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BackgroundMultiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited.PurposeTo investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1.Study typeRetrospective.SubjectsOne thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022.Field strength/sequence3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences.AssessmentIntegrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated.Statistical testsA weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05.ResultsA total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively.Data conclusionSpatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa we

Objective: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [68Ga]PSMA-11-PET (PSMA)-PET, [11C] Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility. Methods: During 2016–2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement. Results: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance (P = 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET. Conclusion: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.

Objective: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [68Ga]PSMA-11-PET (PSMA)-PET, [11C] Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility. Methods: During 2016–2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement. Results: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance (P = 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET. Conclusion: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.

Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localizat

PurposeTo develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.MethodsTwenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.ResultsThe T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).ConclusionA deep learning method was developed to estimate prostate T2 maps retrospe

Objective: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [68Ga]PSMA-11-PET (PSMA)-PET, [11C] Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility. Methods: During 2016–2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement. Results: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance (P = 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET. Conclusion: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.

Objective: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [68Ga]PSMA-11-PET (PSMA)-PET, [11C] Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility. Methods: During 2016–2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement. Results: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance (P = 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET. Conclusion: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.

Purpose: To compare two strategies: Prostate-specific antigen density (PSAd) and lesion volume measurement in ruling out significant prostate cancer (sPCa) in men with equivocal Prostate Imaging Reporting and Data System (PI-RADS) category 3 index lesions on biparametric magnetic resonance imaging. Methods: In total, 130 men from our database had index lesions with PI-RADS scores of 3. Prostate volume was measured using the ellipsoid method, in accordance with PI-RADS version 2.1 criteria. Index lesion volumes were also measured using the ellipsoidal formula on the diffusion-weighted imaging sequence with the highest b-value and sagittal T2 sequences. Results: Among 130 men with PI-RADS category 3 index lesions, 23 (18%) had sPCa. In total, 6 of the 89 men with PSAd < 0.15 ng/mL2 (7%) had sPCa, whereas 8 of the 49 men with index lesion volumes < 0.5 mL (16%) had sPCa. The difference was statistically significant (McNemar, p < 0.0001). Conclusion: The PSAd strategy performed better than the lesion volume strategy in ruling out sPCa in men with equivocal PI-RADS category 3 index lesions.

Purpose: To compare two strategies: Prostate-specific antigen density (PSAd) and lesion volume measurement in ruling out significant prostate cancer (sPCa) in men with equivocal Prostate Imaging Reporting and Data System (PI-RADS) category 3 index lesions on biparametric magnetic resonance imaging. Methods: In total, 130 men from our database had index lesions with PI-RADS scores of 3. Prostate volume was measured using the ellipsoid method, in accordance with PI-RADS version 2.1 criteria. Index lesion volumes were also measured using the ellipsoidal formula on the diffusion-weighted imaging sequence with the highest b-value and sagittal T2 sequences. Results: Among 130 men with PI-RADS category 3 index lesions, 23 (18%) had sPCa. In total, 6 of the 89 men with PSAd < 0.15 ng/mL2 (7%) had sPCa, whereas 8 of the 49 men with index lesion volumes < 0.5 mL (16%) had sPCa. The difference was statistically significant (McNemar, p < 0.0001). Conclusion: The PSAd strategy performed better than the lesion volume strategy in ruling out sPCa in men with equivocal PI-RADS category 3 index lesions.

BackgroundMultiparametric MRI (mpMRI) is commonly recommended as a triage test prior to any prostate biopsy. However, there exists limited consensus on which patients with a negative prostate mpMRI could avoid prostate biopsy.PurposeTo identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach.Study typeRetrospective.SubjectsThree hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included.Field strength/sequenceA 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T2 WI) and diffusion-weighted imaging (DWI).AssessmentThe integrative machine learning (iML) model was trained to predict negative prostate biopsy results, utilizing both radiomics and clinical features. The final study cohort comprised 330 consecutive patients with negative mpMRI (PI-RADS < 3) who underwent systematic transrectal ultrasound-guided (TRUS) or MR-ultrasound fusion (MRUS) biopsy within 6 months. A secondary analysis of biopsy naïve subcohort (n = 227) was also conducted.Statistical testsThe Mann-Whitney U test and Chi-Squared test were utilized to evaluate the significance of difference of clinical features between prostate biopsy positive and negative groups. The model performance was validated using leave-one-out cross-validation (LOOCV) and measured by AUC, sensitivity, specificity, and negative predictive value (NPV).ResultsOverall, 306/330 (NPV 92.7%) of the final study cohort patients had negative biopsies, and 207/227 (NPV 91.2%) of the biopsy naïve subcohort patients had negative biopsies. Our iML model achieved NPVs of 98.3% and 98.0% for the study cohort and subcohort, respectively, superior to prostate-specific antigen density (PSAD)-based risk assessment with NPVs of 94.9% and 93.9%, respectively.Data conclusionThe proposed iML model achieved high performance in predicting negative prostate biopsy results for patients with negative mpMRI.

