Pelvic organ movements in asymptomatic nulliparous and symptomatic premenopausal women with pelvic organ prolapse in dynamic MRI: a feasibility study comparing midsagittal single-slice with multi-slice sequences.

PURPOSE: To compare multi-slice (MS) MRI sequences of the pelvis acquired at rest and straining to dynamic midsagittal single-slice (SS) sequences for the assessment of pelvic organ prolapse (POP). METHODS: This IRB-approved prospective single-center feasibility study included 23 premenopausal symptomatic patients with POP and 22 asymptomatic nulliparous volunteers. MRI of the pelvis at rest and straining was performed with midsagittal SS and MS sequences. Straining effort, visibility of organs and POP grade were scored on both. Organ points (bladder, cervix, anorectum) were measured. Differences between SS and MS sequences were compared with Wilcoxon test. RESULTS: Straining effort was good in 84.4% on SS and in 64.4% on MS sequences (p = 0.003). Organ points were always visible on MS sequences, whereas the cervix was not fully visible in 31.1-33.3% on SS sequences. At rest, there were no statistically significant differences of organ point measurements between SS and MS sequences in symptomatic patients. At straining, positions of bladder, cervix, and anorectum were + 1.1 cm (± 1.8 cm), - 0.7 cm (± 2.9 cm), and + 0.7 cm (± 1.3 cm) on SS and + 0.4 mm (± 1.7 cm), - 1.4 cm (± 2.6 cm), and + 0.4 cm (± 1.3 cm) on MS sequences (p < 0.05). Only 2 cases of higher-grade POP were missed on MS sequences (both with poor straining effort). CONCLUSION: MS sequences increase the visibility of organ points compared to SS sequences. Dynamic MS sequences can depict POP if images are acquired with sufficient straining effort. Further work is needed to optimize the depiction of the maximum straining effort with MS sequences.

Primary staging in patients with intermediate- and high-risk prostate cancer: Multiparametric MRI and 68Ga-PSMA-PET/MRI - What is the value of quantitative data from multiparametric MRI alone or in conjunction with clinical information?

PURPOSE: Comparing mpMRI and 68Ga-PSMA-PET/MRI in primary staging of PCa and investigating the value of quantitative mpMRI-measurements for prediction of extracapsular extension and N-metastases. METHODS: Patients with PCa undergoing 68Ga-PSMA-PET/MRI and mpMRI during January 2016 to February 2019 were retrospectively included. Two readers each on 68Ga-PSMA-PET/MRI or mpMRI rated extraprostatic extension (≥T3) and regional lymph-node-metastasis (N1) on a Likert-scale. A fifth reader measured tumor volume, maximum diameter, and capsular contact length on mpMRI. Probability of lymph-node-metastasis was additionally calculated using the 2018 Briganti model. Interobserver-agreement was assessed by squared Cohen's kappa, and diagnostic accuracy was determined using radical prostatectomy (n = 35/49) as reference standard. RESULTS: 49 patients (median age 66 years [IQR: 61-72 years]) were evaluated. Interobserver-agreement for mpMRI and 68Ga-PSMA-PET/MRI was: ≥T3: κ = 0.58/0.47; N1: κ = 0.55/0.92. Diagnostic accuracy for mpMRI vs 68Ga-PSMA-PET/MRI readers for ≥ T3 was AUC: 0.72, 0.62 vs 0.71, 0.72 (p > 0.38) and for N1 was AUC: 0.39, 0.55 vs 0.72, 0.78 (p < 0.01). Quantitative parameters delivered diagnostic accuracies of: AUC: 0.70-0.72 for ≥ T3. The 2018 Briganti model achieved an AUC of 0.89 for N1. CONCLUSIONS: Interreader-agreement regarding ≥ T3 was similar for mpMRI and 68Ga-PSMA-PET/MRI while for N1 it was higher for 68Ga-PSMA-PET/MRI. Diagnostic accuracy was comparable for ≥ T3 while for N1 it was higher in 68Ga-PSMA-PET/MRI and the 2018 Briganti model. Combining clinical data and quantitative data from mpMRI in the 2018 Briganti model yielded the highest AUC for prediction of lymph node metastasis and may aid in selecting patients who will benefit from 68Ga-PSMA-PET/MRI for primary staging.

Immunotherapy (Cemiplimab)-Induced Bullous Pemphigoid: A Possible Pitfall in 18F-FDG PET/CT.

