Preoperative 18F-FDG PET/CT for CT-Guided Biopsy Planning: Predicting Target Adjustment and Improving Malignant Yield in Large Tumors

Preoperative 18F-FDG PET/CT for CT-Guided Biopsy Planning: Predicting Target Adjustment and Improving Malignant Yield in Large Tumors | 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 Preoperative 18F-FDG PET/CT for CT-Guided Biopsy Planning: Predicting Target Adjustment and Improving Malignant Yield in Large Tumors Fumiyasu Tsushima, Taiki Koshiishi, Tomohiro Shintaku, Sho Maruyama, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8561461/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective To assess the clinical utility of preoperative 18F-FDG PET/CT for CT-guided biopsy planning of large tumors, including predictors of PET-driven target adjustment and malignant yield with FDG hotspot targeting in very large tumors. Methods In this retrospective single-center study, we analyzed 82 patients who underwent CT-guided biopsy between January 2015 and December 2025 with preoperative 18F-FDG PET/CT performed within 90 days before biopsy. Target adjustment was defined as changing the planned intratumoral biopsy target from the contrast-enhanced CT–based target to an alternative intratumoral site based on FDG uptake. Tumor size was evaluated by ROC analysis. Multivariable logistic regression included age, sex, tumor size, PET system (analog vs digital), ΔSUVmax, lesion location (chest vs other), and lymphoma (vs other). In tumors ≥ 52 mm, malignant yield was compared between hotspot (highest uptake) and non-hotspot targeting. Results Target adjustment was performed in 28/82 cases (34.1%). Interobserver agreement was 89% with Cohen’s κ = 0.74 (95% CI, 0.58–0.89). Tumor size predicted target adjustment (AUC 0.847; 95% CI 0.761–0.934), and the Youden-optimal cutoff was 52 mm (sensitivity 0.82, specificity 0.81). Target adjustment rates were 5/49 (10.2%) for < 52 mm and 23/33 (69.7%) for ≥ 52 mm (p < 0.001). In multivariable analysis, tumor size (per 10 mm) was independently associated with target adjustment (OR 2.33; 95% CI 1.56–3.50; p < 0.001), while female sex (OR 0.20; 95% CI 0.03–0.91; p = 0.049) and lymphoma (OR 0.082; 95% CI 0.0058–0.746; p = 0.041) were inversely associated. The multivariable model showed good discrimination (AUC 0.90; 95% CI 0.82–0.98; DeLong). Among tumors ≥ 52 mm, malignant pathology was obtained in 14/14 cases (100%) with hotspot targeting versus 5/9 (55.6%) with non-hotspot targeting (p = 0.014). Conclusions Preoperative 18F-FDG PET/CT supports CT-guided biopsy planning of large tumors by identifying cases likely to require target adjustment and by improving malignant yield when the FDG hotspot is targeted in tumors ≥ 52 mm. 18F-FDG PET/CT CT-guided biopsy tumor heterogeneity SUVmax ROC Figures Figure 1 Figure 2 Figure 3 Introduction CT-guided biopsy is essential for tissue diagnosis, but sampling error remains a concern in large, heterogeneous tumors because necrosis or fibrosis may not be detected on anatomic imaging alone. 18F-FDG PET/CT depicts intratumoral metabolic heterogeneity and may help select viable regions for biopsy.[ 1 , 2 ] Prior studies have incorporated FDG PET information into percutaneous interventions using registration of prior PET/CT to intraprocedural CT,[ 3 , 5 ] multimodality fusion with electromagnetic navigation,[ 4 ] and PET/CT–CBCT fusion guidance.[ 6 ] We evaluated whether preoperative PET/CT changes intratumoral target selection (target adjustment) and whether hotspot targeting improves malignant yield in very large tumors. Large tumors pose a particular challenge because viable tumor, necrosis, hemorrhage, and fibrosis often coexist, increasing sampling error when the intratumoral target is selected based solely on morphologic CT findings. 18F-FDG PET/CT provides a whole-tumor map of glucose metabolism and can highlight metabolically active regions that may better represent viable tumor. However, the incremental value of PET/CT for biopsy planning has been inconsistently reported, in part because prior studies frequently included smaller lesions and did not explicitly quantify when PET information changes the planned target. In routine practice, hotspot identification also depends on how PET images are displayed and fused with CT, yet these practical steps are rarely described in detail. Accordingly, we sought to (i) quantify PET-driven target adjustment as a clinically meaningful decision endpoint, (ii) determine whether tumor size can predict the need for adjustment, and (iii) evaluate whether FDG hotspot targeting improves malignant yield in very large tumors. Materials and Methods Patients Ethics approval: This study was approved by the Institutional Review Board (approval No. 2024 − 101). The requirement for written informed consent was waived, and an opt-out consent procedure was used. Biopsy planning was performed by reviewing contrast-enhanced CT (when available) and the most recent 18F-FDG PET/CT. The intended intratumoral target was first determined using CT findings and procedural feasibility (safe needle trajectory avoiding major vessels and bowel). PET/CT was then used to assess intratumoral metabolic heterogeneity and to refine the target toward metabolically active tissue when appropriate. Because of the retrospective design, it was not always possible to