Multiparametric MRI (mpMRI) is validated for the diagnosis of clinically significant prostate cancer (csPCa). 68Ga-PSMA-11 PET/CT (68Ga-PSMA PET/CT) combined with mpMRI has improved negative predictive value over mpMRI alone for csPCa. The aim of this post hoc analysis of the PRIMARY study was to evaluate the clinical significance of patterns of intraprostatic PSMA activity, proposing a 5-point PRIMARY score to optimize the accuracy of 68Ga-PSMA PET/CT for csPCa in a low-prevalence population. Methods: The PRIMARY trial was a prospective multicenter phase II imaging trial that enrolled men with suspected PCa, no prior biopsy, and a recent mpMRI examination (6 mo) and for whom prostate biopsy was planned. In total, 291 men underwent mpMRI, 68Ga-PSMA PET/CT, and systematic biopsy with or without targeted biopsy. The mpMRI was read separately using the Prostate Imaging Reporting and Data System (PI-RADS) (version 2). 68Ga-PSMA PET/CT (pelvis only) was acquired a minimum of 60 min after injection. 68Ga-PSMA PET/CT was centrally read for pattern (diffuse transition zone [TZ], symmetric central zone [CZ], focal TZ, or focal peripheral zone [PZ]) and intensity (SUVmax). In this post hoc analysis, a 5-level PRIMARY score was assigned on the basis of analysis of the central read: no pattern (score of 1), diffuse TZ or CZ (not focal) (score of 2), focal TZ (score of 3), focal PZ (score of 4), or an SUVmax of at least 12 (score of 5). Two further readers independently assigned a PRIMARY score to 118 scans to determine interrater agreement. Associations between PRIMARY score and csPCa (International Society of Urological Pathology grade group ≥ 2) were evaluated. Results: Of the 291 men enrolled, 162 (56%) had csPCa. A PRIMARY score of 1 was present in 16% (47); a score of 2, in 19% (55); a score of 3, in 10% (29); a score of 4 in 40% (117); and a score of 5, in 15% (43). The proportion of patients with csPCa and a PRIMARY score of 1, 2, 3, 4, and 5 was 8.5% (4/47), 27% (15/55)

Characterization of hypoxia and tissue acidosis could advance the understanding of glioma biology and improve patient management. In this study, we evaluated the ability of a pH- and oxygen-sensitive magnetic resonance imaging (MRI) technique to differentiate glioma genotypes, including isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion, and epidermal growth factor receptor (EGFR) amplification, and investigated its prognostic value. A total of 159 adult glioma patients were scanned with pH- and oxygen-sensitive MRI at 3T. We quantified the pH-sensitive measure of magnetization transfer ratio asymmetry (MTRasym) and oxygen-sensitive measure of R2’ within the tumor region-of-interest. IDH mutant gliomas showed significantly lower MTRasym × R2’ (p < 0.001), which differentiated IDH mutation status with sensitivity and specificity of 90.0% and 71.9%. Within IDH mutants, 1p/19q codeletion was associated with lower tumor acidity (p < 0.0001, sensitivity 76.9%, specificity 91.3%), while IDH wild-type, EGFR-amplified gliomas were more hypoxic (R2’ p = 0.024, sensitivity 66.7%, specificity 76.9%). Both R2’ and MTRasym × R2’ were significantly associated with patient overall survival (R2’: p = 0.045; MTRasym × R2’: p = 0.002) and progression-free survival (R2’: p = 0.010; MTRasym × R2’: p < 0.0001), independent of patient age, treatment status, and IDH status. The pH- and oxygen-sensitive MRI is a clinically feasible and potentially valuable imaging technique for distinguishing glioma subtypes and providing additional prognostic value to clinical practice.

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

In this study, the objective was to characterize the MR signatures of the various benign prostate tissues and to differentiate them from cancer. Data was from seventy prostate cancer patients who underwent multiparametric MRI (mpMRI) and subsequent prostatectomy. The scans included T2-weighted imaging (T2W), diffusion weighted imaging, dynamic contrast-enhanced MRI (DCE MRI), and MR spectroscopic imaging. Histopathology tissue information was translated to MRI images. The mpMRI parameters were characterized separately per zone and by tissue type. The tissues were ordered according to trends in tissue parameter means. The peripheral zone tissue order was cystic atrophy, high grade prostatic intraepithelial neoplasia (HGPIN), normal, atrophy, inflammation, and cancer. Decreasing values for tissue order were exhibited by ADC (1.8 10-3 mm2/s to 1.2 10-3 mm2/s) and T2W intensity (3447 to 2576). Increasing values occurred for DCE MRI peak (143% to 157%), DCE MRI slope (101%/min to 169%/min), fractional anisotropy (FA) (0.16 to 0.19), choline (7.2 to 12.2), and choline / citrate (0.3 to 0.9). The transition zone tissue order was cystic atrophy, mixed benign prostatic hyperplasia (BPH), normal, atrophy, inflammation, stroma, anterior fibromuscular stroma, and cancer. Decreasing values occurred for ADC (1.6 10-3 mm2/s to 1.1 10-3 mm2/s) and T2W intensity (2863 to 2001). Increasing values occurred for DCE MRI peak (143% to 150%), DCE MRI slope (101%/min to 137%/min), FA (0.18 to 0.25), choline (7.9 to 11.7), and choline / citrate (0.3 to 0.7). Logistic regression was used to create parameter model fits to differentiate cancer from benign prostate tissues. The fits achieved AUCs ≥0.91. This study quantified the mpMRI characteristics of benign prostate tissues and demonstrated the capability of mpMRI to discriminate among benign as well as cancer tissues, potentially aiding future discrimination of cancer from benign confounders.

