Can F-18 FDG PET/CT Metabolic Parameters Predict STAS in Non-Small Cell Lung Cancer Patients?

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Batuhan Kocabeyoglu, Goktug Aydogan, Pinar Gürsoy, Recep Halit Tokac, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8065101/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 Objective In patients with Non-Small Cell Lung Cancer (NSCLC), spread through air spaces (STAS) describes tumor cells within alveolar spaces beyond the main tumor margin and is considered an invasive pattern associated with recurrence. This study aimed to preoperatively predict STAS using metabolic parameters obtained from F-18 FDG PET/CT imaging. Methods This retrospective single-center study analyzed 108 patients who underwent surgical resection between 2019 and 2024. Histological assessment determined the presence and subtype of STAS as limited or extended. PET/CT parameters included SUVmax, MTV, and TLG, measured by both a fixed threshold (42% of SUVmax) and patient-specific adaptive thresholds. Peritumoral rim values were calculated volumetrically. Variables associated with STAS in univariate analysis were tested with ROC analysis to determine diagnostic performance, and survival outcomes were evaluated using the Kaplan–Meier method. Results STAS was detected in 56.5% (n = 61) of patients, with the extended subtype observed in 67.2% (n = 41) of these cases. SUVmax was significantly higher in STAS-positive tumors and demonstrated modest discriminative ability, with an AUC of 0.619. A cut-off value of 5.3 achieved very high sensitivity (0.97) but only moderate specificity (0.64). None of the volumetric parameters, including MTV and TLG, differentiated between limited and extended STAS. Survival was numerically worse in patients with STAS, and extended STAS tended to have shorter median overall survival compared with limited STAS, though these differences did not reach statistical significance. Conclusion It was concluded that STAS, which plays a crucial role in the management of lung cancer treatment and prognosis prediction, can be predicted by SUVmax and may serve as a useful non-invasive parameter in the preoperative evaluation. However, other volumetric parameters showed no significant predictive value, and the high sensitivity but moderate specificity of SUVmax limit its standalone clinical utility. Future studies combining metabolic, radiomic, and morphological features may yield more accurate tools for preoperative STAS detection and tailored surgical planning. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung cancer continues to be the primary contributor to cancer-related deaths globally, responsible for approximately 1.8 million fatalities each year[1]. Among the histopathological subtypes, non-small cell lung cancer (NSCLC) represents nearly 85% of all diagnosed cases, with adenocarcinoma being the most prevalent form [2,3]. Despite progress in both diagnostic methodologies and therapeutic approaches, the prognosis remains poor, particularly for tumors exhibiting aggressive behavior and invasive morphological features [4]. In 2015, the World Health Organization (WHO) introduced “spread through air spaces” (STAS) as a distinct invasion pattern specific to lung adenocarcinomas [1]. Histologically, STAS is identified by tumor cells whether isolated, in micropapillary formations, or in solid aggregates found detached within the alveolar spaces beyond the tumor's margin. Multiple studies have consistently linked the presence of STAS to a significantly higher risk of local recurrence, especially in patients undergoing limited surgical procedures such as segmentectomy or wedge resection [2,3,5,6,7]. These findings support the consideration of STAS as a critical adverse prognostic marker during preoperative surgical planning. Since STAS can only be confirmed by examining surgically removed lung tissue under a microscope, there is still a need for reliable, non-invasive ways to predict it before surgery. Although frozen section analysis during surgery is sometimes used, studies have shown that while frozen section analysis has high specificity in the detection of STAS, but its sensitivity remains limited [8,9]. Consequently, attention has turned toward advanced imaging methods, most notably 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT), which offers metabolic insights into tumor biology that could serve as indirect indicators of invasive potential. Emerging evidence suggests that 18 F-FDG PET/CT derived metabolic parameters including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) may correlate with tumor aggressiveness and potential for dissemination [4,10]. While conventional approaches to STAS prediction have largely focused on SUVmax, CT morphological features, and clinical data, the predictive accuracy of these parameters remains limited due to inconsistent definitions and methodological variability across studies [11–14]. In response, recent studies employing advanced CT and PET/CT-based techniques such as 3D deep learning models incorporating peritumoral regions and radiomics-clinical fusion frameworks have demonstrated promising discriminatory performance [15–19]. These developments reflect a growing interest in radiomics and peritumoral imaging analyses as more objective and robust tools to improve preoperative STAS prediction and guide personalized surgical decision-making [20]. Recent efforts have focused on subclassifying STAS into “limited” and “extended” forms, based on the number and spatial extent of detached tumor clusters within alveolar spaces. Limited STAS refers to a smaller number or short-distance spread of tumor cells, while extended STAS implies more widespread involvement. In a recent study, it was demonstrated that extended STAS is associated with significantly worse disease-free and overall survival, independent of other tumor characteristics [21]. This growing interest in STAS subtypes has raised the need for accurate, non-invasive preoperative assessment tools to predict both the presence and the extent of STAS. This study aims to determine whether F-18 FDG PET/CT metabolic parameters including both whole-tumor and peritumoral measurements can reliably predict the presence of STAS in NSCLC patients before surgery. A reliable, non-invasive imaging-based model may support preoperative decision-making and contribute to improved clinical outcomes. Materials and Methods This retrospective, single-institution study reviewed the records of 237 patients who underwent 18 F-FDG PET/CT imaging at our department between 2019 and 2024 for metabolic characterization or staging of solitary pulmonary nodules. Of these, 108 patients who fulfilled the histopathological and clinical inclusion criteria were included in the analysis, while the remaining patients were excluded due to ineligibility. All patients subsequently underwent surgical resection without receiving any neoadjuvant chemotherapy or radiotherapy prior to surgery. Inclusion criteria for the study were as follows: a confirmed diagnosis of NSCLC, performance of 18 F-FDG PET/CT prior to surgical intervention, absence of any prior neoadjuvant treatment, and availability of complete pathological data, including evaluation for spread through air spaces [STAS). The patients whose data were utilized in this research were included in the study following the principles outlined in the Declaration of Helsinki. Prior to data collection, approval was obtained from the Ethics Committee for Medical Research at Ege University (Approval No: 25-2.1T/58). Additionally, written informed consent was secured from all patients for the use of their medical data within the scope of this study. 