verify whether PET/CT images were actively reviewed at the time of biopsy planning; therefore, PET-driven target adjustment was defined based on the recorded planning decision rather than confirmed real-time image review. PET/CT acquisition PET/CT was performed using an analog system until December 2020 and a digital system from April 2021 onward. Reconstruction conditions and injected dose distributions are provided in Supplementary Tables S1–S2. PET/CT visualization optimization and fusion To facilitate intratumoral hotspot identification for biopsy planning, PET images were reviewed both alone and in fused PET/CT views. The PET display window level/width (WL/WW) was adjusted according to overall lesion uptake as follows: SUVmax around 25: WL 12, WW 18; SUVmax around 15: WL 1.5, WW 15; and lower uptake (< 15): WL 4, WW 8. When available, fusion/registration with procedural CT (or contrast-enhanced CT) was used for anatomical correlation and target selection based on the FDG distribution (Ref 6). Image interpretation and adjudication All PET/CT images used for biopsy planning were independently reviewed by two board-certified radiologists. Target adjustment (yes/no) and hotspot identification were determined independently; discrepancies were resolved by consensus discussion. Definition of target adjustment and PET-derived metrics Target adjustment was defined as changing the planned intratumoral biopsy target from the contrast-enhanced CT–based target to an alternative intratumoral site based on FDG uptake. The FDG hotspot was defined as the region with the maximum SUV (SUVmax). ΔSUVmax was defined as SUVmax(hotspot) minus the SUVmax of the lowest-uptake intratumoral viable region within the same tumor. For lesions with visually negligible FDG uptake, hotspot SUVmax was recorded as 0 and ΔSUVmax was set to 0 because intratumoral metabolic heterogeneity could not be assessed. Statistical analysis Continuous variables are presented as median [interquartile range] and compared using the Wilcoxon rank-sum test; categorical variables are presented as n (%) and compared using Fisher’s exact test. Tumor size was evaluated by ROC analysis (DeLong 95% CI) [ 7 , 8 ] using the pROC package,[ 9 ] and the optimal cutoff was selected by the Youden index.[ 10 ] Multivariable logistic regression modeled target adjustment using age, sex, tumor size, PET system, ΔSUVmax, lesion location, and lymphoma. Model discrimination was assessed by AUC (DeLong 95% CI).[ 7 , 8 ] Interobserver agreement for target adjustment was assessed using percent agreement and Cohen’s κ.[ 11 ] In tumors ≥ 52 mm, malignant yield was compared between hotspot and non-hotspot targeting using Fisher’s exact test. All analyses were performed using R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria). Results Target adjustment was performed in 28/82 cases (34.1%). Baseline characteristics are summarized in Table 1 . Table 1 Baseline characteristics by target adjustment (N = 82) Characteristic Overall (N = 82) No adjustment (N = 54) Target adjustment (N = 28) p value Age (years) 68.0 [61.2, 74.8] 69.5 [63.2, 75.0] 65.0 [56.8, 72.2] 0.043 Sex 0.098 F 31 (38%) 24 (44%) 7 (25%) M 51 (62%) 30 (56%) 21 (75%) Tumor size (mm) 43.5 [27.2, 65.0] 33.0 [17.2, 47.0] 69.0 [53.5, 95.2] < 0.001 PET system 0.356 Analog 47 (57%) 33 (61%) 14 (50%) Digital 35 (43%) 21 (39%) 14 (50%) Hotspot SUVmax (FT) 9.0 [5.9, 14.2] 8.0 [3.6, 12.9] 11.8 [8.8, 15.7] 0.009 ΔSUVmax 3.5 [1.6, 8.0] 2.2 [0.6, 5.5] 6.2 [3.2, 9.2] 0.002 Lesion location > 0.9 Chest 58 (71%) 38 (70%) 20 (71%) Other 24 (29%) 16 (30%) 8 (29%) Lymphoma 0.788 Lymphoma 19 (23%) 12 (22%) 7 (25%) Other 63 (77%) 42 (78%) 21 (75%) Data are median [Q1, Q3] or n (%). P values: Wilcoxon rank-sum test for continuous variables and Fisher’s exact test for categorical variables. ΔSUVmax indicates SUVmax(hotspot) – SUVmax (lowest-uptake intratumoral viable region). Hotspot SUVmax (FT) was defined as the maximum SUV within the intratumoral FDG hotspot. In lesions with negligible uptake, hotspot SUVmax (FT) and ΔSUVmax were recorded as 0. In sensitivity analysis restricted to FDG-avid tumors (hotspot SUVmax > 0), ΔSUVmax remained higher in the target-adjustment group (Wilcoxon rank-sum test, p = 0.032). Interobserver agreement for target adjustment was 89%, with Cohen’s κ = 0.74 (95% CI, 0.58–0.89; n = 82). Tumor size predicted target adjustment (AUC 0.847; 95% CI 0.761–0.934; Fig. 1 ). A cutoff of 52 mm yielded sensitivity of 0.82 and specificity of 0.81. Target adjustment rates were 5/49 (10.2%; 95% CI 3.4–22.2%) for < 52 mm and 23/33 (69.7%; 95% CI 51.3–84.4%) for ≥ 52 mm (p < 0.001; Fig. 2 ). In multivariable analysis, tumor size (per 10 mm) was independently associated with target adjustment (OR 2.33; 95% CI 1.56–3.50; p < 0.001). Female sex (OR 0.20; 95% CI 0.03–0.91; p = 0.049) and lymphoma (OR 0.082; 95% CI 0.0058–0.746; p = 0.041) were inversely associated. The multivariable model showed good discrimination (AUC 0.90; 95% CI 0.82–0.98; DeLong) (Table 2 .). Table 2 Multivariable logistic regression for target adjustment (N = 82) Predictor OR 95% CI p value Age (per 10 years) 0.65 0.29–1.45 0.293 Female (vs male) 0.20 0.04–0.96 0.045 Tumor size (per 10 mm) 2.33 1.56–3.50 < 0.001 Digital PET (vs analog) 1.12 0.27–4.62 0.877 ΔSUVmax (per 5 units) 0.76 0.40–1.44 0.403 Chest lesion (vs other) 