Backround/aim: In prostate cancer, extraprostatic extension (EPE) is an unfavorable prognostic factor, and the grade of EPE is correlated with the prognosis. This study aims to evaluate the utility of length of capsular contact (LCC ) in predicting the grade of EPE by correlating the measurements from MRI images and the measurements performed from radical prostatectomy specimens. Materials and methods: MR images and specimens of 110 tumors are analyzed retrospectively. The specimens are used as reference to validate the presence of EPE and to measure the ground truth LCC. MR images are evaluated by two radiologists to identify the presence of EPE and to predict the LCC indirectly. Reliability, accuracy, sensitivity, and specificity of the evaluations are analyzed in comparison with the findings obtained from the specimens. Results: In detection of EPE existence, the radiologists achieve almost the same performance (all AUCs = 0.73) with optimal cut-off values lead to moderate sensitivity and specificity pairs (For cut-off = 15.8 mm; Se = 0.69, Sp = 0.68 and for cut-off of 14.5 mm: Se = 0.77, Sp = 0.62). In distinguishing high-grade EPE from low-grade EPE, the radiologists accomplish very similar performances (AUCs = 0.73 and 0.72) Optimal thresholds of 20.0 mm and 18.5 mm for the readers retrospectively reveal medium sensitivity and specificity pairs (Se = 0.64, Sp = 0.67; Se = 0.64, Sp = 0.67). Conclusion: Consistent LCC estimates can be obtained from MR images providing a beneficial metric for detecting the existence of EPE and for discriminating the grades of EPE.

Backround/aim: In prostate cancer, extraprostatic extension (EPE) is an unfavorable prognostic factor, and the grade of EPE is correlated with the prognosis. This study aims to evaluate the utility of length of capsular contact (LCC ) in predicting the grade of EPE by correlating the measurements from MRI images and the measurements performed from radical prostatectomy specimens. Materials and methods: MR images and specimens of 110 tumors are analyzed retrospectively. The specimens are used as reference to validate the presence of EPE and to measure the ground truth LCC. MR images are evaluated by two radiologists to identify the presence of EPE and to predict the LCC indirectly. Reliability, accuracy, sensitivity, and specificity of the evaluations are analyzed in comparison with the findings obtained from the specimens. Results: In detection of EPE existence, the radiologists achieve almost the same performance (all AUCs = 0.73) with optimal cut-off values lead to moderate sensitivity and specificity pairs (For cut-off = 15.8 mm; Se = 0.69, Sp = 0.68 and for cut-off of 14.5 mm: Se = 0.77, Sp = 0.62). In distinguishing high-grade EPE from low-grade EPE, the radiologists accomplish very similar performances (AUCs = 0.73 and 0.72) Optimal thresholds of 20.0 mm and 18.5 mm for the readers retrospectively reveal medium sensitivity and specificity pairs (Se = 0.64, Sp = 0.67; Se = 0.64, Sp = 0.67). Conclusion: Consistent LCC estimates can be obtained from MR images providing a beneficial metric for detecting the existence of EPE and for discriminating the grades of EPE.

BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

Altres ajuts: Acord transformatiu CRUE-CSIC, Objective: To analyse the current predictive value of isolated high-grade prostatic intraepithelial neoplasia (HGPIN) for clinically significant prostate cancer (csPCa) detection in repeat biopsies. Patients and Methods: A cohort of 293 men with isolated HGPIN detected in previous biopsies performed without multiparametric magnetic resonance imaging (mpMRI), and who underwent repeat biopsy within 1 to 3 years, was analysed. Pre-repeat biopsy mpMRI and guided biopsies to suspicious lesions (Prostate Imaging - Reporting and Data System [PI-RADS] ≥3) and/or and systematic biopsies were performed. Persistent prostate cancer (PCa) suspicion, defined as sustained serum prostatespecific antigen level >4 ng/mL and/or abnormal digital rectal examination, was present in 248 men (84.6%), and was absent in 45 men (15.4%). A control group of 190 men who had no previous HGPIN, atypical small acinar proliferation or HGPIN with atypia who were scheduled to undergo repeat biopsy due to persistent PCa suspicion were also analysed. csPCa was defined as tumours of Grade Group ≥2.Results: In the subset of 45 men with isolated HGPIN, in whom PCa suspicion disappeared, only one csPCa (2.2%) and one insignificant PCa (iPCa) were detected. csPCa was detected in 34.7% of men with persistent PCa suspicion and previous HGPIN, and in 28.4% of those without previous HGPIN (P =0.180). iPCa was detected in 12.1% and 6.3%, respectively (P =0.039). Logistic regression analysis showed that the risk of csPCa detection was not predicted by previous HGPIN: odds ratio (OR) 1.369 (95% confidence interval [CI] 0.894-2.095; P =0.149); however, previous HGPIN increased the risk of iPCa detection: OR 2.043 (95% CI 1.016-4.109; P =0.006). Conclusion: The risk of csPCa in men with isolated HGPIN, in whom PCa suspicion disappears, is extremely low. Moreover, in those men in whom PCa suspicion persists, the risk of csPCa is not influenced by the previous finding of HGPIN. However, previous HGPIN increases the ris

BackgroundEarly identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.MethodsIn this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.ResultsFrom a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR).ConclusionAddition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.

