How Should SUV Thresholds Be Set to Exclude Physiological Bone Uptake of 68Ga-Pentixafor?

How Should SUV Thresholds Be Set to Exclude Physiological Bone Uptake of 68Ga-Pentixafor? | 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 How Should SUV Thresholds Be Set to Exclude Physiological Bone Uptake of 68 Ga-Pentixafor? Ranbie Tang, Zhanwen Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7008259/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: 68 Ga-Pentixafor PET/CT enables non-invasive imaging of C-X-C chemokine receptor type 4 expression and holds significant value in hematologic malignancies. However, physiological bone marrow uptake may interfere with lesion identification. This study aimed to evaluate the impact of various SUV thresholds on bone segmentation and to identify thresholds that exclude physiological uptake, thereby improving the accuracy of bone lesion delineation and detection. Method: This retrospective study included 15 patients with confirmed aldosterone-producing adenomas who underwent 68 Ga-Pentixafor PET/CT. TotalSegmentator was used for automated volumes of interest (VOI) delineation (bone, liver, spleen), recording SUVmean, SUVmax, and volume. Semi-automatic segmentation applied multiple thresholds: 40% bone SUVmax, 1×, 1.5× and 2× liver SUVmean and SUVmax, and spleen SUVmean. Acceptability criteria were post-segmentation volume <5% or <3% of the original VOI. Results: Spleen SUVmax consistently exceeded bone uptake. With <5% volume retention, thresholds using spleen SUVmean, 1.5× and 2× liver SUVmax achieved stable segmentation in all cases (15/15). 2× liver SUVmean (10/15) and 2× liver SUVmax (13/15) also performed well. Under stricter criteria (<3%), 2× liver SUVmean (9/15) and SUVmax (12/15) remained robust. In contrast, 40% bone SUVmax showed poor stability (7/15 or 3/15), while 1×/1.5× liver SUVmean led to over segmentation (0/15 and 1/15). Conclusion: For optimal 68 Ga-Pentixafor PET/CT segmentation, thresholds of 2× liver SUVmean or 1× liver SUVmax effectively minimize bone interference, providing robust performance suitable for subsequent image analysis and disease quantification studies. Nuclear Medicine & Medical Imaging 68Ga-Pentixafor physiological uptake Semi-automatic segmentation Figures Figure 1 Introduction 68 Ga-Pentixafor is a novel PET tracer that specifically targets the C-X-C chemokine receptor type 4 (CXCR4), a transmembrane G protein-coupled receptor that is widely overexpressed in various hematological malignancies and endocrine diseases 1 , 2 . In recent years, CXCR4-targeted imaging using 68 Ga-Pentixafor PET/CT has garnered increasing attention for its ability to non-invasively visualize CXCR4 expression in vivo, providing valuable information for tumor detection 3 – 5 . CXCR4 plays a pivotal role in tumor biology by mediating cell migration, invasion, and microenvironmental homing through its interaction with the chemokine ligand stromal cell-derived factor-1. This axis is not only crucial for hematopoietic cell trafficking and bone marrow niche maintenance under physiological conditions but is also hijacked by malignant cells to facilitate tumor progression and metastasis 6 – 8 . Bone marrow uptake is commonly observed in healthy individuals due to physiological expression of CXCR4 on hematopoietic cells. This physiological uptake is often diffuse and mild to moderate intensity, yet it may vary with patient age, hematopoietic activity, or recent chemotherapy 9 . In the context of malignancies such as lymphoma or multiple myeloma—which frequently involve bone and marrow—delineating true lesions from physiological background becomes critically important 10 . While experienced readers may distinguish pathological from physiological uptake based on distribution patterns and clinical context, such discrimination becomes more difficult in the setting of semi-automatic or quantitative image analysis. In particular, segmentation approaches that rely on SUV-based thresholds face the risk of including physiological bone marrow uptake, potentially compromising the accuracy of quantitative parameters such as metabolic tumor volume or total lesion uptake. To address this issue, our study aimed to investigate the effect of different SUV-based thresholding methods on bone marrow segmentation in 68 Ga-Pentixafor PET/CT, and to explore which thresholds can effectively exclude most physiological uptake. By identifying optimal segmentation criteria, we hope to facilitate a more accurate semi-automated analysis of 68 Ga-Pentixafor PET/CT in hematologic malignancies, especially for quantitative imaging biomarkers used in diagnosis, prognostication, or treatment response assessment. Materials and methods Patients This retrospective study included 15 patients who underwent 68 Ga-Pentixafor PET/CT at our institution between January 1, 2023, and December 31, 2023, and subsequently confirmed to have aldosterone-producing tumors via postoperative histopathology. These patients were categorized as the healthy