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Purpose Growing evidence proved the efficacy of multi-parametric MRI (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT)-guided targeted biopsy (TB) in prostate cancer (PCa) diagnosis, but there is no direct comparison between mpMRI-TB and PSMA PET/CT-TB. Gastrin-releasing peptide receptor (GRPR) is highly expressed in PCa, which can compensate for the unstable expression of PSMA in PCa. Therefore, we designed a study to compare the efficiency of mpMRI-TB, dual-tracer (GRPR and PSMA) PET/CT-TB, systematic biopsy, and combined biopsy for the diagnosis of prostate cancer. Methods One hundred twelve suspicious PCa patients were enrolled from September 2020 to June 2021. Patients with anyone of positive dual-tracer PET/CT or mpMRI underwent TB, and all enrolled patients underwent systematic biopsy (SB) after TB. The primary outcome was the detection rates of PCa in different biopsy strategies. Secondary outcomes were the performance of three imaging methods, omission diagnostic rates, and upgrading and downgrading of biopsy samples relative to those of prostatectomy specimens in different biopsy strategies. McNemar’s tests and Bonferroni correction in multiple comparisons were used to compare the primary and secondary outcomes. Results In 112 men, clinically significant PCa (grade group[GG] ≥ 2) accounted for 34.82% (39/112), and nonclinically significant PCa (GG = 1) accounted for 4.46% (5/112). 68  Ga-PSMA PET/CT-TB achieved higher PCa detection rate (69.77%) and positive ratio of biopsy cores (0.44) compared with SB (39.29% and 0.12) and mpMRI-TB (36.14% and 0.23), respectively ( P  < 0.005). Dual-tracer PET/CT screen out patients for avoiding 52.67% (59/112) unnecessary biopsy, whereas dual-tracer PET/CT-TB plus SB achieved high detection rate (77.36%) without misdiagnosis of csPCa. Conclusion Dual-tracer PET/CT might screen patients for avoiding unnecessary biopsy. Dual-tracer PET/CT-TB plus SB might be a more effective and promising strategy for the definite diagnosis of clinically significant PCa than mpMRI-TB.

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists’ accuracy and speed.

IntroductionBiparametric magnetic resonance imaging (MRI) preserves enough information to enable the prediction of prostate cancer (PCa). This fast, cost-effective, and non-invasive modality includes acquisition of T2-weighted images, and accelerated diffusion-weighted imaging (DWI) sequences with corresponding apparent diffusion coefficient (ADC) maps. In this proof-of-concept study, we aimed to assess the prediction of PCa using a tumor location-(L) dependent risk score (LADCT2) generated from an ADC and T2 images - based model.MethodsThe single-center institutional retrospective cohort study used 113 patients who underwent multiparametric MRI (mpMRI) for the diagnosis and management of PCa. A discovery cohort (n = 58) and an evaluation cohort (n = 55) were identified from a prospectively maintained institutional cancer registry. The discovery cohort consisted of patients who underwent MRI-guided TRUS biopsies, whereas the evaluation cohort consisted of patients who received only standard TRUS biopsy. Among the discovery cohort, we developed a predictive risk score (LADCT2) using a multivariable logistic regression model that incorporated tumor location (L) with normalized mean signal differences of T2-and ADC- tumor region of interest. The primary outcome assessed the predictive accuracy of the LADCT2 risk score in predicting PCa.ResultsOur results demonstrated that the LADCT2 score exhibited excellent predictive accuracy for PCa among both the evaluation (AUC = 0.84, OR = 2.80 [95% CI, 1.04-7.52]; P = .04), and discovery (AUC = 0.77, OR = 2.71 [95% CI, 1.38-5.35]; P = .003) cohorts. Additionally, it also predicted for clinically significant PCa among both the discovery (AUC = 0.71, OR = 2.11 [95% CI, 1.16-3.84]; P = .01), and evaluation (AUC = 0.65, OR = 1.94 [95% CI, 1.02-3.69]; P = .04) cohorts.ConclusionThe novel LADCT2 risk score may function as an effective risk stratification tool to support clinical decision-making in the management of PCa.IntroductionBiparametric magnetic resonance imaging (MRI) preserves enough information to enable the prediction of prostate cancer (PCa). This fast, cost-effective, and non-invasive modality includes acquisition of T2-weighted images, and accelerated diffusion-weighted imaging (DWI) sequences with corresponding apparent diffusion coefficient (ADC) maps. In this proof-of-concept study, we aimed to assess the prediction of PCa using a tumor location-(L) dependent risk score (LADCT2) generated from an ADC and T2 images - based model.MethodsThe single-center institutional retrospective cohort study used 113 patients who underwent multiparametric MRI (mpMRI) for the diagnosis and management of PCa. A discovery cohort (n = 58) and an evaluation cohort (n = 55) were identified from a prospectively maintained institutional cancer registry. The discovery cohort consisted of patients who underwent MRI-guided TRUS biopsies, whereas the evaluation cohort consisted of patients who received only standard TRUS biopsy. Among the discovery cohort, we developed a predictive risk score (LADCT2) using a multivariable logistic regression model that incorporated tumor location (L) with normalized mean signal differences of T2-and ADC- tumor region of interest. The primary outcome assessed the predictive accuracy of the LADCT2 risk score in predicting PCa.ResultsOur results demonstrated that the LADCT2 score exhibited excellent predictive accuracy for PCa among both the evaluation (AUC = 0.84, OR = 2.80 [95% CI, 1.04-7.52]; P = .04), and discovery (AUC = 0.77, OR = 2.71 [95% CI, 1.38-5.35]; P = .003) cohorts. Additionally, it also predicted for clinically significant PCa among both the discovery (AUC = 0.71, OR = 2.11 [95% CI, 1.16-3.84]; P = .01), and evaluation (AUC = 0.65, OR = 1.94 [95% CI, 1.02-3.69]; P = .04) cohorts.ConclusionThe novel LADCT2 risk score may function as an effective risk stratification tool to support clinical decision-making in the management of PCa.

