Can transrectal prostate ultrasound compete with multiparametric MRI in the detection of clinically significant prostate cancer?
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ABSTRACT:
SUBMITTER: Grey ADR
PROVIDER: S-EPMC7354342 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
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Can transrectal prostate ultrasound compete with multiparametric MRI in the detection of clinically significant prostate cancer?
Grey Alistair D R ADR Connor Martin J MJ Tam Joseph J Loch Tillmann T
Translational andrology and urology 20200601 3
We consider the current and future role of transrectal ultrasound imaging in the diagnosis of prostate cancer, with a particular focus on the pre-biopsy localization and targeting role that multiparametric MRI (mpMRI) has come to occupy for some men in recent years. We draw a distinction between transrectal ultrasound (TRUS) used only as a means of distributing zonal biopsies with its employment as a means for identifying and targeting sonographically abnormal lesions. The role of AI in lesion i ...[more]
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Project description:PurposeGiven the limitations of prostate specific antigen and standard biopsies for detecting prostate cancer, we evaluated the cancer detection rate and external validity of a magnetic resonance imaging/transrectal ultrasound fusion guided prostate biopsy system used at the National Institutes of Health.Materials and methodsWe performed a phase III trial of a magnetic resonance imaging/transrectal ultrasound fusion guided prostate biopsy system with participants enrolled between 2012 and 2013. A total of 153 men consented to the study and underwent 3 Tesla multiparametric magnetic resonance imaging with an endorectal coil for clinical suspicion of prostate cancer. Lesions were classified as low or moderate/high risk for prostate cancer. Magnetic resonance imaging/transrectal ultrasound fusion guided biopsy and standard 12-core prostate biopsy were performed and 105 men were eligible for analysis.ResultsMean patient age was 65.8 years and mean prostate specific antigen was 9.5 ng/ml. The overall cancer detection rate was 62.9% (66 of 105 patients). The cancer detection rate in those with moderate/high risk on imaging was 72.3% (47 of 65) vs 47.5% (19 of 40) in those classified as low risk for prostate cancer (p<0.05). Mean tumor core length was 4.6 and 3.7 mm for fusion biopsy and standard 12-core biopsy, respectively (p<0.05). Magnetic resonance imaging/transrectal ultrasound fusion guided biopsy detected prostate cancer that was missed by standard 12-core biopsy in 14.3% of cases (15 of 105), of which 86.7% (13 of 15) were clinically significant. This biopsy upgraded 23.5% of cancers (4 of 17) deemed clinically insignificant on 12-core biopsy to clinically significant prostate cancer necessitating treatment.ConclusionsMagnetic resonance imaging/transrectal ultrasound fusion guided biopsy can improve prostate cancer detection. The results of this trial support the external validity of this platform and may be the next step in the evolution of prostate cancer management.
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Project description:A prospective single-arm clinical trial was conducted to determine whether 18F-choline PET/mpMRI can improve the specificity of multiparametric MRI (mpMRI) of the prostate for Gleason ? 3+4 prostate cancer. Methods: Before targeted and systematic prostate biopsy, mpMRI and 18F-choline PET/CT were performed on 56 evaluable subjects with 90 Likert score 3-5 mpMRI target lesions, using a 18F-choline target-to-background ratio of greater than 1.58 to indicate a positive 18F-choline result. Prostate biopsies were performed after registration of real-time transrectal ultrasound with T2-weighted MRI. A mixed-effects logistic regression was applied to measure the performance of mpMRI (based on prospective Likert and retrospective Prostate Imaging Reporting and Data System, version 2 [PI-RADS], scores) compared with 18F-choline PET/mpMRI to detect Gleason ? 3+4 cancer. Results: The per-lesion accuracy of systematic plus targeted biopsy for mpMRI alone was 67.8% (area under receiver-operating-characteristic curve [AUC], 0.73) for Likert 4-5 and 70.0% (AUC, 0.76) for PI-RADS 3-5. Several PET/MRI models incorporating 18F-choline with mpMRI data were investigated. The most promising model selected all high-risk disease on mpMRI (Likert 5 or PI-RADS 5) plus low- and intermediate-risk disease (Likert 4 or PI-RADS 3-4), with an elevated 18F-choline target-to-background ratio greater than 1.58 as positive for significant cancer. Using this approach, the accuracy on a per-lesion basis significantly improved to 88.9% for Likert (AUC, 0.90; P < 0.001) and 91.1% for PI-RADS (AUC, 0.92; P < 0.001). On a per-patient basis, the accuracy improved to 92.9% for Likert (AUC, 0.93; P < 0.001) and to 91.1% for PI-RADS (AUC, 0.91; P = 0.009). Conclusion: 18F-choline PET/mpMRI improved the identification of Gleason ? 3+4 prostate cancer compared with mpMRI, with the principal effect being improved risk stratification of intermediate-risk mpMRI lesions.
