csPCa ( 5 , 6 ). Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging(DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate ( 7 ). Therefore, the PI-RADS Steering Committee supported the ongoing study of bpMRI in various clinical protocols and recognized the potential advantages of bpMRI, including the avoidance of contrast-related adverse reactions, shorter test times and lower costs ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

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bpMRI and mpMRI for detecting prostate cancer: A retrospective cohort study

et al. 2023

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BackgroundWe aimed to compare the detection rates of prostate cancer (PCa) and clinically significant prostate cancer(csPCa) by biparametric (bp-) and multiparameter magnetic resonance imaging (mpMRI).Materials and MethodsA total of 699 patients who underwent transperineal prostate biopsy in the Department of Urology, the Second Affiliated Hospital of Nantong University from January 2018 to December 2021 were retrospectively reviewed. Multivariate analysis was used to explore the influencing factors associated with the detection rates of PCa and csPCa. According to MRI examination before biopsy, the patients were divided into bpMRI group and mpMRI group. The detection rates of PCa and csPCa by bpMRI and mpMRI were compared. Furthermore, stratified analysis was performed for patients in these two groups to compare the detection rates of PCa and csPCa at different tPSA intervals, different prostate volume (PV) intervals and different PI-RADS V2 scores.ResultsA total of 571 patients were finally analyzed in this study after exclusion, and the overall detection rate of PCa was 54.5%. Multivariate analysis showed that patient age, tPSA level, prostate volume and PI-RADS V2 score were independent risk factors affecting the detection rates of PCa and csPCa. The detection rates of PCa and csPCa by bpMRI and mpMRI were comparable (51.3% vs. 57.9%, 44.0% vs. 48.0%, both P > 0.05), with no statistical significance. In the tPSA 10–20 ng/ml interval, the detection rates of PCa (59.72% vs. 40.35%, P = 0.011) and csPCa (51.39% vs. 28.82%, P = 0.005) by mpMRI were significantly higher than those by bpMRI, while in other tPSA interval (tPSA < 4 ng/ml, 4–10 ng/ml, 20–100 ng/ml), different PVs (≤30 ml, 30–60 ml, >60 ml) and different PI-RADS V2 scores (3, 4, and 5), the detection rates of PCa and csPCa were comparable between the two groups.ConclusionFor detecting PCa and csPCa, bpMRI and mpMRI had similar diagnostic efficacies, whereas mpMRI detected more PCa and csPCa in the tPSA interval of 10–20 ng/ml.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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bpMRI and mpMRI for detecting prostate cancer: A retrospective cohort study

et al. 2023

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“…Conversely, DCE has a secondary role in the PI-RADS evaluation of clinically significant PCa [43]. However, when used together with T 2 W and DWI images (depending on the lesion location), it can be useful for detecting small lesions and characterizing indeterminate findings [42,44].…”

Section: Standardization and Reproducibilitymentioning

confidence: 99%

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Corradini

Brizi

Gaudiano

et al. 2021

Cancers

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Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

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“…In 2019, the PI-RADS version 2.1 was newly described, and a simplified biparametric MRI (bpMRI) comprising T2WI and DWI was proposed (5). To date, many studies have demonstrated that the use of bpMRI protocols don't significantly reduce PCa detection rates and is comparable to mpMRI protocols (6)(7)(8). Meanwhile, bpMR protocols also have several advantages, such as a shorter examination time, reducing cost and avoiding potential adverse effects of gadolinium-based contrast agents.…”

Section: Introductionmentioning

confidence: 99%

MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI

et al. 2022

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PurposeTo compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI).MethodsA total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis.ResultsThe radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated.ConclusionsOur study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.

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Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement

Cited by 10 publications

References 86 publications

bpMRI and mpMRI for detecting prostate cancer: A retrospective cohort study

bpMRI and mpMRI for detecting prostate cancer: A retrospective cohort study

Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI

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