Standardized uptake values of 99mTc-MDP in normal vertebrae assessed using quantitative SPECT/CT for differentiation diagnosis of benign and malignant bone lesions

Prospective Study Comparing [99mTc]Tc-DP-FAPI Quantitative SPECT/CT with [68Ga]Ga-FAPI-04 PET/CT in Patients with Gastrointestinal Tumors

Over the past decade, [68Ga]Ga-FAPI-04 positron emission tomography (PET)/CT imaging has been widely used for the treatment of various tumors. However, the application of 99mTC-labeled fibroblast activation protein inhibitors in tumors has been less studied. Our team previously demonstrated the safe biological distribution of [99mTc]Tc-DP-FAPI in the human body. Based on this, this study reports the accuracy of [99mTc]Tc-DP-FAPI in the imaging diagnosis of gastrointestinal tumors and compares it with that of [68Ga]Ga-FAPI-04 to evaluate the differences. A total of 24 patients with clinically diagnosed gastrointestinal tumors were prospectively included. All patients received [99mTc]Tc-DP-FAPI quantitative SPECT/CT imaging on the first day and [68Ga]Ga-FAPI-04 PET/CT imaging on the second day. And the effectiveness of the two imaging probes in detecting suspicious lesions was analyzed and compared. The primary tumors of all 24 patients were well detected by two imaging probes, and the sensitivity of [99mTc]Tc-DP-FAPI and [68Ga]Ga-FAPI-04 to the primary lesions was 100%. [99mTc]Tc-DP-FAPI examined 21 lymph nodes with a sensitivity and specificity of 32.8% and 10.9%, and [68Ga]Ga-FAPI-04 detected 57 lymph nodes with a sensitivity and specificity of 89.1% and 67.2%, respectively. Three distant metastases were detected by [99mTc]Tc-DP-FAPI and nine metastases by [68Ga]Ga-FAPI-04. The study showed that [99mTc]Tc-DP-FAPI is highly sensitive to detecting primary lesions of gastrointestinal tumors. Compared with [68Ga]Ga-FAPI-04, [99mTc]Tc-DP-FAPI has the same sensitivity in detecting primary tumors but has certain limitations in detecting metastases. [99mTc]Tc-DP-FAPI is of great value for preliminary screening of tumor lesions and early diagnosis of disease in patients who are suspected of having gastrointestinal tumors.

Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy

BACKGROUND

To evaluate the clinical performance of two deep learning methods, one utilizing real clinical pairs and the other utilizing simulated datasets, in enhancing image quality for two-dimensional (2D) fast whole-body scintigraphy (WBS).

METHODS

A total of 83 patients with suspected bone metastasis were retrospectively enrolled. All patients underwent single-photon emission computed tomography (SPECT) WBS at speeds of 20 cm/min (1x), 40 cm/min (2x), and 60 cm/min (3x). Two deep learning models were developed to generate high-quality images from real and simulated fast scans, designated 2x-real and 3x-real (images from real fast data) and 2x-simu and 3x-simu (images from simulated fast data), respectively. A 5-point Likert scale was used to evaluate the image quality of each acquisition. Accuracy, sensitivity, specificity, and the area under the curve (AUC) were used to evaluate diagnostic efficacy. Learned perceptual image patch similarity (LPIPS) and the Fréchet inception distance (FID) were used to assess image quality. Additionally, the count-level consistency of WBS was compared between the two models.

RESULTS

Subjective assessments revealed that the 1x images had the highest general image quality (Likert score: 4.40 ± 0.45). The 2x-real, 2x-simu and 3x-real, 3x-simu images demonstrated significantly better quality than the 2x and 3x images (Likert scores: 3.46 ± 0.47, 3.79 ± 0.55 vs. 2.92 ± 0.41, P < 0.0001; 2.69 ± 0.40, 2.61 ± 0.41 vs. 1.36 ± 0.51, P < 0.0001), respectively. Notably, the quality of the 2x-real images was inferior to that of the 2x-simu images (Likert scores: 3.46 ± 0.47 vs. 3.79 ± 0.55, P = 0.001). The diagnostic efficacy for the 2x-real and 2x-simu images was indistinguishable from that of the 1x images (accuracy: 81.2%, 80.7% vs. 84.3%; sensitivity: 77.27%, 77.27% vs. 87.18%; specificity: 87.18%, 84.63% vs. 87.18%. All P > 0.05), whereas the diagnostic efficacy for the 3x-real and 3x-simu was better than that for the 3x images (accuracy: 65.1%, 66.35% vs. 59.0%; sensitivity: 63.64%, 63.64% vs. 64.71%; specificity: 66.67%, 69.23% vs. 55.1%. All P < 0.05). Objectively, both the real and simulated models achieved significantly enhanced image quality from the accelerated scans in the 2x and 3x groups (FID: 0.15 ± 0.18, 0.18 ± 0.18 vs. 0.47 ± 0.34; 0.19 ± 0.23, 0.20 ± 0.22 vs. 0.98 ± 0.59.