18 F-FDG PET/CT Imaging Patients underwent PET-CT imaging following intravenous administration of 18 F-FDG at a dose of 0.1 mCi/kg, after a fasting period of at least 4–6 hours. Imaging was initiated approximately 45–60 minutes post-injection, with scanning performed from the head to the proximal thighs using a clinical PET-CT scanner (Biograph high-definition 16-slice CT, Siemens Healthcare, Erlangen, Germany). CT imaging was conducted using the following technical settings: tube voltage of 80 kV, tube current of 120 mA, rotation time of 0.6 seconds, slice collimation of 0.6 mm, and image reconstruction with kernel B31f. PET acquisition was carried out at a rate of 1 mm/min per bed position using a 512×512 matrix. PET images were reconstructed using an attenuation-corrected iterative algorithm with three iterations and 24 subsets, a zoom factor of 10 (isotropic), a 512 matrix size, and a uniform isotropic resolution of 2 mm across the field of view, with CT resolution standardized at 0.24 mm. For image analysis and visual interpretation, PET and CT datasets were transferred to a 3D workstation. Evaluation was done using both attenuation-corrected and non-corrected images, as well as maximum intensity projection views of PET, CT, and fused PET-CT, in axial, sagittal, and coronal planes. The quantitative assessment of 18 F-FDG uptake in the primary tumor was carried out by determining the maximum standardized uptake value (SUVmax), mean of standardized uptake value (SUVmean9, metabolic tumor volume (MTV) defined using a threshold of 42% of SUVmax and total lesion glycolysis (TLG), which was calculated as the product of MTV and the mean SUV (SUVmean). Quantitative measurements were performed using the Syngo® Oncology Engine equipped with Siemens Syngo.via software (Siemens Healthcare). In addition, peritumoral MTV and TLG values were calculated by expanding the metabolic volume beyond the primary lesion through threshold reduction and subtracting the core MTV and TLG measurements from the resulting volumes. This method facilitated the targeted evaluation of metabolic activity in the surrounding peritumoral tissue. Pathological Assessment Surgical specimens were assessed by two experienced pathologists using WHO 2015 criteria. STAS was confirmed when detached tumor cells, clusters, or nests were observed in the alveolar spaces beyond the margin of the main tumor. Additionally, STAS was further evaluated by measuring the maximum spread distance (MSD) from the edge of the tumor to its most distant extension, and categorized as limited (MSD ≤ 1000 µm) or extended (MSD > 1000 µm). Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics for Version 25.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro–Wilk test. Variables that did not follow a normal distribution were summarized as median and interquartile range [IQR), while categorical variables were expressed as frequencies and percentages. Differences in metabolic parameters (SUVmax, SUVmean,MTV, TLG, MTVmax, TLGmax, peritumoral MTV, and peritumoral TLG) between STAS-positive and STAS-negative groups were evaluated using the Mann–Whitney U test. Similarly, comparisons between limited and extended STAS subgroups were also conducted with the Mann–Whitney U test due to non-parametric data distribution. Receiver Operating Characteristic (ROC) curve analysis was subsequently performed to evaluate the diagnostic accuracy of those metabolic PET/CT parameters that exhibited statistically significant differences between STAS-positive and STAS-negative groups, as determined by the Mann–Whitney U test. The area under the curve (AUC) was calculated for each of these parameters, and optimal cut-off values were established using the Youden Index. Kaplan–Meier survival analysis was conducted to compare overall survival (OS) between patient groups stratified by STAS status and PET/CT metabolic thresholds. Log-rank tests were used to determine statistical significance between survival curves. A p-value < 0.05 was considered statistically significant for all tests. Results Patient Characteristics A total of 108 patients were included in the study, comprising 86 males and 22 females, with a mean age of 67 years (range: 45–87 years). Surgical procedures consisted of wedge resection in 47 patients, lobectomy in 54 patients, and pneumonectomy in 7 patients. The most common histological subtype was adenocarcinoma in 60 patients, followed by squamous cell carcinoma in 38 patients (19 were non-keratinizing, 14 were keratinizing, and 5 lacked further subtyping). In addition, adenosquamous carcinoma was observed in 4 patients, mucinous adenocarcinoma in 2 patients, and other rare subtypes in the remaining cases. STAS was detected in 61 patients (56.5%), while it was absent in 47 patients (43.5%). Among the STAS-positive cases, 41 patients (67.2%) were classified as having extended STAS, whereas 20 patients (32.8%) had limited STAS. The overall number of events (deaths) was 29, with 20 occurring in the STAS-positive group and 9 in the STAS-negative group. ( Table 1 . ) Association between metabolic metabolic parameters and STAS Non-parametric testing using the Mann–Whitney U test revealed a statistically significant difference in SUVmax between STAS-positive and STAS-negative groups ( p = 0.035 ), with higher median values observed in the STAS-positive group. No significant differences were found in other metabolic PET/CT parameters including SUVmean (p = 0.071), MTVmax (p = 0.941) , TLGmax (p = 0.270) , MTV (p = 0.635) , TLG (p = 0.351) , peritumoral MTV (p = 0.459) , and peritumoral TLG (p = 0.223) . Receiver Operating Characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of PET/CT-derived metabolic parameters in predicting the presence of STAS. SUVmax demonstrated the highest discriminative ability among all parameters, with an area under the curve (AUC) of 0.619 . Using a cut-off value of SUVmax ≥ 5.3 , the model achieved a sensitivity of 97% and a specificity of 64% for identifying STAS-positive cases. ( Fig. 1 . ) Subgroup Analysis of STAS-Positive Patients Among patients with STAS-positive tumors, further subgroup analysis was conducted to evaluate whether metabolic