Purpose The detection rate for significant prostate cancer of mMRI/TRUS fusion targeted biopsy versus saturation prostate biopsy was prospectively evaluated in men enrolled in active surveillance (AS) protocol. Methods From May 2013 to January 2015, 40 men aged 66 years (median) with very low-risk PCa were enrolled in an AS protocol, and eligible criteria were: life expectancy greater than 10 years, cT1C, PSA below 10 ng/ml, PSA density <0.20, ≤2 unilateral positive biopsy cores, Gleason score (GS) equal to 6, greatest percentage of cancer (GPC) in a core ≤50 %. All patients underwent 3.0-Tesla pelvic mpMRI before confirmatory transperineal saturation biopsy (SPBx; median 30 cores) combined with mpMRI/TRUS fusion targeted biopsy (median 4 cores) of suspicious lesions (PI-RADS 4–5). Results Ten out of 40 (25 %) patients were reclassified by SPBx based on upgraded GS ≥ 7; mpMRI found all the lesions predictive of significant PCa showing a false-positive rate equal to 5 %; on the contrary, mpMRI/TRUS targeted biopsy missed 3/10 (30 %) significant PCa characterised by the presence of a single positive core of GS ≥ 7 and GPC ≤ 5 %, suggesting that reduced number of targeted biopsies could miss small but significant PCa. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value of mpMRI in diagnosing significant PCa were 95.2, 100, 93.8, 83.4, 100 %, respectively. Conclusions Although mpMRI provided high diagnostic accuracy (about 95 %) in diagnosing clinically significant PCa, mpMRI/TRUS fusion targeted biopsy cannot replace SPBx at confirmatory biopsy of men enrolled in AS protocols.

This study intended to evaluate the diagnostic accuracy of the prostate imaging reporting and data system (PI-RADS) and prostate-specific antigen density (PSAD) for clinically significant prostate cancer (csPCa) with PSA levels of 4–10 ng/ml. Between July 2018 and June 2022, a total of 453 patients with PSA levels of 4–10 ng/ml were retrospectively included, which were randomly assigned to the training group (323 patients) and validation group (130 patients). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with their 95% CI were calculated. The overall diagnostic performance was determined with area under the receiver operating characteristic curve (AUC), and an integrated nomogram combining PI-RADS score and PSAD was constructed and tested in a validation cohort. In the training group, the AUC for PI-RADS 2.1 and PSAD alone were 0.875 (95% CI 0.834–0.916) and 0.712 (95% CI 0.648–0.775). At the cutoff PI-RADS score ≥ 4, the sensitivity and specificity were 86.2% (95% CI 77.4–1.9%) and 84.7% (95% CI 79.6–88.8%), respectively. For PSAD, the sensitivity and specificity were 73.3% (95% CI 63.0–82.4%) and 62.1% (95% CI 55.8–68.5%) at the cutoff 0.162 ng/ml/ml. While combining PI-RADS with PSAD, the diagnostic performance was improved significantly, with AUC of 0.893 (95% CI 0.853–0.933). In the validation group, the nomogram yielded a AUC of 0.871 (95% CI 0.807–0.934), which is significantly higher than PI-RADS alone (0.829, 95% CI 0.759–0.899, P  = 0.02). For patients with PSA levels of 4–10 ng/ml, PSAD demonstrated moderate diagnostic accuracy whereas PI-RADS showed high performance. By combination of PSAD and PI-RADS together, the diagnostic performance could be improved significantly.