LPIPS

0.17 ± 0.05, 0.16 ± 0.04 vs. 0.19 ± 0.05; 0.18 ± 0.05, 0.19 ± 0.05 vs. 0.23 ± 0.04. All P < 0.05). The count-level consistency with the 1x images was excellent for all four sets of model-generated images (P < 0.0001).

CONCLUSIONS

Ultrafast 2x speed (real and simulated) images achieved comparable diagnostic value to that of standardly acquired images, but the simulation algorithm does not necessarily reflect real data.

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.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features

BACKGROUND

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.

PURPOSE

We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.

METHODS

A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.

RESULTS

The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.

CONCLUSIONS

Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Correlation analysis between the quantitative parameters of iodine-131 single-photon emission computed tomography-computed tomography thyroid bed uptake and the efficacy of radioactive iodine adjuvant therapy for papillary thyroid cancer

Background

Single-photon emission computed tomography-computed tomography (SPECT/CT) quantification has emerged as a valuable tool for assessing disease prognosis by accurately identifying and characterizing abnormal lesions with accumulated radionuclides. Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, and radioactive iodine (RAI) therapy is a standard treatment following total thyroidectomy. This study aimed to explore the potential utility the quantitative parameters of the thyroid bed under iodine-131 (I-131) SPECT/CT in the efficacy of RAI adjuvant therapy for patients with PTC.

Methods

The retrospective cohort study enrolled 107 patients with PTC who underwent RAI adjuvant therapy from June 2020 to January 2023. Three days after the RAI adjuvant therapy, all patients underwent I-131 whole-body scans and SPECT/CT imaging. The quantitative parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and percent injected dose (%ID), were measured using image analysis software based on I-131 SPECT/CT thyroid bed uptake. Successful therapy was defined as inhibitory thyroglobulin (Tg) <0.2 ng/mL with negative thyroglobulin antibody (TgAb) and negative imaging examination 6 months after RAI adjuvant therapy. The relationship between the quantitative parameters and the treatment efficacy, in addition to the potential influencing factors, were analyzed.

Results

The quantitative parameters from the successful group [SUVmax: median 6.15 g/mL, interquartile range (IQR) 2.34-13.80 g/mL; SUVmean: median 2.02 g/mL, IQR 0.89-4.93 g/mL; %ID: median 2.00%, IQR 1.00-4.00%] were significantly lower than those from the unsuccessful group (SUVmax: median 19.03 g/mL, IQR 5.31-45.10 g/mL, SUVmean 4.64 g/mL, IQR 2.07-19.05 g/mL; %ID: median 8.00%, IQR 3.00-18.00%) (SUVmax: Z=-3.755; SUVmean; Z=-3.671; %ID: Z=-4.070; all P values <0.001). SUVmax, SUVmean and %ID were positively correlated with the stimulated thyroglobulin (sTg) and inhibitory Tg at 6 months after RAI adjuvant therapy, respectively (all P values <0.001). SUVmax [odds ratio (OR) =1.045], SUVmean (OR =1.130), and %ID (OR =1.092) were predictive factors for the failure of RAI adjuvant therapy (all P values <0.001).

Conclusions

Our study suggested that quantitative parameters (SUVmax, SUVmean, and %ID) derived from I-131 SPECT/CT imaging of the thyroid bed can serve as useful tools for predicting therapy outcomes following RAI adjuvant therapy.

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

BACKGROUND

Bone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination.

PURPOSE

Machine learning analysis of [99mTc]-methylene diphosphate (99mTc-MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer.

METHODS

One hundred forty-one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK-SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics-clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test.

RESULTS

The optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively.

CONCLUSIONS

The machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics-clinical integrated model demonstrated the best performance.

