Differences in Suvmax Spect/ct Bone Scan Between Bone Metastatic Lesions and Non-metastatic Lesions in Breast Cancer Patients

Differences in Suvmax Spect/ct Bone Scan Between Bone Metastatic Lesions and Non-metastatic Lesions in Breast Cancer Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differences in Suvmax Spect/ct Bone Scan Between Bone Metastatic Lesions and Non-metastatic Lesions in Breast Cancer Patients Nitami Oktavia Indiarti, Hendra Budiawan, Trias Nugrahadi, A. Hussein S Kartamihardja This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7636568/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Breast cancer is the most common cancer among women in Indonesia. Approximately 65–75% breast cancer cases have spread to the bone. Early detection of bone metastases is crucial for determining patient management strategies. Bone metastatic lesions in breast cancer are typically osteolytic, but invasion into bone tissue will still stimulate an osteoblastic response. This osteoblastic activity can be visualized using 99m Tc-MDP SPECT/CT bone scan. Quantitative parameters, such as SUVmax, are used to strengthen a qualitative interpretation visually and have the potential to differentiate between metastatic and non-metastatic bone lesions objectively. This study aimed to evaluate the accuracy of SUVmax from SPECT/CT bone scan in differentiating bone metastatic lesions from non-metastatic lesions in breast cancer patients. Methods This retrospective study was conducted on breast cancer patients who underwent bone scan and SPECT/CT at Dr. Hasan Sadikin General Hospital from May 2024 to June 2025. SUVmax was obtained by determining the volume of interest (VOI) based on fused SPECT/CT images. Statistical analysis used a Mann–Whitney to compare SUVmax between groups and ROC curve analysis to assess threshold value, sensitivity, specificity, and accuracy. Results A total of 182 SPECT/CT bone scan lesions from 62 subjects with breast cancer, consisting of 134 metastatic and 48 non-metastatic lesions, were analyzed in this study. The SUVmax in the metastatic group was (28.60 ± 11.40) g/mL, significantly higher than in the non-metastatic group (10.10 ± 3.65) g/mL ( p < 0.001 ) . ROC analysis showed an optimal SUVmax cutoff 16.7 g/mL, with an AUC of 0.964. Based on this cut-off, sensitivity, specificity, PPV, NPV, and accuracy were 92.48%, 94.55%, 99%, 82%, and 94% respectively. Conclusion Metastatic lesions had significantly higher SUVmax than non-metastatic lesions. SUVmax can be used as a quantitative parameter to support the diagnosis of bone metastases in breast cancer patients. SUVmax bone scan SPECT/CT breast cancer bone metastases Figures Figure 1 Figure 2 Introduction Breast cancer stands as the most prevalent type of cancer worldwide. According to the GLOBOCAN 2020 report, more than 2.2 million new breast cancer cases were recorded globally, making it the leading cause of cancer-related mortality among women.[ 1 ] Breast cancer remains the most prevalent malignancy among women worldwide, accounting for approximately 25.1% of all cancers. Globally, more than 1.6–2.2 million new cases are reported annually, representing nearly one-quarter of the overall cancer burden in women, with rising incidence across Asia; national statistics further indicate a substantial burden in Indonesia and Malaysia.[ 2 – 7 ] In Indonesia, breast cancer ranks first in prevalence, with an estimated incidence of 42.1 cases per 100,000 women and a mortality rate of 17 per 100,000 population.[ 8 ] Projections suggest that by 2040, the incidence will rise by more than 50%, reaching approximately 90,000 new cases annually if current epidemiological trends persist.[ 9 ] These figures highlight the urgent need to strengthen early detection and improve treatment strategies, particularly in advanced stages where bone metastases are frequently observed.[ 10 , 11 ] Bone metastasis represents one of the most common complications in advanced breast cancer. Approximately 65–75% of metastatic breast cancer patients develop skeletal involvement, most often affecting the vertebrae, femur, and pelvis. Such metastases not only impair patients’ quality of life but also worsen long-term prognosis.[ 2 , 12 ] Consequently, early identification of bone metastasis is pivotal for tailoring appropriate therapeutic strategies for breast cancer patients. Within the disease trajectory, bone is the most frequent site of metastasis. Various studies estimate that 30%-85% of patients will develop bone metastases, and skeletal involvement is commonly observed in the majority of patients who succumb to breast cancer, with a median survival of about 24 months in the metastatic setting.[ 2 , 7 , 13 ] The axial skeleton, particularly the thoracic and lumbar vertebrae, pelvis, ribs, and sternum, is most frequently affected, with occasional extension to the femur and skull. Clinical complications include pain, pathological fractures, spinal cord compression, and hypercalcemia, making early detection and accurate characterization crucial for staging, treatment selection, and monitoring therapeutic response.[ 10 , 14 – 19 ] Single-photon emission computed tomography (SPECT) has long been recognized as a primary imaging modality for detecting skeletal lesions. Compared to conventional planar imaging, SPECT offers superior accuracy in both visualization and localization of metastatic sites.[ 10 ] With its ability to generate three-dimensional images and provide enhanced anatomical precision, SPECT/CT has become the preferred approach for diagnosing bone metastases in breast cancer patients. Nevertheless, interpretation of SPECT images often remains subjective, particularly when differentiating between metastatic lesions and degenerative bone changes. To address this limitation, quantitative approaches using the maximum Standardized Uptake Value (SUVmax) have been increasingly applied to improve diagnostic accuracy. SUVmax reflects the degree of radiopharmaceutical uptake within lesions, serving as a more objective indicator of metabolic activity. Studies demonstrate that higher SUVmax values are frequently associated with metastatic lesions, whereas degenerative changes tend to exhibit lower uptake.[ 4 , 20 ] Whole-body bone scintigraphy with ⁹⁹ᵐTc-phosphonates (e.g., ⁹⁹ᵐTc-MDP/⁹⁹ᵐTc-HDP) has long been recognized as a sensitive, widely available, and cost-effective modality for screening skeletal involvement in breast cancer. However, its specificity is limited, as tracer uptake may also increase in benign conditions (degenerative, traumatic, or inflammatory), resulting in false positives and equivocal findings that complicate interpretation.[ 2 , 11 , 21 , 22 ] SPECT improves lesion detection by 20%–50% compared with planar imaging and provides higher sensitivity and specificity. Hybrid SPECT/CT approaches enable precise correlation of functional and anatomical information, thereby reducing equivocal findings and enhancing lesion classification, especially in the spine and pelvis.[ 7 , 11 , 22 – 24 ] Nonetheless, low-dose CT within hybrid systems may be constrained by limited resolution for small osteolytic lesions, leaving a subset of lesions undetected.[ 25 , 26 ] Advances reconstruction algorithms (e.g., OSEM), CT-based attenuation and scatter correction, and system calibration now allow SPECT/CT to be evaluated quantitatively using the standardized uptake value (SUV), analogous to PET.[ 27 – 29 ] SUV represents the measured tracer concentration normalized to injected activity and anthropometric parameters (e.g., body weight, lean body mass [LBM], or body surface area [BSA]). The choice of normalization impacts variability, with some studies indicating greater robustness for BSA normalization. SUVmax is commonly applied, being reproducible and relatively independent of VOI size.[ 27 – 33 ] Reference studies on normal thoracolumbar vertebrae in breast cancer patients demonstrate consistent SUVmean and SUVmax values across reports, providing physiological anchors for lesion interpretation; degenerative changes generally present higher SUVs than normal vertebrae but remain lower than metastases.[ 10 ] This quantitative strategy not only facilitates differentiation between metastatic and degenerative lesions but also enhances diagnostic accuracy in clinical investigations. Several studies have reported that SUV_max values in patients with bone metastases are significantly higher compared to those observed in degenerative lesions, which are more common among elderly individuals with pre-existing degenerative disorders. 2,34 Hence, integrating SUV_max measurements into SPECT/CT evaluations provides highly valuable information for treatment planning and monitoring therapeutic response. Given the critical importance of accurate and early detection of bone metastases in breast cancer patients, this study aims to analyze the diagnostic utility of SUV_max in identifying skeletal metastases using SPECT. By adopting this quantitative approach, the study is expected to yield deeper insights that may strengthen diagnostic precision and improve treatment outcomes for breast cancer patients with bone metastasis. 20,34 Based on the background, the research problem is formulated as follows: how effective is the quantitative SUV_max derived from SPECT in detecting bone metastases among breast cancer patients? This study aims to evaluate the diagnostic performance of the quantitative Standard Uptake Value (SUV_max) obtained from SPECT in detecting bone metastases in breast cancer patients. Methods 1. Population and Sample The study population consisted of all breast cancer patients who underwent bone scintigraphy with SPECT/CT at the Department of Nuclear Medicine and Molecular Theranostics, Dr. Hasan Sadikin General Hospital, Bandung, between May 2024 and June 2025. The study sample included patients who met the inclusion and exclusion criteria and for whom quantitative SUV_max values could be obtained. Inclusion criteria comprised patients with histopathologically and/or clinically confirmed breast cancer, those who underwent SPECT/CT bone scans using 99m Tc-MDP, patients with imaging suitable for SUV_max quantification, and patients with complete medical records. Exclusion criteria were limited to patients with incomplete medical records. 2. Study Setting and Duration This research was conducted at the Department of Nuclear Medicine and Molecular Theranostics, Dr. Hasan Sadikin General Hospital, Bandung. Data were collected from May 2024 to June 2025. 