ABSTRACT: A 78-year-old man with multiple squamous cell carcinomas of the skin underwent 18F-FDG-PET/CT for restaging after 4 cycles of cemiplimab. The scan showed new disseminated FDG-avid skin lesions. Dermatologic examination and biopsy revealed bullous pemphigoid. Discontinuation of cemiplimab and treatment with corticosteroids led to clinical improvement, after which treatment with cemiplimab was resumed. A broad spectrum of inflammatory adverse events can occur in patients treated with immune checkpoint inhibitors, and FDG avidity of these lesions may mimic metastases. Knowledge of such imaging pitfalls is essential for interpreting 18F-FDG-PET/CT, particularly if they occur in the same organ as the primary tumor.

Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?

OBJECTIVES: To evaluate whether a magnetic resonance imaging (MRI) radiomics-based machine learning classifier can predict postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) and to compare its performance to T1 signal intensity ratio (T1 SIratio). METHODS: Sixty-two patients who underwent 3 T MRI before PD between 2008 and 2018 were retrospectively analyzed. POPF was graded and split into clinically relevant POPF (CR-POPF) vs. biochemical leak or no POPF. On T1- and T2-weighted images, 2 regions of interest were placed in the pancreatic corpus and cauda. 173 radiomics features were extracted using pyRadiomics. Additionally, the pancreas-to-muscle T1 SIratio was measured. The dataset was augmented and split into training (70 %) and test sets (30 %). A Boruta algorithm was used for feature reduction. For prediction of CR-POPF models were built using a gradient-boosted tree (GBT) and logistic regression from the radiomics features, T1 SIratio and a combination of the two. Diagnostic accuracy of the models was compared using areas under the receiver operating characteristics curve (AUCs). RESULTS: Five most important radiomics features were identified for prediction of CR-POPF. A GBT using these features achieved an AUC of 0.82 (95 % Confidence Interval [CI]: 0.74 - 0.89) when applied on the original (non-augmented) dataset. Using T1 SIratio, a GBT model resulted in an AUC of 0.75 (CI: 0.63 - 0.84) and a logistic regression model delivered an AUC of 0.75 (CI: 0.63 - 0.84). A GBT model combining radiomics features and T1 SIratio resulted in an AUC of 0.90 (CI 0.84 - 0.95). CONCLUSION: MRI-radiomics with routine sequences provides promising prediction of CR-POPF.

Value of an online PI-RADS v2.1 score calculator for assessment of prostate MRI.

PURPOSE: To evaluate the value of a browser-based PI-RADS Score Calculator (PCalc) compared to MRI reporting using the official PI-RADS v2.1 document (PDoc) for non-specialized radiologists in terms of reporting efficiency, interrater agreement and diagnostic accuracy for detection of clinically significant prostate cancer (PCa). METHODS: Between 09/2013 and 04/2015, 100 patients (median age, 64.8; range 47.5-78.2) who underwent prostate-MRI at a 3 T scanner and who received transperineal prostate mapping biopsy within <6 months were included in this retrospective study. Two non-specialized radiology residents (R1, R2) attributed a PI-RADS version 2.1 score for the most suspect (i. e. index) lesion (i) using the original PI-RADS v2.1 document only and after a 6-week interval (ii) using a browser-based PCalc. Reading time was measured. Reading time differences were assessed using Wilcoxon signed rank test. Intraclass-correlation Coefficient (ICC) was used to assess interrater agreement (IRA). Parameters of diagnostic accuracy and ROC curves were used for assessment of lesion-based diagnostic accuracy. RESULTS: Cumulative reading time was 32:55 (mm:ss) faster when using the PCalc, the difference being statistically significant for both readers (p < 0.05). The difference in IRA between the image sets (ICC 0.55 [0.40, 0.68]) and 0.75 [0.65, 0.82] for the image set with PDoc and PCalc, respectively) was not statistically significant. There was no statistically significant difference in lesion-based diagnostic accuracy (AUC 0.83 [0.74, 0.92] and 0.82 [95 %CI: 0.74, 0.91]) for images assessed with PDoc as compared to PCalc (AUC 0.82 [0.74, 0.91] and 0.74 [95 %CI: 0.64, 0.83]) for R1 and R2, respectively. CONCLUSION: Non-specialized radiologists may increase reading speed in prostate MRI with the help of a browser-based PI-RADS Score Calculator compared to reporting using the official PI-RADS v2.1 document without impairing interreader agreement or lesion-based diagnostic accuracy for detection of clinically significant PCa.