Risk factors for rib metastases of lung cancer patients with high-uptake rib foci on 99Tcm-MDP SPECT/CT

BACKGROUND

99Tcm-MDP SPECT/CT is widely used to diagnose early bone metastasis. Ribs are high-risk bone metastasis sites, while few study is related to ribs. The study is to investigate the risk factors of rib metastases in lung cancer patients.

METHODS

We retrospectively analyzed the patients' clinical characteristics and SPECT/CT imaging features. The patients were divided into a rib metastasis group (108 cases) and a non-rib metastasis group (103 cases).

RESULTS

In 211 patients, rib metastases were closely related to tumor markers, T stage, N stage, clinical staging, lymph node (LN) involvement, number of rib foci, localization on rib and foci type (P<0.05). In 93 patients with pure rib foci, rib metastases were affected by clinical staging, LN involvement, localization on the rib and primary lung cancer localization (P<0.001, 0.038,<0.001, 0.034, respectively). In 100 patients with a solitary rib focus, rib metastases were associated with clinical staging, localization on the rib, and LN involvement (P<0.001, 0.001, and 0.014, respectively). In all 633 rib foci, localization on the rib was an effective risk factor for rib metastases (P<0.001).

CONCLUSIONS

Patients with increased tumor markers, stage IV lung adenocarcinoma and multiple rib foci located ipsilaterally with the primary lung tumor, or rib foci accompanied other bone foci are more likely to develop rib metastasis. Patients with pure rib foci or a solitary rib focus, especially in the anterior rib with negative LN involvement, have a low probability of rib metastasis.

Detectability of cold tumors by xSPECT bone technology compared with hot tumors: a supine phantom study

Detecting cold as well as hot tumors is vital for interpreting bone tumors on single-photon emission computed tomography (SPECT) images. This study aimed to visually and quantitatively demonstrate the detectability of cold tumors using xSPECT technology compared with that of hot tumors in the phantom study. Five tumors of different sizes and normal bone contained a mixture of 99mTc and K2HPO4 in a spine phantom. We acquired SPECT data using an xSPECT protocol and transverse images were reconstructed using xSPECT Bone (xB) and xSPECT Quant (xQ). Mean standardized uptake values (SUVmean) in volumes of interest (VOI) were calculated. Recovery coefficients (RCs) for each tumor site were calculated with reference to radioactive concentrations. The SUVmeans of the whole vertebral body for hot tumor bone image in cortical bone phantom reconstructed by with xB and xQ were 5.77 and 4.86 respectively. The SUVmean of xB was similar to the true value. The SUVmeans for xB and xQ reconstructed images of cold tumors were both approximately 0.16. The RC of the cold tumor on xQ images increased as the tumor diameter decreased, whereas that of xB remained almost constant regardless of the tumor diameter. In conclusion, the quantitative accuracy of detecting hot and cold tumors was higher in the xB image than in the xQ image. Moreover, the visual detectability of cold tumors was also excellent in xB images.

Effect of Gaussian Smoothing Filter Size for CT-Based Attenuation Correction on Quantitative Assessment of Bone SPECT/CT: A Phantom Study

This study aims to determine the effect of Gaussian filter size for CT-based attenuation correction (CTAC) on the quantitative assessment of bone SPECT. An experiment was performed using a cylindrical phantom containing six rods, of which one was filled with water and five were filled with various concentrations of K2HPO4 solution (120-960 mg/cm3) to simulate different bone densities. 99mTc-solution of 207 kBq/ml was also included within the rods. SPECT data were acquired at 120 views for 30 s/view. CT for attenuation correction were obtained at 120 kVp and 100 mA. Sixteen different CTAC maps processed with different Gaussian filter sizes (ranging from 0 to 30 mm in 2 mm increments) were generated. SPECT images were reconstructed for each of the 16 CTAC maps. Attenuation coefficients and radioactivity concentrations in the rods were compared with those in the water-filled rod without K2HPO4 solution as a reference. Gaussian filter sizes below 14-16 mm resulted in an overestimation of radioactivity concentrations for rods with high concentrations of K2HPO4 (≥ 666 mg/cm3). The overestimation of radioactivity concentration measurement was 3.8% and 5.5% for 666 mg/cm3 and 960 mg/cm3 K2HPO4 solutions, respectively. The difference in radioactivity concentration between the water rod and the K2HPO4 rods was minimal at 18-22 mm. The use of Gaussian filter sizes smaller than 14-16 mm caused an overestimation of radioactivity concentration in regions of high CT values. Setting the Gaussian filter size to 18-22 mm enables radioactivity concentration to be measured with the least influence on bone density.