PET/CT parameters could differentiate between limited and extended patterns of STAS dissemination. However, no statistically significant differences were observed in SUVmax (p = 0.753), SUVmean (p = 0.585), MTVmax (p = 0.070), TLGmax (p = 0.147), MTV (p = 0.062), TLG (p = 0.192), peritumoral MTV (p = 0.171), or peritumoral TLG (p = 0.167) between the two subgroups. ( Fig. 2 . ) Overall Survival in Relation to STAS Presence and Extent The median overall survival (OS) was shorter in the STAS-positive group than in the STAS-negative group (55.7 vs. 64.7 months); however, this difference did not reach statistical significance according to the Kaplan–Meier analysis (log-rank p = 0.140). Among STAS-positive patients, those classified with limited STAS demonstrated a numerically longer median OS (59.3 months) compared to those with extended STAS (53.5 months), although this difference was likewise not statistically significant (log-rank p = 0.582). ( Fig. 3 ). Discussion In this retrospective study, we explored the potential of metabolic parameters derived from 18 F-FDG PET/CT to predict both the presence and extent of spread through air spaces (STAS) in patients with surgically resected non-small cell lung cancer (NSCLC). STAS was a frequent histological finding, and the extended form appeared more commonly among affected cases. Survival outcomes were numerically worse in the STAS-positive group, yet the difference did not reach statistical significance. A similar nonsignificant pattern was observed when comparing survival between limited and extended STAS subtypes. Among all evaluated metabolic indicators, only SUVmax showed a meaningful association with STAS presence. While it demonstrated a modest ability to discriminate between STAS-positive and STAS-negative tumors, other parameters such as SUVmean, MTV, TLG, and threshold-based or peritumoral measurements did not show statistically significant findings within the resolution limits of PET/CT. The prognostic and predictive role of STAS in NSCLC has been increasingly recognized, with multiple studies confirming its association with higher recurrence rates and worse survival outcomes, particularly among early-stage tumors treated with sublobar resection [2,3,5,6,7,22]. Our findings are consistent with these reports, as STAS-positive patients in our cohort exhibited shorter overall survival, although the difference was not statistically significant. This may be attributed to the retrospective design and relatively limited sample size. In terms of imaging-based prediction, previous research has highlighted the potential of FDG PET/CT parameters to reflect tumor aggressiveness and predict histopathological features such as STAS. Li et al. [4] and Nishimori et al. [10] reported significantly higher SUVmax values in STAS-positive tumors, aligning with our results. Falay et al. [12] similarly found SUVmax to be a useful preoperative indicator, although the overall predictive accuracy varied across studies. Hanin et al. [23] emphasized that increased FDG uptake, particularly elevated SUVmax, was associated with poorer prognosis in early-stage NSCLC, and in addition, other authors have explored the utility of metabolic tumor volume (MTV) and total lesion glycolysis (TLG). For example, Kang et al. [24], Agarwal et al. [25], Yoo et al [26] and Park et al. [27] demonstrated that elevated metabolic tumor burden correlates with worse outcomes and more aggressive histology, while Wang et al. [28] suggested that higher MTV and TLG values were associated with STAS-positive adenocarcinomas. However, in our study, neither MTV nor TLG nor their threshold-based or peritumoral derivatives showed significant associations with STAS presence. These discrepancies may reflect heterogeneity in segmentation methods, tumor subtypes, and imaging protocols across institutions. In the present study, SUVmax demonstrated modest discriminatory ability for predicting the presence of STAS, with an AUC of 0.619. A threshold of ≥ 5.3 yielded a high sensitivity (97%). but only moderate specificity 864%). These results indicate that a lower SUVmax cut-off may be useful in minimizing false-negative predictions of STAS, although the overall diagnostic performance remains limited. This aligns with prior studies that reported relatively low-to-moderate AUC values for conventional PET parameters. For instance, Falay et al. [12] reported SUVmax cut-off values ranging between 4.5 and 6.2, with variable sensitivity and specificity depending on the analytic method and tumor subtype. To overcome such limitations, recent investigations have explored more advanced imaging approaches. Zheng et al. [15] developed a 3D deep learning model based on PET/CT images, achieving an AUC of 0.89 for STAS prediction in early-stage adenocarcinoma. Similarly, Chen et al. [16] incorporated PET/CT-derived radiomic features into a clinical fusion model and demonstrated improved accuracy compared to SUVmax alone. Another study by Tao et al. [17] applied 3D convolutional neural networks on contrast-enhanced CT and achieved superior performance in identifying STAS patterns. These data collectively indicate that while SUVmax remains a readily accessible parameter with some predictive utility, it is likely insufficient as a standalone marker for preoperative STAS detection. Moreover, recent studies have emphasized the importance of incorporating peritumoral or radiomic features extracted from both the tumor and surrounding lung parenchyma. Models proposed by Liao et al. [19] and Li et al. [18], which combine tumor geometry, radiomic texture, and spatial distribution, have demonstrated AUC values exceeding 0.80, offering a more robust and objective framework for individualized surgical planning. The subclassification of STAS into limited and extended forms has gained increasing attention due to its potential prognostic relevance. In our study, the majority of STAS-positive cases (67.2%) were classified as extended STAS. Although median overall survival was numerically shorter in the extended subgroup (53.5 months) compared to the limited subgroup (59.3 months), this difference did not reach statistical significance. These findings are in line with the recent work by Hashinokuchi et al. [21], who reported that extended STAS was independently associated with poorer disease-free and overall survival. Similarly, Toki et al. [29] highlighted that the extent of STAS dissemination may reflect underlying biological aggressiveness and could potentially influence postoperative therapeutic decisions. While our results did not demonstrate a statistically significant survival gap between limited and extended STAS groups, the observed numerical trend suggests that this subclassification may still hold clinical relevance, particularly in early-stage tumors being considered for sublobar resection. Importantly, none of the metabolic PET/CT parameters including SUVmax, SUVmean, MTV, TLG, and their