1.94 0.33–11.30 0.459 Lymphoma (vs other) 0.11 0.01–1.11 0.062 Model discrimination: AUC 0.899 (95% CI 0.816–0.966; bootstrap, 5,000 resamples). Among tumors ≥ 52 mm, malignant pathology was obtained in 14/14 cases (100%) with hotspot targeting and in 5/9 cases (55.6%) with non-hotspot targeting (Fisher’s exact test, p = 0.014) (Table 3 .). Table 3 Malignant yield in tumors ≥ 52 mm: hotspot vs non-hotspot targeting Malignant tumors Others High uptake (hotspot) 14 0 Low uptake (non-hotspot) 5 4 Fisher’s exact test (two-sided): p = 0.014. Odds ratio = ∞ (95% CI 1.27–∞) due to a zero cell. Case presentation Case 1 (Fig. 3 A): A heterogeneous hypermetabolic tumor was seen on preoperative FDG PET/CT, with intratumoral uptake ranging from SUVmax 10.0 to 18.5. On contrast-enhanced CT alone, the optimal intratumoral target was not obvious; therefore, the target was adjusted toward the FDG hotspot (SUVmax 18.5) on fused PET/CT, and CT-guided biopsy yielded squamous cell carcinoma. Case 2 (Fig. 3 B): A large tumor showed heterogeneous and relatively low FDG uptake (intratumoral SUVmax approximately 8.9–11.0). Biopsy targeting based mainly on CT resulted in a non-malignant specimen (alveolar tissue with alveolitis), and the patient was subsequently diagnosed with small cell carcinoma based on other findings, suggesting that PET-based hotspot targeting may help avoid sampling metabolically inactive components in selected cases. Discussion This study demonstrates that preoperative 18F-FDG PET/CT provides practical value for CT-guided biopsy planning of large tumors. Tumor size was a strong predictor of PET-driven target adjustment, and a size threshold of approximately 52 mm identified a subgroup in which target adjustment was frequent. Importantly, in tumors ≥ 52 mm, targeting the FDG hotspot was associated with a higher malignant yield than targeting non-hotspot regions. Notably, prior reports of PET-informed or PET-fusion biopsy guidance often included lesions across a broad size range and, in many cohorts, the median lesion size was smaller than in our study, with limited emphasis on intratumoral heterogeneity as a function of tumor size. By focusing on large tumors, we demonstrate that PET-driven target adjustment becomes frequent beyond approximately 52 mm, which is biologically plausible given increasing necrosis/fibrosis and metabolic heterogeneity with tumor growth. This size-dependent effect may explain why the incremental value of PET/CT was most evident in very large tumors in our cohort. These findings support the concept that metabolic heterogeneity increases with tumor size and that PET/CT can help avoid sampling low-viability regions. [ 1 , 12 ] PET system type (analog vs digital) was included as a covariate, and detailed acquisition/reconstruction conditions are provided in the Supplementary Appendix. From a practical standpoint, our findings support incorporating a structured PET/CT review step into the biopsy workflow for large tumors. Tumor size is readily available on preprocedural imaging, and a size threshold around 52 mm may serve as a simple trigger to scrutinize intratumoral FDG heterogeneity and to consider PET-guided refinement of the target. Importantly, the decision endpoint in this study—PET-driven target adjustment—reflects real-world clinical impact rather than a purely technical comparison of guidance methods. We also observed substantial inter-reader agreement for the adjustment decision, suggesting that with standardized visualization and fusion practices, this workflow can be applied reproducibly. Although digital PET systems with TOF/PSF and penalized-likelihood reconstruction may improve image quality and lesion delineation, we did not identify PET system type as an independent predictor after adjustment. This likely reflects that the clinical decision to adjust the target is dominated by the presence of marked intratumoral metabolic heterogeneity in very large tumors, which is detectable on both analog and digital platforms when images are reviewed carefully. Limitations include the retrospective single-center design, possible selection bias in patients undergoing PET/CT prior to biopsy, and the modest sample size for the hotspot analysis among tumors ≥ 52 mm. Because PET/CT information was used during planning, incorporation bias cannot be fully excluded; nonetheless, we chose a clinically relevant decision endpoint and evaluated malignant yield in the very large tumor subgroup to mitigate this concern. The 52-mm threshold was derived internally using the Youden index and may vary across institutions and protocols; external validation and, ideally, prospective evaluation are warranted. We did not perform voxel-wise radiopathologic correlation to confirm that the hotspot always corresponds to the highest viable tumor fraction, and ΔSUVmax may be influenced by partial-volume effects and reconstruction settings. Future multicenter studies using standardized acquisition and reconstruction parameters, together with predefined targeting criteria, will be important to confirm generalizability and to refine practical thresholds. Second, because we could not reliably determine whether PET/CT was reviewed in real time during biopsy planning for every case, misclassification of “target adjustment” is possible. This limitation may partly explain why some biopsies targeted low-uptake regions despite the availability of PET/CT. Such non-differential misclassification would be expected to bias associations toward the null, suggesting that the observed effects of tumor size and hotspot targeting may be conservative estimates. Conclusion Preoperative 18F-FDG PET/CT supports CT-guided biopsy planning in large tumors by identifying cases likely to require target adjustment and by improving malignant yield when the FDG hotspot is targeted in tumors ≥ 52 mm. Declarations Compliance with ethical standards Funding This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the institutional review board (approval No. 2024-101). 