The influence of colour scale in lesion detection and patient-based sensitivity in [68Ga]Ga-PSMA-PET/CT.

OBJECTIVE: To investigate the influence of colour scales on the interpretation of [68Ga]Ga-PSMA-11 PET/CT for the diagnosis of recurrent prostate cancer. METHODS: 50 consecutive patients who underwent [68Ga]Ga-PSMA-11 PET/CT for recurrent prostate cancer were selected for this retrospective study. The scans were randomised, anonymised and read by five different readers first in the visually nonlinear colour scale 'PET-rainbow'. Scans were then rerandomised and read in the visually linear colour scale 'hot-metal new'. For each scan in each colour scale the numbers of pathological, equivocal and benign lesions were noted. Scans where the majority of readers (≥3) reported at least one PET-positive lesion were recorded as 'pathological'. Patient-level sensitivity was obtained by composite standard with 14.8 ± 1.2 months of follow-up. RESULTS: Increased numbers of lesions per patient were reported for all readers in PET-rainbow compared to hot-metal new (37.4 ± 15.2 vs. 33.9 ± 16.4, respectively, P = 0.0005). On a per-patient basis, 43 scans were rated pathological in PET-rainbow, compared to 39 in hot-metal new. Follow-up was available for 30 patients confirming 26 pathological scans with positive follow-up in PET-rainbow, and 23 in hot-metal new. Three pathological scans were missed in hot-metal new. Patient-level sensitivity was higher for PET-rainbow (0.96) compared to hot-metal new (0.85). Inter-reader reliability was higher for hot-metal new (Fleiss κ = 0.76) compared to PET-rainbow (Fleiss κ = 0.60). CONCLUSION: Use of PET-rainbow was associated with improved lesion detection and sensitivity compared to hot-metal new, although at cost of reduced inter-rater agreement. Consequently, the use of PET-rainbow for clinical routine and future studies involving [68Ga]Ga-PSMA-11 is recommended.

One-Stop Shop: 18F-Flortaucipir PET Differentiates Amyloid-Positive and -Negative Forms of Neurodegenerative Diseases.

Tau protein aggregations are a hallmark of amyloid-associated Alzheimer disease and some forms of non-amyloid-associated frontotemporal lobar degeneration. In recent years, several tracers for in vivo tau imaging have been under evaluation. This study investigated the ability of 18F-flortaucipir PET not only to assess tau positivity but also to differentiate between amyloid-positive and -negative forms of neurodegeneration on the basis of different 18F-flortaucipir PET signatures. Methods: The 18F-flortaucipir PET data of 35 patients with amyloid-positive neurodegeneration, 19 patients with amyloid-negative neurodegeneration, and 17 healthy controls were included in a data-driven scaled subprofile model (SSM)/principal-component analysis (PCA) identifying spatial covariance patterns. SSM/PCA pattern expression strengths were tested for their ability to predict amyloid status in a receiver-operating-characteristic analysis and validated with a leave-one-out approach. Results: Pattern expression strengths predicted amyloid status with a sensitivity of 0.94 and a specificity of 0.83. A support vector machine classification based on pattern expression strengths in 2 different SSM/PCA components yielded a prediction accuracy of 98%. Anatomically, prediction performance was driven by parietooccipital gray matter in amyloid-positive patients versus predominant white matter binding in amyloid-negative patients. Conclusion: SSM/PCA-derived binding patterns of 18F-flortaucipir differentiate between amyloid-positive and -negative neurodegenerative diseases with high accuracy. 18F-flortaucipir PET alone may convey additional information equivalent to that from amyloid PET. Together with a perfusion-weighted early-phase acquisition (18F-FDG PET-equivalent), a single scan potentially contains comprehensive information on amyloid (A), tau (T), and neurodegeneration (N) status as required by recent biomarker classification algorithms (A/T/N).

Diagnostic Potential of Pulsed Arterial Spin Labeling in Alzheimer's Disease.

Alzheimers disease (AD) is the most common cause of dementia. Although the underlying pathology is still not completely understood, several diagnostic methods are available. Frequently, the most accurate methods are also the most invasive. The present work investigates the diagnostic potential of Pulsed Arterial Spin Labeling (PASL) for AD: a non-invasive, MRI-based technique for the quantification of regional cerebral blood flow (rCBF). In particular, we propose a pilot computer aided diagnostic (CAD) procedure able to discriminate between healthy and diseased subjects, and at the same time, providing visual informative results. This method encompasses the creation of a healthy model, the computation of a voxel-wise likelihood function as comparison between the healthy model and the subject under examination, and the correction of the likelihood function via prior distributions. The discriminant analysis is carried out to maximize the accuracy of the classification. The algorithm has been trained on a dataset of 81 subjects and achieved a sensitivity of 0.750 and a specificity of 0.875. Moreover, in accordance with the current pathological knowledge, the parietal lobe, and limbic system are shown to be the main discriminant factors.