3. Study Design The study applied a retrospective design. Data were retrieved from SPECT/CT scans performed with 99m Tc-MDP. Quantitative SUVmaxBW values were calculated for skeletal lesions with increased tracer uptake. Lesions were classified by nuclear medicine specialists. Metastatic lesions were defined as skeletal lesions demonstrating characteristics consistent with bone metastases based on tracer intensity, lesion morphology, clinical history, and distribution patterns. Degenerative lesions were identified as non-malignant skeletal changes, typically located in joints or vertebrae, showing symmetrical uptake and degenerative findings on CT. SUVmax values were compared between the metastatic and degenerative groups to evaluate diagnostic utility. 4. Data Analysis All data obtained in this study were analyzed using SPSS software version 30. The study population was divided into two groups: patients with bone metastases and those with non-metastatic/degenerative bone lesions, based on SPECT/CT interpretations performed by nuclear medicine specialists at Dr. Hasan Sadikin General Hospital. Quantitative SUVmax values were derived using Xeleris 5.0 software equipped with the Q.Metrix module. The first step of the analysis involved descriptive statistics of patient characteristics in both groups. The variables included sex, age, laterality of the primary breast cancer (left or right), treatment history (chemotherapy, hormonal therapy, or others), immunohistochemistry (IHC) subtypes such as Luminal A, Luminal B, HER2-enriched, and triple-negative, as well as Ki-67 expression. Categorical variables were presented as frequencies and percentages, whereas continuous variables such as age and SUVmax were expressed as mean ± standard deviation for normally distributed data or median with interquartile range (IQR) for non-normally distributed data. To examine whether there was a significant difference in SUVmax values between metastatic and degenerative groups, comparative analyses were performed. Before testing, data distribution was assessed using the Shapiro–Wilk test. If the data were normally distributed, an independent t-test was applied. For non-normal distributions, the non-parametric Mann–Whitney U test was used. A p-value of < 0.05 was considered statistically significant. To determine the optimal SUVmax cut-off value that differentiates metastatic from degenerative lesions, Receiver Operating Characteristic (ROC) curve analysis was conducted. In this analysis, SUVmax was defined as the test variable, while lesion status (metastatic = 1, degenerative = 0) served as the state variable. ROC analysis yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC). The optimal cut-off point was established using the Youden Index, which identifies the best trade-off between sensitivity and specificity. The AUC values were interpreted according to general criteria: AUC 0.9 indicates high accuracy. 5. Ethical Considerations This study was retrospective in design and used secondary data from bone SPECT/CT examinations and medical records of breast cancer patients who had undergone imaging at Dr. Hasan Sadikin General Hospital. Data were obtained from the hospital’s PACS (Picture Archiving and Communication System) and electronic medical records, collected during routine clinical services. No additional procedures or exposures were performed. All patient data were anonymized before analysis. Identifiable information such as names, medical record numbers, birth dates, and addresses was removed or coded. Only clinical and SUV_max data were included in the analysis. Data were stored on password-protected devices with restricted access to authorized investigators only. Given the absence of direct patient involvement and additional risk, this study qualified for a waiver of informed consent. Ethical clearance was sought and obtained from the Health Research Ethics Committee, Faculty of Medicine, Universitas Padjadjaran. The research complied with biomedical ethics principles: respect for persons (anonymization of data), beneficence and non-maleficence (no additional risk to patients), and justice (equal inclusion without discrimination). Results 1. Descriptive Statistics Table 1 Patient Characteristic Characteristic Non-metastatic (N = 25) Metastatic (N = 37) p value Age < 0.001 - Mean (SD) 61.24 ± 10.61 50.56 ± 10.03 - Range 44–78 31–78 Body Weight 0.634 - Mean (SD) 57.08 ± 11.47kg 58.541 ± 11.987 - Range 32–82 kg 37–92 kg Height 0.489 - Mean (SD) 155.72 ± 7.185 cm 154.40 ± 7.377 cm - Range 143–168 cm 140–172 cm Treatment History 0.571 – No treatment 0(0.0%) 1(2.7%) – Surgery only 7(28.0%) 7(18.9%) – Surgery + chemotherapy 16(64.0%) 23(62.2%) – Surgery + chemotherapy + radiotherapy 2(8.0%) 6(16.2%) IHC Results ER 0.724 – ER positive 21(84.0%) 29(78.3%) – ER negative 3(12.0%) 7(18.9%) – No result 1(4%.0) 1(2.8%) PR 0.955 – PR positive 17(68.0%) 24(64.9%) – PR negative 7(28.0%) 12(32.4%) – No result 1(4.0%) 1(2.7%) HER2 0.941 – HER2 positive 3(12.0%) 6(16.2%) – HER2 negative 21(84.0%) 30(81.0%) – No result 1(4.0%) 1(2.8%) Ki-67 Expression 0,032 – <20% 7(28.0%) 2(5.4%) – ≥20% 17(68.0%) 34(91.9%) – No result 1(4.0%) 1(2.7%) Table 1 summarizes the baseline clinical and pathological characteristics of breast cancer patients with and without bone metastases. A total of 62 patients were included, comprising 25 non-metastatic and 37 metastatic cases. There was a statistically significant age difference between groups (p < 0.001). Patients with metastatic disease were generally younger, with a mean age of 50.56 years (SD ± 10.03; range 31–78 years), compared to non-metastatic patients who had a mean age of 61.24 years (SD ± 10.61; range 44–78 years). Body weight and height were comparable between groups, with no significant differences. The mean body weight was 57.08 ± 11.47 kg in the non-metastatic group and 58.54 ± 11.99 kg in the metastatic group (p = 0.634). Mean height was 155.72 ± 7.19 cm versus 154.40 ± 7.38 cm, respectively (p = 0.489). Previous treatments did not differ significantly (p = 0.571). In the non-metastatic group, 28.0% underwent surgery only, 64.0% received surgery plus chemotherapy, and 8.0% received the combination of surgery, chemotherapy, and radiotherapy. Similar proportions were observed in the metastatic group: 18.9%, 62.2%, and 16.2%, respectively. Only one metastatic patient (2.7%) had no prior treatment. The expression of ER, PR, and HER2 did not differ significantly between groups. ER positivity was high in both groups (84.0% vs. 78.3%, p = 0.724). PR was positive in 68.0% of non-metastatic and 64.9% of metastatic patients (p = 0.955). HER2 positivity was detected in 12.0% of non-metastatic and 16.2% of metastatic patients (p = 0.941). Ki-67 expression. Notably, Ki-67 status showed a significant association with bone metastasis (p = 0.032). A high proliferation index (≥ 20%) was found in 91.9% of metastatic patients compared to 68.0% in non-metastatic patients, whereas low expression (< 20%) was more common in the non-metastatic group (28.0% vs. 5.4%). Table 3 Description of Lesion Status Status No. Min. Mean Median Std.dev. p value Degenerative 48 3.34 10.10 9.98 3.65 < 0.001 Metastasis 134 11.4 28.60 24.50 11.40 The table presents the descriptive and comparative statistics of SUV Max values between degenerative and metastatic bone lesions. Degenerative lesions (n = 48). The minimum SUV Max was 3.34, with a mean of 10.10 and a median of 9.98, indicating that the distribution of values is relatively symmetrical. The standard deviation of 3.65 reflects a narrower spread of values, suggesting that degenerative lesions tend to cluster around a lower SUV Max range. Metastatic lesions (n = 134). The minimum SUV Max was markedly higher (11.4) compared to degenerative lesions. The mean was 28.60 and the median was 24.50, demonstrating a right-skewed distribution with several lesions showing very high uptake values. The standard deviation of 11.40 indicates a much wider variability in SUV Max, consistent with the heterogeneous biological behavior of metastatic disease. Statistical significance that the comparison yielded a p-value < 0.001, confirming a statistically significant difference in SUV Max between degenerative and metastatic lesions. This finding validates the discriminative power of SUV Max in differentiating between benign degenerative changes and malignant bone involvement. The diagnostic performance of SUV max in differentiating metastatic from degenerative lesions is illustrated in Fig. 8, which presents the ROC curve. The area under the curve (AUC) was 0.96 (95% CI: 93.71–99.16), indicating excellent discriminative ability. At the threshold of 16.7, SUV max achieved a sensitivity of 92.48% and a specificity of 94.55%, confirming its robustness as a diagnostic parameter in distinguishing metastatic lesions from degenerative changes. Discussions This study demonstrated that bone SPECT/CT SUVmax values were significantly higher in metastatic lesions compared with non-metastatic lesions in breast cancer patients, with mean values of 28.60 ± 11.40 g/mL versus 10.10 ± 3.65 g/mL (p < 0.001). Diagnostic performance was excellent, with an AUC of 0.964 at a cutoff of 16.7 g/mL, yielding a sensitivity of 92.48%, specificity of 94.55%, PPV of 99%, NPV of 82%, and overall accuracy of 94%. These findings highlight the added value of semi-quantitative analysis as a complement to qualitative interpretation in SPECT/CT, particularly in cases complicated by artifacts, degenerative changes, or the limited resolution of low-dose CT morphology. This study demonstrated that quantitative assessment of SUVmax derived from SPECT/CT significantly improves diagnostic accuracy in distinguishing metastatic from degenerative bone lesions in breast cancer patients. Consistent with prior findings[ 35 , 36 ], metastatic lesions exhibited markedly higher SUVmax values compared to degenerative lesions, reflecting increased osteoblastic activity and tumor-driven metabolic turnover. In this study, degenerative lesions clustered around lower SUVmax values (< 15), while metastatic lesions demonstrated a broader range, often exceeding 20 and in some cases surpassing 50. The difference between the two groups was statistically significant (p < 0.001), reinforcing the robustness of SUVmax as a quantitative biomarker. Importantly, ROC analysis confirmed the diagnostic power of SUVmax, yielding an AUC of 0.964, which denotes excellent discriminative ability. A threshold value of 16.7 provided high sensitivity (92%), specificity (98%), and overall diagnostic accuracy (94%), surpassing conventional planar bone scintigraphy that typically suffers from lower specificity.[ 25 , 26 ] The extremely high positive predictive value (99%) underscores the reliability of SUVmax in confirming metastatic disease. Our results align closely with the prospective cohort reported by Gherghe et al.