Pascal A. T. Baltzer

The efficacy of radioligand therapy (RLT) targeting prostate-specific membrane antigen (PSMA) is currently being investigated for its application in patients with early-stage prostate cancer (PCa). However, little is known about PSMA expression in healthy organs in this cohort. Collectively, 202 [68Ga]Ga-PSMA-11 positron emission tomography (PET) scans from 152 patients were studied. Of these, 102 PET scans were from patients with primary PCa and hormone-sensitive biochemically recurrent PCa and 50 PET scans were from patients with metastatic castration-resistant PCa (mCRPC) before and after three cycles of [177Lu]Lu-PSMA-RLT. PSMA-standardized uptake values (SUV) were measured in multiple organs and PSMA-total tumor volume (PSMA-TTV) was determined in all cohorts. The measured PET parameters of the different cohorts were normalized to the bloodpool and compared using t- or Mann–Whitney U tests. Patients with early-stage PCa had lower PSMA-TTVs (10.39 mL vs. 462.42 mL, p < 0.001) and showed different SUVs in the thyroid, submandibular glands, heart, liver, kidneys, intestine, testes and bone marrow compared to patients with advanced CRPC, with all tests showing p < 0.05. Despite the differences in the PSMA-TTV of patients with mCRPC before and after [177Lu]Lu-PSMA-RLT (462.42 mL vs. 276.29 mL, p = 0.023), no significant organ differences in PET parameters were detected. These suggest different degrees of PSMA-ligand binding among patients with different stages of PCa that could influence radiotoxicity during earlier stages of disease in different organs when PSMA-RLT is administered.

The aim of the current study was to compare the performance of fully automated software with human expert interpretation of single‐voxel proton magnetic resonance spectroscopy (1H‐MRS) spectra in the assessment of breast lesions. Breast magnetic resonance imaging (MRI) (including contrast‐enhanced T1‐weighted, T2‐weighted, and diffusion‐weighted imaging) and 1H‐MRS images of 74 consecutive patients were acquired on a 3‐T positron emission tomography‐MRI scanner then automatically imported into and analyzed by SpecTec‐ULR 1.1 software (LifeTec Solutions GmbH). All ensuing 117 spectra were additionally independently analyzed and interpreted by two blinded radiologists. Histopathology of at least 24 months of imaging follow‐up served as the reference standard. Nonparametric Spearman's correlation coefficients for all measured parameters (signal‐to‐noise ratio [SNR] and integral of total choline [tCho]), Passing and Bablok regression, and receiver operating characteristic analysis, were calculated to assess test diagnostic performance, as well as to compare automated with manual reading. Based on 117 spectra of 74 patients, the area under the curve for tCho SNR and integrals ranged from 0.768 to 0.814 and from 0.721 to 0.784 to distinguish benign from malignant tissue, respectively. Neither method displayed significant differences between measurements (automated vs. human expert readers, p > 0.05), in line with the results from the univariate Spearman's rank correlation coefficients, as well as the Passing and Bablok regression analysis. It was concluded that this pilot study demonstrates that 1H‐MRS data from breast MRI can be automatically exported and interpreted by SpecTec‐ULR 1.1 software. The diagnostic performance of this software was not inferior to human expert readers.

Hintergrund Die MR-Mammografie (MRM) ist als sensitivstes Verfahren zur Detektion von Brustkrebs integraler Bestandteil der modernen Mammadiagnostik. Aufgrund umfangreicher multiparametrischer Bildinformationen gilt die Befundung der MRM jedoch als schwierig. Klinische Entscheidungsregeln kombinieren diagnostische Kriterien in einem Algorithmus. Damit unterstützen sie Radiologen dabei, objektive und exakte sowie weitgehend von der Untersuchererfahrung unabhängige MRM-Diagnosen zu stellen. Methodik Narrativer review. Der Kaiser-Score (KS) als klinische Entscheidungsregel für die MRM wird eingeführt. Befundkriterien werden erläutert, Strategien zur klinischen Entscheidungsfindung diskutiert und illustriert. Ergebnisse Entwickelt mit Methoden des maschinellen Lernens wurde der Kaiser-Score in internationalen Studien unabhängig validiert. Dabei ist der KS unabhängig von der Untersuchungstechnik. Anhand von auf T2w- und kontrastangehobenen T1w-Aufnahmen fassbaren diagnostischen BI-RADS-Kriterien ermöglicht der KS die objektive und genaue Differenzialdiagnose von benignen und malignen Befunden in der MRM. Ein Flowchart leitet den Leser über maximal 3 Zwischenschritte zu einem Punktwert, entsprechend einer Malignomwahrscheinlichkeit. Damit lässt sich der KS direkt einer konkreten BI-RADS-Kategorie zuordnen. Individuelle Managemententscheidungen sollten dabei auch den klinischen Kontext berücksichtigen, was anhand von typischen Beispielen dargestellt wird. Kernaussagen: Zitierweise