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08:43:50","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65850,"visible":true,"origin":"","legend":"","description":"","filename":"ANMED26000220structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/2f19d77c6391f5b477bc0718.xml"},{"id":100594755,"identity":"e2832531-51e7-4334-851d-fd089a736988","added_by":"auto","created_at":"2026-01-19 13:44:49","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75258,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/acf5e77ab4ff714d424e2eb7.html"},{"id":100560778,"identity":"a047657a-5145-4cb1-b2a1-b5fb3009bf50","added_by":"auto","created_at":"2026-01-19 08:43:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1658646,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of tumor size for predicting PET-driven target adjustment prior to CT-guided biopsy. The dot indicates the Youden-optimal cutoff (52 mm).\u003c/p\u003e","description":"","filename":"Fig1ROCtumorsizeFTTiff.png","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/064e09fc7309bdb23d73a8ac.png"},{"id":100560507,"identity":"4350f290-32c8-4664-a126-017b0786bfe6","added_by":"auto","created_at":"2026-01-19 08:43:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2593368,"visible":true,"origin":"","legend":"\u003cp\u003eTarget adjustment rate by tumor size category (\u0026lt;52 mm vs ≥52 mm). Error bars indicate 95% confidence intervals; p value was calculated using Fisher’s exact test.\u003c/p\u003e","description":"","filename":"Fig2targetadjustmentrateTiff.png","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/8997882acc00cd611b40fa11.png"},{"id":100560298,"identity":"1ea3c92f-3158-4512-b47b-64b13aacb50f","added_by":"auto","created_at":"2026-01-19 08:43:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4538137,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative cases demonstrating optimized PET/CT-assisted target selection for CT-guided biopsy. (A) Case 1: Target adjustment toward the FDG hotspot; biopsy yielded squamous cell carcinoma. (B) Case 2: CT-based targeting sampled non-malignant tissue; the patient was later diagnosed with small cell carcinoma.\u003c/p\u003e","description":"","filename":"Fig3representativecasesTiff.png","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/ced893263a5a352a8409a4ce.png"},{"id":100597269,"identity":"8f55e85e-d36a-4791-89c3-0a736665a4bc","added_by":"auto","created_at":"2026-01-19 14:16:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8310043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/1d9034a3-c34e-4e6c-b1e3-95fa56f4aee9.pdf"},{"id":100560253,"identity":"2b375d34-94c1-4389-92f1-a8984a2d5e30","added_by":"auto","created_at":"2026-01-19 08:43:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15768,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8561461/v1/5911d32fed31396992aa6d6c.docx"}],"financialInterests":"","formattedTitle":"Preoperative 18F-FDG PET/CT for CT-Guided Biopsy Planning: Predicting Target Adjustment and Improving Malignant Yield in Large Tumors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCT-guided biopsy is essential for tissue diagnosis, but sampling error remains a concern in large, heterogeneous tumors because necrosis or fibrosis may not be detected on anatomic imaging alone. 18F-FDG PET/CT depicts intratumoral metabolic heterogeneity and may help select viable regions for biopsy.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Prior studies have incorporated FDG PET information into percutaneous interventions using registration of prior PET/CT to intraprocedural CT,[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] multimodality fusion with electromagnetic navigation,[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and PET/CT\u0026ndash;CBCT fusion guidance.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] We evaluated whether preoperative PET/CT changes intratumoral target selection (target adjustment) and whether hotspot targeting improves malignant yield in very large tumors.\u003c/p\u003e \u003cp\u003eLarge tumors pose a particular challenge because viable tumor, necrosis, hemorrhage, and fibrosis often coexist, increasing sampling error when the intratumoral target is selected based solely on morphologic CT findings. 18F-FDG PET/CT provides a whole-tumor map of glucose metabolism and can highlight metabolically active regions that may better represent viable tumor.