[ 1 ] involving 70 patients (415 lesions) with ⁹⁹ᵐTc-HDP, which showed an AUC of 0.974 and an SUVmax cutoff of 16.6 g/mL with 91.5% sensitivity and 93.3% specificity in distinguishing metastatic from degenerative lesions.[ 7 , 13 , 14 ] In another breast cancer cohort with sclerotic lesions (⁹⁹ᵐTc-MDP), Saminathan et al.[ 2 ] similarly, observed higher SUVmax was observed in metastatic lesions (20.66 ± 14.36) compared with benign lesions (10.18 ± 12.79, p < 0.001), with a cutoff of 10.8 (AUC 0.870; sensitivity 82.6%; specificity 84.2%). Importantly, mean CT attenuation < 240.86 HU further enhanced performance (AUC 0.933; sensitivity 98.7%; specificity 88.1%).[ 15 , 17 , 37 ] In high-risk breast cancer populations, quantitative ⁹⁹ᵐTc-MDP analyses revealed parallel trends (33.04 ± 15.3 vs. 13.25 ± 5.46 g/mL), with a proposed cutoff of 22.75 g/mL and SUVmax emerging as a strong logistic regression predictor of metastasis.[ 10 , 18 , 19 ] Cross-entity support was also observed in prostate cancer cohorts, which, despite differing tumor biology, demonstrated an AUC of 0.874 and a cutoff ≥ 20 (sensitivity 73.8%; specificity 85.4%), underscoring the consistency of osteoblastic uptake elevation in metastatic versus degenerative lesions within the axial skeleton.[ 21 , 22 , 38 ] Similarly, in breast cancer patients imaged with ⁹⁹ᵐTc-MDP SPECT/CT (32 patients, 185 sclerotic lesions), metastatic lesions exhibited higher SUVmax (20.66 ± 14.36) than benign lesions (10.18 ± 12.79, p < 0.001), with an optimal cutoff of 10.8 (sensitivity 82.6%, specificity 84.2%). CT attenuation analysis yielded an HU cutoff of 240.86, with an AUC of 0.933, a sensitivity of 98.7%, and a specificity of 88.1%.[ 2 ] In high-risk breast cancer cohorts (⁹⁹ᵐTc-MDP SPECT/CT), metastatic lesions demonstrated SUVmax of 33.04 ± 15.3 compared with 13.25 ± 5.46 for degenerative lesions, with boxplot analyses suggesting a cutoff of 22.75. SUVmax emerged as a significant predictor of metastasis in logistic regression, while complementary studies reported strong correlations between ¹⁸F-NaF PET SUV and ⁹⁹ᵐTc-HDP SPECT SUV (R² ≥ 0.80).[ 4 ] In prostate cancer and spinal metastases, SUVmax values were likewise higher for metastatic lesions (36.64 ± 24.84) compared to degenerative joint disease (12.59 ± 9.01), with an AUC of 0.874 and a cutoff ≥ 20 yielding 73.8% sensitivity and 85.4% specificity, underscoring the consistency of osteoblastic uptake differences across axial skeletal cohorts.[ 11 ] The excellent diagnostic performance (AUC 0.964) and particularly high PPV (99%) at a cutoff of 16.7 g/mL carry direct clinical implications. When SUVmax ≥ 16.7 g/mL, the probability of metastasis is extremely high, thereby enhancing clinical confidence, expediting systemic treatment decisions, and reducing the proportion of equivocal findings.[ 3 , 23 , 39 ] This is especially relevant in settings where PET/CT availability is limited, as quantitative SPECT/CT offers a more affordable yet diagnostically competitive alternative for detecting bone metastases.[ 24 , 26 ] Moreover, integration of functional parameters (SUVmax) with morphological indices (mean HU), as demonstrated by Saminathan et al.[ 2 ], may further refine lesion discrimination and reduce the subjectivity of purely qualitative readings.[ 15 , 26 , 37 ] Although mean differences were clear, we observed a gray zone between ~ 10–20 g/mL, consistent with prior reports of SUVmax overlap (10.90–27.00) in some lesions.[ 27 – 29 ] In this range, interpretation should incorporate clinical context, CT morphology, and lesion distribution patterns. A multimodal approach combining SUVmax, mean HU, typical metastatic sites (vertebral column/pelvis), and clinical history may improve classification accuracy.[ 15 , 30 , 37 ] The variability in cutoff values across studies (≈ 10.8–22.75 g/mL) can be explained by population heterogeneity, differences in acquisition/reconstruction protocols, tracer type (MDP vs. HDP), and normalization strategy (body weight vs. LBM/BSA).[ 31 – 33 ] Partial volume effects (PVE) in small lesions typically underestimate SUV; phantom-based corrections reduce absolute SUV and cutoff values (e.g., to ~ 10) but preserve relative performance, making this particularly important for small lesions and pre-therapeutic dosimetry.[ 40 – 42 ] Large-scale normative data further suggest that BSA-based normalization (SUVbsa) is more robust to anthropometric variability than body-weight-based SUV (SUVbw), supporting its consideration for inter-center harmonization.[ 33 , 43 , 44 ] Additionally, the limited resolution of low-dose CT in hybrid systems for detecting small osteolytic lesions remains a potential source of under-calling, necessitating correlation with clinical data or complementary imaging when discordance is suspected.[ 6 , 45 ] From a clinicopathological perspective, our cohort also revealed that younger age was more frequent among metastatic patients, and high Ki-67 expression (≥ 20%) was significantly associated with bone metastasis. This is consistent with evidence linking Ki-67 to higher metastatic risk and poorer prognosis.[ 46 – 48 ] Our findings corroborate those of Afkari et al.[ 7 ], who identified Ki-67 as the strongest molecular marker for predicting bone metastasis in breast cancer, whereas ER/PR/HER2 status was not significantly correlated.[ 49 , 50 ] Such findings may aid in risk stratification and imaging interpretation in selected patients.[ 7 , 51 , 52 ] The growing body of evidence supports integrating biological and clinical data with quantitative SPECT/CT metrics (SUVmax, HU) to improve the accuracy of clinical decision-making. 7,38,48,49 The strengths of this study include an adequate lesion sample size (n = 182) and the application of standardized quantification protocols on a calibrated system, resulting in an AUC comparable to or exceeding prior reports.[ 2 , 3 , 7 ] However, the retrospective single-center design may limit generalizability, and we did not apply routine PVE correction at the lesion level, which may have led to underestimation in small lesions and slight shifts in absolute cutoff values.[ 40 , 41 , 55 ] Additionally, low-dose CT may miss a subset of small osteolytic lesions, and biological variability (treatment status, lesion heterogeneity) may influence uptake, underscoring the need for holistic interpretation.[ 6 , 56 , 57 ] Based on these data, an SUVmax threshold of ≥ 16.7 g/mL may serve as an operational reference point to improve diagnostic confidence for bone metastasis in breast cancer. Nevertheless, lesions within the 10–20 g/mL range warrant multimodal assessment, including HU, lesion distribution, and clinicopathological correlates such as Ki-67.[ 3 , 15 , 23 ]. Future efforts should focus on protocol harmonization (acquisition, reconstruction parameters such as iteration/subset numbers), adoption of more robust normalization strategies, and multicenter cross-platform validation to reduce variability and establish generalizable cutoffs.[ 33 , 57 , 58 ] Prospective studies integrating functional metrics (SUVmax), morphology (HU), PVE correction, and biological markers are expected to further reduce equivocal findings, improve inter-reader and inter-center consistency, and optimize therapeutic decision-making and response monitoring.[ 15 , 25 , 41 ] ,50 This research reinforces the evidence that SUVmax derived from SPECT/CT is a powerful quantitative parameter for differentiating metastatic from non-metastatic bone lesions in breast cancer. It provides excellent diagnostic performance and high practical utility, particularly in resource-limited settings and in scenarios where alternative modalities are either unavailable or less sensitive for bone involvement.[ 3 , 24 , 59 ] ,51 Beyond imaging, this study also revealed significant associations between patient characteristics and bone metastasis. Younger age and elevated Ki-67 expression were strongly correlated with the metastatic group, in line with previous evidence linking tumor aggressiveness and proliferative activity with skeletal involvement.[ 60 ] By contrast, classical markers such as ER, PR, and HER2 status showed no significant differences, suggesting that metabolic imaging provides independent and complementary diagnostic information. Despite its excellent performance, some overlap was noted between degenerative and metastatic SUVmax values within the 10–20 range. This gray zone highlights the continued importance of multimodal assessment, including clinical evaluation, histopathology, and additional imaging, to avoid misclassification. Future multicenter studies with larger cohorts are warranted to validate the cut-off values and assess inter-system reproducibility across different SPECT/CT platforms. The molecular markers ER, PR, HER2, and Ki-67 are routinely assessed in breast cancer patients, both at the time of screening and diagnosis. For years, these markers have served as critical indicators of tumor proliferation, prognosis, and therapeutic decision-making. The present study specifically sought to determine which of these markers exerts the greatest influence on the risk of developing bone metastases in untreated breast cancer patients, as evaluated using bone scintigraphy at the time of diagnosis.[ 7 ] Bone scintigraphy remains one of the most frequently employed diagnostic modalities in routine clinical practice, with technetium-99m-labeled methylene diphosphonate ( 99m Tc-MDP) serving as the tracer of choice. This radiotracer exhibits a high affinity for calcium-rich tissues and binds to the mineral phase of bone hydroxyapatite. Owing to its cost-effectiveness and wide availability, bone scintigraphy has long been regarded as a reference standard for detecting metastatic bone disease. Bone metastases, most often affecting the axial skeleton and pelvis, are particularly prevalent among patients with primary tumors of the prostate, breast, lung, kidney, or thyroid. When integrated with single-photon emission computed tomography/computed tomography (SPECT/CT), scintigraphy achieves improved diagnostic sensitivity by directly correlating functional uptake patterns with anatomical structures through fused imaging and attenuation-corrected reconstructions.