Imaging signs and the qualitative diagnosis of solitary rib lesions using 99mtechnetium-methylene diphosphonate whole-body bone imaging in patients with a malignant tumor

Background

The aim of this study was to summarize the valuable information for qualitative diagnosis by investigating the imaging signs from the whole-body bone imaging of solitary rib lesions.

Methods

A retrospective analysis was conducted of the data from 313 patients with malignant tumors and solitary rib lesions identified using whole-body bone imaging in Department of Nuclear Medicine of Central South University Xiangya School Affiliated Haikou Hospital between January 2015 and December 2017. Based on the final comprehensive diagnosis of the rib lesions, the patients were divided into a bone metastasis group, fracture group, other benign lesions group, and an uncertain group, and the characteristic imaging changes in rib lesions in each group were explored.

Results

(I) Significant differences were identified among the 4 groups (P<0.001) in the distribution of lesions in the anterior, posterior, and lateral ribs and proximal costal cartilage. The fracture group had the highest proportion of lesions in the anterior ribs (99/121, 81.8%) and proximal costal cartilage (74.4%, 90/121). (II) Significant differences were detected in morphology, concentration, boundaries, and radioactivity distribution among the 4 groups of patients (P<0.001). The bone metastasis group had the highest proportion of lesions appearing as stripes (35/67, 52.2%), and the fracture group had the highest proportion of lesions appearing as spots (94.2%, 114/121) and the lowest proportion appearing as stripes (3/121, 2.5%). (III) Significant differences were found in the longitudinal diameter, transverse diameter, aspect ratio, and tumor-to-normal tissue ratio between the 4 groups (P<0.001). The longitudinal diameter (27.8±16.0 mm) and aspect ratio (1.9±1.0) of the bone metastasis group were the highest, whereas the longitudinal diameter (15.2±3.9 mm) and aspect ratio (1.0±0.2) of the fracture group were the smallest.

Conclusions

This study revealed that different types of solitary rib lesions had relatively characteristic imaging signs in whole-body bone imaging.

Quantitative SPECT/CT Parameters in the Assessment of Transthyretin Cardiac Amyloidosis-A New Dimension of Molecular Imaging

AIMS

Cardiac transthyretin amyloidosis (ATTR) represents the accumulation of misfolded transthyretin in the heart interstitium. Planar scintigraphy with bone-seeking tracers has long been established as one of the three main steps in the non-invasive diagnosis of ATTR, but lately, single-photon emission computed tomography (SPECT) has gained wide recognition for its abilities to exclude false positive results and offer a possibility for amyloid burden quantitation. We performed a systematic review of the existing literature to provide an overview of the available SPECT-based parameters and their diagnostic performances in the assessment of cardiac ATTR. Methods and Methods: Among the 43 papers initially identified, 27 articles were screened for eligibility and 10 met the inclusion criteria. We summarised the available literature based on radiotracer, SPECT acquisition protocol, analysed parameters and their correlation to planar semi-quantitative indices.

RESULTS

Ten articles provided accurate details about SPECT-derived parameters in cardiac ATTR and their diagnostic potential. Five studies performed phantom studies for accurate calibration of the gamma cameras. All papers described good correlation of quantitative parameters to the Perugini grading system.

CONCLUSIONS

Despite little published literature on quantitative SPECT in the assessment of cardiac ATTR, this method offers good prospects in the appraisal of cardiac amyloid burden and treatment monitoring.

Comparison of the detectability of hot lesions on bone SPECT using six state-of-the-art SPECT/CT systems: a multicenter phantom study to optimize reconstruction parameters