Purpose: To develop a novel risk tool that allows the prediction of lymph node invasion (LNI) among patients with prostate cancer (PCa) treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). Methods: We retrospectively identified 742 patients treated with RARP + ePLND at a single center between 2012 and 2018. All patients underwent multiparametric magnetic resonance imaging (mpMRI) and were diagnosed with targeted biopsies. First, the nomogram published by Briganti et al. was validated in our cohort. Second, three novel multivariable logistic regression models predicting LNI were developed: (1) a complete model fitted with PSA, ISUP grade groups, percentage of positive cores (PCP), extracapsular extension (ECE), and Prostate Imaging Reporting and Data System (PI-RADS) score; (2) a simplified model where ECE score was not included (model 1); and (3) a simplified model where PI-RADS score was not included (model 2). The predictive accuracy of the models was assessed with the receiver operating characteristic-derived area under the curve (AUC). Calibration plots and decision curve analyses were used. Results: Overall, 149 patients (20%) had LNI. In multivariable logistic regression models, PSA (OR: 1.03; P= 0.001), ISUP grade groups (OR: 1.33; P= 0.001), PCP (OR: 1.01; P= 0.01), and ECE score (ECE 4 vs. 3 OR: 2.99; ECE 5 vs. 3 OR: 6.97; P< 0.001) were associated with higher rates of LNI. The AUC of the Briganti et al. model was 74%. Conversely, the AUC of model 1 vs. model 2 vs. complete model was, respectively, 78% vs. 81% vs. 81%. Simplified model 1 (ECE score only) was then chosen as the best performing model. A nomogram to calculate the individual probability of LNI, based on model 1 was created. Setting our cut-off at 5% we missed only 2.6% of LNI patients. Conclusions: We developed a novel nomogram that combines PSA, ISUP grade groups, PCP, and mpMRI-derived ECE score to predict the probability of LNI at fin

Purpose: To determine the diagnostic yield of magnetic resonance imaging (MRI) guided in-bore biopsy in pa-tients with high likelihood multiparametric MRI (mpMRI) findings, regarding overall and clinically significant prostate cancer (csPCa) detection rates and concordance of biopsy and radical prostatectomy (RP) Gleason scores (GS). Methods: This retrospective study consisted of 277 Prostate Imaging Reporting and Data System (PI-RADS) assessment category 4 and 5 targets in 246 patients (mean age, 65.7 years; median prostate specific antigen value, 7.75 ng/mL) who had undergone in-bore biopsy at our institution between 2012 and 2020. Eighty-one patients who underwent RP were eligible for the concordance analysis of biopsy and RP specimen GS. Results: Overall PCa detection rates were 80.5 % per patient (198/246) and 78 % per target (216/277) and 83.5 % and 67.4 % in primary (biopsy naive) and secondary (at least one negative prior biopsy) settings. csPCa was found in 63 % overall, 66 % of patients (132/200) in the primary, and 50 % of patients (23/46) in the secondary biopsy settings (p 0.001). The prostate cancer detection rate was 68 % and 92 % in PI-RADS 4 and 5, respectively (p 0.001). In the radical prostatectomy subcohort, 27.2 % of patients were upgraded, 8.6 % of patients were downgraded from needle biopsy. Significant complications occurred in 1.2 % of patients. Conclusions: MRI-guided in-bore prostate biopsy has a high detection rate of csPCa in primary and secondary biopsy cohorts. Biopsy results were satisfactory in terms of the number of positive cores, cancer percentage in positive cores, and concordance of GS in needle biopsy and RP specimen.

Purpose: To determine the diagnostic yield of magnetic resonance imaging (MRI) guided in-bore biopsy in pa-tients with high likelihood multiparametric MRI (mpMRI) findings, regarding overall and clinically significant prostate cancer (csPCa) detection rates and concordance of biopsy and radical prostatectomy (RP) Gleason scores (GS). Methods: This retrospective study consisted of 277 Prostate Imaging Reporting and Data System (PI-RADS) assessment category 4 and 5 targets in 246 patients (mean age, 65.7 years; median prostate specific antigen value, 7.75 ng/mL) who had undergone in-bore biopsy at our institution between 2012 and 2020. Eighty-one patients who underwent RP were eligible for the concordance analysis of biopsy and RP specimen GS. Results: Overall PCa detection rates were 80.5 % per patient (198/246) and 78 % per target (216/277) and 83.5 % and 67.4 % in primary (biopsy naive) and secondary (at least one negative prior biopsy) settings. csPCa was found in 63 % overall, 66 % of patients (132/200) in the primary, and 50 % of patients (23/46) in the secondary biopsy settings (p 0.001). The prostate cancer detection rate was 68 % and 92 % in PI-RADS 4 and 5, respectively (p 0.001). In the radical prostatectomy subcohort, 27.2 % of patients were upgraded, 8.6 % of patients were downgraded from needle biopsy. Significant complications occurred in 1.2 % of patients. Conclusions: MRI-guided in-bore prostate biopsy has a high detection rate of csPCa in primary and secondary biopsy cohorts. Biopsy results were satisfactory in terms of the number of positive cores, cancer percentage in positive cores, and concordance of GS in needle biopsy and RP specimen.