threshold-based or peritumoral variants were able to distinguish between limited and extended STAS subtypes in our cohort. This highlights the current limitations of metabolic imaging in detecting not only the presence but also the spatial extent of STAS. Future efforts combining functional imaging with spatially-aware radiomics or AI-based volumetric modeling may offer better resolution in predicting such subpatterns preoperatively. The identification of STAS prior to surgery is of particular clinical importance, especially in patients being considered for limited resections such as wedge resection or segmentectomy. Previous studies have consistently shown that STAS-positive tumors are associated with higher rates of local recurrence following sublobar resections compared to lobectomy [5–7]. Consequently, preoperative identification of STAS may influence surgical decision-making and justify a more extensive resection, even in early-stage disease. Despite its clinical significance, STAS remains a histopathological diagnosis, and intraoperative frozen section analysis has demonstrated only limited sensitivity in reliably detecting it [8,9]. This underscores the urgent need for non-invasive preoperative predictors to guide individualized treatment strategies. Our study contributes to this effort by evaluating the role of metabolic imaging an easily accessible and widely used modality in predicting STAS status. Although the predictive power of SUVmax was modest, its high sensitivity may allow it to serve as a preliminary screening tool to flag patients at risk for STAS. This could be particularly useful in conjunction with other clinical or radiologic predictors when making operative decisions. In current PET/CT systems, spatial resolution has improved to approximately 0.4 cm, corresponding to a tissue mass of about 0.1–0.5 to 1 g or roughly 10⁸–10⁹ tumor cells. However, due to the partial volume effect and the inherent limitations in detecting lesions below this threshold, small-volume disease or subtle peritumoral extensions such as early manifestations of STAS may not be adequately visualized. This limitation is particularly relevant when evaluating invasive patterns that extend beyond the primary tumor mass but remain below the spatial resolution capacity of the imaging system. Emerging decision-support frameworks such as the stepwise algorithm proposed by Suh et al. [30], which integrates imaging findings with histologic and anatomic factors, underscore the evolving role of multi-parameter models in guiding the extent of surgical intervention. While our findings do not yet justify a change in practice based solely on PET metrics, they reinforce the potential utility of metabolic data in multi-disciplinary planning. This study has several notable strengths. First, it represents one of the few investigations to evaluate a wide spectrum of PET/CT-derived metabolic parameters including core, threshold-based, and peritumoral volumetrics in relation to both the presence and extent of STAS. Second, the use of a standardized imaging protocol and consistent pathological assessment, including subclassification into limited and extended STAS, enhances the internal validity of our findings. Finally, the incorporation of survival analysis adds prognostic context to the imaging metrics evaluated. However, some limitations must be acknowledged. The retrospective and single-center nature of the study introduces potential selection and information bias. Additionally, the sample size, particularly in subgroup analyses of STAS extent, may have been underpowered to detect subtle differences. Moreover, our analysis focused exclusively on metabolic imaging features, without incorporating other potentially relevant variables such as tumor morphology, molecular mutations, or detailed radiologic features that may further enhance STAS prediction. Lastly, the absence of radiomic or deep learning-based analyses which have shown promise in recent literature limits the predictive depth of the current approach. Future multicenter prospective studies incorporating advanced quantitative imaging tools and external validation cohorts are needed to refine STAS prediction models and translate them into clinical practice. Conclusion Given the clinical significance of STAS as an aggressive invasion pattern influencing surgical strategy, 18 F-FDG PET/CT may offer valuable insights for its non-invasive preoperative detection. Among the metabolic parameters evaluated in our study, only SUVmax was found to be significantly associated with the presence of STAS, demonstrating high sensitivity but limited specificity. However, none of the volumetric measurements were able to differentiate between limited and extended STAS subtypes. These findings suggest that while metabolic imaging, particularly SUVmax, may serve as a preliminary screening tool for STAS, it remains insufficient as a standalone predictive marker. Future studies integrating radiomics, tumor morphology, and AI-assisted models may provide more powerful tools for preoperative STAS detection and personalized surgical planning. Declarations Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Declaration Aysegul Akgun (Corresponding author) and Batuhan Kocabeyoglu (Corresponding author) conceived and designed the study. Data were acquired by B.K., Goktug Aydogan, and Pinar Gursoy. Data and algorithm quality control were performed by A.A. and Deniz Nart. B.K., Recep Halit Tokac, and A.A. analyzed and interpreted the data. Statistical analyses were conducted by B.K. and R.H.T. B.K. prepared the main manuscript draft. Manuscript editing was performed by B.K., G.A., and R.H.T. A.A. and D.N. critically reviewed the manuscript. All authors approved the final version of the manuscript. Contact to corresponding authors Email address of the corresponding authors are [email protected] and [email protected] . Consent to Participate Declaration The authors affirm that all necessary approvals were obtained prior to data collection and that participants rights, privacy, and confidentiality were fully protected throughout the study. Data Availability The data that support the findings of this study are available from Ege University Faculty of Medicine Hospital, but restrictions apply to the availability of these data, which were used under licence for the current study and are therefore not publicly available. The data can be made available upon reasonable request and with permission from Ege University Faculty of Medicine Hospital. Ethics Declaration The patients whose data were utilized in this research were included in the study following the principles outlined in the Declaration of Helsinki. Prior to data collection, approval was obtained from the Ethics Committee for Medical Research at Ege University (Approval No: 25-2.1T/58). 