Multiparametric Magnetic Resonance Imaging Should Be Preferred Over Digital Rectal Examination for Prostate Cancer Local Staging and Disease Risk Classification

Soeterik, Timo; Melick, Harm H.E. van; Dijksman, Lea M.; Biesma, Douwe H.; Witjes, J. Alfred; Basten, Jean‐Paul A. van

doi:10.1016/j.urology.2020.08.089

Cited by 32 publications

(26 citation statements)

References 21 publications

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“…This is most likely the consequence of the main advantage that mpMRI has over DRE for determining local tumour extent, which is the visualization of the prostate gland as a whole and improved detection of non-organ-confined disease. Our study group has confirmed this in a recent study, as the reported sensitivity for the detection of non-organ-confined disease was significantly lower for DRE compared with mpMRI (51% vs 12%; P < 0.001) [28].…”

Section: Clinical Usefulnesssupporting

confidence: 89%

“…Second, use of mpMRI compared with DRE would lead to upstaging of clinical T‐stage in one‐third of patients [28]. Although mpMRI can provide valuable prognostic information for specific patients, including those with non‐organ‐confined disease which was not detected during DRE, the high upstaging rates bear the risk of overstaging and hence overtreatment in patients with favourable‐risk disease [28].…”

Section: Discussionmentioning

confidence: 99%

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External validation of the Memorial Sloan Kettering Cancer Centre and Briganti nomograms for the prediction of lymph node involvement of prostate cancer using clinical stage assessed by magnetic resonance imaging

et al. 2021

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To evaluate the impact of using clinical stage assessed by multiparametric magnetic resonance imaging (mpMRI) on the performance of two established nomograms for the prediction of pelvic lymph node involvement (LNI) in patients with prostate cancer. Patients and MethodsPatients undergoing robot-assisted extended pelvic lymph node dissection (ePLND) from 2015 to 2019 at three teaching hospitals were retrospectively evaluated. Risk of LNI was calculated four times for each patient, using clinical tumour stage (T-stage) assessed by digital rectal examination (DRE) and by mpMRI, in the Memorial Sloan Kettering Cancer Centre (MSKCC; 2018) and Briganti (2012) nomograms. Discrimination (area under the curve [AUC]), calibration, and the net benefit of these four strategies were assessed and compared. ResultsA total of 1062 patients were included, of whom 301 (28%) had histologically proven LNI. Using DRE T-stage resulted in AUCs of 0.71 (95% confidence interval [CI] 0.70-0.72) for the MSKCC and 0.73 (95% CI 0.72-0.74) for the Briganti nomogram. Using mpMRI T-stage, the AUCs were 0.72 (95% CI 0.71-0.73) for the MSKCC and 0.75 (95% CI 0.74-0.76) for the Briganti nomogram. mpMRI T-stage resulted in equivalent calibration compared with DRE T-stage. Combined use of mpMRI T-stage and the Briganti 2012 nomogram was shown to be superior in terms of AUC, calibration, and net benefit. Use of mpMRI T-stage led to increased sensitivity for the detection of LNI for all risk thresholds in both models, countered by a decreased specificity, compared with DRE T-stage.Conclusion T-stage as assessed by mpMRI is an appropriate alternative for T-stage assessed by DRE to determine nomogram-based risk of LNI in patients with prostate cancer, and was associated with improved model performance of both the MSKCC 2018 and Briganti 2012 nomograms.

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Section: Clinical Usefulnesssupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

External validation of the Memorial Sloan Kettering Cancer Centre and Briganti nomograms for the prediction of lymph node involvement of prostate cancer using clinical stage assessed by magnetic resonance imaging

et al. 2021

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“…In the last few decades, multiparametric magnetic resonance imaging (mpMRI) has been widely used to characterize PCa preoperatively and determine the clinical stage. While, the use of MRI instead of DRE leads to a signi cant upstaging of clinical T-stage and risk grouping [12][13][14][15]. In addition, despite considerable efforts such as alternative high-resolution imaging and new grading approaches, the diagnostic accuracy of MRI for T3a-staging revealed a poor and heterogeneous sensitivity of 30%-70% [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study

Hou

Zhang

Bao

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose: A balance between preserving urinary continence and achievement of negative margins is of clinical relevance while implementary di culty. Preoperatively accurate detection of prostate cancer (PCa) extracapsular extension (ECE) is thus crucial for determining appropriate treatment options. We aimed to develop and clinically validate an arti cial intelligence (AI)-assisted tool for the detection of ECE in patients with PCa using multiparametric MRI.Methods: 849 patients with localized PCa underwent multiparametric MRI before radical prostatectomy were retrospectively included from two medical centers. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts' prior knowledges (PAGNet) from 596 training data sets. The tool was validated in 150 internal and 103 external data sets, respectively; and its clinical applicability was compared with expert-based interpretation and AI-expert interaction.Results: An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% con dence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867) and 0.728 (95% CI, 0.631-0.811) in the training, internal test and external test cohorts, compared to the conventional ResNeXt networks. For experts, the inter-reader agreement was observed in only 437/849 (51.5%) patients with a Kappa value 0.343. And the performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When expert' interpretations were adjusted by the AI assessments, the performance of both two experts was improved. Conclusion:Our AI tool, showing improved accuracy, offers a promising alternative to human experts for imaging staging of PCa ECE using multiparametric MRI.

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“…Preoperatory extraprostatic involvement is defined by specific digital rectal examination and imagistic findings. However, both methods have clear limitations, as DRE has an average accuracy below 60% [ 49 ], while the rate of upstaging to extracapsular extension at the final pathological report compared to the mpMRI prediction is up to 29% [ 50 ]. Using radiomics-based computer-assisted algorithms to weight in upon the diagnosis of ECE increased the radiologists’ accuracy up to 76% [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

Telecan

Andraș

Crişan

et al. 2022

JPM

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(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

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Multiparametric Magnetic Resonance Imaging Should Be Preferred Over Digital Rectal Examination for Prostate Cancer Local Staging and Disease Risk Classification

Cited by 32 publications

References 21 publications

External validation of the Memorial Sloan Kettering Cancer Centre and Briganti nomograms for the prediction of lymph node involvement of prostate cancer using clinical stage assessed by magnetic resonance imaging

External validation of the Memorial Sloan Kettering Cancer Centre and Briganti nomograms for the prediction of lymph node involvement of prostate cancer using clinical stage assessed by magnetic resonance imaging

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study

More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review

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