control group for analysis purposes. The inclusion criteria were as follows: (1) age > 18 years; (2) No obvious signs of infection or anemia; (3) no prior history of malignancy; and (4) at least one year of post-imaging follow-up without any evidence of malignancy. The exclusion criteria were: (1) coexisting autoimmune diseases; (2) impaired hepatic or renal function; (3) history of major illness or significant trauma within the past three months; and (4) incomplete clinical records. This study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (Ethics Approval Number: KY2025316). Preparation and imaging of 68 Ga-Pentixafor The preparation process for 68 Ga-Pentixafor followed the methods reported in the literature 11 . No special preparation was required from the patient prior to imaging. The radiotracer was administered intravenously at a body weight–adjusted dose of 0.05 mCi/kg, followed by PET/CT scanning 40–60 minutes post-injection. Imaging was performed using a uMI 780 scanner (United Imaging Healthcare, Shanghai, China), covering the region from the vertex to the upper thighs. Other scanning parameters were set based on established protocols reported in the literature 11 . Setting volume of interest The TotalSegmentator tool (V2.4.0, https://github.com/wasserth/TotalSegmentator ) was used to automatically delineate volumes of interest (VOI) for the skeleton, liver, and spleen. For each VOI, the SUVmean, SUVmax, and volume were recorded. To minimize interference from adrenal lesions or adjacent kidneys on SUVmax, we progressively contracted the VOI boundaries inward by 1 mm increments (up to a maximum of 5 mm) until SUVmax values stabilized, at which point the final value was recorded. The same approach was applied to VOIs adjacent to the bladder and pubic bone. Based on the skeletal VOI, semi-automatic segmentation was further performed using various thresholding criteria, and the resulting volumes were recorded. The thresholds used included: (1) 40% of SUVmax (a commonly used standard in PET/CT studies of solid tumors); (2) 1×, 1.5×, and 2× the liver SUVmean; (3) 1×, 1.5×, and 2× the liver SUVmax; (4) spleen SUVmean. Results This study included 15 patients with aldosterone-producing tumors (11 females and 4 males), with a mean age of 46 ± 12 years. The relevant parameters of each VOI under different segmentation thresholds are summarized in Table 1. Notably, in all patients, the spleen SUVmax exceeded that of the skeleton. Figure 1 illustrates the segmentation results obtained using different thresholding strategies. Using a post-segmentation volume of less than 5% of the original VOI as the acceptability criterion, thresholds based on spleen SUVmean (15/15) and 1.5× (15/15) and 2× (15/15) liver SUVmax, yielded stable and acceptable segmentation outcomes across all patients. Thresholds based on 2× liver SUVmean (10/15) and 2× liver SUVmax (13/15) also produced satisfactory results, with only a few cases exhibiting slightly expanded segmentation volumes. When a stricter criterion was applied (post-segmentation volume < 3% of the original VOI), thresholds of 2× liver SUVmean (9/15) and 2× liver SUVmax (12/15) still demonstrated good stability and applicability. In contrast, the semi-automatic segmentation results based on 40% of the SUVmax of the skeletal VOI were not ideal in this study (7/15 or 3/15), exhibiting segmentation instability. The segmentation results using 1×(0/15) and 1.5×(1/15) the liver SUVmean as thresholds were significantly inadequate, with overly large segmentation ranges. Discussion The skeleton is one of the most common sites of metastasis in malignant tumors, and multifocal bone involvement is frequently observed in clinical practice. In this context, accurate quantification of intraosseous lesions is of particular importance. Although manual segmentation is considered the gold standard and provides precise VOI delineation, it is time-consuming and highly labor-intensive, especially in cases with diffuse or multifocal lesions, which limits its clinical feasibility. As a result, semi-automatic segmentation based on SUV has emerged as an efficient and reproducible alternative. However, the widespread physiological expression of CXCR4 in the bone marrow may blur the distinction between true lesions and normal marrow uptake, posing challenges for fixed-threshold–based segmentation strategies. To address this, we referred to the Deauville scoring system and adopted liver and spleen uptake values as internal references to define multiple SUV thresholds. We then systematically evaluated the performance of these thresholds in whole-skeleton segmentation. Preliminary results of this study suggest that using 2× liver SUVmean and 1× liver SUVmax as thresholds yields the most balanced segmentation performance in terms of VOI number, volume, and lesion detectability. Although a small amount of background tissue remained within the VOI at these thresholds, the segmentation results were acceptable and the ability to identify lesions was