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

Core-shell hybrid liposomes: Transforming imaging diagnostics and therapeutic strategies

For the last few years, researchers and industry have intensified efforts to develop a diverse array of diagnostic and therapeutic approaches to fight diseases such as cancer, diabetes, and viral infections. Among the emerging technologies, hybrid liposomes (HLs) stand out for their ability to address key limitations of conventional liposomes and deliver multifunctional solutions more effectively. While several novel nanosystems, including polymerlipid conjugates and inorganic nanoparticles (NPs), have shown great potential in the preclinical and clinical phases for the diagnosis and treatment of diseases, particularly cancer, HLs can integrate the best of both worlds, combining drug delivery properties with imaging capabilities. HLs, particularly those with core-shell structures, can surpass conventional liposomes by offering improved physicochemical properties, multifunctionality, and the capacity to overcome critical delivery challenges. The integration of natural and synthetic polymers has rapidly emerged as a preferred strategy in the development of HLs, providing significant advantages, such as enhanced stability, stimuli-responsive drug release, prolonged circulation, and improved therapeutic efficacy. Additionally, the customizable structure of HLs allows the incorporation of diverse materials, such as metals, ligands, and functional lipids, improving diagnosis and enhancing targeted delivery and cellular uptake far beyond what conventional liposomes offer. This review provides a critical and updated analysis of core-shell structure exhibiting HLs, with a focus on their preparation, characterization, and functional enhancements. We also examine in vitro/in vivo outcomes in imaging diagnosis and drug delivery while addressing the current barriers to clinical translation and future prospects for these versatile nanoplatforms.

Response Evaluation Using 68 Ga-DOTA-IBA in a Case of Metastatic Prostate Cancer

ABSTRACT

We report a case of a patient with metastatic prostate cancer receiving first-line endocrine therapy. Clinical symptoms, PSA level, and CT confirmed the significant progression of his bone metastatic lesions. In comparison to images at baseline, follow-up bone scan incorrectly showed remission of the bone lesions, whereas follow-up 68 Ga-DOTA-IBA correctly showed disease progression.

Synthesizing [18F]PSMA-1007 PET bone images from CT images with GAN for early detection of prostate cancer bone metastases: a pilot validation study

BACKGROUND

[18F]FDG PET/CT scan combined with [18F]PSMA-1007 PET/CT scan is commonly conducted for detecting bone metastases in prostate cancer (PCa). However, it is expensive and may expose patients to more radiation hazards. This study explores deep learning (DL) techniques to synthesize [18F]PSMA-1007 PET bone images from CT bone images for the early detection of bone metastases in PCa, which may reduce additional PET/CT scans and relieve the burden on patients.

METHODS

We retrospectively collected paired whole-body (WB) [18F]PSMA-1007 PET/CT images from 152 patients with clinical and pathological diagnosis results, including 123 PCa and 29 cases of benign lesions. The average age of the patients was 67.48 ± 10.87 years, and the average lesion size was 8.76 ± 15.5 mm. The paired low-dose CT and PET images were preprocessed and segmented to construct the WB bone structure images. 152 subjects were randomly stratified into training, validation, and test groups in the number of 92:41:19. Two generative adversarial network (GAN) models-Pix2pix and Cycle GAN-were trained to synthesize [18F]PSMA-1007 PET bone images from paired CT bone images. The performance of two synthesis models was evaluated using quantitative metrics of mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM), as well as the target-to-background ratio (TBR).

RESULTS

The results of DL-based image synthesis indicated that the synthesis of [18F]PSMA-1007 PET bone images from low-dose CT bone images was highly feasible. The Pix2pix model performed better with an SSIM of 0.97, PSNR of 44.96, MSE of 0.80, and MAE of 0.10, respectively. The TBRs of bone metastasis lesions calculated on DL-synthesized PET bone images were highly correlated with those of real PET bone images (Pearson's r > 0.90) and had no significant differences (p < 0.05).

CONCLUSIONS

It is feasible to generate synthetic [18F]PSMA-1007 PET bone images from CT bone images by using DL techniques with reasonable accuracy, which can provide information for early detection of PCa bone metastases.