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Suh JW, Jeong YH, Cho A, Kim DJ, Chung KY, Shim HS, Lee CY. Stepwise flowchart for decision making on sublobar resection through the estimation of spread through air space in early stage lung cancer1. Lung Cancer. 2020 Apr;142:28-33. doi: 10.1016/j.lungcan.2020.02.001. Epub 2020 Feb 4. Erratum in: Lung Cancer. 2020 Aug;146:391. doi: 10.1016/j.lungcan.2020.06.011. PMID: 32065918. Yoo IeR, Chung SK, Park HL, Choi WH, Kim YK, Lee KY, Wang YP. Prognostic value of SUVmax and metabolic tumor volume on 18F-FDG PET/CT in early stage non-small cell lung cancer patients without LN metastasis. Biomed Mater Eng. 2014;24(6):3091-103. doi: 10.3233/BME-141131. PMID: 25227018. Park SY, Cho A, Yu WS, Lee CY, Lee JG, Kim DJ, Chung KY. Prognostic value of total lesion glycolysis by 18F-FDG PET/CT in surgically resected stage IA non-small cell lung cancer. J Nucl Med. 2015 Jan;56(1):45-9. doi: 10.2967/jnumed.114.147561. Epub 2014 Dec 18. PMID: 25525185. Li C, Jiang C, Gong J, Wu X, Luo Y, Sun G. A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma. Quant Imaging Med Surg. 2020 Oct;10(10):1984-1993. doi: 10.21037/qims-20-724. PMID: 33014730; PMCID: PMC7495322. Liao G, Huang L, Wu S, Zhang P, Xie D, Yao L, Zhang Z, Yao S, Shanshan L, Wang S, Wang G, Wing-Chi Chan L, Zhou H. Preoperative CT-based peritumoral and tumoral radiomic features prediction for tumor spread through air spaces in clinical stage I lung adenocarcinoma. Lung Cancer. 2022 Jan;163:87-95. doi: 10.1016/j.lungcan.2021.11.017. Epub 2021 Dec 6. PMID: 34942493. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1..docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":619323,"visible":true,"origin":"","legend":"\u003cp\u003eThe yellow ROI (No. 1) represents the automatically delineated volume using the classical (%42) threshold method, while the blue ROI (No. 2) corresponds to the SUVmax-adjusted adaptive threshold.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8065101/v1/fa84750e26801f8acd1671c2.png"},{"id":97503464,"identity":"b9c6e4ef-830f-41a8-b51e-8a6730f7494e","added_by":"auto","created_at":"2025-12-05 06:53:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109526,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves evaluating the predictive efficieny of SUVmax\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8065101/v1/40f88a348f8bbd7fdead5ea8.png"},{"id":97503466,"identity":"0f36b594-2109-449e-8dea-201465045c4e","added_by":"auto","created_at":"2025-12-05 06:53:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45667,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of Overall Survival(OS) according to STAS presence\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8065101/v1/f25963aab20ef3712141e573.png"},{"id":97503467,"identity":"11960511-2364-428d-949a-3e602e22b28b","added_by":"auto","created_at":"2025-12-05 06:53:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43770,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of Overall Survival(OS) according to STAS extent\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8065101/v1/0503cd070bb99f6b63727172.png"},{"id":98777804,"identity":"183e9539-41a1-46e4-91a4-d81677e13a9e","added_by":"auto","created_at":"2025-12-22 12:28:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1902753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8065101/v1/5a997805-b897-4e85-bf9c-1254d3553dca.pdf"},{"id":97503469,"identity":"39692fba-357c-4c74-95c0-c8a213ffb125","added_by":"auto","created_at":"2025-12-05 06:53:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18240,"visible":true,"origin":"","legend":"","description":"","filename":"Table1..docx","url":"https://assets-eu.researchsquare.com/files/rs-8065101/v1/4f2cf2e2457402846632a975.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can F-18 FDG PET/CT Metabolic Parameters Predict STAS in Non-Small Cell Lung Cancer Patients?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer continues to be the primary contributor to cancer-related deaths globally, responsible for approximately 1.8\u0026nbsp;million fatalities each year[1]. Among the histopathological subtypes, non-small cell lung cancer (NSCLC) represents nearly 85% of all diagnosed cases, with adenocarcinoma being the most prevalent form [2,3]. Despite progress in both diagnostic methodologies and therapeutic approaches, the prognosis remains poor, particularly for tumors exhibiting aggressive behavior and invasive morphological features [4].\u003c/p\u003e\u003cp\u003eIn 2015, the World Health Organization (WHO) introduced \u0026ldquo;spread through air spaces\u0026rdquo; (STAS) as a distinct invasion pattern specific to lung adenocarcinomas [1]. Histologically, STAS is identified by tumor cells whether isolated, in micropapillary formations, or in solid aggregates found detached within the alveolar spaces beyond the tumor's margin. Multiple studies have consistently linked the presence of STAS to a significantly higher risk of local recurrence, especially in patients undergoing limited surgical procedures such as segmentectomy or wedge resection [2,3,5,6,7]. These findings support the consideration of STAS as a critical adverse prognostic marker during preoperative surgical planning.\u003c/p\u003e\u003cp\u003eSince STAS can only be confirmed by examining surgically removed lung tissue under a microscope, there is still a need for reliable, non-invasive ways to predict it before surgery. Although frozen section analysis during surgery is sometimes used, studies have shown that while frozen section analysis has high specificity in the detection of STAS, but its sensitivity remains limited [8,9]. Consequently, attention has turned toward advanced imaging methods, most notably \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose positron emission tomography/computed tomography (\u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT), which offers metabolic insights into tumor biology that could serve as indirect indicators of invasive potential. Emerging evidence suggests that \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT derived metabolic parameters including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) may correlate with tumor aggressiveness and potential for dissemination [4,10].\u003c/p\u003e\u003cp\u003eWhile conventional approaches to STAS prediction have largely focused on SUVmax, CT morphological features, and clinical data, the predictive accuracy of these parameters remains limited due to inconsistent definitions and methodological variability across studies [11\u0026ndash;14]. In response, recent studies employing advanced CT and PET/CT-based techniques such as 3D deep learning models incorporating peritumoral regions and radiomics-clinical fusion frameworks have demonstrated promising discriminatory performance [15\u0026ndash;19]. These developments reflect a growing interest in radiomics and peritumoral imaging analyses as more objective and robust tools to improve preoperative STAS prediction and guide personalized surgical decision-making [20].