Single-photon emission computed tomography with X-ray computed tomography (SPECT/CT) systems have diversified due to the remarkable developments made by each manufacturer. This study aimed to optimize the reconstruction parameters of six state-of-the-art SPECT/CT systems and compare their image quality of bone SPECT. SPECT images were acquired on SPECT/CT systems, including Symbia Intevo, Discovery NM/CT 670, Discovery NM/CT 870 CZT, Brightview XCT, and VERITON-CT. SIM² bone phantom with tough lung phantoms on both sides of the spinal inserts that simulate the thorax was used for image quality assessment. SPECT images were obtained at individual workstations using an ordered subset expectation maximization method with three-dimensional resolution recovery, as well as CT attenuation and scatter correction, subset 2, iteration 12-84, and a full width at half maximum 10-mm Gaussian smooth filter. An automatic image analysis software dedicated to SIM² bone phantom was used to assess the contrast-to-noise ratio (CNR), relative recovery coefficient, percentage of coefficient of variance, contrast, and detectability. The optimal parameters for each system were defined with superior detectability of spherical lesions and noise characteristics, as well as the highest CNR. All systems exhibited better image quality indexes using the optimal parameters than using the manufacturer's recommended parameters. The detectability of all systems was in agreement while using the optimal parameters. Detectability agreement can be achieved by optimizing the reconstruction parameters for different reconstruction algorithms, which can further improve the image quality. Therefore, future research should focus on optimal reconstruction parameters for SPECT alone.

A Simple Kit Formulation for Preparation and Exploratory Human Studies of a Novel 99mTc-Labeled Fibroblast Activation Protein Inhibitor Tracer for Imaging of the Fibroblast Activation Protein in Cancers

Fibroblast activation protein (FAP) is a potential target for tumor diagnosis and treatment because it is selectively expressed on the cell membrane of cancer-associated fibroblasts in most solid tumor stroma. The aim of this study was to develop a ^99mTc-labeled fibroblast activation protein inhibitor (FAPI) tracer, evaluate its imaging efficacy in nude mice, and further explore its biodistribution in healthy volunteers and uptake in tumor patients. An FAPI-derived ligand (DP-FAPI) containing d-proline was designed and synthesized as a linker, and a stable hydrophilic ^99mTc-labeled complex ([^99mTc]Tc-DP-FAPI) was obtained by kit formulation. In vitro cellular uptake and saturation binding assays were performed in FAP-transfected HT-1080 cells (FAP-HT-1080). The biodistribution was characterized, and micro-single-photon emission computed tomography (SPECT) imaging was performed in BALB/c nude mice bearing U87 MG tumors. Furthermore, a first-in-man application was performed in four healthy volunteers and three patients with gastrointestinal tumors. In vitro, the nanomolar K_d values of [^99mTc]Tc-DP-FAPI indicated that it had significantly high target affinity for FAP. Biodistribution and micro-SPECT imaging studies showed that [^99mTc]Tc-DP-FAPI exhibited high uptake and high tumor-to-nontargeted ratios. The calculated effective dose for [^99mTc]Tc-DP-FAPI was approximately <5 mSv in four healthy volunteers. In three patients with gastrointestinal tumors, [^99mTc]Tc-DP-FAPI quantitative SPECT/CT revealed high and reliable uptake. [^99mTc]Tc-DP-FAPI exhibited high selectivity and affinity for FAP in vitro. The safety and effectiveness of [^99mTc]Tc-DP-FAPI in primary tumor imaging have been confirmed by animal and clinical studies, revealing the potential clinical application value of this tracer.

Quantitative assessment of 99mTc-methylene diphosphonate bone SPECT/CT for assessing bone metastatic burden and its prognostic value in patients with castration-resistant prostate cancers: initial results in a single-center retrospective study

PURPOSE

To evaluate the prognostic value of the quantitative assessment of ^99mTc-methylene diphosphonate (^99mTc-MDP) bone SPECT/CT in castration-resistant prostate cancer (CRPC) patients with bone metastases.

METHODS

A total of 103 patients who underwent ^99mTc-MDP bone SPECT/CT imaging from the neck to the proximal femur were included. First, in 65 patients without bone metastases, the normal range of standardized uptake value (SUV) of non-pathological bone was evaluated to determine an SUV threshold to reliably exclude most normal osseous activity. Then, in 38 CRPC patients with bone metastases, lesion uptake volume (LUV), which is the extracted volume of bone metastases exhibiting high accumulation above the SUV threshold, was calculated. The relation between LUV and prostate-related mortality was statistically evaluated.

RESULTS

Based on the SUV measurements of non-pathological bones, the optimal SUV threshold, which defines abnormal bone SPECT uptake, was determined to be 8. Median LUV was 39 mL (interquartile range 4.0-104.3 mL) in the CRPC subjects with bone metastases. Kaplan-Meier survival analysis showed a significant relation between prostate cancer-specific survival and LUV (cut-off value, 19.95 mL; P = 0.001). Multivariate analysis revealed LUV as an independent prognostic factor for the survival (P = 0.008, hazard ratio 23.424). Global chi-square test showed that LUV had significant incremental prognostic value in addition to prostate-specific antigen and the interval from progression to CRPC until bone SPECT/CT (P = 0.022).

