A Prognostic Risk Score for Prostate Cancer Based on PSMA PET-derived Organ-specific Tumor Volumes.

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.

Excretion of glucose analogue with SGLT2 affinity predicts response effectiveness to sodium glucose transporter 2 inhibitors in patients with type 2 diabetes mellitus.

PURPOSE: Sodium-glucose cotransporter 2 inhibitor (SGLT2i) regulation, developed as treatment for patients with type 2 diabetes, can be imaged with the glucose analogue alpha-methyl-4-deoxy-4-[18F]fluoro-D-glucopyranoside (Me4FDG), a positron emission tomography (PET) tracer with a high affinity for SGLT1 and SGLT2 proteins. With regard to therapy effectiveness, we aimed to investigate whether clinical parameters or Me4FDG excretion could predict response to SGLT2i in patients with type 2 diabetes. METHODS: In a longitudinal, prospective study, 19 patients with type 2 diabetes underwent Me4FDG combined PET and magnetic resonance imaging (PET/MRI) scans at baseline and 2 weeks after initiation of therapy with SGLT2i, accompanied by the collection of blood and urine samples. Me4FDG-excretion was determined from the Me4FDG uptake in the bladder. Long-term response was determined by HbA1c level after 3 months; a strong response to the therapy was defined as a reduction of HbA1c by at least 10% from baseline. RESULTS: SGLT2i resulted in significantly increased Me4FDG excretion (4.8 vs. 45.0, P < 0.001) and urine glucose (56 vs. 2806 mg/dl, P < 0.001). Baseline urine glucose and baseline Me4FDG excretion correlated both with long-term decline in HbA1c with r = 0.55 (P < 0.05). However, only Me4FDG excretion was a predictor of a strong response to SGLT2i (P = 0.005, OR 1.9). CONCLUSIONS: Using Me4FDG-PET, we demonstrated for the first time renal SGLT2-related excretion before and after short-term SGLT2i treatment. In contrary to other clinical parameters, SGLT2-related excretion before treatment was a robust predictor of long-term HbA1c response in patients with type 2 diabetes, suggesting that therapy effectiveness is only dependent of endogenous SGLT2 processes.

Myofibroblast stroma differentiation in infiltrative basal cell carcinoma is accompanied by regulatory T-cells.

INTRODUCTION: The implications of infiltrative compared to non-infiltrative growth of cutaneous basal cell carcinoma (BCC) on the tumor stroma and immune cell landscape are unknown. This is of clinical importance, because infiltrative BCCs, in contrast to other BCC subtypes, are more likely to relapse after surgery and radiotherapy. MATERIALS AND METHODS: This descriptive cross-sectional study analyzed 38 BCCs collected from 2018 to 2021. In the first cohort (n = 28), immune cells were characterized by immunohistochemistry and multiplex immunofluorescence staining for CD3, CD8, CD68, Foxp3, and α-SMA protein expression. In the second cohort (n = 10) with matched characteristics (age, sex, location, and BCC subtype), inflammatory parameters, including TGF-ß1, TGF-ß2, ACTA2, IL-10, IL-12A, and Foxp3, were quantified via RT-qPCR after isolating mRNA from BCC tissue samples and perilesional skin. RESULTS: Infiltrative BCCs showed significantly increased levels of α-SMA expression in fibroblasts (p = 0.0001) and higher levels of Foxp3+ (p = 0.0023) and CD3+ (p = 0.0443) T-cells compared to non-infiltrative BCCs. CD3+ (p = 0.0171) and regulatory T-cells (p = 0.0026) were significantly increased in α-SMA-positive tumor stroma, whereas CD8+ T-cells (p = 0.1329) and CD68+ myeloid cells (p = 0.2337) were not affected. TGF-ß1 and TGF-ß2 correlated significantly with ACTA2/α-SMA mRNA expression (p = 0.020, p = 0.005). CONCLUSION: Infiltrative growth of BCCs shows a myofibroblastic stroma differentiation and is accompanied by an immunosuppressive tumor microenvironment.

The role of [18F]F-DOPA PET/CT in diagnostic and prognostic assessment of medullary thyroid cancer: a 15-year experience with 109 patients.