\u003c/p\u003e\u003cp\u003eRecent efforts have focused on subclassifying STAS into \u0026ldquo;limited\u0026rdquo; and \u0026ldquo;extended\u0026rdquo; forms, based on the number and spatial extent of detached tumor clusters within alveolar spaces. Limited STAS refers to a smaller number or short-distance spread of tumor cells, while extended STAS implies more widespread involvement. In a recent study, it was demonstrated that extended STAS is associated with significantly worse disease-free and overall survival, independent of other tumor characteristics [21]. This growing interest in STAS subtypes has raised the need for accurate, non-invasive preoperative assessment tools to predict both the presence and the extent of STAS.\u003c/p\u003e\u003cp\u003eThis study aims to determine whether F-18 FDG PET/CT metabolic parameters including both whole-tumor and peritumoral measurements can reliably predict the presence of STAS in NSCLC patients before surgery. A reliable, non-invasive imaging-based model may support preoperative decision-making and contribute to improved clinical outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis retrospective, single-institution study reviewed the records of 237 patients who underwent \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging at our department between 2019 and 2024 for metabolic characterization or staging of solitary pulmonary nodules. Of these, 108 patients who fulfilled the histopathological and clinical inclusion criteria were included in the analysis, while the remaining patients were excluded due to ineligibility. All patients subsequently underwent surgical resection without receiving any neoadjuvant chemotherapy or radiotherapy prior to surgery. Inclusion criteria for the study were as follows: a confirmed diagnosis of NSCLC, performance of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT prior to surgical intervention, absence of any prior neoadjuvant treatment, and availability of complete pathological data, including evaluation for spread through air spaces [STAS). The patients whose data were utilized in this research were included in the study following the principles outlined in the Declaration of Helsinki. Prior to data collection, approval was obtained from the Ethics Committee for Medical Research at Ege University (Approval No: 25-2.1T/58). Additionally, written informed consent was secured from all patients for the use of their medical data within the scope of this study.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e18\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eF-FDG PET/CT Imaging\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients underwent PET-CT imaging following intravenous administration of \u003csup\u003e18\u003c/sup\u003eF-FDG at a dose of 0.1 mCi/kg, after a fasting period of at least 4\u0026ndash;6 hours. Imaging was initiated approximately 45\u0026ndash;60 minutes post-injection, with scanning performed from the head to the proximal thighs using a clinical PET-CT scanner (Biograph high-definition 16-slice CT, Siemens Healthcare, Erlangen, Germany).\u003c/p\u003e\u003cp\u003eCT imaging was conducted using the following technical settings: tube voltage of 80 kV, tube current of 120 mA, rotation time of 0.6 seconds, slice collimation of 0.6 mm, and image reconstruction with kernel B31f. PET acquisition was carried out at a rate of 1 mm/min per bed position using a 512\u0026times;512 matrix. PET images were reconstructed using an attenuation-corrected iterative algorithm with three iterations and 24 subsets, a zoom factor of 10 (isotropic), a 512 matrix size, and a uniform isotropic resolution of 2 mm across the field of view, with CT resolution standardized at 0.24 mm. For image analysis and visual interpretation, PET and CT datasets were transferred to a 3D workstation. Evaluation was done using both attenuation-corrected and non-corrected images, as well as maximum intensity projection views of PET, CT, and fused PET-CT, in axial, sagittal, and coronal planes.\u003c/p\u003e\u003cp\u003eThe quantitative assessment of \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in the primary tumor was carried out by determining the maximum standardized uptake value (SUVmax), mean of standardized uptake value (SUVmean9, metabolic tumor volume (MTV) defined using a threshold of 42% of SUVmax and total lesion glycolysis (TLG), which was calculated as the product of MTV and the mean SUV (SUVmean). Quantitative measurements were performed using the Syngo\u0026reg; Oncology Engine equipped with Siemens Syngo.via software (Siemens Healthcare).\u003c/p\u003e\u003cp\u003eIn addition, peritumoral MTV and TLG values were calculated by expanding the metabolic volume beyond the primary lesion through threshold reduction and subtracting the core MTV and TLG measurements from the resulting volumes. This method facilitated the targeted evaluation of metabolic activity in the surrounding peritumoral tissue.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePathological Assessment\u003c/h2\u003e\u003cp\u003eSurgical specimens were assessed by two experienced pathologists using WHO 2015 criteria. STAS was confirmed when detached tumor cells, clusters, or nests were observed in the alveolar spaces beyond the margin of the main tumor. Additionally, STAS was further evaluated by measuring the maximum spread distance (MSD) from the edge of the tumor to its most distant extension, and categorized as limited (MSD\u0026thinsp;\u0026le;\u0026thinsp;1000 \u0026micro;m) or extended (MSD\u0026thinsp;\u0026gt;\u0026thinsp;1000 \u0026micro;m).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics for Version 25.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. Variables that did not follow a normal distribution were summarized as median and interquartile range [IQR), while categorical variables were expressed as frequencies and percentages.\u003c/p\u003e\u003cp\u003eDifferences in metabolic parameters (SUVmax, SUVmean,MTV, TLG, MTVmax, TLGmax, peritumoral MTV, and peritumoral TLG) between STAS-positive and STAS-negative groups were evaluated using the Mann\u0026ndash;Whitney U test. Similarly, comparisons between limited and extended STAS subgroups were also conducted with the Mann\u0026ndash;Whitney U test due to non-parametric data distribution.\u003c/p\u003e\u003cp\u003eReceiver Operating Characteristic (ROC) curve analysis was subsequently performed to evaluate the diagnostic accuracy of those metabolic PET/CT parameters that exhibited statistically significant differences between STAS-positive and STAS-negative groups, as determined by the Mann\u0026ndash;Whitney U test. The area under the curve (AUC) was calculated for each of these parameters, and optimal cut-off values were established using the Youden Index.\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier survival analysis was conducted to compare overall survival (OS) between patient groups stratified by STAS status and PET/CT metabolic thresholds. Log-rank tests were used to determine statistical significance between survival curves. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all tests.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eA total of 108 patients were included in the study, comprising 86 males and 22 females, with a mean age of 67 years (range: 45\u0026ndash;87 years). Surgical procedures consisted of wedge resection in 47 patients, lobectomy in 54 patients, and pneumonectomy in 7 patients.\u003c/p\u003e\u003cp\u003eThe most common histological subtype was adenocarcinoma in 60 patients, followed by squamous cell carcinoma in 38 patients (19 were non-keratinizing, 14 were keratinizing, and 5 lacked further subtyping). In addition, adenosquamous carcinoma was observed in 4 patients, mucinous adenocarcinoma in 2 patients, and other rare subtypes in the remaining cases.\u003c/p\u003e\u003cp\u003eSTAS was detected in 61 patients (56.5%), while it was absent in 47 patients (43.5%). Among the STAS-positive cases, 41 patients (67.2%) were classified as having extended STAS, whereas 20 patients (32.8%) had limited STAS. The overall number of events (deaths) was 29, with 20 occurring in the STAS-positive group and 9 in the STAS-negative group. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation between metabolic metabolic parameters and STAS\u003c/h3\u003e\n\u003cp\u003eNon-parametric testing using the \u003cb\u003eMann\u0026ndash;Whitney U test\u003c/b\u003e revealed a statistically significant difference in \u003cb\u003eSUVmax\u003c/b\u003e between STAS-positive and STAS-negative groups (\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.035\u003c/b\u003e), with higher median values observed in the STAS-positive group. No significant differences were found in other metabolic PET/CT parameters including \u003cb\u003eSUVmean (p\u0026thinsp;=\u0026thinsp;0.071), MTVmax (p\u0026thinsp;=\u0026thinsp;0.941)\u003c/b\u003e, \u003cb\u003eTLGmax (p\u0026thinsp;=\u0026thinsp;0.270)\u003c/b\u003e, \u003cb\u003eMTV (p\u0026thinsp;=\u0026thinsp;0.635)\u003c/b\u003e, \u003cb\u003eTLG (p\u0026thinsp;=\u0026thinsp;0.351)\u003c/b\u003e, \u003cb\u003eperitumoral MTV (p\u0026thinsp;=\u0026thinsp;0.459)\u003c/b\u003e, and \u003cb\u003eperitumoral TLG (p\u0026thinsp;=\u0026thinsp;0.223)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReceiver Operating Characteristic (ROC) curve analysis\u003c/b\u003e was conducted to evaluate the diagnostic performance of PET/CT-derived metabolic parameters in predicting the presence of STAS. \u003cb\u003eSUVmax\u003c/b\u003e demonstrated the highest discriminative ability among all parameters, with an \u003cb\u003earea under the curve (AUC) of 0.619\u003c/b\u003e. Using a cut-off value of \u003cb\u003eSUVmax\u0026thinsp;\u0026ge;\u0026thinsp;5.3\u003c/b\u003e, the model achieved a \u003cb\u003esensitivity of 97%\u003c/b\u003e and a \u003cb\u003especificity of 64%\u003c/b\u003e for identifying STAS-positive cases. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analysis of STAS-Positive Patients\u003c/h2\u003e\u003cp\u003eAmong patients with STAS-positive tumors, further subgroup analysis was conducted to evaluate whether metabolic PET/CT parameters could differentiate between \u003cb\u003elimited\u003c/b\u003e and \u003cb\u003eextended\u003c/b\u003e patterns of STAS dissemination. However, no statistically significant differences were observed in SUVmax (p\u0026thinsp;=\u0026thinsp;0.753), SUVmean (p\u0026thinsp;=\u0026thinsp;0.585), MTVmax (p\u0026thinsp;=\u0026thinsp;0.070), TLGmax (p\u0026thinsp;=\u0026thinsp;0.147), MTV (p\u0026thinsp;=\u0026thinsp;0.062), TLG (p\u0026thinsp;=\u0026thinsp;0.192), peritumoral MTV (p\u0026thinsp;=\u0026thinsp;0.171), or peritumoral TLG (p\u0026thinsp;=\u0026thinsp;0.167) between the two subgroups. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOverall Survival in Relation to STAS Presence and Extent\u003c/h3\u003e\n\u003cp\u003eThe median overall survival (OS) was shorter in the STAS-positive group than in the STAS-negative group (55.7 vs. 64.7 months); however, this difference did not reach statistical significance according to the Kaplan\u0026ndash;Meier analysis (log-rank p\u0026thinsp;=\u0026thinsp;0.140). Among STAS-positive patients, those classified with limited STAS demonstrated a numerically longer median OS (59.3 months) compared to those with extended STAS (53.5 months), although this difference was likewise not statistically significant (log-rank p\u0026thinsp;=\u0026thinsp;0.582). \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study, we explored the potential of metabolic parameters derived from \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT to predict both the presence and extent of spread through air spaces (STAS) in patients with surgically resected non-small cell lung cancer (NSCLC). STAS was a frequent histological finding, and the extended form appeared more commonly among affected cases. Survival outcomes were numerically worse in the STAS-positive group, yet the difference did not reach statistical significance. A similar nonsignificant pattern was observed when comparing survival between limited and extended STAS subtypes.\u003c/p\u003e\u003cp\u003eAmong all evaluated metabolic indicators, only SUVmax showed a meaningful association with STAS presence. While it demonstrated a modest ability to discriminate between STAS-positive and STAS-negative tumors, other parameters such as SUVmean, MTV, TLG, and threshold-based or peritumoral measurements did not show statistically significant findings within the resolution limits of PET/CT.\u003c/p\u003e\u003cp\u003eThe prognostic and predictive role of STAS in NSCLC has been increasingly recognized, with multiple studies confirming its association with higher recurrence rates and worse survival outcomes, particularly among early-stage tumors treated with sublobar resection [2,3,5,6,7,22]. Our findings are consistent with these reports, as STAS-positive patients in our cohort exhibited shorter overall survival, although the difference was not statistically significant. This may be attributed to the retrospective design and relatively limited sample size.\u003c/p\u003e\u003cp\u003eIn terms of imaging-based prediction, previous research has highlighted the potential of FDG PET/CT parameters to reflect tumor aggressiveness and predict histopathological features such as STAS. Li et al. [4] and Nishimori et al. [10] reported significantly higher SUVmax values in STAS-positive tumors, aligning with our results. Falay et al. [12] similarly found SUVmax to be a useful preoperative indicator, although the overall predictive accuracy varied across studies.