[ 29 ] Conclusion In conclusion, SUVmax quantification from SPECT/CT significantly enhances the ability to differentiate metastatic from degenerative bone lesions in breast cancer patients. The optimal threshold of 16.7 demonstrated excellent diagnostic accuracy, with sensitivity, specificity, and predictive values surpassing conventional imaging approaches. Moreover, the association between younger age, high Ki-67 expression, and metastatic disease underscores the potential of integrating metabolic imaging with molecular pathology for improved risk stratification. These findings suggest that SUVmax may play a pivotal role in advancing personalized diagnostic strategies, particularly in healthcare systems where PET/CT availability is limited. Ultimately, quantitative SPECT/CT emerges as a promising and practical tool in the multidisciplinary management of breast cancer with suspected bone metastases, bridging the gap between advanced molecular imaging and real-world clinical needs. The findings of this study provide several important clinical implications: 1. Improved Diagnostic Precision. Quantitative SUVmax analysis minimizes inter-observer variability and strengthens diagnostic confidence in daily nuclear medicine practice. 2. Guidance for Treatment Decisions. A reliable SUVmax threshold (≥ 16.7) allows oncologists to distinguish metastatic from degenerative lesions more accurately, ensuring timely initiation of systemic therapy and avoiding overtreatment in benign cases. 3. Integration with Tumor Biology. Combining SUVmax with Ki-67 and other molecular markers offers a pathway toward risk-based stratification and personalized treatment planning. 4. Accessibility and Cost-Effectiveness. In resource-limited settings such as Indonesia, quantitative SPECT/CT provides a cost-efficient alternative to PET/CT, with comparable diagnostic value for bone metastasis detection. 5. Future Personalized Oncology. SUVmax may serve not only as a diagnostic marker but also as part of predictive models for disease progression and therapeutic response monitoring. Declarations Acknowledgement This research was supported by the I would like to thank Indonesia Endowment Fund for Education (LPDP) from the Ministry of Finance, Republic of Indonesia, for granting the scholarship number: LOG-20759/LPDP.3/2024. The authors would like to express their deepest gratitude to the translator and academic advisors for their invaluable contributions to this work. Their constructive feedback, insightful suggestions, and meticulous attention to detail have greatly enhanced the clarity, coherence, and overall quality of the manuscript. The refinement of this study owes much to their expertise and dedication, which ensured its alignment with scientific standards and academic rigor. The authors sincerely appreciate their guidance and support, which have been instrumental in bringing this research to completion. References Gherghe M, Mutuleanu MD, Stanciu AE, Irimescu I, Lazar A, Bacinschi X, et al. Quantitative Analysis of SPECT-CT Data in Metastatic Breast Cancer Patients—The Clinical Significance. Cancers (Basel). 2022;14:273. Saminathan ST, Ahmed WAW, Nawi NM, Tagiling N, Aziz I, Udin Y, et al. Correlation between the maximum standard uptake value and mean Hounsfield unit on single-photon emission computed tomography-computed tomography to discriminate benign and metastatic lesions among patients with breast cancer. 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Kaneta T, Ogawa M, Daisaki H, Nawata S, Yoshida K, Inoue T. SUV measurement of normal vertebrae using SPECT/CT with Tc-99m methylene diphosphonate. Am J Nucl Med Mol Imaging. 2016;6:262. Liu G, Hu Y, Zhao Y, Yu H, Hu P, Shi H. Variations of the liver standardized uptake value in relation to background blood metabolism: An 2-[: 18: F] Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography study in a large population from China. Med (Baltim). 2018;97:e0699. Jochimsen TH, Schulz J, Busse H, Werner P, Schaudinn A, Zeisig V, et al. Lean body mass correction of standardized uptake value in simultaneous whole-body positron emission tomography and magnetic resonance imaging. Phys Med Biol. 2015;60:4651. Sarikaya I, Albatineh A, Sarikaya A. Revisiting weight-normalized SUV and lean-body-mass–normalized SUV in PET studies. J Nucl Med Technol. 2020;48:163–7. Acayan E, Aquino C, Bandong I. Diagnostic accuracy of bone scintigraphy, positron emission tomography/computed tomography (PET/CT) and whole body magnetic resonance imaging (MRI) in the detection of bone metastasis among patients with breast cancer using histopathologic report and/or c. Eur Congr Radiol. 2017. 2017. Gherghe M, Mutuleanu MD, Stanciu AE, Irimescu I, Lazar A, Bacinschi X et al. Quantitative Analysis of SPECT-CT Data in Metastatic Breast Cancer Patients—The Clinical Significance. Cancers (Basel). 2022;14. Even-Sapir E, Metser U, Mishani E, Lievshitz G, Lerman H, Leibovitch I. The detection of bone metastases in patients with high-risk prostate cancer: 99mTc-MDP Planar bone scintigraphy, single-and multi-field-of-view SPECT, 18F-fluoride PET, and 18F-fluoride PET/CT. J Nucl Med. 2006;47:287–97. Macedo F, Ladeira K, Pinho F, Saraiva N, Bonito N, Pinto L, et al. Bone metastases: an overview. Oncol Rev. 2017;11:321. Rohani MFM, Nawi NM, Shamim SE, Sohaimi WFW, Zainon WMNW, Musarudin M, et al. Maximum standardized uptake value from quantitative bone single-photon emission computed tomography/computed tomography in differentiating metastatic and degenerative joint disease of the spine in prostate cancer patients. Ann Nucl Med. 2020;34:39–48. Almuqbil N, Mansour S, Alnuwaiser S. Quantitative analysis of 99mTc-MDP SPECT-CT data in diagnosing bone metastases in breast cancer. J Nucl Med. Saminathan ST, Ahmed WAW, Nawi NM. Discrimination of benign and metastatic lesions using SPECT-CT SUVmax. Asian Spine J 18:99–107. Beck M, Sanders J, Ritt P, Reinfelder J, Kuwert T. Longitudinal analysis of bone metabolism using SPECT/CT and 99mTc-diphosphono-propanedicarboxylic acid: comparison of visual and quantitative analysis. EJNMMI Res. 2016;6:60. Bettinardi V, Castiglioni I, De Bernardi E, Gilardi M. PET quantification: strategies for partial volume correction. Clin Transl Imaging. 2014;2:199–218. Nyathi M, Sithole M. Quantification of partial volume effects in single photon emission computed tomography. Int J Electr Comput Sci IJECS-IJENS. 2016;16:1–9. Muzahir S, Jeraj R, Liu G, Hall LT, Rio AM, Del, Perk T, et al. Differentiation of metastatic vs degenerative joint disease using semi-quantitative analysis with 18F-NaF PET/CT in castrate resistant prostate cancer patients. Am J Nucl Med Mol Imaging. 2015;5:162. Kuji I, Yamane T, Seto A, Yasumizu Y, Shirotake S, Oyama M. Skeletal standardized uptake values obtained by quantitative SPECT/CT as an osteoblastic biomarker for the discrimination of active bone metastasis in prostate cancer. Eur J Hybrid Imaging. 2017;1:2. Win A, Aparici C. Normal SUV values measured from NaF18-PET/CT bone scan studies. PLoS ONE. 2014;9:e108429. Win A, Aparici C. Factors affecting uptake of NaF-18 by the normal skeleton. J Clin Med Res. 2014;6:435. Voinea S, Sandru A, Gherghe M. Pitfalls in cutaneous melanoma lymphatic drainage. Chirurgia (Bucur). 2016;111:87–9. Amalo RB, Tuti Y, Dewi AR. Diagnostic Challenges in a Case of Suspected Breast Cancer with Low FDG Uptake and an Incidental Thyroid Lesion: A Case Report and Literature Review. World J Nucl Med. 2025;1–4. Gradishar WJ, Moran MS, Abraham J, Abramson V, Aft R, Agnese D, et al. Breast Cancer, Version 3.2024. JNCCN J Natl Compr Cancer Netw. 2024;22:331–57. Inari H, Suganuma N, Kawachi K, Yoshida T, Yamanaka T, Nakamura Y, et al. Clinicopathological and prognostic significance of Ki-67 immunohistochemical expression of distant metastatic lesions in patients with metastatic breast cancer. Breast Cancer. 2017;24:748–55. Harries M, Taylor A, Holmberg L, Agbaje O, Garmo H, Kabilan S, et al. Incidence of bone metastases and survival after a diagnosis of bone metastases in breast cancer patients. Cancer Epidemiol. 2014;38:427–34. Nishimura R, Osako T, Okumura Y, Hayashi M, Toyozumi Y, Arima N. Ki-67 as a prognostic marker according to breast cancer subtype and a predictor of recurrence time in primary breast cancer. Exp Ther Med. 2010;1:747–54. Nishimura R, Osako T, Nishiyama Y, Tashima R, Nakano M, Fujisue M, et al. Prognostic significance of Ki-67 index value at the primary breast tumor in recurrent breast cancer. Mol Clin Oncol. 2014;2:1062–8. Lee Mil, Jung YJ, Kim D, Il, Lee S, Jung CS, Kang SK, et al. Prognostic value of SUV. Med (Baltim). 2021;100:1–7. Dong A, Wang Y, Lu J, Zuo C. Spectrum of the breast lesions with increased 18F-FDG uptake on PET/CT. Clin Nucl Med. 2016;41:543–57. Murakami R, Fukushima Y, Tani H, Iwata K, Kumita S, Nakai M, et al. Prognostic Value of SUVmax of 18F-FDG PET/CT in Early Stage Breast Cancer with No LN Metastasis. Open J Med Imaging. 2017;7:112–23. Öberg K, Reubi J, Kwekkeboom D, Krenning E. Role of somatostatins in gastroenteropancreatic neuroendocrine tumor development and therapy. Gastroenterology. 2010;139:742–53. Johnbeck C, Knigge U, Kjær A. PET tracers for somatostatin receptor imaging of neuroendocrine tumors: current status and review of the literature. Futur Oncol. 2014;10:2259–77. Zeng M, Zhou J, Wen L, Zhu Y, Luo Y, Wang W. The relationship between the expression of Ki-67 and the prognosis of osteosarcoma. BMC Cancer. 2021;21:210. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":1283202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe following is an example of VOI (Volume of Interest) taking in a 47-year-old breast cancer patient with metastatic images in several vertebrae\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7636568/v1/aec13080bb0cf22bf0182475.png"},{"id":92584406,"identity":"3b3f60cd-6a28-4379-a6e6-477981937b7b","added_by":"auto","created_at":"2025-10-01 10:11:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7636568/v1/2c0dc8ef37032ba11c8fd2ec.png"},{"id":93447299,"identity":"291353f9-e330-4edb-8899-86e1283f2fb6","added_by":"auto","created_at":"2025-10-14 02:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2017933,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7636568/v1/d4dcb2db-3d1f-4d71-bf09-2bffc871e568.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDifferences in Suvmax Spect/ct Bone Scan Between Bone Metastatic Lesions and Non-metastatic Lesions in Breast Cancer Patients\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer stands as the most prevalent type of cancer worldwide. According to the GLOBOCAN 2020 report, more than 2.2\u0026nbsp;million new breast cancer cases were recorded globally, making it the leading cause of cancer-related mortality among women.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Breast cancer remains the most prevalent malignancy among women worldwide, accounting for approximately 25.1% of all cancers. Globally, more than 1.6\u0026ndash;2.2\u0026nbsp;million new cases are reported annually, representing nearly one-quarter of the overall cancer burden in women, with rising incidence across Asia; national statistics further indicate a substantial burden in Indonesia and Malaysia.