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.

Bone metastasis develops in multiple malignancies with a wide range of incidence. The presence of multiple bone metastases, leading to a multitude of complications and poorer prognosis. The corresponding refractory bone pain is still a challenging issue managed through multidisciplinary approaches to enhance the quality of life. Radiopharmaceuticals are mainly used in the latest courses of the disease. Bone-pain palliation with easy-to-administer radionuclides offers advantages, including simultaneous treatment of multiple metastatic foci, the repeatability and also the combination with other therapies. Several β¯- and α-emitters as well as pharmaceuticals, from the very first [⁸⁹Sr]strontium-dichloride to recently introduced [²²³Ra]radium-dichloride, are investigated to identify an optimum agent. In addition, the combination of bone-seeking radiopharmaceuticals with chemotherapy or radiotherapy has been employed to enhance the outcome. Radiopharmaceuticals demonstrate an acceptable response rate in pain relief. Nevertheless, survival benefits have been documented in only a limited number of studies. In this review, we provide an overview of bone-seeking radiopharmaceuticals used for bone-pain palliation, their effectiveness and toxicity, as well as the results of the combination with other therapies. Bone-pain palliation with radiopharmaceuticals has been employed for eight decades. However, there are still new aspects yet to be established.

Background/Objectives: Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In ⁶⁸Ga-PSMA PET, however, artifacts such as respiratory motion, halo effects, and truncation errors in CT-based AC (CT-AC) images compromise image quality and impair model training for deep learning-based AC. This study proposes a novel artifact-refinement framework that filters out corrupted PET-CT images to create a clean dataset for training an image-domain AC model, eliminating the need for anatomical reference scans. Methods: A residual neural network (ResNet) was trained using paired PET non-AC and PET CT-AC images from a dataset of 828 whole-body ⁶⁸Ga-PSMA PET-CT scans. An initial model was trained using all data and employed to identify artifact-affected samples via voxel-level error metrics. These outliers were excluded, and the refined dataset was used to retrain the model with an L2 loss function. Performance was evaluated using metrics including mean error (ME), mean absolute error (MAE), relative error (RE%), RMSE, and SSIM on both internal and external test datasets. Results: The model trained with the artifact-free dataset demonstrated significantly improved performance: ME = −0.009 ± 0.43 SUV, MAE = 0.09 ± 0.41 SUV, and SSIM = 0.96 ± 0.03. Compared to the model trained on unfiltered data, the purified data model showed enhanced quantitative accuracy and robustness in external validation. Conclusions: The proposed data purification framework significantly enhances the performance of deep learning-based AC for ⁶⁸Ga-PSMA PET by mitigating artifact-induced errors. This approach facilitates reliable PET imaging in the absence of anatomical references, advancing clinical applicability and image fidelity.

A 76-year-old man with castration-resistant prostate cancer underwent ⁶⁸Ga–prostate-specific membrane antigen (PSMA) PET/CT for restaging. A large PSMA-avid tumor with invasion to adjacent organs was noted causing gross hematuria and symptomatic anemia. Two cycles of ¹⁷⁷Lu-PSMA were administered, and the patient showed significant reduction of hematuria as well as declining in PSA levels. ¹⁷⁷Lu-PSMA therapy can be a good treatment option in patients with locally invasive tumors.

Background: While prostate-specific membrane antigen (PSMA)-targeted imaging has revolutionized metastatic detection, unspecific bone uptake (UBU)—particularly in the ribs—is a common but diagnostically challenging finding in prostate cancer (PCa) patients. This review aims to synthesize current evidence on PSMA-avid rib lesions in PCa and to propose a structured approach for differentiating true metastases from benign mimics. Methods: A comprehensive literature search across PubMed, EMBASE, Scopus, and Web of Science identified relevant studies on PSMA imaging interpretation, tracer-specific patterns, rib lesion morphology, and clinical correlates. Data on uptake intensity, CT features, lesion number, location, tracer type, patient-specific risk factors, and follow-up behavior were extracted and analyzed. Results: Most solitary rib lesions are benign, particularly in low-risk patients or when located in the anterior/lateral arcs. Metastatic lesions are more likely to present as multiple foci, show cortical destruction on CT, exhibit high uptake intensity, and occur in patients with elevated PSA, high Gleason score, or ongoing androgen deprivation. ¹⁸F-PSMA-1007 is especially prone to UBU in the ribs compared to ⁶⁸Ga-PSMA-11. Based on these variables, we propose a clinical decision tree to guide interpretation of PSMA-avid rib lesions. Conclusions: Accurate interpretation of rib lesions on PSMA PET/CT requires a multimodal, context-sensitive approach. Our diagnostic decision tree guides precise differentiation of benign versus metastatic rib lesions, enhancing staging accuracy and clinical decision-making. Biomarker-guided therapies offer potential for personalized treatment, though rib-specific validation remains a critical need.

Objectives: We aimed to develop and evaluate a novel computer-aided detection (CADe) approach for identifying small metastatic biochemically recurrent (BCR) prostate cancer (PCa) lesions on PSMA-PET images, utilizing multi-angle Maximum Intensity Projections (MA-MIPs) and state-of-the-art (SOTA) object detection algorithms. Methods: We fine-tuned and evaluated 16 SOTA object detection algorithms (selected across four main categories of model types) applied to MA-MIPs as extracted from rotated 3D PSMA-PET volumes. Predicted 2D bounding boxes were back-projected to the original 3D space using the Ordered Subset Expectation Maximization (OSEM) algorithm. A fine-tuned Medical Segment-Anything Model (MedSAM) was then also used to segment the identified lesions within the bounding boxes. Results: The proposed method achieved a high detection performance for this difficult task, with the FreeAnchor model reaching an F1-score of 0.69 and a recall of 0.74. It outperformed several 3D methods in efficiency while maintaining comparable accuracy. Strong recall rates were observed for clinically relevant areas, such as local relapses (0.82) and bone metastases (0.80). Conclusion: Our fully automated CADe tool shows promise in assisting physicians as a “second reader” for detecting small metastatic BCR PCa lesions on PSMA-PET images. By leveraging the strength and computational efficiency of 2D models while preserving 3D spatial information of the PSMA-PET volume, the proposed approach has the potential to improve detectability and reduce workload in cancer diagnosis and management.