relatively favorable. In contrast, higher thresholds such as spleen SUVmean, 1.5×, and 2× liver SUVmax significantly reduced background interference and resulted in nearly zero VOI volumes; however, these thresholds tended to underestimate lesion burden by missing low-uptake lesions during image processing. While such high thresholds may be acceptable when focusing solely on lesions with high radiotracer accumulation, caution is warranted, as low-uptake lesions may still carry important prognostic information. In addition, thresholds based on 40% of bone SUVmax, 1×, and 1.5× liver SUVmean failed to effectively exclude physiological bone or marrow uptake, indicating limited specificity of these approaches. In previous studies on multiple myeloma, researchers have used total skeletal uptake to predict patient prognosis and obtained meaningful results 12 – 14 . Kaur et al. further applied a threshold of 2× liver uptake to define 68 Ga-Pentixafor positive lesions and evaluate therapeutic response, with encouraging outcomes 4 . A more precise segmentation strategy may help isolate true lesion areas, facilitating the analysis of tumor heterogeneity rather than heterogeneity between tumor and normal tissues. This concept is also applicable to radiomics analysis. Radiomics relies on high-throughput extraction of imaging features from VOI, and the reliability of these features largely depends on the representativeness of the VOI. Inclusion of substantial normal tissue within the VOI may introduce noise or erroneous features, compromising the performance of predictive models. Similar strategies could also be extended to other imaging agents, such as PSMA PET/CT in prostate cancer patients with multifocal bone metastases or novel bone-targeting radiotracers used for detecting skeletal lesions. Defining appropriate segmentation thresholds in these scenarios would improve lesion identification and enhance the scientific validity of subsequent analyses. This study has several limitations. First, the SUV thresholds were explored with relatively coarse intervals of 0.5× and a maximum of 2×, which may have restricted the identification of optimal cutoff values. Second, the sample size was small and derived from a single center, limiting the generalizability of the findings. Third, only non-oncologic patients were included, and the clinical applicability of these thresholds in confirmed cancer patients has not yet been validated. Future studies will aim to expand the sample size, include disease cohorts, and refine the gradient of SUV thresholds to further optimize automated segmentation strategies for bone lesions. References Rossi D, Zlotnik A. The biology of chemokines and their receptors. Annu Rev Immunol . 2000;18:217-242. Buck AK, Haug A, Dreher N, et al. Imaging of C-X-C Motif Chemokine Receptor 4 Expression in 690 Patients with Solid or Hematologic Neoplasms Using (68)Ga-Pentixafor PET. J Nucl Med . 2022;63:1687-1692. Tang R, Pu J, Huang Z. Clinical value of CXCR4-targeted PET-CT in primary aldosteronism: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging . 2025. Kaur H, Kumar S, Watts A, et al. 68Ga-Pentixafor PET/CT-Based Response Evaluation and its Prognostic Value in Multiple Myeloma: Comparison With IMWG and 18F-FDG-Based Response. Clin Nucl Med . 2025. Zamanian M, Albano D, Treglia G, et al. The Clinical Role of CXCR4-Targeted PET on Lymphoproliferative Disorders: A Systematic Review. J Clin Med . 2024;13. Huynh C, Dingemanse J, Meyer Zu Schwabedissen HE, et al. Relevance of the CXCR4/CXCR7-CXCL12 axis and its effect in pathophysiological conditions. Pharmacol Res . 2020;161:105092. Mehrpouri M. The contributory roles of the CXCL12/CXCR4/CXCR7 axis in normal and malignant hematopoiesis: A possible therapeutic target in hematologic malignancies. Eur J Pharmacol . 2022;920:174831. Yang Y, Li J, Lei W, et al. CXCL12-CXCR4/CXCR7 Axis in Cancer: from Mechanisms to Clinical Applications. Int J Biol Sci . 2023;19:3341-3359. Moll NM, Ransohoff RM. CXCL12 and CXCR4 in bone marrow physiology. Expert Rev Hematol . 2010;3:315-322. Ooi LL, Dunstan CR. CXCL12/CXCR4 axis in tissue targeting and bone destruction in cancer and multiple myeloma. J Bone Miner Res . 2009;24:1147-1149. Liu M, Chen X, Ding H, et al. Comparison of [(18)F]FDG and [(68) Ga]pentixafor PET/CT in Nasopharyngeal Carcinoma. Mol Imaging Biol . 2024;26:658-667. Chen Z, Yang A, Chen A, et al. [(68)Ga]Pentixafor PET/CT for staging and prognostic assessment of newly diagnosed multiple myeloma: comparison to [(18)F]FDG PET/CT. Eur J Nucl Med Mol Imaging . 2024;51:1926-1936. Pan Q, Cao X, Luo Y, et al. Chemokine receptor-4 targeted PET/CT with (68)Ga-Pentixafor in assessment of newly diagnosed multiple myeloma: comparison to (18)F-FDG PET/CT. Eur J Nucl Med Mol Imaging . 2020;47:537-546. Gauthaman DK, Muthukrishnan I, Acharya KA, et al. Ga-68 Pentixafor PET/CT in multiple myeloma and its correlation with clinical parameters: institutional pilot study. Ann Nucl Med . 