[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] In Indonesia, breast cancer ranks first in prevalence, with an estimated incidence of 42.1 cases per 100,000 women and a mortality rate of 17 per 100,000 population.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Projections suggest that by 2040, the incidence will rise by more than 50%, reaching approximately 90,000 new cases annually if current epidemiological trends persist.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] These figures highlight the urgent need to strengthen early detection and improve treatment strategies, particularly in advanced stages where bone metastases are frequently observed.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eBone metastasis represents one of the most common complications in advanced breast cancer. Approximately 65\u0026ndash;75% of metastatic breast cancer patients develop skeletal involvement, most often affecting the vertebrae, femur, and pelvis. Such metastases not only impair patients\u0026rsquo; quality of life but also worsen long-term prognosis.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Consequently, early identification of bone metastasis is pivotal for tailoring appropriate therapeutic strategies for breast cancer patients. Within the disease trajectory, bone is the most frequent site of metastasis. Various studies estimate that 30%-85% of patients will develop bone metastases, and skeletal involvement is commonly observed in the majority of patients who succumb to breast cancer, with a median survival of about 24 months in the metastatic setting.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] The axial skeleton, particularly the thoracic and lumbar vertebrae, pelvis, ribs, and sternum, is most frequently affected, with occasional extension to the femur and skull. Clinical complications include pain, pathological fractures, spinal cord compression, and hypercalcemia, making early detection and accurate characterization crucial for staging, treatment selection, and monitoring therapeutic response.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSingle-photon emission computed tomography (SPECT) has long been recognized as a primary imaging modality for detecting skeletal lesions. Compared to conventional planar imaging, SPECT offers superior accuracy in both visualization and localization of metastatic sites.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] With its ability to generate three-dimensional images and provide enhanced anatomical precision, SPECT/CT has become the preferred approach for diagnosing bone metastases in breast cancer patients. Nevertheless, interpretation of SPECT images often remains subjective, particularly when differentiating between metastatic lesions and degenerative bone changes. To address this limitation, quantitative approaches using the maximum Standardized Uptake Value (SUVmax) have been increasingly applied to improve diagnostic accuracy. SUVmax reflects the degree of radiopharmaceutical uptake within lesions, serving as a more objective indicator of metabolic activity. Studies demonstrate that higher SUVmax values are frequently associated with metastatic lesions, whereas degenerative changes tend to exhibit lower uptake.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWhole-body bone scintigraphy with ⁹⁹ᵐTc-phosphonates (e.g., ⁹⁹ᵐTc-MDP/⁹⁹ᵐTc-HDP) has long been recognized as a sensitive, widely available, and cost-effective modality for screening skeletal involvement in breast cancer. However, its specificity is limited, as tracer uptake may also increase in benign conditions (degenerative, traumatic, or inflammatory), resulting in false positives and equivocal findings that complicate interpretation.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] SPECT improves lesion detection by 20%\u0026ndash;50% compared with planar imaging and provides higher sensitivity and specificity. Hybrid SPECT/CT approaches enable precise correlation of functional and anatomical information, thereby reducing equivocal findings and enhancing lesion classification, especially in the spine and pelvis.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Nonetheless, low-dose CT within hybrid systems may be constrained by limited resolution for small osteolytic lesions, leaving a subset of lesions undetected.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAdvances reconstruction algorithms (e.g., OSEM), CT-based attenuation and scatter correction, and system calibration now allow SPECT/CT to be evaluated quantitatively using the standardized uptake value (SUV), analogous to PET.[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] SUV represents the measured tracer concentration normalized to injected activity and anthropometric parameters (e.g., body weight, lean body mass [LBM], or body surface area [BSA]). The choice of normalization impacts variability, with some studies indicating greater robustness for BSA normalization. SUVmax is commonly applied, being reproducible and relatively independent of VOI size.[\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31 CR32\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Reference studies on normal thoracolumbar vertebrae in breast cancer patients demonstrate consistent SUVmean and SUVmax values across reports, providing physiological anchors for lesion interpretation; degenerative changes generally present higher SUVs than normal vertebrae but remain lower than metastases.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThis quantitative strategy not only facilitates differentiation between metastatic and degenerative lesions but also enhances diagnostic accuracy in clinical investigations. Several studies have reported that SUV_max values in patients with bone metastases are significantly higher compared to those observed in degenerative lesions, which are more common among elderly individuals with pre-existing degenerative disorders.\u003csup\u003e2,34\u003c/sup\u003e Hence, integrating SUV_max measurements into SPECT/CT evaluations provides highly valuable information for treatment planning and monitoring therapeutic response.\u003c/p\u003e\u003cp\u003eGiven the critical importance of accurate and early detection of bone metastases in breast cancer patients, this study aims to analyze the diagnostic utility of SUV_max in identifying skeletal metastases using SPECT. By adopting this quantitative approach, the study is expected to yield deeper insights that may strengthen diagnostic precision and improve treatment outcomes for breast cancer patients with bone metastasis.\u003csup\u003e20,34\u003c/sup\u003e Based on the background, the research problem is formulated as follows: how effective is the quantitative SUV_max derived from SPECT in detecting bone metastases among breast cancer patients? This study aims to evaluate the diagnostic performance of the quantitative Standard Uptake Value (SUV_max) obtained from SPECT in detecting bone metastases in breast cancer patients.\u003c/p\u003e"},{"header":"Methods","content":"\n\u003ch3\u003e1. Population and Sample\u003c/h3\u003e\n\u003cp\u003eThe study population consisted of all breast cancer patients who underwent bone scintigraphy with SPECT/CT at the Department of Nuclear Medicine and Molecular Theranostics, Dr. Hasan Sadikin General Hospital, Bandung, between May 2024 and June 2025. The study sample included patients who met the inclusion and exclusion criteria and for whom quantitative SUV_max values could be obtained. Inclusion criteria comprised patients with histopathologically and/or clinically confirmed breast cancer, those who underwent SPECT/CT bone scans using \u003csup\u003e99m\u003c/sup\u003eTc-MDP, patients with imaging suitable for SUV_max quantification, and patients with complete medical records. Exclusion criteria were limited to patients with incomplete medical records.\u003c/p\u003e\n\u003ch3\u003e2. Study Setting and Duration\u003c/h3\u003e\n\u003cp\u003eThis research was conducted at the Department of Nuclear Medicine and Molecular Theranostics, Dr. Hasan Sadikin General Hospital, Bandung. Data were collected from May 2024 to June 2025.\u003c/p\u003e\n\u003ch3\u003e3. Study Design\u003c/h3\u003e\n\u003cp\u003eThe study applied a retrospective design. Data were retrieved from SPECT/CT scans performed with \u003csup\u003e99m\u003c/sup\u003eTc-MDP. Quantitative SUVmaxBW values were calculated for skeletal lesions with increased tracer uptake. Lesions were classified by nuclear medicine specialists. Metastatic lesions were defined as skeletal lesions demonstrating characteristics consistent with bone metastases based on tracer intensity, lesion morphology, clinical history, and distribution patterns. Degenerative lesions were identified as non-malignant skeletal changes, typically located in joints or vertebrae, showing symmetrical uptake and degenerative findings on CT. SUVmax values were compared between the metastatic and degenerative groups to evaluate diagnostic utility.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e4. Data Analysis\u003c/h3\u003e\n\u003cp\u003eAll data obtained in this study were analyzed using SPSS software version 30. The study population was divided into two groups: patients with bone metastases and those with non-metastatic/degenerative bone lesions, based on SPECT/CT interpretations performed by nuclear medicine specialists at Dr. Hasan Sadikin General Hospital. Quantitative SUVmax values were derived using Xeleris 5.0 software equipped with the Q.Metrix module. The first step of the analysis involved descriptive statistics of patient characteristics in both groups. The variables included sex, age, laterality of the primary breast cancer (left or right), treatment history (chemotherapy, hormonal therapy, or others), immunohistochemistry (IHC) subtypes such as Luminal A, Luminal B, HER2-enriched, and triple-negative, as well as Ki-67 expression. Categorical variables were presented as frequencies and percentages, whereas continuous variables such as age and SUVmax were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data or median with interquartile range (IQR) for non-normally distributed data.\u003c/p\u003e\u003cp\u003eTo examine whether there was a significant difference in SUVmax values between metastatic and degenerative groups, comparative analyses were performed. Before testing, data distribution was assessed using the Shapiro\u0026ndash;Wilk test. If the data were normally distributed, an independent t-test was applied. For non-normal distributions, the non-parametric Mann\u0026ndash;Whitney U test was used. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. To determine the optimal SUVmax cut-off value that differentiates metastatic from degenerative lesions, Receiver Operating Characteristic (ROC) curve analysis was conducted. In this analysis, SUVmax was defined as the test variable, while lesion status (metastatic\u0026thinsp;=\u0026thinsp;1, degenerative\u0026thinsp;=\u0026thinsp;0) served as the state variable. ROC analysis yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC). The optimal cut-off point was established using the Youden Index, which identifies the best trade-off between sensitivity and specificity. The AUC values were interpreted according to general criteria: AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7 indicates low diagnostic accuracy, 0.7\u0026ndash;0.9 indicates moderate accuracy, and \u0026gt;\u0026thinsp;0.9 indicates high accuracy.\u003c/p\u003e\n\u003ch3\u003e5. Ethical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study was retrospective in design and used secondary data from bone SPECT/CT examinations and medical records of breast cancer patients who had undergone imaging at Dr. Hasan Sadikin General Hospital. Data were obtained from the hospital\u0026rsquo;s PACS (Picture Archiving and Communication System) and electronic medical records, collected during routine clinical services. No additional procedures or exposures were performed. All patient data were anonymized before analysis. Identifiable information such as names, medical record numbers, birth dates, and addresses was removed or coded. Only clinical and SUV_max data were included in the analysis. Data were stored on password-protected devices with restricted access to authorized investigators only. Given the absence of direct patient involvement and additional risk, this study qualified for a waiver of informed consent. Ethical clearance was sought and obtained from the Health Research Ethics Committee, Faculty of Medicine, Universitas Padjadjaran. The research complied with biomedical ethics principles: respect for persons (anonymization of data), beneficence and non-maleficence (no additional risk to patients), and justice (equal inclusion without discrimination).\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Descriptive Statistics\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-metastatic (N\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMetastatic (N\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.24\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u0026ndash;78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u0026ndash;78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody Weight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.634\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.47kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.541\u0026thinsp;\u0026plusmn;\u0026thinsp;11.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u0026ndash;82 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37\u0026ndash;92 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.489\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.185 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.377 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e- Range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143\u0026ndash;168 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140\u0026ndash;172 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment History\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.571\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; No treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; Surgery only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; Surgery\u0026thinsp;+\u0026thinsp;chemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(64.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(62.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; Surgery\u0026thinsp;+\u0026thinsp;chemotherapy\u0026thinsp;+\u0026thinsp;radiotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIHC Results\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eER\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.724\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; ER positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21(84.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29(78.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; ER negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; No result\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(4%.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.955\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; PR positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(64.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; PR negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; No result\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHER2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.941\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; HER2 positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; HER2 negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21(84.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(81.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; No result\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKi-67 Expression\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0,032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; \u0026lt;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; \u0026ge;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34(91.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ndash; No result\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline clinical and pathological characteristics of breast cancer patients with and without bone metastases. A total of 62 patients were included, comprising 25 non-metastatic and 37 metastatic cases. There was a statistically significant age difference between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with metastatic disease were generally younger, with a mean age of 50.56 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03; range 31\u0026ndash;78 years), compared to non-metastatic patients who had a mean age of 61.24 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61; range 44\u0026ndash;78 years). Body weight and height were comparable between groups, with no significant differences. The mean body weight was 57.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.47 kg in the non-metastatic group and 58.54\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99 kg in the metastatic group (p\u0026thinsp;=\u0026thinsp;0.634). Mean height was 155.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19 cm versus 154.40\u0026thinsp;\u0026plusmn;\u0026thinsp;7.38 cm, respectively (p\u0026thinsp;=\u0026thinsp;0.489).\u003c/p\u003e\u003cp\u003ePrevious treatments did not differ significantly (p\u0026thinsp;=\u0026thinsp;0.571). In the non-metastatic group, 28.0% underwent surgery only, 64.0% received surgery plus chemotherapy, and 8.0% received the combination of surgery, chemotherapy, and radiotherapy. Similar proportions were observed in the metastatic group: 18.9%, 62.2%, and 16.2%, respectively. Only one metastatic patient (2.7%) had no prior treatment. The expression of ER, PR, and HER2 did not differ significantly between groups. ER positivity was high in both groups (84.0% vs. 78.3%, p\u0026thinsp;=\u0026thinsp;0.724). PR was positive in 68.0% of non-metastatic and 64.9% of metastatic patients (p\u0026thinsp;=\u0026thinsp;0.955). HER2 positivity was detected in 12.0% of non-metastatic and 16.2% of metastatic patients (p\u0026thinsp;=\u0026thinsp;0.941). Ki-67 expression. Notably, Ki-67 status showed a significant association with bone metastasis (p\u0026thinsp;=\u0026thinsp;0.032). A high proliferation index (\u0026ge;\u0026thinsp;20%) was found in 91.9% of metastatic patients compared to 68.0% in non-metastatic patients, whereas low expression (\u0026lt;\u0026thinsp;20%) was more common in the non-metastatic group (28.0% vs. 5.4%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of Lesion Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd.dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDegenerative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe table presents the descriptive and comparative statistics of SUV Max values between degenerative and metastatic bone lesions. Degenerative lesions (n\u0026thinsp;=\u0026thinsp;48). The minimum SUV Max was 3.34, with a mean of 10.10 and a median of 9.98, indicating that the distribution of values is relatively symmetrical. The standard deviation of 3.65 reflects a narrower spread of values, suggesting that degenerative lesions tend to cluster around a lower SUV Max range. Metastatic lesions (n\u0026thinsp;=\u0026thinsp;134). The minimum SUV Max was markedly higher (11.4) compared to degenerative lesions. The mean was 28.60 and the median was 24.50, demonstrating a right-skewed distribution with several lesions showing very high uptake values. The standard deviation of 11.40 indicates a much wider variability in SUV Max, consistent with the heterogeneous biological behavior of metastatic disease. Statistical significance that the comparison yielded a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, confirming a statistically significant difference in SUV Max between degenerative and metastatic lesions. This finding validates the discriminative power of SUV Max in differentiating between benign degenerative changes and malignant bone involvement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe diagnostic performance of SUV max in differentiating metastatic from degenerative lesions is illustrated in Fig.\u0026nbsp;8, which presents the ROC curve. The area under the curve (AUC) was 0.96 (95% CI: 93.71\u0026ndash;99.16), indicating excellent discriminative ability. At the threshold of 16.7, SUV max achieved a sensitivity of 92.48% and a specificity of 94.55%, confirming its robustness as a diagnostic parameter in distinguishing metastatic lesions from degenerative changes.\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eThis study demonstrated that bone SPECT/CT SUVmax values were significantly higher in metastatic lesions compared with non-metastatic lesions in breast cancer patients, with mean values of 28.60\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40 g/mL versus 10.