Background: We assessed the 5-year risk of being diagnosed with significant prostate cancer following a low-suspicion biparametric magnetic resonance imaging result. Methods: The study population was derived from a prospective database used to assess the diagnostic accuracy of biparametric magnetic resonance imaging for significant prostate cancer detection in 1020 biopsy-naïve men. Significant prostate cancer was defined as any core with Gleason grade group ≥3 or a maximum cancerous core length greater than 50% of Gleason grade group 2. A secondary definition of significant prostate cancer was also included: any core with prostate cancer Gleason grade group ≥2. Of the 1020 men, 305 had a low-suspicion biparametric magnetic resonance imaging result (Prostate Imaging Reporting and Data System score of 1 or 2) but four men were excluded from follow-up. Thus, the final study population consisted of 301 men, who were clinically followed-up from inclusion (November 2015 to June 2017) until 1 June 2021. Findings: Overall, 1·7% (5/301) of the study population had significant prostate cancer diagnosed within 5 years (median 1480 days, Interquartile Range (1587–1382)) of their low-suspicion result and corresponding set of biopsies. When the secondary definition of significant prostate cancer was applied, this increased to 5% (15/301) of the study population. Interpretation: The 5-year risk of being diagnosed with significant prostate cancer after a prebiopsy low-suspicion prebiopsy biparametric magnetic resonance imaging result was 1·7%.

Background: We assessed the 5-year risk of being diagnosed with significant prostate cancer following a low-suspicion biparametric magnetic resonance imaging result. Methods: The study population was derived from a prospective database used to assess the diagnostic accuracy of biparametric magnetic resonance imaging for significant prostate cancer detection in 1020 biopsy-naïve men. Significant prostate cancer was defined as any core with Gleason grade group ≥3 or a maximum cancerous core length greater than 50% of Gleason grade group 2. A secondary definition of significant prostate cancer was also included: any core with prostate cancer Gleason grade group ≥2. Of the 1020 men, 305 had a low-suspicion biparametric magnetic resonance imaging result (Prostate Imaging Reporting and Data System score of 1 or 2) but four men were excluded from follow-up. Thus, the final study population consisted of 301 men, who were clinically followed-up from inclusion (November 2015 to June 2017) until 1 June 2021. Findings: Overall, 1·7% (5/301) of the study population had significant prostate cancer diagnosed within 5 years (median 1480 days, Interquartile Range (1587–1382)) of their low-suspicion result and corresponding set of biopsies. When the secondary definition of significant prostate cancer was applied, this increased to 5% (15/301) of the study population. Interpretation: The 5-year risk of being diagnosed with significant prostate cancer after a prebiopsy low-suspicion prebiopsy biparametric magnetic resonance imaging result was 1·7%.

This study sought to assess the value of multiparametric magnetic resonance image (mp-MRI) in patients with a prostate cancer (PCa) Gleason score of 6 or less under consideration for or already in active surveillance and to determine the rate of upgrading by target biopsy. Three hundred and fifty-four consecutive men with an initial transrectal ultrasound-guided (TRUS) biopsy-confirmed PCa Gleason score of 6 or less under clinical consideration for or already in active surveillance underwent mp-MRI and were retrospectively reviewed. One hundred and nineteen of 354 patients had cancer-suspicious regions (CSRs) at mp-MRI. Each CSR was assigned a Prostate Imaging Reporting and Data System (PI-RADS) score based on PI-RADS v2. One hundred and eight of 119 patients underwent confirmatory imaging-guided biopsy for CSRs. Pathology results including Gleason score (GS) and percentage of specimens positive for PCa were recorded. Associations between PI-RADS scores and findings at target biopsy were evaluated using logistic regression. At target biopsy, 81 of 108 patients had PCa (75%). Among them, 77 patients had upgrading (22%, 77 of 354 patients). One hundred and forty-six CSRs in 108 patients had PI-RADS 3 n = 28, 4 n = 66, and 5 n = 52. The upgraded rate for each category of CSR was for PI-RADS 3 (5 of 28, 18%), 4 (47 of 66, 71%) and 5 (49 of 52, 94%). Using logistic regression analysis, differences in PI-RADS scores from 3 to 5 are significantly associated with the probability of disease upgrade (20%, 73%, and 96% for PI-RADS score of 3, 4, and 5, respectively). Adding mp-MRI to patients under consideration for or already in active surveillance helps to identify undiagnosed PCa of a higher GS or higher volume resulting in upgrading in 22%.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patients tumor on multiparametric MRI is insufficient to predict that patients response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation.

This study sought to assess the value of multiparametric magnetic resonance image (mp-MRI) in patients with a prostate cancer (PCa) Gleason score of 6 or less under consideration for or already in active surveillance and to determine the rate of upgrading by target biopsy. Three hundred and fifty-four consecutive men with an initial transrectal ultrasound-guided (TRUS) biopsy-confirmed PCa Gleason score of 6 or less under clinical consideration for or already in active surveillance underwent mp-MRI and were retrospectively reviewed. One hundred and nineteen of 354 patients had cancer-suspicious regions (CSRs) at mp-MRI. Each CSR was assigned a Prostate Imaging Reporting and Data System (PI-RADS) score based on PI-RADS v2. One hundred and eight of 119 patients underwent confirmatory imaging-guided biopsy for CSRs. Pathology results including Gleason score (GS) and percentage of specimens positive for PCa were recorded. Associations between PI-RADS scores and findings at target biopsy were evaluated using logistic regression. At target biopsy, 81 of 108 patients had PCa (75%). Among them, 77 patients had upgrading (22%, 77 of 354 patients). One hundred and forty-six CSRs in 108 patients had PI-RADS 3 n = 28, 4 n = 66, and 5 n = 52. The upgraded rate for each category of CSR was for PI-RADS 3 (5 of 28, 18%), 4 (47 of 66, 71%) and 5 (49 of 52, 94%). Using logistic regression analysis, differences in PI-RADS scores from 3 to 5 are significantly associated with the probability of disease upgrade (20%, 73%, and 96% for PI-RADS score of 3, 4, and 5, respectively). Adding mp-MRI to patients under consideration for or already in active surveillance helps to identify undiagnosed PCa of a higher GS or higher volume resulting in upgrading in 22%.