2025. Table Table 1. Relevant parameters of different volumes of interest Characteristic SUVmax SUVmean Volume (cm 3 ) Liver 2.4 (2.1 - 2.7) 1.1 (0.9 - 1.2) 1189 (1013 - 1553) Spleen 5.9 (4.9 - 7.0) 1.1 (0.9 - 1.2) 152 (133 - 267) Bone and bone marrow 4.2 (3.4 - 4.7) 1.0 (0.9 - 1.1) 3261 (3024 - 3943) Threshold for semi - automatic segmentation - 40% of bone SUVmax - 2.2 (2.0 - 2.4) 190 (98 - 432) - Liver SUVmean 1x - 1.5 (1.4 - 1.6) 1172 (823 - 1399) - Liver SUVmean 1.5x - 2.0 (1.9 - 2.1) 404 (234 - 565) - Liver SUVmean 2x - 2.3 (2.5 - 2.6) 78 (38 - 225) - Liver SUVmax 1x - 2.7 (2.3 - 3.0) 37 (8 - 62) - Liver SUVmax 1.5x - 3.4 (0 - 4.1) 0.2 (0 - 1.1) - Liver SUVmax 2x - 0 (0 - 4.4) 0 (0 - 0.03) - Spleen SUVmean - 3.6 (0.9 - 1.1) 0.2 (0 - 1.7) SUV: standardized uptake value; 1x: 1 times; 1.5x: 1.5 times; 2x: 2 times; Continuous variables are displayed using the median (interquartile range). Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7008259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478287495,"identity":"d41e96ce-638e-4d53-9459-4617ba85f377","order_by":0,"name":"Ranbie Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDCCAwwGDDxA2oC9gTQtBgwGPAdI1iKRQKQOvuOHN354U/Mn31zy8cYbDDU20QS1SJ5JK5acc8zAcufstGILhmNpuQ2EtBgcyDGQ5mEzMDC4nWMmwdhwmAgt598Y/+b5B9Ry8wyxWm7kmEnztgG13OAhUovkjWdllnP7jA0se4B+SSDGL3znkzffePNNzsCc/fDGGx9qbAhrQXEk0VGDpIVUHaNgFIyCUTAyAAD/ZEBJn+v65AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0005-7902-2417","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ranbie","middleName":"","lastName":"Tang","suffix":""},{"id":478287496,"identity":"7cb9f763-431a-47c0-9f23-a10fe74fdbd5","order_by":1,"name":"Zhanwen Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACAwh1AEIl/vlvx8/MfPgB8VoeNjAnS7azpRkQrYURqIVxw3keBQl8Wswlkp89/PLnjjyQcexB4g42ZuPDPECDamyicWmxnJFmbizD88xw54y0dIPEMzx8Zod5DzxgOJaW24DLYTcSzKQlJA4zbridYyaRwCbBbHaYL8GAseEwHi3p36QlDA7bb7id/w2oxYBxczOPgQR+LTlmkh8SDicCbWGTSGxLYNzATEjLmTdl0gwHDidvuP8M6LAzB5IlDgMDOQGfX46nb5P88eew7YYzh59J/qg4YMfff/jwgw81Nji1gAAzD4ZQAh7lIMD4g4CCUTAKRsEoGOEAAHJ/YgCkT9ORAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhanwen","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-06-30 08:39:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7008259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7008259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86240405,"identity":"2d27c148-ef02-4e66-a5cc-a1c4ccc51a49","added_by":"auto","created_at":"2025-07-08 10:33:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2125184,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of semi-automatic segmentation methods when excluding physiological uptake. Acceptable criteria are segmentation volumes less than 5% (A) and 3% (B) of the original VOI volume.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7008259/v1/4a2a1108e9726cfb4d38bdc2.png"},{"id":86241774,"identity":"bd441090-665e-4d07-9b8d-b0ae6fd0389f","added_by":"auto","created_at":"2025-07-08 10:50:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2379457,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7008259/v1/d1aeff4c-7a29-4040-b905-b7dbb43e943e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHow Should SUV Thresholds Be Set to Exclude Physiological Bone Uptake of \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa-Pentixafor?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa-Pentixafor is a novel PET tracer that specifically targets the C-X-C chemokine receptor type 4 (CXCR4), a transmembrane G protein-coupled receptor that is widely overexpressed in various hematological malignancies and endocrine diseases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In recent years, CXCR4-targeted imaging using \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT has garnered increasing attention for its ability to non-invasively visualize CXCR4 expression in vivo, providing valuable information for tumor detection\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCXCR4 plays a pivotal role in tumor biology by mediating cell migration, invasion, and microenvironmental homing through its interaction with the chemokine ligand stromal cell-derived factor-1. This axis is not only crucial for hematopoietic cell trafficking and bone marrow niche maintenance under physiological conditions but is also hijacked by malignant cells to facilitate tumor progression and metastasis\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBone marrow uptake is commonly observed in healthy individuals due to physiological expression of CXCR4 on hematopoietic cells. This physiological uptake is often diffuse and mild to moderate intensity, yet it may vary with patient age, hematopoietic activity, or recent chemotherapy\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In the context of malignancies such as lymphoma or multiple myeloma\u0026mdash;which frequently involve bone and marrow\u0026mdash;delineating true lesions from physiological background becomes critically important\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile experienced readers may distinguish pathological from physiological uptake based on distribution patterns and clinical context, such discrimination becomes more difficult in the setting of semi-automatic or quantitative image analysis. In particular, segmentation approaches that rely on SUV-based thresholds face the risk of including physiological bone marrow uptake, potentially compromising the accuracy of quantitative parameters such as metabolic tumor volume or total lesion uptake.