Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa

Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone

Lymphatic spread determines treatment decisions in prostate cancer (PCa) patients. 68Ga-PSMA-PET/CT can be performed, although cost remains high and availability is limited. Therefore, computed tomography (CT) continues to be the most used modality for PCa staging. We assessed if convolutional neural networks (CNNs) can be trained to determine 68Ga-PSMA-PET/CT-lymph node status from CT alone. In 549 patients with 68Ga-PSMA PET/CT imaging, 2616 lymph nodes were segmented. Using PET as a reference standard, three CNNs were trained. Training sets balanced for infiltration status, lymph node location and additionally, masked images, were used for training. CNNs were evaluated using a separate test set and performance was compared to radiologists' assessments and random forest classifiers. Heatmaps maps were used to identify the performance determining image regions. The CNNs performed with an Area-Under-the-Curve of 0.95 (status balanced) and 0.86 (location balanced, masked), compared to an AUC of 0.81 of experienced radiologists. Interestingly, CNNs used anatomical surroundings to increase their performance, "learning" the infiltration probabilities of anatomical locations. In conclusion, CNNs have the potential to build a well performing CT-based biomarker for lymph node metastases in PCa, with different types of class balancing strongly affecting CNN performance

Quantitative biparametric analysis of hybrid 18F-FET PET/MR-neuroimaging for differentiation between treatment response and recurrent glioma

We investigated the diagnostic potential of simultaneous 18F-FET PET/MR-imaging for differentiation between recurrent glioma and post-treatment related effects (PTRE) using quantitative volumetric (3D-VOI) lesion analysis. In this retrospective study, a total of 42 patients including 32 patients with histologically proven glioma relapse and 10 patients with PTRE (histopathologic follow-up, n = 4, serial imaging follow-up, n = 6) were evaluated regarding recurrence. PET/MR-imaging was semi-automatically analysed based on FET tracer uptake using conservative SUV thresholding (isocontour 80%) with emphasis on the metabolically most active regions. Mean (relative) apparent diffusion coefficient (ADCmean, rADCmean), standardised-uptake-value (SUV) including target-to-background (TBR) ratio were determined. Glioma relapse presented higher ADCmean (MD ± SE, 284 ± 91, p = 0.003) and TBRmax (MD ± SE, 1.10 ± 0.45, p = 0.02) values than treatment-related changes. Both ADCmean (AUC ± SE = 0.82 ± 0.07, p-value < 0.001) and TBRmax (AUC ± SE = 0.81 ± 0.08, p-value < 0.001) achieved reliable diagnostic performance in differentiating glioma recurrence from PTRE. Bivariate analysis based on a combination of ADCmean and TBRmax demonstrated highest diagnostic accuracy (AUC ± SE = 0.90 ± 0.05, p-value < 0.001), improving clinical (false negative and false positive) classification. In conclusion, biparametric analysis using DWI and FET PET, both providing distinct information regarding the underlying pathophysiology, presented best diagnostic accuracy and clinical benefit in differentiating recurrent glioma from treatment-related changes

Author Correction: Quantitative biparametric analysis of hybrid 18F-FET PET/MR-neuroimaging for differentiation between treatment response and recurrent glioma

An amendment to this paper has been published and can be accessed via a link at the top of the paper

68Ga-PSMA-PET/CT for the evaluation of liver metastases in patients with prostate cancer

BACKGROUND: The purpose of this study was to evaluate the imaging properties of hepatic metastases in 68Ga-PSMA positron emission tomography (PET) in patients with prostate cancer (PC). METHODS: 68Ga-PSMA-PET/CT scans of PC patients available in our database were evaluated retrospectively for liver metastases. Metastases were identified using 68Ga-PSMA-PET, CT, MRI and follow-up scans. Different parameters including, maximum standardized uptake values (SUVmax) of the healthy liver and liver metastases were assessed by two- and three-dimensional regions of interest (2D/3D ROI). RESULTS: One hundred three liver metastases in 18 of 739 PC patients were identified. In total, 80 PSMA-positive (77.7%) and 23 PSMA-negative (22.3%) metastases were identified. The mean SUVmax of PSMA-positive liver metastases was significantly higher than that of the normal liver tissue in both 2D and 3D ROI (p ≤ 0.05). The mean SUVmax of PSMA-positive metastases was 9.84 ± 4.94 in 2D ROI and 10.27 ± 5.28 in 3D ROI; the mean SUVmax of PSMA-negative metastases was 3.25 ± 1.81 in 2D ROI and 3.40 ± 1.78 in 3D ROI, and significantly lower than that of the normal liver tissue (p ≤ 0.05). A significant (p ≤ 0.05) correlation between SUVmax in PSMA-positive liver metastases and both size (ρSpearman = 0.57) of metastases and PSA serum level (ρSpearman = 0.60) was found. CONCLUSIONS: In 68Ga-PSMA-PET, the majority of liver metastases highly overexpress PSMA and is therefore directly detectable. For the analysis of PET images, it has to be taken into account that also a significant portion of metastases can only be detected indirectly, as these metastases are PSMA-negative

Accuracy of standard clinical 3T prostate MRI for pelvic lymph node staging: Comparison to 68Ga-PSMA PET-CT

The aim was to assess the performance of prostate 3T MRI for pelvic lymph node (LN) staging in prostate cancer (PCa), in comparison to 68Gallium-prostate specific membrane antigen PET-CT (68Ga-PSMA PET-CT) as reference standard for LN detection. 130 patients with PCa underwent non-contrast-enhanced multiparametric prostate 3T MRI and 68Ga-PSMA-PET-CT within 180 days at our institution. Overall, 187 LN metastases (n = 43 patients) detected by 68Ga-PSMA-PET-CT were characterized by calculating maximum standardized uptake value (SUVmax), area, diameter and anatomical location including iliac, obturator, presacral and inguinal region. MRI achieved an overall sensitivity, specificity, positive and negative predictive value of 81.6% (CI 71.1-88.9%), 98.6% (CI 97.6-99.2%), 73.5% (CI 52.1-87.6%) and 99.5% (CI 98.8-99.8%), respectively. On a region-based analysis, detection rates differed non-significantly (ps > 0.12) in the anatomical regions. On a size-dependent analysis, detection of LN > 10 mm did not differ significantly (ps > 0.09) from LN ≤ 10 mm. In comparison to single T1 sequence evaluation, additional use of the T2 weighted sequences did not improve the overall performance significantly (p > 0.05). 3T prostate MRI represented an accurate tool for the detection of LN compared to 68Ga-PSMA-PET-CT. Especially for LN metastases smaller than 10 mm, MRI was less accurate compared to 68Ga-PSMA-PET-CT