We tested the ability of Prostate Imaging Reporting and Data System (PI-RADS) score and prostate-specific antigen density (PSAD) in predicting the risk of reclassification during active surveillance. Three hundred eighty-nine patients underwent multiparametric magnetic resonance imaging and subsequent confirmatory or follow-up biopsy. PSAD ≥ 0.20 ng/mL2 may improve predictive accuracy of multiparametric magnetic resonance imaging results for reclassification of patients in active surveillance, whereas PSAD < 0.10 ng/mL2 may help selection of patients at lower risk of harboring clinically significant prostate cancer.

Objective:To compare the negative predictive value (NPV) of multiparametric MRI for Gleason score (GS) ≥ 3+4 cancer and evaluate predictors of these tumors in men with suspected disease and under active surveillance (AS). Materials and Methods:This retrospective study included 38 men with suspected prostate cancer and 38 under AS with scans assigned PI-RADS v2 scores 1 or 2 between May 2016 and September 2017. Biopsy results were no cancer, GS = 3+3, or GS ≥ 3+4. Pre-MRI PSA, gland volume, and PSA density were recorded. Chi-square, equality of proportions, and logistic regressions were used to analyze the data. Results:Intermediate to high-grade cancer was found in 12.8% (95% CI = 2.3-23.3) and 35.9% (95% CI = 20.8-50.9) of men with suspected cancer, and under AS (p = 0.02), respectively. The NPV for GS ≥ 3+4 were 87.2% (suspected cancer; 76.7-97.7) and 64.1% (AS; 49.0-79.2). In neither group PSA significantly predicted cancer grade (p = 0.75 and 0.63). Although it did not reach conventional statistical significance, PSA density was a good predictor of cancer grade in men with suspected disease (p = 0.06), but not under AS (p = 0.62). Conclusion:The NPV of multiparametric MRI for GS ≥ 3+4 is higher in men with suspected prostate cancer than in men under AS. PSA density ≤ 0.15 improved the prediction of intermediate to high-grade disease in patients without known cancer.

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with ‘ground truth’ histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC = 0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87–0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a promising tool for prostate cancer (PCa) localization. Due to the complex acquisition procedure and analysis, along with a questioned diagnostic contribution, the inclusion of DCE-MRI in the updated Prostate Imaging and Reporting Data System (PI-RADS2) has been limited to qualitative assessment only, with a marginal weight on the final score. However, lower diagnostic performance has been reported for PI-RADS2 compared to original PI-RADS, evidencing the need to reconsider the role of DCE-MRI. Recently, magnetic resonance dispersion imaging (MRDI) has shown promise for quantitative DCE-MRI of the prostate. In MRDI, the adopted pharmacokinetic (PK) model includes description of the contrast agent intravascular transport, enabling additional assessment of the microvascular architecture. Compared to standard PK analysis in MRI, separate estimation of an arterial input function is no longer necessary, facilitating the clinical routine. Here we evaluate the diagnostic performance of MRDI for PCa localization compared to standard PK analysis by the Tofts model (TM) in a retrospective study including 80 patients from three different Dutch institutions.

ObjectiveTo compare the negative predictive value (NPV) of multiparametric MRI for Gleason score (GS) ≥ 3+4 cancer and evaluate predictors of these tumors in men with suspected disease and under active surveillance (AS).Materials and methodsThis retrospective study included 38 men with suspected prostate cancer and 38 under AS with scans assigned PI-RADS v2 scores 1 or 2 between May 2016 and September 2017. Biopsy results were no cancer, GS = 3+3, or GS ≥ 3+4. Pre-MRI PSA, gland volume, and PSA density were recorded. Chi-square, equality of proportions, and logistic regressions were used to analyze the data.ResultsIntermediate to high-grade cancer was found in 12.8% (95% CI = 2.3-23.3) and 35.9% (95% CI = 20.8-50.9) of men with suspected cancer, and under AS (p = 0.02), respectively. The NPV for GS ≥ 3+4 were 87.2% (suspected cancer; 76.7-97.7) and 64.1% (AS; 49.0-79.2). In neither group PSA significantly predicted cancer grade (p = 0.75 and 0.63). Although it did not reach conventional statistical significance, PSA density was a good predictor of cancer grade in men with suspected disease (p = 0.06), but not under AS (p = 0.62).ConclusionThe NPV of multiparametric MRI for GS ≥ 3+4 is higher in men with suspected prostate cancer than in men under AS. PSA density ≤ 0.15 improved the prediction of intermediate to high-grade disease in patients without known cancer.