CONCLUSION

Quantitative assessment of ^99mTc-MDP bone SPECT images can provide valuable prognostic information in CRPC patients with bone metastases.

Standardized Uptake Values on SPECT/CT: A Promising Alternative Tool for Treatment Evaluation and Prognosis of Metastatic Neuroendocrine Tumours

(1) Background: The aim of our study was to assess the feasibility of 99mTcEDDA/HYNIC-TOC SPECT/CT quantitative analysis in evaluating treatment response and disease progression in patients with NETs. (2) Methods: This prospective monocentric study evaluated 35 SPECT/CT examinations performed on 14 patients with neuroendocrine tumours who underwent a baseline and at least one follow-up 99mTcEDDA/HYNIC-TOC scan as part of their clinical management. The examination protocol included a whole-body scan acquired 2 h after the radiotracer’s administration, with the SPECT/CT performed 4 h post-injection. Images were analyzed by two experienced physicians and patients were classified into response categories based on their changes in SUV values. (3) Results: We evaluated 14 baseline studies and 21 follow-up scans, accounting for 123 lesions. A statistically positive correlation has been found between the SUVmax and SUVpeak values in tumoral lesions (p < 0.05). No correlation has been found between the SUV values and the ki67 proliferation index. Finally, 64.29% patients were classified as SD at the end of the study, with only 14.29% of patients exhibiting PD and 21.43% patients with PR. (4) Conclusions: The quantitative analysis of 99mTcEDDA/HYNIC-TOC SPECT/CT data in patients with neuroendocrine tumours could represent an alternative to 68Ga-DOTA-peptides PET/CT for the monitoring and prognosis of NETs.

Preliminary Study for Quantitative Assessment of Sacroiliitis Activity Using Bone SPECT/CT: Comparison of Diagnostic Performance of Quantitative Parameters

Purpose

We compared the feasibility of quantitative analysis methods using bone SPECT/CT with those using planar bone scans to assess active sacroiliitis.

Methods

We retrospectively reviewed whole-body bone scans and pelvic bone SPECT/CTs of 8 patients who had clinically confirmed sacroiliitis and enrolled 24 patients without sacroiliitis as references. The volume of interest of each sacroiliac joint, including both the ilium and sacrum, was drawn. Active arthritis zone (AAZ) was defined as the zone of voxels with higher SUV than sacral mean SUV within the VOI of SI joint. Then, the following SPECT/CT quantitative parameters, SUVmax (maximum SUV), SUV50% (mean SUV in highest 50% of SUV), and SUV-AAZ, and the ratio of those values to sacral mean SUV (SUVmax/S, SUV50%/S, SUV-AAZ/S) were calculated. For the planar bone scan, the mean count ratio of SI joint/sacrum (SI/S) was conventionally measured.

Results

Most of the SPECT/CT parameters of the sacroiliitis group were significantly higher than the normal group, whereas SI/S of the planar bone scan was not significantly different between the two groups. In receiver operating characteristic curve analysis, SUV-AAZ/S showed the highest AUC of 0.992, followed by SUV50%/S and SUVmax/S. All ratio parameters of the SPECT/CT showed higher AUC values than the SUV parameters of SI joint or SI/S of the planar scan.

Conclusions

The quantitative analyses of bone SPECT/CT showed better performance in assessing active sacroiliitis than the planar bone scan. SPECT/CT parameters using the ratio of the SI joint to sacrum showed more favorable results than SUV parameters such as SUVmax, SUV50%, and SUV-AAZ.

Impact of patient body habitus on image quality and quantitative value in bone SPECT/CT

OBJECTIVE

The first edition of guidelines for standardization of bone single photon emission computed tomography (SPECT) imaging was published in 2017, and the optimization and standardization are widely promoted. To the purpose, clarification of the factors related to image quality and quantitative values and their influence are required. The present study aimed to clarify and optimize the influence of patient body habitus on image quality and quantitative values in bone SPECT/CT.