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Project description:ObjectivesTo create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa).MethodsThis study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3-5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets.ResultsThe best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980-0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920-0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution.ConclusionsThe phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa.Key points• T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance. • Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest. • The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.
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Project description:ObjectiveTo investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer.MethodsBased on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC).ResultsIndependent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855.ConclusionEstablishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.
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Project description: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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Project description:Purpose To compare the diagnostic performance of a short dual-pulse sequence magnetic resonance (MR) imaging protocol versus a standard six-pulse sequence multiparametric MR imaging protocol for detection of clinically significant prostate cancer. Materials and Methods This HIPAA-compliant study was approved by the regional ethics committee. Between July 2013 and March 2015, 63 patients from a prospectively accrued study population who underwent MR imaging of the prostate including transverse T1-weighted; transverse, coronal, and sagittal T2-weighted; diffusion-weighted; and dynamic contrast material-enhanced MR imaging with a 3-T imager at a single institution were included in this retrospective study. The short MR imaging protocol image set consisted of transverse T2-weighted and diffusion-weighted images only. The standard MR imaging protocol image set contained images from all six pulse sequences. Three expert readers from different institutions assessed the likelihood of prostate cancer on a five-point scale. Diagnostic performance on a quadrant basis was assessed by using areas under the receiver operating characteristic curves, and differences were evaluated by using 83.8% confidence intervals. Intra- and interreader agreement was assessed by using the intraclass correlation coefficient. Transperineal template saturation biopsy served as the standard of reference. Results At histopathologic evaluation, 84 of 252 (33%) quadrants were positive for cancer in 38 of 63 (60%) men. There was no significant difference in detection of tumors larger than or equal to 0.5 mL for any of the readers of the short MR imaging protocol, with areas under the curve in the range of 0.74-0.81 (83.8% confidence interval [CI]: 0.64, 0.89), and for readers of the standard MR imaging protocol, areas under the curve were 0.71-0.77 (83.8% CI: 0.62, 0.86). Ranges for sensitivity were 0.76-0.95 (95% CI: 0.53, 0.99) and 0.76-0.86 (95% CI: 0.53, 0.97) and those for specificity were 0.84-0.90 (95% CI: 0.79, 0.94) and 0.82-0.90 (95% CI: 0.77, 0.94) for the short and standard MR protocols, respectively. Ranges for interreader agreement were 0.48-0.60 (83.8% CI: 0.41, 0.66) and 0.49-0.63 (83.8% CI: 0.42, 0.68) for the short and standard MR imaging protocols. Conclusion For the detection of clinically significant prostate cancer, no difference was found in the diagnostic performance of the short MR imaging protocol consisting of only transverse T2-weighted and diffusion-weighted imaging pulse sequences compared with that of a standard multiparametric MR imaging protocol. © RSNA, 2017 Online supplemental material is available for this article.
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Project description:Our purpose was to explore whether 68Ga-PSMA PET/CT alone (PET/CT) or in combination with multiparametric MRI (PET/MRI) can improve the detection of clinically significant prostate cancer (PCa). Methods: We retrospectively enrolled 54 patients who underwent both MRI and PET/CT before radical prostatectomy. Regions of interest on MR images, PET/CT images, and pathologic images were marked. A lesion was defined as a region of interest marked on images obtained with any of the 3 modalities. All lesions were characterized using the prostate imaging reporting and data system (PI-RADS), the molecular imaging PSMA expression score, and the pathologic results and analyzed. Diagnostic performance was analyzed by receiver-operating-characteristic analysis. Specific improvement for lesions with different PI-RADS scores was analyzed using the net reclassification index (NRI). Results: In total, 90 lesions from 54 patients were analyzed, among which 66 lesions represented clinically significant PCa. Receiver-operating-characteristic analysis showed PET/MRI to perform better than MRI in detecting clinically significant PCa (change in area under the curve, 0.06; 95% confidence interval, 0.01-0.12; P < 0.05). With the calculated cutoff, PET/MRI performed significantly better than MRI (NRI, 21.9%; P < 0.01), with an improvement in sensitivity (89% vs. 76%, P < 0.01) at no sacrifice of specificity (96% vs. 88%, P > 0.05). Improvement in diagnosing clinically significant PCa occurred for lesions classified as PI-RADS 3 (NRI, 66.7%; P < 0.01). Conclusion: PET/MRI improves the detection of clinically significant PCa for PI-RADS 3 lesions.
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