\u003c/p\u003e\u003cp\u003eTo address this issue, our study aimed to investigate the effect of different SUV-based thresholding methods on bone marrow segmentation in \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT, and to explore which thresholds can effectively exclude most physiological uptake. By identifying optimal segmentation criteria, we hope to facilitate a more accurate semi-automated analysis of \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT in hematologic malignancies, especially for quantitative imaging biomarkers used in diagnosis, prognostication, or treatment response assessment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective study included 15 patients who underwent \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT at our institution between January 1, 2023, and December 31, 2023, and subsequently confirmed to have aldosterone-producing tumors via postoperative histopathology. These patients were categorized as the healthy control group for analysis purposes. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026gt;\u0026thinsp;18 years; (2) No obvious signs of infection or anemia; (3) no prior history of malignancy; and (4) at least one year of post-imaging follow-up without any evidence of malignancy. The exclusion criteria were: (1) coexisting autoimmune diseases; (2) impaired hepatic or renal function; (3) history of major illness or significant trauma within the past three months; and (4) incomplete clinical records. This study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (Ethics Approval Number: KY2025316).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePreparation and imaging of\u003c/b\u003e \u003csup\u003e\u003cb\u003e68\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eGa-Pentixafor\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe preparation process for \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor followed the methods reported in the literature\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. No special preparation was required from the patient prior to imaging. The radiotracer was administered intravenously at a body weight\u0026ndash;adjusted dose of 0.05 mCi/kg, followed by PET/CT scanning 40\u0026ndash;60 minutes post-injection. Imaging was performed using a uMI 780 scanner (United Imaging Healthcare, Shanghai, China), covering the region from the vertex to the upper thighs. Other scanning parameters were set based on established protocols reported in the literature\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSetting volume of interest\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe TotalSegmentator tool (V2.4.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wasserth/TotalSegmentator\u003c/span\u003e\u003cspan address=\"https://github.com/wasserth/TotalSegmentator\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to automatically delineate volumes of interest (VOI) for the skeleton, liver, and spleen. For each VOI, the SUVmean, SUVmax, and volume were recorded. To minimize interference from adrenal lesions or adjacent kidneys on SUVmax, we progressively contracted the VOI boundaries inward by 1 mm increments (up to a maximum of 5 mm) until SUVmax values stabilized, at which point the final value was recorded. The same approach was applied to VOIs adjacent to the bladder and pubic bone.\u003c/p\u003e\u003cp\u003eBased on the skeletal VOI, semi-automatic segmentation was further performed using various thresholding criteria, and the resulting volumes were recorded. The thresholds used included: (1) 40% of SUVmax (a commonly used standard in PET/CT studies of solid tumors); (2) 1\u0026times;, 1.5\u0026times;, and 2\u0026times; the liver SUVmean; (3) 1\u0026times;, 1.5\u0026times;, and 2\u0026times; the liver SUVmax; (4) spleen SUVmean.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 15 patients with aldosterone-producing tumors (11 females and 4 males), with a mean age of 46\u0026thinsp;\u0026plusmn;\u0026thinsp;12 years. The relevant parameters of each VOI under different segmentation thresholds are summarized in Table\u0026nbsp;1. Notably, in all patients, the spleen SUVmax exceeded that of the skeleton. Figure\u0026nbsp;1 illustrates the segmentation results obtained using different thresholding strategies.