METHODS

National Electrical Manufacturers Association body phantom (S-size) and custom-made large body phantoms (M-size and L-size) that simulate the abdomens of Japanese patients weighing 60, 80, and 100 kg, were used. Each phantom was filled with ^99mTc-solutions of 108  and 18 kBq/mL for the hot spheres and background, respectively. Dynamic SPECT acquisition was performed for 6000 s (150 s /rotation × 40 rotation). The data were divided into six projection data and reconstructed each acquisition time (150, 300, 450, 600, 750, 900 s, and single projection 6000 s). Image quality was evaluated for contrast (Q_{H, 17 mm}), background noise (N_{B, 17 mm}), contrast-to-noise ratio (CNR), maximum standardized uptake value (SUV_{max, 17 mm}), and visual assessment for a 17 mm hot sphere.

RESULTS

Image quality in the 300 s acquisition showed that values of Q_{H, 17 mm}, CNR, and SUV_{max, 17 mm} decreased (-16.7%, -11.8%, and -11.3%) for M-size and (-28.2%, -30.1%, and -21.7%) for L-size compared with S-size, respectively. No significant difference was observed in N_{B, 17 mm} values. M-size and L-size required 1.2 and 2.3 times longer acquisition, to achieve same CNR as S-size. In visual assessment, 17 mm hot sphere could not be detected only in the L-size. When the Japanese bone SPECT guidelines criteria were applied in 600 s, the sphere could be detected between all phantoms.

CONCLUSIONS

Patient body habitus significantly affects image quality and decreases the quantitative value in bone SPECT/CT. For the optimization, extend acquisition time according to the patient body habitus is effective for image quality. And for the standardization, it is important to achieve imaging conditions that meet the Japanese bone SPECT guidelines criteria to ensure adequate detectability.

What validation tests can be done by the clinical medical physicist while waiting for the standardization of quantitative SPECT/CT imaging?

OBJECTIVE

The aim of the study was to assess the accuracy of quantitative SPECT/CT imaging in a clinical setting and to compare test results from two nuclear medicine departments.

METHODS

Phantom studies were carried out with two gamma cameras manufactured by GE Healthcare: Discovery NM/CT 670 and NM/CT 850, used in two nuclear medicine departments. The data were collected using a cylindrical uniform phantom and a NEMA/IEC NU2 Body Phantom, filled with ^99mTc-pertechnetate.

RESULTS

The convergence of activity concentration recovery was validated for the two gamma cameras operating in two medical centers using the cylindrical uniform phantom. The comparison of results revealed a 5% difference in the background calibration factor Bg. cal; 6% difference in COV, and a 0.6% difference in total activity deviation ∆A_tot. Recovery coefficients (RC_max) for activity concentration in spheres of the NEMA/IEC NU2 Body Phantom were measured for different image reconstruction techniques. RC_max was in the range of 0.2-0.4 for the smallest sphere (ϕ 10 mm), and 1.3-1.4 for the largest sphere (ϕ 37 mm). Conversion factors for SUVmax and SUVmean for the gamma camera systems used were 0.99 and 1.13, respectively.

CONCLUSIONS

(1) Measurements taken in our study confirmed the clinical suitability of 5 parameters of image quality (Bg. cal-background calibration factor, ∆A_tot-total activity deviation, COV-coefficient of variation used for image noise assessment, Q_H-hot contrast, AM-accuracy of measurements, or RC-recovery coefficient) for the validation of SPECT/CT system performance in terms of correct quantitative acquisitions of images. (2) This work shows that absolute SPECT/CT quantification is achievable in clinical nuclear medicine centers. Results variation of quantitative analyses between centers is mainly related to the use of different reconstruction methods. (3) It is necessary to standardize the technique of measuring the SUV conversion factor obtained with different SPECT/CT scanners.

Absolute Quantification in Diagnostic SPECT/CT: The Phantom Premise

The application of absolute quantification in SPECT/CT has seen increased interest in the context of radionuclide therapies where patient-specific dosimetry is a requirement within the European Union (EU) legislation. However, the translation of this technique to diagnostic nuclear medicine outside this setting is rather slow. Clinical research has, in some examples, already shown an association between imaging metrics and clinical diagnosis, but the applications, in general, lack proper validation because of the absence of a ground truth measurement. Meanwhile, additive manufacturing or 3D printing has seen rapid improvements, increasing its uptake in medical imaging. Three-dimensional printed phantoms have already made a significant impact on quantitative imaging, a trend that is likely to increase in the future. In this review, we summarize the data of recent literature to underpin our premise that the validation of diagnostic applications in nuclear medicine using application-specific phantoms is within reach given the current state-of-the-art in additive manufacturing or 3D printing.