\u003c/p\u003e \u003cp\u003eHowever, the incremental value of PET/CT for biopsy planning has been inconsistently reported, in part because prior studies frequently included smaller lesions and did not explicitly quantify when PET information changes the planned target. In routine practice, hotspot identification also depends on how PET images are displayed and fused with CT, yet these practical steps are rarely described in detail. Accordingly, we sought to (i) quantify PET-driven target adjustment as a clinically meaningful decision endpoint, (ii) determine whether tumor size can predict the need for adjustment, and (iii) evaluate whether FDG hotspot targeting improves malignant yield in very large tumors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval:\u003c/strong\u003e \u003cp\u003eThis study was approved by the Institutional Review Board (approval No. 2024\u0026thinsp;\u0026minus;\u0026thinsp;101). The requirement for written informed consent was waived, and an opt-out consent procedure was used.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eBiopsy planning was performed by reviewing contrast-enhanced CT (when available) and the most recent 18F-FDG PET/CT. The intended intratumoral target was first determined using CT findings and procedural feasibility (safe needle trajectory avoiding major vessels and bowel). PET/CT was then used to assess intratumoral metabolic heterogeneity and to refine the target toward metabolically active tissue when appropriate.\u003c/p\u003e \u003cp\u003eBecause of the retrospective design, it was not always possible to verify whether PET/CT images were actively reviewed at the time of biopsy planning; therefore, PET-driven target adjustment was defined based on the recorded planning decision rather than confirmed real-time image review.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePET/CT acquisition\u003c/h3\u003e\n\u003cp\u003ePET/CT was performed using an analog system until December 2020 and a digital system from April 2021 onward. Reconstruction conditions and injected dose distributions are provided in Supplementary Tables S1\u0026ndash;S2.\u003c/p\u003e\n\u003ch3\u003ePET/CT visualization optimization and fusion\u003c/h3\u003e\n\u003cp\u003eTo facilitate intratumoral hotspot identification for biopsy planning, PET images were reviewed both alone and in fused PET/CT views. The PET display window level/width (WL/WW) was adjusted according to overall lesion uptake as follows: SUVmax around 25: WL 12, WW 18; SUVmax around 15: WL 1.5, WW 15; and lower uptake (\u0026lt;\u0026thinsp;15): WL 4, WW 8. When available, fusion/registration with procedural CT (or contrast-enhanced CT) was used for anatomical correlation and target selection based on the FDG distribution (Ref 6).\u003c/p\u003e\n\u003ch3\u003eImage interpretation and adjudication\u003c/h3\u003e\n\u003cp\u003eAll PET/CT images used for biopsy planning were independently reviewed by two board-certified radiologists. Target adjustment (yes/no) and hotspot identification were determined independently; discrepancies were resolved by consensus discussion.\u003c/p\u003e\n\u003ch3\u003eDefinition of target adjustment and PET-derived metrics\u003c/h3\u003e\n\u003cp\u003eTarget adjustment was defined as changing the planned intratumoral biopsy target from the contrast-enhanced CT\u0026ndash;based target to an alternative intratumoral site based on FDG uptake. The FDG hotspot was defined as the region with the maximum SUV (SUVmax). ΔSUVmax was defined as SUVmax(hotspot) minus the SUVmax of the lowest-uptake intratumoral viable region within the same tumor. For lesions with visually negligible FDG uptake, hotspot SUVmax was recorded as 0 and ΔSUVmax was set to 0 because intratumoral metabolic heterogeneity could not be assessed.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as median [interquartile range] and compared using the Wilcoxon rank-sum test; categorical variables are presented as n (%) and compared using Fisher\u0026rsquo;s exact test. Tumor size was evaluated by ROC analysis (DeLong 95% CI) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] using the pROC package,[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and the optimal cutoff was selected by the Youden index.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Multivariable logistic regression modeled target adjustment using age, sex, tumor size, PET system, ΔSUVmax, lesion location, and lymphoma. Model discrimination was assessed by AUC (DeLong 95% CI).[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Interobserver agreement for target adjustment was assessed using percent agreement and Cohen\u0026rsquo;s κ.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] In tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm, malignant yield was compared between hotspot and non-hotspot targeting using Fisher\u0026rsquo;s exact test. All analyses were performed using R (version 4.5.2; R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTarget adjustment was performed in 28/82 cases (34.1%). Baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \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\u003eBaseline characteristics by target adjustment (N\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eOverall (N\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo adjustment (N\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget adjustment (N\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.0 [61.2, 74.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.5 [63.2, 75.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.0 [56.8, 72.