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a promising tool for prostate cancer (PCa) localization. Due to the complex acquisition procedure and analysis, along with a questioned diagnostic contribution, the inclusion of DCE-MRI in the updated Prostate Imaging and Reporting Data System (PI-RADS2) has been limited to qualitative assessment only, with a marginal weight on the final score. However, lower diagnostic performance has been reported for PI-RADS2 compared to original PI-RADS, evidencing the need to reconsider the role of DCE-MRI. Recently, magnetic resonance dispersion imaging (MRDI) has shown promise for quantitative DCE-MRI of the prostate. In MRDI, the adopted pharmacokinetic (PK) model includes description of the contrast agent intravascular transport, enabling additional assessment of the microvascular architecture. Compared to standard PK analysis in MRI, separate estimation of an arterial input function is no longer necessary, facilitating the clinical routine. Here we evaluate the diagnostic performance of MRDI for PCa localization compared to standard PK analysis by the Tofts model (TM) in a retrospective study including 80 patients from three different Dutch institutions.

Prostate cancer (PCa) is the most commonly diagnosed cancer type in males in Australia and radiotherapy provides an efficacious treatment option. However, traditional approaches of radiotherapy apply a uniform high radiation dose to the entire prostate, which can cause side effects as healthy tissues are irradiated. The treatment-related side effects have a substantial impact on patients’ quality of life, particularly for the increasing number of low-risk and intermediate-risk patients who typically have a long life-expectancy (10 – 20 years). Focal therapy provides a solution to reduce treatment-related side effects by selectively treating the tumour subvolume and hence spares healthy tissues. Our group has proposed a specific form of focal therapy, termed “bio-focused radiotherapy” (BiRT), where the actual radiation dose to be delivered to the tumour volume further depends on the biological characteristics of the tumour, including cell density, tumour aggressiveness and the presence of hypoxia. To implement BiRT, reliable estimation of tumour location and characteristics is required. The aim of this thesis is to develop predictive models for tumour location and biological characteristics from multiparametric MRI (mpMRI) for the clinical implementation of BiRT. In vivo mpMRI scans were acquired from 30 patients who subsequently underwent radical prostatectomy. Histology was obtained from prostate specimens after surgery, which was used to retrieve the “ground truth” information of tumour location and characteristics for predictive model development using supervised learning. To enable voxel-wise analysis, in vivo mpMRI data was co-registered with ex vivo histology using an advanced 3D deformable registration framework. This provided the necessary materials for predictive model development, using mpMRI data as features (predictors) and ground truths from histology as labels. To facilitate the investigation of specific models, a multi-purposed scalable machine learni

Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density-weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUVmax was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.

Radiotherapy is a treatment with curative intent, both in patients with primary diagnosis of prostate cancer (PCa) and in patients presenting with biochemical recurrence after radical prostatectomy (RP). Moreover, the use of stereotactic body radiotherapy as a metastasis directed therapy in patients with oligometastatic PCa has significantly increased in the recent years. Conventional imaging techniques, including transrectal ultrasound, computed tomography (CT), morphologic magnetic resonance and bone scintigraphy have traditionally played a minor role in all those clinical scenarios due to its low diagnostic accuracy. The recent development of the positron emission tomography (PET) radiotracer 68Ga-PSMA binding to the prostate specific membrane antigen (PSMA), a transmembrane glycoprotein overexpressed in PCa cells, has shown promising results. Detection rates for PCa lesions are higher than CT and higher than the best technique available, the PET/CT with choline. Its superiority has been demonstrated even at very low PSA levels (<1 ng/ml). This increase in diagnostic accuracy represents a potential impact on patient management, especially in radiotherapy. Even if this imaging technique is already available for routine clinical practice in some European countries, in Spain, unfortunately, there is very limited access. In this review, we analyze the main studies that investigate the usefulness of 68Ga-PSMA PET/CT in patients with PCa and its potential impact on radiotherapy treatments. In addition, we compared the 68Ga-PSMA PET/CT, with the multiparametric magnetic resonance imaging and the PET/CT with choline, in the different clinical scenarios., SCOPUS: ar.j, info:eu-repo/semantics/published

Prostate specific membrane antigen (PSMA) scanning is a sensitive method of prostate cancer detection. In a 71 y.o. man with a PSA of 49 (6%F), 4 negative MRI studies and 6 negative biopsies over an 8 year interval, a 68Ga-PSMA PET/CT scan showed a PSMA-avid spot in the prostate. Using image fusion technology, the lesion was target-biopsied and Gleason 3 + 4 = 7 (cancer core length of 12 mm) was identified. This case may herald a new application for PSMA scanning and prostate cancer imaging.

The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with ‘ground truth’ histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.

Prostate specific membrane antigen (PSMA) scanning is a sensitive method of prostate cancer detection. In a 71 y.o. man with a PSA of 49 (6%F), 4 negative MRI studies and 6 negative biopsies over an 8 year interval, a 68Ga-PSMA PET/CT scan showed a PSMA-avid spot in the prostate. Using image fusion technology, the lesion was target-biopsied and Gleason 3 + 4 = 7 (cancer core length of 12 mm) was identified. This case may herald a new application for PSMA scanning and prostate cancer imaging.