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65 g/mL (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Diagnostic performance was excellent, with an AUC of 0.964 at a cutoff of 16.7 g/mL, yielding a sensitivity of 92.48%, specificity of 94.55%, PPV of 99%, NPV of 82%, and overall accuracy of 94%. These findings highlight the added value of semi-quantitative analysis as a complement to qualitative interpretation in SPECT/CT, particularly in cases complicated by artifacts, degenerative changes, or the limited resolution of low-dose CT morphology.\u003c/p\u003e\u003cp\u003eThis study demonstrated that quantitative assessment of SUVmax derived from SPECT/CT significantly improves diagnostic accuracy in distinguishing metastatic from degenerative bone lesions in breast cancer patients. Consistent with prior findings[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], metastatic lesions exhibited markedly higher SUVmax values compared to degenerative lesions, reflecting increased osteoblastic activity and tumor-driven metabolic turnover. In this study, degenerative lesions clustered around lower SUVmax values (\u0026lt;\u0026thinsp;15), while metastatic lesions demonstrated a broader range, often exceeding 20 and in some cases surpassing 50. The difference between the two groups was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing the robustness of SUVmax as a quantitative biomarker. Importantly, ROC analysis confirmed the diagnostic power of SUVmax, yielding an AUC of 0.964, which denotes excellent discriminative ability. A threshold value of 16.7 provided high sensitivity (92%), specificity (98%), and overall diagnostic accuracy (94%), surpassing conventional planar bone scintigraphy that typically suffers from lower specificity.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] The extremely high positive predictive value (99%) underscores the reliability of SUVmax in confirming metastatic disease.\u003c/p\u003e\u003cp\u003eOur results align closely with the prospective cohort reported by Gherghe et al.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] involving 70 patients (415 lesions) with ⁹⁹ᵐTc-HDP, which showed an AUC of 0.974 and an SUVmax cutoff of 16.6 g/mL with 91.5% sensitivity and 93.3% specificity in distinguishing metastatic from degenerative lesions.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] In another breast cancer cohort with sclerotic lesions (⁹⁹ᵐTc-MDP), Saminathan et al.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] similarly, observed higher SUVmax was observed in metastatic lesions (20.66\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36) compared with benign lesions (10.18\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a cutoff of 10.8 (AUC 0.870; sensitivity 82.6%; specificity 84.2%). Importantly, mean CT attenuation\u0026thinsp;\u0026lt;\u0026thinsp;240.86 HU further enhanced performance (AUC 0.933; sensitivity 98.7%; specificity 88.1%).[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] In high-risk breast cancer populations, quantitative ⁹⁹ᵐTc-MDP analyses revealed parallel trends (33.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3 vs. 13.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46 g/mL), with a proposed cutoff of 22.75 g/mL and SUVmax emerging as a strong logistic regression predictor of metastasis.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Cross-entity support was also observed in prostate cancer cohorts, which, despite differing tumor biology, demonstrated an AUC of 0.874 and a cutoff\u0026thinsp;\u0026ge;\u0026thinsp;20 (sensitivity 73.8%; specificity 85.4%), underscoring the consistency of osteoblastic uptake elevation in metastatic versus degenerative lesions within the axial skeleton.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSimilarly, in breast cancer patients imaged with ⁹⁹ᵐTc-MDP SPECT/CT (32 patients, 185 sclerotic lesions), metastatic lesions exhibited higher SUVmax (20.66\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36) than benign lesions (10.18\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an optimal cutoff of 10.8 (sensitivity 82.6%, specificity 84.2%). CT attenuation analysis yielded an HU cutoff of 240.86, with an AUC of 0.933, a sensitivity of 98.7%, and a specificity of 88.1%.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In high-risk breast cancer cohorts (⁹⁹ᵐTc-MDP SPECT/CT), metastatic lesions demonstrated SUVmax of 33.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3 compared with 13.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46 for degenerative lesions, with boxplot analyses suggesting a cutoff of 22.75. SUVmax emerged as a significant predictor of metastasis in logistic regression, while complementary studies reported strong correlations between \u0026sup1;⁸F-NaF PET SUV and ⁹⁹ᵐTc-HDP SPECT SUV (R\u0026sup2; \u0026ge; 0.80).[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] In prostate cancer and spinal metastases, SUVmax values were likewise higher for metastatic lesions (36.64\u0026thinsp;\u0026plusmn;\u0026thinsp;24.84) compared to degenerative joint disease (12.59\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01), with an AUC of 0.874 and a cutoff\u0026thinsp;\u0026ge;\u0026thinsp;20 yielding 73.8% sensitivity and 85.4% specificity, underscoring the consistency of osteoblastic uptake differences across axial skeletal cohorts.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe excellent diagnostic performance (AUC 0.964) and particularly high PPV (99%) at a cutoff of 16.7 g/mL carry direct clinical implications. When SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;16.7 g/mL, the probability of metastasis is extremely high, thereby enhancing clinical confidence, expediting systemic treatment decisions, and reducing the proportion of equivocal findings.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] This is especially relevant in settings where PET/CT availability is limited, as quantitative SPECT/CT offers a more affordable yet diagnostically competitive alternative for detecting bone metastases.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Moreover, integration of functional parameters (SUVmax) with morphological indices (mean HU), as demonstrated by Saminathan et al.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], may further refine lesion discrimination and reduce the subjectivity of purely qualitative readings.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAlthough mean differences were clear, we observed a gray zone between ~\u0026thinsp;10\u0026ndash;20 g/mL, consistent with prior reports of SUVmax overlap (10.90\u0026ndash;27.00) in some lesions.[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] In this range, interpretation should incorporate clinical context, CT morphology, and lesion distribution patterns. A multimodal approach combining SUVmax, mean HU, typical metastatic sites (vertebral column/pelvis), and clinical history may improve classification accuracy.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe variability in cutoff values across studies (\u0026asymp;\u0026thinsp;10.8\u0026ndash;22.75 g/mL) can be explained by population heterogeneity, differences in acquisition/reconstruction protocols, tracer type (MDP vs. HDP), and normalization strategy (body weight vs. LBM/BSA).[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Partial volume effects (PVE) in small lesions typically underestimate SUV; phantom-based corrections reduce absolute SUV and cutoff values (e.g., to ~\u0026thinsp;10) but preserve relative performance, making this particularly important for small lesions and pre-therapeutic dosimetry.[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Large-scale normative data further suggest that BSA-based normalization (SUVbsa) is more robust to anthropometric variability than body-weight-based SUV (SUVbw), supporting its consideration for inter-center harmonization.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] Additionally, the limited resolution of low-dose CT in hybrid systems for detecting small osteolytic lesions remains a potential source of under-calling, necessitating correlation with clinical data or complementary imaging when discordance is suspected.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eFrom a clinicopathological perspective, our cohort also revealed that younger age was more frequent among metastatic patients, and high Ki-67 expression (\u0026ge;\u0026thinsp;20%) was significantly associated with bone metastasis. This is consistent with evidence linking Ki-67 to higher metastatic risk and poorer prognosis.[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Our findings corroborate those of Afkari et al.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], who identified Ki-67 as the strongest molecular marker for predicting bone metastasis in breast cancer, whereas ER/PR/HER2 status was not significantly correlated.[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] Such findings may aid in risk stratification and imaging interpretation in selected patients.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] The growing body of evidence supports integrating biological and clinical data with quantitative SPECT/CT metrics (SUVmax, HU) to improve the accuracy of clinical decision-making.\u003csup\u003e7,38,48,49\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe strengths of this study include an adequate lesion sample size (n\u0026thinsp;=\u0026thinsp;182) and the application of standardized quantification protocols on a calibrated system, resulting in an AUC comparable to or exceeding prior reports.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] However, the retrospective single-center design may limit generalizability, and we did not apply routine PVE correction at the lesion level, which may have led to underestimation in small lesions and slight shifts in absolute cutoff values.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] Additionally, low-dose CT may miss a subset of small osteolytic lesions, and biological variability (treatment status, lesion heterogeneity) may influence uptake, underscoring the need for holistic interpretation.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eBased on these data, an SUVmax threshold of \u0026ge;\u0026thinsp;16.7 g/mL may serve as an operational reference point to improve diagnostic confidence for bone metastasis in breast cancer. Nevertheless, lesions within the 10\u0026ndash;20 g/mL range warrant multimodal assessment, including HU, lesion distribution, and clinicopathological correlates such as Ki-67.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Future efforts should focus on protocol harmonization (acquisition, reconstruction parameters such as iteration/subset numbers), adoption of more robust normalization strategies, and multicenter cross-platform validation to reduce variability and establish generalizable cutoffs.