State-of-the-art of multidisciplinary approach of bone metastasis-directed therapy: review and challenging questions for preparation of a GEMO practice guidelines

Bone is a common secondary site of dissemination during the course of cancer. Bone metastases (BM) can be associated with skeletal-related events (SRE) such as disabling pain, hypercalcemia, and bone instability that leads to pathological fractures or spinal cord compression. SRE contribute to high morbidity as well as, mortality, and have a negative economic impact. Modern management of BM integrates focal treatments (such as radiotherapy, surgery, and interventional radiology), orthoses, and antiresorptive and systemic oncological treatment. The choice of a metastasis-directed therapy depends on the objective of the treatment, the patient characteristics, and the complete assessment of the bone lesion (pain, neurological risk, and instability). In the narrative review present herein, we aim to provide an updated summary of the literature, with description of the advantages and disadvantages of current and emerging strategies in the multimodal treatment of BM and, based on these data, an updated algorithm for the management of BM.

Potential of Technetium and Rhenium Theranostics

While theranostics has transformed the precision medicine landscape over the last decade, there is scope for the development of true theranostic pairs, e.g. diagnostic and therapeutic partners in which any physical, chemical, and biological differences are negligible to in vivo application. Although simple to state in theory, there are, in fact, limited options exhibiting optimal physical characteristics and wholly shared elements. Further compounding real-world application of the traditional theranostic method are additional barriers. The use of PET/CT as the cornerstone of the diagnostic pair in theranostics creates inequity of access and opportunity based on socioeconomic and geographic factors, and the growing demand for both 68Ga and 177Lu is straining production capabilities globally. Improving access to theranostics globally will require novel thinking and infrastructure investment to ensure that patients of all economic and social backgrounds have access to this transformative technology. An approach which is underdeveloped, but which may address gaps in health inequities and improve outcomes, is the application of the widely available generator-produced 99mTc for imaging and 188Re for therapy. Despite favourable and near identical radiochemistry, the search for the next generation of theranostic radionuclide pairs seldom references technetium or rhenium radionuclides. Advances in SPECT/CT instrumentation and radiochemistry provide an opportunity to deliver theranostics to communities not serviced by PET-based theranostics. The 188Re and 99mTc supply by daily elution of a generator affords significant convenience, flexibility and delayed biomolecule imaging. Low abundance gamma emissions of 188Re allow serial imaging and dosimetry calculations. 99mTc / 188Re theranostics could address inequity in access and opportunity to cutting edge theranostics.

Preparation and preclinical evaluation of 68Ga-labeled alendronate analogs for diagnosis of bone metastases

Bone is one of the most common target organs for distant metastases of solid tumors, which imposes a heavy burden on society. Early diagnosis of bone metastases is of great significance and positron emission tomography (PET) imaging plays an important role in the diagnosis of bone metastases. PET tracers applied for diagnosing bone metastases are constantly being updated, but they all have certain limitations like a relatively low bone/kidney ratio or no capacity to label therapeutic radionuclides. Alendronate, a representative bisphosphonate (BP), has been usually considered the standard clinical treatment for bone related diseases. In this study, alendronate was strategically modified with different linkers in an attempt to improve target/non-target ratios and 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) was used as the chelator. Finally, three 68Ga-labeled tracers were successfully developed. The results showed that [68Ga]Ga-AABP1/2/3 all exhibited high radiochemical purity, biosafety, and excellent stability. In the biodistribution study of normal BALB/c mice, [68Ga]Ga-AABP3, when modified with phenylalanine and β-alanine as the linker, showed the highest bone/non-bone ratio at 1.5 h. In micro-PET/CT imaging of normal BALB/c mice, [68Ga]Ga-AABP3 showed the highest SUVmax value at the bones (2.24 ± 0.16 at 1.5 h). In micro-PET/CT imaging of the mouse model of bone metastases, compared with [68Ga]Ga-AABP1 and [68Ga]Ga-AABP2, the SUVmax in the foci after injection of [68Ga]Ga-AABP3 was the highest (2.64 ± 0.08 at 0.5 h and 2.67 ± 0.10 at 1.5 h), significantly higher than that of the contralateral normal bone. Besides, [68Ga]Ga-AABP3 showed the highest tumor/non-tumor ratio at 1.5 h. The results suggest that [68Ga]Ga-AABP3 has the potential for diagnosis of bone metastases. Furthermore, AABP3 with the chelator DOTA could also be labeled with 177Lu or 225Ac, providing possibility for further application in radioligand therapy.