The purpose of this study was to characterize prostate cancer (PCa) based on multiparametric MR (mpMR) measures derived from MRI, diffusion, spectroscopy, and dynamic contrast-enhanced (DCE) MRI, and to validate mpMRI in detecting PCa and predicting PCa aggressiveness by correlating mpMRI findings with whole-mount histopathology. Seventy-eight men with untreated PCa received 3 T mpMR scans prior to radical prostatectomy. Cancerous regions were outlined, graded, and cancer amount estimated on whole-mount histology. Regions of interest were manually drawn on T2 -weighted images based on histopathology. Logistic regression was used to identify optimal combinations of parameters for the peripheral zone and transition zone to separate: (i) benign from malignant tissues; (ii) Gleason score (GS) ≤3 + 3 disease from ≥GS3 + 4; and (iii) ≤ GS3 + 4 from ≥GS4 + 3 cancers. The performance of the models was assessed using repeated fourfold cross-validation. Additionally, the performance of the logistic regression models created under the assumption that one or more modality has not been acquired was evaluated. Logistic regression models yielded areas under the curve (AUCs) of 1.0 and 0.99 when separating benign from malignant tissues in the peripheral zone and the transition zone, respectively. Within the peripheral zone, combining choline, maximal enhancement slope, apparent diffusion coefficient (ADC), and citrate measures for separating ≤GS3 + 3 from ≥GS3 + 4 PCa yielded AUC = 0.84. Combining creatine, choline, and washout slope yielded AUC = 0.81 for discriminating ≤GS3 + 4 from ≥GS4 + 3 disease. Within the transition zone, combining washout slope, ADC, and creatine yielded AUC = 0.93 for discriminating ≤GS3 + 3 and ≥GS3 + 4 cancers. When separating ≤GS3 + 4 from ≥GS4 + 3 PCa, combining choline and washout slope y

INTRODUCTION:Uveal melanoma is a rare intraocular tumor with heterogeneous biological behavior, and additional noninvasive markers that may predict outcome are needed. Diffusion- and perfusion-weighted imaging may prove useful but have previously been limited in their ability to evaluate ocular tumors. Our purpose was to show the feasibility and potential value of a multiparametric (mp-) MRI protocol employing state of the art diffusion- and perfusion-weighted imaging techniques. METHODS:Sixteen patients with uveal melanoma were imaged with mp-MRI. Multishot readout-segmented echoplanar diffusion-weighted imaging, quantitative dynamic contrast-enhanced (DCE) MR perfusion imaging, and anatomic sequences were obtained. Regions of interest (ROIs) were drawn around tumors for calculation of apparent diffusion coefficient (ADC) and perfusion metrics (K (trans) , v e , k ep , and v p ). A generalized linear fit model was used to compare various MRI values with the American Joint Commission on Cancer (AJCC) tumor group and monosomy 3 status with significance set at P < 0.05. RESULTS:mp-MRI was performed successfully in all cases. MRI tumor height (mean [standard deviation]) was 6.5 mm (3.0). ROI volume was 278 mm(3) (222). ADC was 1.07 (0.27) × 10-3 mm(2)/s. DCE metrics were K (trans) 0.085/min (0.063), v e 0.060 (0.052), k ep 1.20/min (0.32), and v p 1.48 % (0.82). Patients with >33 % monosomy 3 had higher K (trans) and higher v e values than those with disomy 3 or ≤33 % monosomy (P < 0.01). There were no significant differences between ADC (P = 0.07), k ep (P = 0.37), and v p with respect to monosomy 3. CONCLUSION:mp-MRI for ocular tumor imaging using multishot EPI DWI and quantitative DCE perfusion is technically feasible. mp-MRI may help predict monosomy 3 in uveal melanoma.

Object: Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only. Materials and methods: Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2–7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes. Results: The highest similarity index between manual and automatic segmentation (SImanual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R2 = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R2 = 0.96). Conclusion: Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.

INTRODUCTION:Uveal melanoma is a rare intraocular tumor with heterogeneous biological behavior, and additional noninvasive markers that may predict outcome are needed. Diffusion- and perfusion-weighted imaging may prove useful but have previously been limited in their ability to evaluate ocular tumors. Our purpose was to show the feasibility and potential value of a multiparametric (mp-) MRI protocol employing state of the art diffusion- and perfusion-weighted imaging techniques. METHODS:Sixteen patients with uveal melanoma were imaged with mp-MRI. Multishot readout-segmented echoplanar diffusion-weighted imaging, quantitative dynamic contrast-enhanced (DCE) MR perfusion imaging, and anatomic sequences were obtained. Regions of interest (ROIs) were drawn around tumors for calculation of apparent diffusion coefficient (ADC) and perfusion metrics (K (trans) , v e , k ep , and v p ). A generalized linear fit model was used to compare various MRI values with the American Joint Commission on Cancer (AJCC) tumor group and monosomy 3 status with significance set at P < 0.05. RESULTS:mp-MRI was performed successfully in all cases. MRI tumor height (mean [standard deviation]) was 6.5 mm (3.0). ROI volume was 278 mm(3) (222). ADC was 1.07 (0.27) × 10-3 mm(2)/s. DCE metrics were K (trans) 0.085/min (0.063), v e 0.060 (0.052), k ep 1.20/min (0.32), and v p 1.48 % (0.82). Patients with >33 % monosomy 3 had higher K (trans) and higher v e values than those with disomy 3 or ≤33 % monosomy (P < 0.01). There were no significant differences between ADC (P = 0.07), k ep (P = 0.37), and v p with respect to monosomy 3. CONCLUSION:mp-MRI for ocular tumor imaging using multishot EPI DWI and quantitative DCE perfusion is technically feasible. mp-MRI may help predict monosomy 3 in uveal melanoma.

Object: Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only. Materials and methods: Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2–7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes. Results: The highest similarity index between manual and automatic segmentation (SImanual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R2 = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R2 = 0.96). Conclusion: Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.