A Retrospective Comparative Study of Sodium Fluoride (NaF-18)-PET/CT and Fluorocholine (F-18-CH) PET/CT in the Evaluation of Skeletal Metastases in Metastatic Prostate Cancer Using a Volumetric 3-D Radiomics Analysis

Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [18F]NaF PET/CT images

BACKGROUND

[18F]NaF is a potential biomarker for assessing cardiac risk. Automated analysis of [18F]NaF positron emission tomography (PET) images, specifically through quantitative image analysis ("radiomics"), can potentially enhance diagnostic accuracy and personalised patient management. However, it is essential to evaluate the reproducibility and reliability of radiomic features to ensure their clinical applicability. This study aimed to (i) develop and evaluate an automated model for coronary artery segmentation using [18F]NaF PET and calcium scoring computed tomography (CSCT) images, (ii) assess inter- and intra-observer radiomic reproducibility from manual segmentations, and (iii) evaluate the radiomics reliability from AI-derived segmentations by comparison with manual segmentations.

RESULTS

141 patients from the "effects of Vitamin K and Colchicine on vascular calcification activity" (VikCoVac, ACTRN12616000024448) trial were included. 113 were used to train an auto-segmentation model using nnUNet on [18F]NaF PET and CSCT images. Reproducibility of inter- and intra-observer radiomics and reliability of radiomics from AI-derived segmentations was assessed using lower bound of intraclass correlation coefficient (ICC). The auto-segmentation model achieved an average Dice Similarity Coefficient of 0.61 ± 0.05, having no statistically significant difference compared to the intra-observer variability (p = 0.922). For the unfiltered images, 47(12.6%) CT and 25(7.5%) PET radiomics were inter-observer reproducible, while 133(35.8%) CT and 57(15.3%) PET radiomics were intra-observer reproducible. 7(9.7%) CT and 18(25.0%) PET first-order features, as well as 17(17.7%) CT GLCM features, were reproducible for both inter- and intra-observer analyses. 9.8% and 16.8% of radiomics from AI-derived segmentations showed excellent and good reliability. First-order features were most reliable (ICC > 0.75; 78/144[54.2%]) and shape features least (2/112[1.8%]). CT features demonstrated greater reliability (147/428[34.3%]) than PET (81/428 [18.9%]). Features from the left anterior descending (76/214[35.5%]) and right coronary artery (75/214[35.0%]) were more reliability than the circumflex (49/214[22.9%]) and left main (28/214[13.1%]) arteries.

CONCLUSIONS

An effective segmentation model for coronary arteries was developed and reproducible [18F]NaF PET/CSCT radiomics were identified through inter- and intra-observer assessments, supporting their clinical applicability. The reliability of radiomics from AI-derived segmentations compared to manual segmentations was highlighted. The novelty of [18F]NaF as a biomarker underscores its potential in providing unique insights into vascular calcification activity and cardiac risk assessment.

CLINICAL TRIAL REGISTRATION

VIKCOVAC trial ("effects of Vitamin K and Colchicine on vascular calcification activity"). Unique identifier: ACTRN12616000024448. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368825 .

SPECT/CT, PET/CT, and PET/MRI for Response Assessment of Bone Metastases

Recent developments in hybrid SPECT/CT systems and the use of cadmium-zinc-telluride (CZT) detectors have improved the diagnostic accuracy of bone scintigraphy. These advancements have paved the way for novel quantitative approaches to accurate and reproducible treatment monitoring of bone metastases. PET/CT imaging using [18F]F-FDG and [18F]F-NaF have shown promising clinical utility in bone metastases assessment and monitoring response to therapy and prediction of treatment response in a broad range of malignancies. Additionally, specific tumor-targeting tracers like [99mTc]Tc-PSMA, [68Ga]Ga-PSMA, or [11C]C- or [18F]F-Choline revealed high diagnostic performance for early assessment and prognostication of bone metastases, particularly in prostate cancer. PET/MRI appears highly accurate imaging modality, but has associated limitations notably, limited availability, more complex logistics and high installation costs. Advances in artificial intelligence (Al) seem to improve the accuracy of imaging modalities and provide an assistant role in the evaluation of treatment response of bone metastases.

A Retrospective Comparative Study of Sodium Fluoride Na18F-PET/CT and 68Ga-PSMA-11 PET/CT in the Bone Metastases of Prostate Cancer Using a Volumetric 3-D Radiomic Analysis

Bone is the most common metastatic site in prostate cancer (PCa). 68Ga-PSMA-11 (or gozetotide) and sodium fluoride-18 (Na18F) are rather new radiopharmaceuticals for assessing PCa-associated bone metastases. Gozetotide uptake reflects cell membrane enzyme activity and the sodium fluoride uptake measures bone mineralization in advanced PCa. Here, we aim to characterize this difference and possibly provide a new method for patient selection in targeted therapies. Methods: The study consisted of 14 patients with advanced PCa (M group > 5 lesions), who had had routine PET/CT both with PSMA and NaF over consecutive days, and 12 PCa patients with no skeletal metastases (N). The bone regions in CT were used to coregister the two PET/CT scans. The whole skeleton volume(s) of interest (VOIs) were defined using the CT component of PET (HU > 150); similarly, the sclerotic/dense bone was defined as HU > 600. Additional VOIs were defined for PET, with pathological threshold values for PSMA (SUV > 3.0) and NaF (SUV > 10). Besides the pathological bone volumes measured with each technique (CT, NaF, and PSMA-PET) and their contemporaneous combinations, overlapping VOIs with the CT-based skeletal and sclerotic volumes were also recorded. Additionally, thresholds of 4.0, 6.0, and 10.0 were tested for SUVPSMA. Results: In group M, the skeletal VOI volumes were 8.77 ± 1.80 L, and the sclerotic bone volumes were 1.32 ± 0.50 L; in contrast, in group N, they were 8.73 ± 1.43 L (skeletal) and 1.23 ± 0.28 L (sclerosis). The total enzyme activity for PSMA was 2.21 ± 5.15 in the M group and 0.078 ± 0.053 in the N group (p < 0.0002). The total bone demineralization activity for NaF varied from 4.31 ± 6.17 in the M group and 0.24 ± 0.56 in group N (p < 0.0002). The pathological PSMA volume represented 0.44−132% of the sclerotic bone volume in group M and 0.55−2.3% in group N. The pathological NaF volume in those patients with multiple metastases represented 0.27−68% of the sclerotic bone volume, and in the control group, only 0.00−6.5% of the sclerotic bone volume (p < 0.0003). Conclusions: These results confirm our earlier findings that CT alone does not suit the evaluation of the extent of active skeletal metastases in PCa. PSMA and NaF images give complementary information about the extent of the active skeletal disease, which has a clinical impact and may change its management. The PSMA and NaF absolute volumes could be used for planning targeted therapies. A cut-off value 3.0 for SUVPSMA given here is the best correlation in the presentation of active metastatic skeletal disease.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Applications of Artificial Intelligence in 18F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:: Current State and Future Directions

This review discusses the current state of artificial intelligence (AI) in ¹⁸F-NaF-PET/CT imaging and the potential applications to come in diagnosis, prognostication, and improvement of care in patients with bone diseases, with emphasis on the role of AI algorithms in CT bone segmentation, relying on their prevalence in medical imaging and utility in the extraction of spatial information in combined PET/CT studies.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.