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images

OBJECTIVES

This study utilizes [99mTc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine learning algorithms can identify microscopic early bone metastases. Additionally, we also determine the optimal machine learning approach.

MATERIALS AND METHODS

We retrospectively studied 63 patients with early bone metastasis from July 2020 to March 2023. The ITK-SNAP software was used to delineate early bone metastases and normal bone tissue in SPECT images of each patient, which were then registered onto CT images to outline the volume of interest (VOI). The VOI includes 63 early bone metastasis volumes and 63 normal bone tissue volumes. 126 VOIs were randomly distributed in a 7:3 ratio between the training and testing groups, and 944 radiomics features were extracted from every VOI. We established 20 machine learning models using 5 feature selection algorithms and 4 classification methods. Evaluate the performance of the model using the area under the receiver operating characteristic curve (AUC).

RESULTS

Most machine learning models demonstrated outstanding discriminative capacity, with AUCs higher than 0.70. Notably, the K-Nearest Neighbors (KNN) classifier exhibited significant performance improvement compared to the other four classifiers. Specifically, the model constructed utilizing eXtreme Gradient Boosting (XGBoost) feature selection method integrated with KNN classifier achieved the maximum AUC, which is 0.989 in the training set and 0.975 in the testing set.

CONCLUSIONS

Radiomics features integrated with machine learning methods can identify early bone metastases that are not visible on CT images. In our analysis, KNN is considered the optimal classification method.

Diagnostic Ability of Quantitative Parameters of Whole-Body Bone SPECT/CT Using a Full-Ring 360° Cadmium-Zinc-Telluride Camera for Detecting Bone Metastasis in Patients with Prostate Cancer

BACKGROUND/OBJECTIVES

This study aimed to assess the diagnostic capability of quantitative parameters from whole-body bone single-photon emission computed tomography/computed tomography (SPECT/CT) in detecting bone metastases in prostate cancer patients; Methods: We retrospectively analyzed 82 prostate cancer patients who underwent staging bone scintigraphy with a full-ring 360° Cadmium-Zinc-Telluride (CZT) SPECT/CT system. From the SPECT/CT images, we measured the maximum (SUVmax) and mean (SUVmean) standardized uptake values at six normal bone sites (skull, humerus, thoracic spine, lumbar spine, iliac bone, and femur), and the SUVmax for both metastatic and benign bone lesions. Ratios of lesion SUVmax-to-maximum and mean uptake values at the skull, humerus, and femur were computed for each lesion; Results: SUVmax and SUVmean at the skull and femur exhibited significantly lower variance compared to those at the thoracic spine, lumbar spine, and iliac bone, and revealed no significant differences between patients with and without bone metastasis. In receiver operating characteristic curve analysis for detecting bone metastasis among 482 metastatic lesions, 132 benign bone lesions, and 477 normal bone sites, the lesion-to-femur mean uptake ratio demonstrated the highest area under the curve value (0.955) among SPECT/CT parameters. Using a cut-off value of 5.38, the lesion-to-femur mean uptake ratio achieved a sensitivity of 94.8% and a specificity of 86.5%; Conclusions: The bone lesion-to-femur mean uptake ratio was the most effective quantitative bone SPECT/CT parameter for detecting bone metastasis in prostate cancer patients. Quantitative analysis of bone SPECT/CT images could thus play a crucial role in diagnosing bone metastasis.

Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers Part II. [18F]F-FLT, [18F]F-FET, [11C]C-MET and Other Less-Commonly Used Radiotracers

Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than 18F-fluorodeoxyglucose ([18F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, 18F-O-(2-fluoroethyl)-L-tyrosine ([18F]F-FET), 18F-Fluorothymidine ([18F]F-FLT) and 11C-Methionine ([11C]C-MET). [18F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [18F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [18F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [18F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [18F]F-FET, the other amino acid tracer which reflects cellular proliferation, [11C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [18F]F-DOPA, [18F]F-FACBC, [18F]F-NaF, [68Ga]Ga-CXCR-4 and [18F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [18F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.