\u003c/p\u003e\u003cp\u003eUsing a post-segmentation volume of less than 5% of the original VOI as the acceptability criterion, thresholds based on spleen SUVmean (15/15) and 1.5\u0026times; (15/15) and 2\u0026times; (15/15) liver SUVmax, yielded stable and acceptable segmentation outcomes across all patients. Thresholds based on 2\u0026times; liver SUVmean (10/15) and 2\u0026times; liver SUVmax (13/15) also produced satisfactory results, with only a few cases exhibiting slightly expanded segmentation volumes. When a stricter criterion was applied (post-segmentation volume\u0026thinsp;\u0026lt;\u0026thinsp;3% of the original VOI), thresholds of 2\u0026times; liver SUVmean (9/15) and 2\u0026times; liver SUVmax (12/15) still demonstrated good stability and applicability.\u003c/p\u003e\u003cp\u003eIn contrast, the semi-automatic segmentation results based on 40% of the SUVmax of the skeletal VOI were not ideal in this study (7/15 or 3/15), exhibiting segmentation instability. The segmentation results using 1\u0026times;(0/15) and 1.5\u0026times;(1/15) the liver SUVmean as thresholds were significantly inadequate, with overly large segmentation ranges.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe skeleton is one of the most common sites of metastasis in malignant tumors, and multifocal bone involvement is frequently observed in clinical practice. In this context, accurate quantification of intraosseous lesions is of particular importance. Although manual segmentation is considered the gold standard and provides precise VOI delineation, it is time-consuming and highly labor-intensive, especially in cases with diffuse or multifocal lesions, which limits its clinical feasibility. As a result, semi-automatic segmentation based on SUV has emerged as an efficient and reproducible alternative.\u003c/p\u003e\u003cp\u003eHowever, the widespread physiological expression of CXCR4 in the bone marrow may blur the distinction between true lesions and normal marrow uptake, posing challenges for fixed-threshold\u0026ndash;based segmentation strategies. To address this, we referred to the Deauville scoring system and adopted liver and spleen uptake values as internal references to define multiple SUV thresholds. We then systematically evaluated the performance of these thresholds in whole-skeleton segmentation.\u003c/p\u003e\u003cp\u003ePreliminary results of this study suggest that using 2\u0026times; liver SUVmean and 1\u0026times; liver SUVmax as thresholds yields the most balanced segmentation performance in terms of VOI number, volume, and lesion detectability. Although a small amount of background tissue remained within the VOI at these thresholds, the segmentation results were acceptable and the ability to identify lesions was relatively favorable. In contrast, higher thresholds such as spleen SUVmean, 1.5\u0026times;, and 2\u0026times; liver SUVmax significantly reduced background interference and resulted in nearly zero VOI volumes; however, these thresholds tended to underestimate lesion burden by missing low-uptake lesions during image processing. While such high thresholds may be acceptable when focusing solely on lesions with high radiotracer accumulation, caution is warranted, as low-uptake lesions may still carry important prognostic information.\u003c/p\u003e\u003cp\u003eIn addition, thresholds based on 40% of bone SUVmax, 1\u0026times;, and 1.5\u0026times; liver SUVmean failed to effectively exclude physiological bone or marrow uptake, indicating limited specificity of these approaches.\u003c/p\u003e\u003cp\u003eIn previous studies on multiple myeloma, researchers have used total skeletal uptake to predict patient prognosis and obtained meaningful results\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Kaur et al. further applied a threshold of 2\u0026times; liver uptake to define \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor positive lesions and evaluate therapeutic response, with encouraging outcomes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. A more precise segmentation strategy may help isolate true lesion areas, facilitating the analysis of tumor heterogeneity rather than heterogeneity between tumor and normal tissues.\u003c/p\u003e\u003cp\u003eThis concept is also applicable to radiomics analysis. Radiomics relies on high-throughput extraction of imaging features from VOI, and the reliability of these features largely depends on the representativeness of the VOI. Inclusion of substantial normal tissue within the VOI may introduce noise or erroneous features, compromising the performance of predictive models. Similar strategies could also be extended to other imaging agents, such as PSMA PET/CT in prostate cancer patients with multifocal bone metastases or novel bone-targeting radiotracers used for detecting skeletal lesions. Defining appropriate segmentation thresholds in these scenarios would improve lesion identification and enhance the scientific validity of subsequent analyses.