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] Prospective studies integrating functional metrics (SUVmax), morphology (HU), PVE correction, and biological markers are expected to further reduce equivocal findings, improve inter-reader and inter-center consistency, and optimize therapeutic decision-making and response monitoring.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003csup\u003e,50\u003c/sup\u003e This research reinforces the evidence that SUVmax derived from SPECT/CT is a powerful quantitative parameter for differentiating metastatic from non-metastatic bone lesions in breast cancer. It provides excellent diagnostic performance and high practical utility, particularly in resource-limited settings and in scenarios where alternative modalities are either unavailable or less sensitive for bone involvement.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003csup\u003e,51\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eBeyond imaging, this study also revealed significant associations between patient characteristics and bone metastasis. Younger age and elevated Ki-67 expression were strongly correlated with the metastatic group, in line with previous evidence linking tumor aggressiveness and proliferative activity with skeletal involvement.[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] By contrast, classical markers such as ER, PR, and HER2 status showed no significant differences, suggesting that metabolic imaging provides independent and complementary diagnostic information. Despite its excellent performance, some overlap was noted between degenerative and metastatic SUVmax values within the 10\u0026ndash;20 range. This gray zone highlights the continued importance of multimodal assessment, including clinical evaluation, histopathology, and additional imaging, to avoid misclassification. Future multicenter studies with larger cohorts are warranted to validate the cut-off values and assess inter-system reproducibility across different SPECT/CT platforms.\u003c/p\u003e\u003cp\u003eThe molecular markers ER, PR, HER2, and Ki-67 are routinely assessed in breast cancer patients, both at the time of screening and diagnosis. For years, these markers have served as critical indicators of tumor proliferation, prognosis, and therapeutic decision-making. The present study specifically sought to determine which of these markers exerts the greatest influence on the risk of developing bone metastases in untreated breast cancer patients, as evaluated using bone scintigraphy at the time of diagnosis.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Bone scintigraphy remains one of the most frequently employed diagnostic modalities in routine clinical practice, with technetium-99m-labeled methylene diphosphonate (\u003csup\u003e99m\u003c/sup\u003eTc-MDP) serving as the tracer of choice. This radiotracer exhibits a high affinity for calcium-rich tissues and binds to the mineral phase of bone hydroxyapatite. Owing to its cost-effectiveness and wide availability, bone scintigraphy has long been regarded as a reference standard for detecting metastatic bone disease. Bone metastases, most often affecting the axial skeleton and pelvis, are particularly prevalent among patients with primary tumors of the prostate, breast, lung, kidney, or thyroid. When integrated with single-photon emission computed tomography/computed tomography (SPECT/CT), scintigraphy achieves improved diagnostic sensitivity by directly correlating functional uptake patterns with anatomical structures through fused imaging and attenuation-corrected reconstructions.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, SUVmax quantification from SPECT/CT significantly enhances the ability to differentiate metastatic from degenerative bone lesions in breast cancer patients. The optimal threshold of 16.7 demonstrated excellent diagnostic accuracy, with sensitivity, specificity, and predictive values surpassing conventional imaging approaches. Moreover, the association between younger age, high Ki-67 expression, and metastatic disease underscores the potential of integrating metabolic imaging with molecular pathology for improved risk stratification. These findings suggest that SUVmax may play a pivotal role in advancing personalized diagnostic strategies, particularly in healthcare systems where PET/CT availability is limited. Ultimately, quantitative SPECT/CT emerges as a promising and practical tool in the multidisciplinary management of breast cancer with suspected bone metastases, bridging the gap between advanced molecular imaging and real-world clinical needs.\u003c/p\u003e\u003cp\u003eThe findings of this study provide several important clinical implications: 1. Improved Diagnostic Precision. Quantitative SUVmax analysis minimizes inter-observer variability and strengthens diagnostic confidence in daily nuclear medicine practice. 2. Guidance for Treatment Decisions. A reliable SUVmax threshold (\u0026ge;\u0026thinsp;16.7) allows oncologists to distinguish metastatic from degenerative lesions more accurately, ensuring timely initiation of systemic therapy and avoiding overtreatment in benign cases. 3. Integration with Tumor Biology. Combining SUVmax with Ki-67 and other molecular markers offers a pathway toward risk-based stratification and personalized treatment planning. 4. Accessibility and Cost-Effectiveness. In resource-limited settings such as Indonesia, quantitative SPECT/CT provides a cost-efficient alternative to PET/CT, with comparable diagnostic value for bone metastasis detection. 5. Future Personalized Oncology. SUVmax may serve not only as a diagnostic marker but also as part of predictive models for disease progression and therapeutic response monitoring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported by the I would like to thank Indonesia Endowment Fund for Education (LPDP) from the Ministry of Finance, Republic of Indonesia, for granting the scholarship number: LOG-20759/LPDP.3/2024. The authors would like to express their deepest gratitude to the translator and academic advisors for their invaluable contributions to this work. Their constructive feedback, insightful suggestions, and meticulous attention to detail have greatly enhanced the clarity, coherence, and overall quality of the manuscript. The refinement of this study owes much to their expertise and dedication, which ensured its alignment with scientific standards and academic rigor. The authors sincerely appreciate their guidance and support, which have been instrumental in bringing this research to completion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGherghe M, Mutuleanu MD, Stanciu AE, Irimescu I, Lazar A, Bacinschi X, et al. Quantitative Analysis of SPECT-CT Data in Metastatic Breast Cancer Patients\u0026mdash;The Clinical Significance. Cancers (Basel). 2022;14:273.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaminathan ST, Ahmed WAW, Nawi NM, Tagiling N, Aziz I, Udin Y, et al. Correlation between the maximum standard uptake value and mean Hounsfield unit on single-photon emission computed tomography-computed tomography to discriminate benign and metastatic lesions among patients with breast cancer. ASJ Asian Spine J. 2024;18:398\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. 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The relationship between the expression of Ki-67 and the prognosis of osteosarcoma. BMC Cancer. 2021;21:210.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SUVmax, bone scan, SPECT/CT, breast cancer, bone metastases","lastPublishedDoi":"10.21203/rs.3.rs-7636568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7636568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBreast cancer is the most common cancer among women in Indonesia. Approximately 65\u0026ndash;75% breast cancer cases have spread to the bone. Early detection of bone metastases is crucial for determining patient management strategies. Bone metastatic lesions in breast cancer are typically osteolytic, but invasion into bone tissue will still stimulate an osteoblastic response. This osteoblastic activity can be visualized using \u003csup\u003e99m\u003c/sup\u003eTc-MDP SPECT/CT bone scan. Quantitative parameters, such as SUVmax, are used to strengthen a qualitative interpretation visually and have the potential to differentiate between metastatic and non-metastatic bone lesions objectively. This study aimed to evaluate the accuracy of SUVmax from SPECT/CT bone scan in differentiating bone metastatic lesions from non-metastatic lesions in breast cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective study was conducted on breast cancer patients who underwent bone scan and SPECT/CT at Dr. Hasan Sadikin General Hospital from May 2024 to June 2025. SUVmax was obtained by determining the volume of interest (VOI) based on fused SPECT/CT images. Statistical analysis used a Mann\u0026ndash;Whitney to compare SUVmax between groups and ROC curve analysis to assess threshold value, sensitivity, specificity, and accuracy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 182 SPECT/CT bone scan lesions from 62 subjects with breast cancer, consisting of 134 metastatic and 48 non-metastatic lesions, were analyzed in this study. The SUVmax in the metastatic group was (28.60\u0026thinsp;\u0026plusmn;\u0026thinsp;11.40) g/mL, significantly higher than in the non-metastatic group (10.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65) g/mL \u003cb\u003e(\u003c/b\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003cb\u003e)\u003c/b\u003e. ROC analysis showed an optimal SUVmax cutoff 16.7 g/mL, with an AUC of 0.964. Based on this cut-off, sensitivity, specificity, PPV, NPV, and accuracy were 92.48%, 94.55%, 99%, 82%, and 94% respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMetastatic lesions had significantly higher SUVmax than non-metastatic lesions. SUVmax can be used as a quantitative parameter to support the diagnosis of bone metastases in breast cancer patients.\u003c/p\u003e","manuscriptTitle":"Differences in Suvmax Spect/ct Bone Scan Between Bone Metastatic Lesions and Non-metastatic Lesions in Breast Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 10:11:10","doi":"10.21203/rs.3.rs-7636568/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce62e3e3-68f2-43ba-b858-14518c0b5305","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T02:01:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 10:11:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7636568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7636568","identity":"rs-7636568","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}