We introduce Multi-Organ Objective Segmentation (MOOSE) software that generates subject-specific, multi-organ segmentations using data-centric AI principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from two PET/CT systems (uEXPLORER and Siemens TruePoint TrueView) was used in training MOOSE. For non-cerebral structures, a total 50 WB-CT images were used; 30 of which were acquired from healthy controls (HC, 14M/16F) and 20 datasets were acquired from oncology patients (14M/6F). Non-cerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas, and skeletal muscle. An expert panel performed manual segmentation of all non-cerebral structures except for subcutaneous, visceral fat, and skeletal muscle, which was semi-automatically segmented using thresholding. A majority-voting algorithm was used to generate a 'reference standard' segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing purposes. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 HC (5M/5F imaged twice) and 14 non-lesional epilepsy patients (7M/7F). Only 18F-FDG PET images were considered for training: 24/34 and 10/34 volumes were used for training and testing, respectively. The dice score coefficient (DSC) was used as the primary and the average symmetric surface distance (ASSD) as a secondary metric to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of non-cerebral tissues showed DSC values of >0.90, while a few organs exhibited lower DSC values (e.g., adrenal glands (0.72), pancreas (0.85), and bladder (0.86)). In comparison, the median DSC values of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of >0.90, while segmentation of 60% of regions yielded a median DSC of 0.80-0.89. Results of the ASSD analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, brain regions) lies within the size of image voxels (2mm). Conclusion: The proposed segmentation pipeline MOOSE allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.

Background Prostate-specific membrane antigen (PSMA) PET has high specificity in localizing primary tumors and metastases in patients with prostate cancer, but the individual overall survival probability is still difficult to estimate. Purpose To develop a prognostic risk score using PSMA PET-derived organ-specific total tumor volumes for predicting overall survival in patients with prostate cancer. Materials and Methods Men with prostate cancer who underwent PSMA PET/CT from January 2014 to December 2018 were evaluated retrospectively. All patients from center A were split into training (80%) and internal validation (20%) cohorts. Randomly selected patients from center B were used for external validation. Organ-specific tumor volumes were automatically quantified from PSMA PET scans by a neural network. A prognostic score was selected using multivariable Cox regression guided by the Akaike information criterion (AIC). The final prognostic risk score fitted on the training set was applied to both validation cohorts. Results A total of 1348 men (mean age, 70 years ± 8 [SD]) were included, with 918 patients in the training cohort, 230 in the internal validation cohort, and 200 in the external validation cohort. The median follow-up time was 55.7 months (IQR, 46.7-65.1 months; >4 years; 429 deaths occurred). A body weight-adjusted prognostic risk score integrating total, bone, and visceral tumor volumes obtained high C index values in the internal (0.82) and external (0.74) validation cohorts, as well as in patients with castration-resistant (0.75) and hormone-sensitive (0.68) disease. The fit of the statistical model for the prognostic score was improved compared with a model containing total tumor volume only (AIC, 3324 vs 3351; likelihood ratio test, P < .001). Calibration plots ascertained good model fit. Conclusion The newly developed risk score that included prostate-specific membrane antigen PET-derived organ-specific tumor volumes had good model fit for predicting overall survival in both internal and external validation cohorts. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Civelek in this issue.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (¹⁸F-FDG, ¹⁸F-fluciclovine, ⁶⁸Ga-PSMA, ⁶⁸Ga-DOTATATE, ¹¹C-Pittsburgh compound B, and ⁸²Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac ⁸²Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios. Visual Abstract

Background/Objectives: Cancer-associated cachexia in head and neck squamous cell carcinoma (HNSCC) is challenging to diagnose due to its complex pathophysiology. This study aimed to identify metabolic biomarkers linked to cachexia and survival in HNSCC patients using [¹⁸F]FDG-PET/CT imaging and machine learning (ML) techniques. Methods: We retrospectively analyzed 253 HNSCC patients from Vienna General Hospital and the MD Anderson Cancer Center. Automated organ segmentation was employed to quantify metabolic and volumetric data from [¹⁸F]FDG-PET/CT scans across 29 tissues and organs. Patients were categorized into low weight loss (LoWL; grades 0–2) and high weight loss (HiWL; grades 3–4) groups, according to the weight loss grading system (WLGS). Machine learning models, combined with Cox regression, were used to identify survival predictors. Shapley additive explanation (SHAP) analysis was conducted to determine the significance of individual features. Results: The HiWL group exhibited increased glucose metabolism in skeletal muscle and adipose tissue (p = 0.01), while the LoWL group showed higher lung metabolism. The one-year survival rate was 84.1% in the LoWL group compared to 69.2% in the HiWL group (p < 0.01). Pancreatic volume emerged as a key biomarker associated with cachexia, with the ML model achieving an AUC of 0.79 (95% CI: 0.77–0.80) and an accuracy of 0.82 (95% CI: 0.81–0.83). Multivariate Cox regression confirmed pancreatic volume as an independent prognostic factor (HR: 0.66, 95% CI: 0.46–0.95; p < 0.05). Conclusions: The integration of metabolic and volumetric data provided a strong predictive model, highlighting pancreatic volume as a key imaging biomarker in the metabolic assessment of cachexia in HNSCC. This finding enhances our understanding and may improve prognostic evaluations and therapeutic strategies.

Objective: [¹⁷⁷Lu]Lu-PSMA radioligand therapy (PSMA-RLT) is a promising therapy for patients with metastatic castration-resistant prostate cancer (mCRPC) and offers a survival benefit particularly to patients with only lymph node metastases. We therefore sought to evaluate the clinical outcome of this therapy in such a cohort. Methods: Of all prostate cancer patients admitted to our department between September 2015 and March 2019 to receive 1–4 courses of PSMA-RLT (each course consisted of three cycles of highly standardized PSMA-RLT every 4 weeks), only 10 consecutive men were found to have nodal metastases only and were analyzed retrospectively. Results: Nine out of 10 patients responded to their first PSMA-RLT course with a mean prostate-specific antigen (PSA) decline of 71.8 ± 25.2%, seven of them demonstrated a PSA decline of ≥50%. Collectively, seven of eight patients responded to further PSMA-RLT courses with a total PSA reduction of 59.8 ± 30.0%, five of which showed a PSA reduction of ≥50%. One patient experienced complete remission. Median progression-free survival was 85 weeks (range 14–255 weeks) and median overall survival was not reached during the median observation time of 209 weeks (30–298 weeks). Univariate Cox-regression identified initial PSA decline as the only predictive parameter for progression-free survival (P = 0.047). Conclusion: mCRPC patients with only lymph node metastases showed favorable survival and excellent response to PSMA-RLT, leading to transient partial remission of the disease in most of them.

Cardiac amyloidosis (CA) is a heterogeneous group of diseases in which extracellular insoluble amyloid proteins are deposited in specific organs and tissues locally or systemically, thereby interfering with physiological function. Transthyretin protein (TTR) and light chain (AL) amyloidosis are the most common types of cardiac amyloidosis. Radionuclide bone scintigraphy has recently become the most common non-invasive test for the diagnosis of TTR-CA but is of limited value for the diagnosis of AL-CA. PET has proved promising for the diagnosis of CA and its applications are expected to expand in the future. This review summarizes the current bone scintigraphy and amyloid-targeting Positron emission tomography (PET) imaging, the binding imaging properties of radiotracers, and the values of diagnosis, prognosis, and monitoring therapy response in CA.