Objective: Correct diagnosis and prognostic evaluation of medullary thyroid cancer (MTC) are crucial to treat patients. The purpose of this study was to evaluate the diagnostic and prognostic value of [18F]F-DOPA PET/CT in patients with MTC. Methods: We reviewed MTC patients who underwent [18F]F-DOPA PET/CT from June 2008 to November 2023. Clinical characteristics, follow-up data, and the following [18F]F-DOPA PET/CT parameters were recorded: maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and SUVmean of multiple organs. The diagnostic value of PET/CT for the detection of tumor lesions was calculated. Serum basal calcitonin (bCt) and stimulated calcitonin (sCt) were determined. Receiver operating characteristics, Kaplan-Meier, and Cox regression analyses were performed. Results: In total, 109 patients (50 women, 59 men; average age, 55 ± 14 years) were included in the analysis. The patient-related sensitivity, specificity, and accuracy of [18F]F-DOPA PET/CT were 95%, 93%, and 94%, respectively. The lesion-related sensitivity, specificity, and accuracy were 65%, 99%, and 72%, respectively. The optimal cutoff values of bCt, sCt, and CEA to obtain positive [18F]F-DOPA PET/CT results were 64 pg/mL, 1808 pg/mL, and 4 µg/L, respectively. Patients with negative [18F]F-DOPA PET/CT had longer overall survival than patients with positive [18F]F-DOPA PET/CT results (P = 0.017). Significant positive correlations were found between bCt, sCt, and CEA with SUVmax, SUVmean, and MTV of [18F]F-DOPA PET/CT (P < 0.001). [18F]F-DOPA PET/CT results and MTV may be useful for the evaluation of the prognosis of patients with recurrent MTC, while age and MTV were independent prognostic factors in patients with primary MTC. For all patients, SUVmean of the left kidney, liver, aorta, and pancreas might be used to independently predict OS. Conclusion: [18F]F-DOPA PET/CT had great value for diagnosis and prognostic assessment in patients with MTC. The DOPA PET/CT parameter SUVmean and MTV showed significant association with OS.

Detection of cancer-associated cachexia in lung cancer patients using whole-body [18F]FDG-PET/CT imaging: A multi-centre study.

BACKGROUND: Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non-imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole-body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC. METHODS: This multi-centre study included 345 LCP who underwent WB [18F]FDG-PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into 'No CAC' (WLGS-0/1 at baseline prior treatment and at first follow-up: N = 158, 51F/107M), 'Dev CAC' (WLGS-0/1 at baseline and WLGS-3/4 at follow-up: N = 90, 34F/56M), and 'CAC' (WLGS-3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake () and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [18F]FDG-PET/CT images. Imaging and non-imaging parameters from laboratory tests were compared statistically. A machine-learning (ML) model was then trained to classify LCP as 'No CAC', 'Dev CAC', and 'CAC' based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient. RESULTS: The three CAC categories displayed multi-organ differences in . In all target organs, was higher in the 'CAC' cohort compared with 'No CAC' (P < 0.01), except for liver and kidneys, where in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in of pancreas (+4%), skeletal-muscle (+7%), subcutaneous adipose-tissue (+11%), and visceral adipose-tissue (+15%). In 'CAC' patients, a strong negative Spearman correlation (ρ = -0.8) was identified between and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'. CONCLUSIONS: WB [18F]FDG-PET/CT imaging reveals groupwise differences in the multi-organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi-centre study has been initiated to address the limitations of the present retrospective analysis.

Systemic Metabolic and Volumetric Assessment via Whole-Body [18F]FDG-PET/CT: Pancreas Size Predicts Cachexia in Head and Neck Squamous Cell Carcinoma.

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.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

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.

Nuclear Molecular Imaging of Disease Burden and Response to Treatment for Cardiac Amyloidosis.

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.

Response to [ 177 Lu]Lu-PSMA radioligand therapy in metastatic castration-resistant prostate cancer patients presenting with only lymph node metastases.

OBJECTIVE: [ 177 Lu]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.

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically 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. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance 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 noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.