This study investigates the effectiveness of combining two imaging techniques, multiparametric magnetic resonance imaging (mpMRI) and [sup.68]Ga-Prostate-specific membrane antigen (PSMA-11) positron emission tomography/computed tomography (PET/CT), to diagnose clinically significant prostate cancer (csPCa). While mpMRI is commonly used, it has limitations in its accuracy, requires further confirmation with prostatic biopsy. This study explores whether adding [sup.68]Ga-PSMA-11 PET/CT enhances diagnostic accuracy. The results show that the combined approach significantly improves the detection of csPCa compared to using either modality alone. Specifically, when both imaging methods are able to detect suspicious lesions, the likelihood of csPCa is high. This study suggests that, in select cases with convincing imaging results, it may be possible to forgo biopsy before surgical treatment. However, further research is needed to validate these findings and develop predictive models for accurate diagnosis without biopsy. Widespread adoption of mpMRI has led to a decrease in the number of patients requiring prostate biopsies. [sup.68]Ga-PSMA-11 PET/CT has demonstrated added benefits in identifying csPCa. Integrating the use of these imaging techniques may hold promise for predicting the presence of csPCa without invasive biopsy. A retrospective analysis of 42 consecutive patients who underwent mpMRI, [sup.68]Ga-PSMA-11 PET/CT, prostatic biopsy, and radical prostatectomy (RP) was carried out. A lesion-based model (n = 122) using prostatectomy histopathology as reference standard was used to analyze the accuracy of [sup.68]Ga-PSMA-11 PET/CT, mpMRI alone, and both in combination to identify ISUP-grade group ≥ 2 lesions. [sup.68]Ga-PSMA-11 PET/CT demonstrated greater specificity and positive predictive value (PPV), with values of 73.3% (vs. 40.0%) and 90.1% (vs. 82.2%), while the mpMRI Prostate Imaging Reporting and Data System (PI-RADS) 4–5 had better sensitivity and negative predictive value (NPV): 90.2% (vs. 78.5%) and 57.1% (vs. 52.4%), respectively. When used in combination, the sensitivity, specificity, PPV, and NPV were 74.2%, 83.3%, 93.2%, and 51.0%, respectively. Subgroup analysis of PI-RADS 3, 4, and 5 lesions was carried out. For PI-RADS 3 lesions, [sup.68]Ga-PSMA-11 PET/CT demonstrated a NPV of 77.8%. For PI-RADS 4–5 lesions, [sup.68]Ga-PSMA-11 PET/CT achieved PPV values of 82.1% and 100%, respectively, with an NPV of 100% in PI-RADS 5 lesions. A combination of [sup.68]Ga-PSMA-11 PET/CT and mpMRI improved the radiological diagnosis of csPCa. This suggests that avoidance of prostate biopsy prior to RP may represent a valid option in a selected subgroup of high-risk patients with a high suspicion of csPCa on mpMRI and [sup.68]Ga-PSMA-11 PET/CT.

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. Results: From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77–0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. Conclusions: To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.

Prostate-specific antigen (PSA) testing as the sole indication for prostate biopsy lacks specificity, resulting in overdiagnosis of indolent prostate cancer (PCa) and missing clinically significant PCa (csPCa). SelectMDx is a biomarker-based risk score to assess urinary HOXC6 and DLX1 mRNA expression combined with traditional clinical risk factors. The aim of this prospective multi-institutional study was to evaluate the diagnostic accuracy of SelectMDx and its association with multiparametric magnetic resonance (mpMRI) when predicting PCa in prostate biopsies. Overall, 310 consecutive subjects were included. All patients underwent mpMRI and SelectMDx prior to prostate biopsy. SelectMDx and mpMRI showed sensitivity and specificity of 86.5% vs. 51.9%, and 73.8% vs. 88.3%, respectively, in predicting PCa at biopsy, and 87.1% vs. 61.3%, and 63.7% vs. 83.9%, respectively, in predicting csPCa at biopsy. SelectMDx was revealed to be a good predictor of PCa, while with regards to csPCa detection, it was demonstrated to be less effective, showing results similar to mpMRI. With analysis of strategies assessed to define the best diagnostic strategy to avoid unnecessary biopsy, SelectMDx appeared to be a reliable pathway after an initial negative mpMRI. Thus, biopsy could be proposed for all cases of mpMRI PI-RADS 4–5 score, and to those with Prostate Imaging-Reporting and Data System (PI-RADS) 1–3 score followed by a positive SelectMDx.

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

IntroductionProstate cancer has traditionally been diagnosed by an elevation in PSA or abnormal exam leading to a systematic transrectal ultrasound (TRUS)-guided biopsy. This diagnostic pathway underdiagnoses clinically significant disease while over diagnosing clinically insignificant disease. In this review, we aim to provide an overview of the recent literature regarding the role of multiparametric MRI (mpMRI) in the management of prostate cancer.Materials and MethodsA thorough literature review was performed using PubMed to identify articles discussing use of mpMRI of the prostate in management of prostate cancer. ConclusionThe incorporation of mpMRI of the prostate addresses the shortcomings of the prostate biopsy while providing several other advantages. mpMRI allows some men to avoid an immediate biopsy and permits visualization of areas likely to harbor clinically significant cancer prior to biopsy to facilitate use of MR-targeted prostate biopsies. This allows for reduction in diagnosis of clinically insignificant disease as well as improved detection and better characterization of higher risk cancers, as well as the improved selection of patients for active surveillance. In addition, mpMRI can be used for selection and monitoring of patients for active surveillance and treatment planning during surgery and focal therapy.