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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 \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.5 [27.2, 65.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.0 [17.2, 47.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.0 [53.5, 95.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePET system\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 \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHotspot SUVmax (FT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 [5.9, 14.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 [3.6, 12.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8 [8.8, 15.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔSUVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 [1.6, 8.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 [0.6, 5.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2 [3.2, 9.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLesion location\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 \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoma\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 \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are median [Q1, Q3] or n (%). P values: Wilcoxon rank-sum test for continuous variables and Fisher\u0026rsquo;s exact test for categorical variables. ΔSUVmax indicates SUVmax(hotspot) \u0026ndash; SUVmax (lowest-uptake intratumoral viable region). Hotspot SUVmax (FT) was defined as the maximum SUV within the intratumoral FDG hotspot. In lesions with negligible uptake, hotspot SUVmax (FT) and ΔSUVmax were recorded as 0.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn sensitivity analysis restricted to FDG-avid tumors (hotspot SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;0), ΔSUVmax remained higher in the target-adjustment group (Wilcoxon rank-sum test, p\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e \u003cp\u003eInterobserver agreement for target adjustment was 89%, with Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.74 (95% CI, 0.58\u0026ndash;0.89; n\u0026thinsp;=\u0026thinsp;82).\u003c/p\u003e \u003cp\u003eTumor size predicted target adjustment (AUC 0.847; 95% CI 0.761\u0026ndash;0.934; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A cutoff of 52 mm yielded sensitivity of 0.82 and specificity of 0.81. Target adjustment rates were 5/49 (10.2%; 95% CI 3.4\u0026ndash;22.2%) for \u0026lt;\u0026thinsp;52 mm and 23/33 (69.7%; 95% CI 51.3\u0026ndash;84.4%) for \u0026ge;\u0026thinsp;52 mm (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn multivariable analysis, tumor size (per 10 mm) was independently associated with target adjustment (OR 2.33; 95% CI 1.56\u0026ndash;3.50; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Female sex (OR 0.20; 95% CI 0.03\u0026ndash;0.91; p\u0026thinsp;=\u0026thinsp;0.049) and lymphoma (OR 0.082; 95% CI 0.0058\u0026ndash;0.746; p\u0026thinsp;=\u0026thinsp;0.041) were inversely associated. The multivariable model showed good discrimination (AUC 0.90; 95% CI 0.82\u0026ndash;0.98; DeLong) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.).\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression for target adjustment (N\u0026thinsp;=\u0026thinsp;82)\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eAge (per 10 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (per 10 mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56\u0026ndash;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eDigital PET (vs analog)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔSUVmax (per 5 units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest lesion (vs other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoma (vs other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel discrimination: AUC 0.899 (95% CI 0.816\u0026ndash;0.966; bootstrap, 5,000 resamples).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm, malignant pathology was obtained in 14/14 cases (100%) with hotspot targeting and in 5/9 cases (55.6%) with non-hotspot targeting (Fisher\u0026rsquo;s exact test, p\u0026thinsp;=\u0026thinsp;0.014) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMalignant yield in tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm: hotspot vs non-hotspot targeting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant tumors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh uptake (hotspot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow uptake (non-hotspot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFisher\u0026rsquo;s exact test (two-sided): p\u0026thinsp;=\u0026thinsp;0.014. Odds ratio = \u0026infin; (95% CI 1.27\u0026ndash;\u0026infin;) due to a zero cell.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCase presentation\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eCase 1\u003c/strong\u003e \u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA): A heterogeneous hypermetabolic tumor was seen on preoperative FDG PET/CT, with intratumoral uptake ranging from SUVmax 10.0 to 18.5. On contrast-enhanced CT alone, the optimal intratumoral target was not obvious; therefore, the target was adjusted toward the FDG hotspot (SUVmax 18.5) on fused PET/CT, and CT-guided biopsy yielded squamous cell carcinoma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCase 2\u003c/strong\u003e \u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB): A large tumor showed heterogeneous and relatively low FDG uptake (intratumoral SUVmax approximately 8.9\u0026ndash;11.0). Biopsy targeting based mainly on CT resulted in a non-malignant specimen (alveolar tissue with alveolitis), and the patient was subsequently diagnosed with small cell carcinoma based on other findings, suggesting that PET-based hotspot targeting may help avoid sampling metabolically inactive components in selected cases.