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the SUV thresholds were explored with relatively coarse intervals of 0.5\u0026times; and a maximum of 2\u0026times;, which may have restricted the identification of optimal cutoff values. Second, the sample size was small and derived from a single center, limiting the generalizability of the findings. Third, only non-oncologic patients were included, and the clinical applicability of these thresholds in confirmed cancer patients has not yet been validated. Future studies will aim to expand the sample size, include disease cohorts, and refine the gradient of SUV thresholds to further optimize automated segmentation strategies for bone lesions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRossi D, Zlotnik A. The biology of chemokines and their receptors. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2000;18:217-242.\u003c/li\u003e\n \u003cli\u003eBuck AK, Haug A, Dreher N, et al. Imaging of C-X-C Motif Chemokine Receptor 4 Expression in 690 Patients with Solid or Hematologic Neoplasms Using (68)Ga-Pentixafor PET. \u003cem\u003eJ Nucl Med\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2022;63:1687-1692.\u003c/li\u003e\n \u003cli\u003eTang R, Pu J, Huang Z. Clinical value of CXCR4-targeted PET-CT in primary aldosteronism: a systematic review and meta-analysis. \u003cem\u003eEur J Nucl Med Mol Imaging\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2025.\u003c/li\u003e\n \u003cli\u003eKaur H, Kumar S, Watts A, et al. 68Ga-Pentixafor PET/CT-Based Response Evaluation and its Prognostic Value in Multiple Myeloma: Comparison With IMWG and 18F-FDG-Based Response. \u003cem\u003eClin Nucl Med\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2025.\u003c/li\u003e\n \u003cli\u003eZamanian M, Albano D, Treglia G, et al. The Clinical Role of CXCR4-Targeted PET on Lymphoproliferative Disorders: A Systematic Review. \u003cem\u003eJ Clin Med\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2024;13.\u003c/li\u003e\n \u003cli\u003eHuynh C, Dingemanse J, Meyer Zu Schwabedissen HE, et al. Relevance of the CXCR4/CXCR7-CXCL12 axis and its effect in pathophysiological conditions. \u003cem\u003ePharmacol Res\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2020;161:105092.\u003c/li\u003e\n \u003cli\u003eMehrpouri M. The contributory roles of the CXCL12/CXCR4/CXCR7 axis in normal and malignant hematopoiesis: A possible therapeutic target in hematologic malignancies. \u003cem\u003eEur J Pharmacol\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2022;920:174831.\u003c/li\u003e\n \u003cli\u003eYang Y, Li J, Lei W, et al. CXCL12-CXCR4/CXCR7 Axis in Cancer: from Mechanisms to Clinical Applications. \u003cem\u003eInt J Biol Sci\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2023;19:3341-3359.\u003c/li\u003e\n \u003cli\u003eMoll NM, Ransohoff RM. CXCL12 and CXCR4 in bone marrow physiology. \u003cem\u003eExpert Rev Hematol\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2010;3:315-322.\u003c/li\u003e\n \u003cli\u003eOoi LL, Dunstan CR. CXCL12/CXCR4 axis in tissue targeting and bone destruction in cancer and multiple myeloma. \u003cem\u003eJ Bone Miner Res\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2009;24:1147-1149.\u003c/li\u003e\n \u003cli\u003eLiu M, Chen X, Ding H, et al. Comparison of [(18)F]FDG and [(68) Ga]pentixafor PET/CT in Nasopharyngeal Carcinoma. \u003cem\u003eMol Imaging Biol\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2024;26:658-667.\u003c/li\u003e\n \u003cli\u003eChen Z, Yang A, Chen A, et al. [(68)Ga]Pentixafor PET/CT for staging and prognostic assessment of newly diagnosed multiple myeloma: comparison to [(18)F]FDG PET/CT. \u003cem\u003eEur J Nucl Med Mol Imaging\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2024;51:1926-1936.\u003c/li\u003e\n \u003cli\u003ePan Q, Cao X, Luo Y, et al. Chemokine receptor-4 targeted PET/CT with (68)Ga-Pentixafor in assessment of newly diagnosed multiple myeloma: comparison to (18)F-FDG PET/CT. \u003cem\u003eEur J Nucl Med Mol Imaging\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2020;47:537-546.\u003c/li\u003e\n \u003cli\u003eGauthaman DK, Muthukrishnan I, Acharya KA, et al. Ga-68 Pentixafor PET/CT in multiple myeloma and its correlation with clinical parameters: institutional pilot study. \u003cem\u003eAnn Nucl Med\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003e2025.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1. Relevant parameters of different volumes of interest\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSUVmax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSUVmean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVolume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLiver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4 (2.1 - 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1 (0.9 - 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1189 (1013 - 1553)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSpleen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.9 (4.9 - 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1 (0.9 - 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152 (133 - 267)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBone and bone marrow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.2 (3.4 - 