Assessment of the compliance with minimum acceptable technical parameters proposed by PI-RADS v2 guidelines in multiparametric prostate MRI acquisition in tertiary referral hospitals in the Republic of Turkey

Coşkun, Mehmet; Sarp, Ali Firat; Karasu, Şebnem; Gelal, Mustafa Fazıl; Türkbey, Barış

doi:10.5152/dir.2019.18537

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…It is interesting to note that in the study by Esses and colleagues 54 in the USA, the rate of adherence to PI-RADS v.2 guidelines was significantly greater at 3T for some parameters on T2-WI and DWI, and similar findings were observed in the study by Coskun et al for T2-WI in Turkey. 56 In both studies, the adherence to temporal resolution <10 s on DCE was low (31% in 54 and 56% in 56 ). It should be noted that the temporal resolution has been decreased to 15s in PI-RADS v.2.1 recommendations, 8 and this means that the spatial resolution should be optimised and not compromised, especially since curve analysis and quantitative kinetic modelling are no longer recommended for routine clinical use.…”

Section: Resultsmentioning

confidence: 85%

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Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

Giganti

Kasivisvanathan

Kirkham

et al. 2021

The British Journal of Radiology

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There is increasing interest in the use of multiparametric magnetic resonance imaging (mpMRI) in the prostate cancer pathway. The European Association of Urology (EAU) and the British Association of Urological Surgeons (BAUS) now advise mpMRI prior to biopsy, and the Prostate Imaging Reporting and Data System (PI-RADS) recommendations set out the minimal technical requirements for the acquisition of mpMRI of the prostate. The widespread and swift adoption of this technique has led to variability in image quality. Suboptimal image acquisition reduces the sensitivity and specificity of mpMRI for the detection and staging of clinically significant prostate cancer. This critical review outlines the studies aimed at improving prostate MR quality that have been published over the last 5 years. These span from the use of specific MR sequences, magnets and coils to patient preparation. The rates of adherence of prostate mpMRI to technical standards in different cohorts across the world are also discussed. Finally, we discuss the first standardised scoring system (i.e., Prostate Imaging Quality, PI-QUAL) that has been created to evaluate image quality, although further iterations of this score are expected in the future.

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Section: Resultsmentioning

confidence: 85%

“…All studies were retrospective, and one of them (25%) was carried out as a survey using a specific questionnaire that was sent to all radiology departments of tertiary referral hospitals in Turkey. 56 …”

Section: Resultsmentioning

confidence: 99%

Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

Giganti

Kasivisvanathan

Kirkham

et al. 2021

The British Journal of Radiology

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“…Nevertheless, while the amount of transferable performance appears to be substantial, there remains uncertainty about the full range of conditions that may affect CNN performance in external settings, given the still small number of external sites and the small amount of data evaluated. This is especially true in the context of prostate imaging, where lack of adherence to PI-RADSv2.1 criteria remains common [ 36 , 37 ] and has the potential to affect diagnostic performance [ 38 ]. Thus, continued study of transferability of established models in use settings is required to further support applicability of models in new environments.…”

Section: Discussionmentioning

confidence: 99%

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability

Netzer,

Eith,

Bethge

et al. 2023

Eur Radiol

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Objectives To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system. Materials and methods In this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy. Performance of UNETM was evaluated by comparing ROC AUC and specificity at typical PI-RADS sensitivity levels. Lesion-level analysis between UNETM segmentations and radiologist-delineated segmentations was performed using Dice coefficient, free-response operating characteristic (FROC), and weighted alternative (waFROC). The influence of using different diffusion sequences was analyzed in cohort A. Results In 250/250/140 exams in cohorts A/B/C, differences in ROC AUC were insignificant with 0.80 (95% CI: 0.74–0.85)/0.87 (95% CI: 0.83–0.92)/0.82 (95% CI: 0.75–0.89). At sensitivities of 95% and 90%, UNETM achieved specificity of 30%/50% in A, 44%/71% in B, and 43%/49% in C, respectively. Dice coefficient of UNETM and radiologist-delineated lesions was 0.36 in A and 0.49 in B. The waFROC AUC was 0.67 (95% CI: 0.60–0.83) in A and 0.7 (95% CI: 0.64–0.78) in B. UNETM performed marginally better on readout-segmented than on single-shot echo-planar-imaging. Conclusion For same-vendor examinations, deep learning provided comparable discrimination of csPCa and non-csPCa lesions and examinations between local and two independent external data sets, demonstrating the applicability of the system to institutions not participating in model training. Clinical relevance statement A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets, indicating the potential of deploying AI models without retraining or fine-tuning, and corroborating evidence that AI models extract a substantial amount of transferable domain knowledge about MRI-based prostate cancer assessment. Key Points • A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets. • Lesion detection performance and segmentation congruence was similar on the institutional and an external data set, as measured by the weighted alternative FROC AUC and Dice coefficient. • Although the system generalized to two external institutions without re-training, achieving expected sensitivity and specificity levels using the deep learning system requires probability thresholds to be adjusted, underlining the importance of institution-specific calibration and quality control.

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“…Although the overall radiologic diagnostic performance differs between bpMRI and mpMRI is likely to be small for experienced readers working in multiple imaging planes [12], we need to be cognizant of the potential for error propagation introduced by axial plane bpMRI evaluations which could mean that the desired biopsy impacts may not be realized because of more uncertain cases. Nevertheless, the way DCE imaging is performed across institutions also varies considerably in terms of sequence/acquisition parameters and bolus timing, thus adding another challenge to the standardization task for tools based on image analysis and their reproducibility [40][41][42].…”

Section: Methodsmentioning

confidence: 99%

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

et al. 2021

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Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

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Assessment of the compliance with minimum acceptable technical parameters proposed by PI-RADS v2 guidelines in multiparametric prostate MRI acquisition in tertiary referral hospitals in the Republic of Turkey

Cited by 10 publications

References 16 publications

Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

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