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that preoperative 18F-FDG PET/CT provides practical value for CT-guided biopsy planning of large tumors. Tumor size was a strong predictor of PET-driven target adjustment, and a size threshold of approximately 52 mm identified a subgroup in which target adjustment was frequent. Importantly, in tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm, targeting the FDG hotspot was associated with a higher malignant yield than targeting non-hotspot regions.\u003c/p\u003e \u003cp\u003eNotably, prior reports of PET-informed or PET-fusion biopsy guidance often included lesions across a broad size range and, in many cohorts, the median lesion size was smaller than in our study, with limited emphasis on intratumoral heterogeneity as a function of tumor size. By focusing on large tumors, we demonstrate that PET-driven target adjustment becomes frequent beyond approximately 52 mm, which is biologically plausible given increasing necrosis/fibrosis and metabolic heterogeneity with tumor growth. This size-dependent effect may explain why the incremental value of PET/CT was most evident in very large tumors in our cohort.\u003c/p\u003e \u003cp\u003eThese findings support the concept that metabolic heterogeneity increases with tumor size and that PET/CT can help avoid sampling low-viability regions. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] PET system type (analog vs digital) was included as a covariate, and detailed acquisition/reconstruction conditions are provided in the Supplementary Appendix.\u003c/p\u003e \u003cp\u003eFrom a practical standpoint, our findings support incorporating a structured PET/CT review step into the biopsy workflow for large tumors. Tumor size is readily available on preprocedural imaging, and a size threshold around 52 mm may serve as a simple trigger to scrutinize intratumoral FDG heterogeneity and to consider PET-guided refinement of the target. Importantly, the decision endpoint in this study\u0026mdash;PET-driven target adjustment\u0026mdash;reflects real-world clinical impact rather than a purely technical comparison of guidance methods. We also observed substantial inter-reader agreement for the adjustment decision, suggesting that with standardized visualization and fusion practices, this workflow can be applied reproducibly. Although digital PET systems with TOF/PSF and penalized-likelihood reconstruction may improve image quality and lesion delineation, we did not identify PET system type as an independent predictor after adjustment. This likely reflects that the clinical decision to adjust the target is dominated by the presence of marked intratumoral metabolic heterogeneity in very large tumors, which is detectable on both analog and digital platforms when images are reviewed carefully.\u003c/p\u003e \u003cp\u003eLimitations include the retrospective single-center design, possible selection bias in patients undergoing PET/CT prior to biopsy, and the modest sample size for the hotspot analysis among tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm. Because PET/CT information was used during planning, incorporation bias cannot be fully excluded; nonetheless, we chose a clinically relevant decision endpoint and evaluated malignant yield in the very large tumor subgroup to mitigate this concern. The 52-mm threshold was derived internally using the Youden index and may vary across institutions and protocols; external validation and, ideally, prospective evaluation are warranted. We did not perform voxel-wise radiopathologic correlation to confirm that the hotspot always corresponds to the highest viable tumor fraction, and ΔSUVmax may be influenced by partial-volume effects and reconstruction settings. Future multicenter studies using standardized acquisition and reconstruction parameters, together with predefined targeting criteria, will be important to confirm generalizability and to refine practical thresholds. Second, because we could not reliably determine whether PET/CT was reviewed in real time during biopsy planning for every case, misclassification of \u0026ldquo;target adjustment\u0026rdquo; is possible. This limitation may partly explain why some biopsies targeted low-uptake regions despite the availability of PET/CT. Such non-differential misclassification would be expected to bias associations toward the null, suggesting that the observed effects of tumor size and hotspot targeting may be conservative estimates.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePreoperative 18F-FDG PET/CT supports CT-guided biopsy planning in large tumors by identifying cases likely to require target adjustment and by improving malignant yield when the FDG hotspot is targeted in tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompliance with ethical standards\u003c/p\u003e\n\u003cp\u003eFunding \u0026nbsp; This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eConflict of interest \u0026nbsp;The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEthical approval \u0026nbsp;All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the institutional review board (approval No. 2024-101).