4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0 (0.9 - 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3261 (3024 - 3943)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eThreshold for semi - automatic segmentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- 40% of bone SUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2 (2.0 - 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e190 (98 - 432)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Liver SUVmean 1x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5 (1.4 - 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1172 (823 - 1399)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Liver SUVmean 1.5x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (1.9 - 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e404 (234 - 565)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Liver SUVmean 2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3 (2.5 - 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78 (38 - 225)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Liver SUVmax 1x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.7 (2.3 - 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (8 - 62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Liver SUVmax 1.5x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4 (0 - 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2 (0 - 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Liver SUVmax 2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0 - 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0 - 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e- Spleen SUVmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;-\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.6 (0.9 - 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2 (0 - 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSUV: standardized uptake value; 1x: 1 times; 1.5x: 1.5 times; 2x: 2 times; Continuous variables are displayed using the median (interquartile range).\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Department of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University","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":"68Ga-Pentixafor, physiological uptake, Semi-automatic segmentation","lastPublishedDoi":"10.21203/rs.3.rs-7008259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7008259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT enables non-invasive imaging of C-X-C chemokine receptor type 4 expression and holds significant value in hematologic malignancies. However, physiological bone marrow uptake may interfere with lesion identification. This study aimed to evaluate the impact of various SUV thresholds on bone segmentation and to identify thresholds that exclude physiological uptake, thereby improving the accuracy of bone lesion delineation and detection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e This retrospective study included 15 patients with confirmed aldosterone-producing adenomas who underwent \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT. TotalSegmentator was used for automated volumes of interest (VOI) delineation (bone, liver, spleen), recording SUVmean, SUVmax, and volume. Semi-automatic segmentation applied multiple thresholds: 40% bone SUVmax, 1×, 1.5× and 2× liver SUVmean and SUVmax, and spleen SUVmean. Acceptability criteria were post-segmentation volume \u0026lt;5% or \u0026lt;3% of the original VOI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSpleen SUVmax consistently exceeded bone uptake. With \u0026lt;5% volume retention, thresholds using spleen SUVmean, 1.5× and 2× liver SUVmax achieved stable segmentation in all cases (15/15). 2× liver SUVmean (10/15) and 2× liver SUVmax (13/15) also performed well. Under stricter criteria (\u0026lt;3%), 2× liver SUVmean (9/15) and SUVmax (12/15) remained robust. In contrast, 40% bone SUVmax showed poor stability (7/15 or 3/15), while 1×/1.5× liver SUVmean led to over segmentation (0/15 and 1/15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e For optimal \u003csup\u003e68\u003c/sup\u003eGa-Pentixafor PET/CT segmentation, thresholds of 2× liver SUVmean or 1× liver SUVmax effectively minimize bone interference, providing robust performance suitable for subsequent image analysis and disease quantification studies.\u003c/p\u003e","manuscriptTitle":"How Should SUV Thresholds Be Set to Exclude Physiological Bone Uptake of 68Ga-Pentixafor?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 10:33:54","doi":"10.21203/rs.3.rs-7008259/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":"6ff7159a-d24b-4578-877d-03879ef3a407","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50776561,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2025-07-08T10:33:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 10:33:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7008259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7008259","identity":"rs-7008259","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}