Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study

Grey, Alistair; Scott, Rachel; Shah, Bina; Acher, Peter; Liyanage, Sidath H.; Pavlou, Menelaos; Omar, Rumana; Chinegwundoh, Frank; Patki, Prasad; Shah, Taimur T.; Haddad, Sami; Ghei, Maneesh; Gilbert, Kayleigh; Campbell, Diane; Brew‐Graves, Chris; Arumainayagam, Nimalan; Chapman, Alex; McLeavy, Laura; Karatziou, Angeliki; Alsaadi, Zayneb; Collins, Tom; Freeman, Alex; Eldred‐Evans, David; Bertoncelli-Tanaka, Mariana; Tam, Henry; Ramachandran, Navin; Madaan, Sanjeev; Winkler, Mathias; Arya, Manit; Emberton, Mark; Ahmed, Hashim U.

doi:10.1016/s1470-2045(22)00016-x

Cited by 34 publications

(31 citation statements)

References 45 publications

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“…In this regard, while the low SP may be overcome by subsequent systematic biopsy, the false positive mpMRI lesion obscures the diagnostic process and prompts unnecessary biopsies [ 2 ]. In fact, despite numerous efforts to standardize settings and reports [ 4 ], mpMRI invariably has limitations, including variability in scan quality or reporting [ 3 ], moderate inter-reader variability [ 5 ] and a steep learning curve.…”

Section: Introductionmentioning

confidence: 99%

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

Gaudiano

Mottola

Bianchi

et al. 2022

Cancers

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The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal–Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68–0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

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Section: Introductionmentioning

confidence: 99%

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

Gaudiano

Mottola

Bianchi

et al. 2022

Cancers

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show abstract

“…Prostate MRI is a powerful diagnostic evaluation tool, such as PI-RADS, whereas MRI elastography plays a supporting role and is not useful as a screening test. A previous study reported that pre-biopsy prostate MRI is only used in 22% of biopsy-naive patients because of its high cost 21 . Alternatively, the CADMUS trial studied the diagnostic performance of multiparametric ultrasonography.…”

Section: Discussionmentioning

confidence: 99%

“…The objective of this trial is agreement in recognization of lesion between multiparametric ultrasound and multiparametric MRI. Multiparametric ultrasound consisted of conventional B-mode images, high frequency or fine flow Doppler images, real-time elastography, and contrast enhanced images 21 . Yet, these data were also qualitative, such as the PI-RADS.…”

Section: Discussionmentioning

confidence: 99%

Role of the elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer and clinically significant prostate cancer

Yoo

Koo

Chung

et al. 2022

Sci Rep

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This study investigated the efficacy of the elastography strain ratio (ESR) as a predictor of prostate cancer (PCa) in targeted prostate biopsy. In total, 257 patients who underwent magnetic resonance imaging-targeted biopsy were enrolled. Before biopsy, we placed regions of interest (zone A and B) in the lesion and levator ani. The ESR was measured as zone A/zone B. Multivariate analyses were performed to predict PCa and clinically significant PCa. There were 206 (71.5%) positive cancer lesions. No difference in digit rectal examination findings was found between patients with and without PCa. For predicting clinically significant PCa, an ESR ≥ 6.8 was significantly higher in the PCa (+) group than in the PCa (−) group (p < 0.001). The area under the receiver operating characteristic curve (AUC) for the conventional variables (model 1) plus the ESR was 0.845, which was significantly higher than that for model 1 (p = 0.001). In prostate imaging reporting and data system score 3 lesions, an ESR ≥ 4.6 was a significant predictor of PCa (p = 0.002). The AUC in model 1 plus the ESR was 0.856, which was significantly higher than that in model 1 alone (p = 0.017). The ESR is useful for predicting clinically significant PCa.

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“…Prostate MRI is a powerful diagnostic evaluation tool, such as PI-RADS, whereas MRI elastography plays a supporting role and is not useful as a screening test. A previous study reported that prebiopsy MRI is only used in 22% of biopsy-naive patients because of its high cost 21 . Alternatively, the CADMUS trial studied the diagnostic performance of multiparametric ultrasonography.…”

Section: Discussionmentioning

confidence: 99%

Role of the elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer and clinically significant prostate cancer

Yoo

Koo

Chung

et al. 2022

Preprint

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INTRODUCTIONThis study investigated the efficacy of the elastography strain ratio (ESR) as a predictor of prostate cancer (PCa) in targeted prostate biopsy.METHODSIn total, 257 patients who underwent magnetic resonance imaging-targeted biopsy were enrolled. Before biopsy, we placed regions of interest (zone A and B) in the lesion and levator ani. The ESR was measured as zone A/zone B. Multivariate analyses were performed to predict PCa and clinically significant PCa.RESULTSThere were 206 (71.5%) positive cancer lesions. No difference in digit rectal examination findings was found between patients with and without PCa. For predicting clinically significant PCa, an ESR ≥ 6.8 was significantly higher in the PCa (+) group than in the PCa (−) group (p < 0.001). The area under the receiver operating characteristic curve (AUC) for the conventional variables (model 1) plus the ESR was 0.845, which was significantly higher than that for model 1 (p = 0.001). In prostate imaging reporting and data system score 3 lesions, an ESR ≥ 4.6 was a significant predictor of PCa (p = 0.002). The AUC in model 1 plus the ESR was 0.856, which was significantly higher than that in model 1 alone (p = 0.017).CONCLUSIONSThe ESR is useful for predicting clinically significant PCa.

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Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study

Cited by 34 publications

References 45 publications

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

Role of the elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer and clinically significant prostate cancer

Role of the elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer and clinically significant prostate cancer

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