\u003c/p\u003e\n\u003cp\u003eInformed consent \u0026nbsp;The requirement for written informed consent was waived, and an opt-out consent procedure was used in accordance with institutional policy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00259-012-2247-0\u003c/span\u003e\u003cspan address=\"10.1007/s00259-012-2247-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID:23064544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFei B, Schuster DM. PET molecular imaging-directed biopsy: a review. 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PMID:26294296.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-nuclear-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anme","sideBox":"Learn more about [Annals of Nuclear Medicine](http://link.springer.com/journal/12149)","snPcode":"12149","submissionUrl":"https://www.editorialmanager.com/anme/default2.aspx","title":"Annals of Nuclear Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"18F-FDG, PET/CT, CT-guided biopsy, tumor heterogeneity, SUVmax, ROC","lastPublishedDoi":"10.21203/rs.3.rs-8561461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8561461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo assess the clinical utility of preoperative 18F-FDG PET/CT for CT-guided biopsy planning of large tumors, including predictors of PET-driven target adjustment and malignant yield with FDG hotspot targeting in very large tumors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective single-center study, we analyzed 82 patients who underwent CT-guided biopsy between January 2015 and December 2025 with preoperative 18F-FDG PET/CT performed within 90 days before biopsy. Target adjustment was defined as changing the planned intratumoral biopsy target from the contrast-enhanced CT\u0026ndash;based target to an alternative intratumoral site based on FDG uptake. Tumor size was evaluated by ROC analysis. Multivariable logistic regression included age, sex, tumor size, PET system (analog vs digital), ΔSUVmax, lesion location (chest vs other), and lymphoma (vs other). In tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm, malignant yield was compared between hotspot (highest uptake) and non-hotspot targeting.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTarget adjustment was performed in 28/82 cases (34.1%). Interobserver agreement was 89% with Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.74 (95% CI, 0.58\u0026ndash;0.89). Tumor size predicted target adjustment (AUC 0.847; 95% CI 0.761\u0026ndash;0.934), and the Youden-optimal cutoff was 52 mm (sensitivity 0.82, specificity 0.81). Target adjustment rates were 5/49 (10.2%) for \u0026lt;\u0026thinsp;52 mm and 23/33 (69.7%) for \u0026ge;\u0026thinsp;52 mm (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable analysis, tumor size (per 10 mm) was independently associated with target adjustment (OR 2.33; 95% CI 1.56\u0026ndash;3.50; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while female sex (OR 0.20; 95% CI 0.03\u0026ndash;0.91; p\u0026thinsp;=\u0026thinsp;0.049) and lymphoma (OR 0.082; 95% CI 0.0058\u0026ndash;0.746; p\u0026thinsp;=\u0026thinsp;0.041) were inversely associated. The multivariable model showed good discrimination (AUC 0.90; 95% CI 0.82\u0026ndash;0.98; DeLong). Among tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm, malignant pathology was obtained in 14/14 cases (100%) with hotspot targeting versus 5/9 (55.6%) with non-hotspot targeting (p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative 18F-FDG PET/CT supports CT-guided biopsy planning of large tumors by identifying cases likely to require target adjustment and by improving malignant yield when the FDG hotspot is targeted in tumors\u0026thinsp;\u0026ge;\u0026thinsp;52 mm.\u003c/p\u003e","manuscriptTitle":"Preoperative 18F-FDG PET/CT for CT-Guided Biopsy Planning: Predicting Target Adjustment and Improving Malignant Yield in Large Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:26:46","doi":"10.21203/rs.3.rs-8561461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-15T06:11:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T11:09:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T02:43:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Nuclear Medicine","date":"2026-01-09T08:27:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-nuclear-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anme","sideBox":"Learn more about [Annals of Nuclear Medicine](http://link.springer.com/journal/12149)","snPcode":"12149","submissionUrl":"https://www.editorialmanager.com/anme/default2.aspx","title":"Annals of Nuclear Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cc46405e-2a38-4ed9-a399-315ccf73a550","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T23:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:26:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8561461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8561461","identity":"rs-8561461","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}