\u003c/p\u003e\u003cp\u003eHanin et al. [23] emphasized that increased FDG uptake, particularly elevated SUVmax, was associated with poorer prognosis in early-stage NSCLC, and in addition, other authors have explored the utility of metabolic tumor volume (MTV) and total lesion glycolysis (TLG). For example, Kang et al. [24], Agarwal et al. [25], Yoo et al [26] and Park et al. [27] demonstrated that elevated metabolic tumor burden correlates with worse outcomes and more aggressive histology, while Wang et al. [28] suggested that higher MTV and TLG values were associated with STAS-positive adenocarcinomas. However, in our study, neither MTV nor TLG nor their threshold-based or peritumoral derivatives showed significant associations with STAS presence. These discrepancies may reflect heterogeneity in segmentation methods, tumor subtypes, and imaging protocols across institutions.\u003c/p\u003e\u003cp\u003eIn the present study, SUVmax demonstrated modest discriminatory ability for predicting the presence of STAS, with an AUC of 0.619. A threshold of \u0026ge;\u0026thinsp;5.3 yielded a high sensitivity (97%). but only moderate specificity 864%). These results indicate that a lower SUVmax cut-off may be useful in minimizing false-negative predictions of STAS, although the overall diagnostic performance remains limited. This aligns with prior studies that reported relatively low-to-moderate AUC values for conventional PET parameters. For instance, Falay et al. [12] reported SUVmax cut-off values ranging between 4.5 and 6.2, with variable sensitivity and specificity depending on the analytic method and tumor subtype.\u003c/p\u003e\u003cp\u003eTo overcome such limitations, recent investigations have explored more advanced imaging approaches. Zheng et al. [15] developed a 3D deep learning model based on PET/CT images, achieving an AUC of 0.89 for STAS prediction in early-stage adenocarcinoma. Similarly, Chen et al. [16] incorporated PET/CT-derived radiomic features into a clinical fusion model and demonstrated improved accuracy compared to SUVmax alone. Another study by Tao et al. [17] applied 3D convolutional neural networks on contrast-enhanced CT and achieved superior performance in identifying STAS patterns. These data collectively indicate that while SUVmax remains a readily accessible parameter with some predictive utility, it is likely insufficient as a standalone marker for preoperative STAS detection.\u003c/p\u003e\u003cp\u003eMoreover, recent studies have emphasized the importance of incorporating peritumoral or radiomic features extracted from both the tumor and surrounding lung parenchyma. Models proposed by Liao et al. [19] and Li et al. [18], which combine tumor geometry, radiomic texture, and spatial distribution, have demonstrated AUC values exceeding 0.80, offering a more robust and objective framework for individualized surgical planning.\u003c/p\u003e\u003cp\u003eThe subclassification of STAS into limited and extended forms has gained increasing attention due to its potential prognostic relevance. In our study, the majority of STAS-positive cases (67.2%) were classified as extended STAS. Although median overall survival was numerically shorter in the extended subgroup (53.5 months) compared to the limited subgroup (59.3 months), this difference did not reach statistical significance. These findings are in line with the recent work by Hashinokuchi et al. [21], who reported that extended STAS was independently associated with poorer disease-free and overall survival. Similarly, Toki et al. [29] highlighted that the extent of STAS dissemination may reflect underlying biological aggressiveness and could potentially influence postoperative therapeutic decisions. While our results did not demonstrate a statistically significant survival gap between limited and extended STAS groups, the observed numerical trend suggests that this subclassification may still hold clinical relevance, particularly in early-stage tumors being considered for sublobar resection.\u003c/p\u003e\u003cp\u003eImportantly, none of the metabolic PET/CT parameters including SUVmax, SUVmean, MTV, TLG, and their threshold-based or peritumoral variants were able to distinguish between limited and extended STAS subtypes in our cohort. This highlights the current limitations of metabolic imaging in detecting not only the presence but also the spatial extent of STAS. Future efforts combining functional imaging with spatially-aware radiomics or AI-based volumetric modeling may offer better resolution in predicting such subpatterns preoperatively.\u003c/p\u003e\u003cp\u003eThe identification of STAS prior to surgery is of particular clinical importance, especially in patients being considered for limited resections such as wedge resection or segmentectomy. Previous studies have consistently shown that STAS-positive tumors are associated with higher rates of local recurrence following sublobar resections compared to lobectomy [5\u0026ndash;7]. Consequently, preoperative identification of STAS may influence surgical decision-making and justify a more extensive resection, even in early-stage disease.\u003c/p\u003e\u003cp\u003eDespite its clinical significance, STAS remains a histopathological diagnosis, and intraoperative frozen section analysis has demonstrated only limited sensitivity in reliably detecting it [8,9]. This underscores the urgent need for non-invasive preoperative predictors to guide individualized treatment strategies.\u003c/p\u003e\u003cp\u003eOur study contributes to this effort by evaluating the role of metabolic imaging an easily accessible and widely used modality in predicting STAS status. Although the predictive power of SUVmax was modest, its high sensitivity may allow it to serve as a preliminary screening tool to flag patients at risk for STAS. This could be particularly useful in conjunction with other clinical or radiologic predictors when making operative decisions.\u003c/p\u003e\u003cp\u003eIn current PET/CT systems, spatial resolution has improved to approximately 0.4 cm, corresponding to a tissue mass of about 0.1\u0026ndash;0.5 to 1 g or roughly 10⁸\u0026ndash;10⁹ tumor cells. However, due to the partial volume effect and the inherent limitations in detecting lesions below this threshold, small-volume disease or subtle peritumoral extensions such as early manifestations of STAS may not be adequately visualized. This limitation is particularly relevant when evaluating invasive patterns that extend beyond the primary tumor mass but remain below the spatial resolution capacity of the imaging system.\u003c/p\u003e\u003cp\u003eEmerging decision-support frameworks such as the stepwise algorithm proposed by Suh et al. [30], which integrates imaging findings with histologic and anatomic factors, underscore the evolving role of multi-parameter models in guiding the extent of surgical intervention. While our findings do not yet justify a change in practice based solely on PET metrics, they reinforce the potential utility of metabolic data in multi-disciplinary planning.\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. First, it represents one of the few investigations to evaluate a wide spectrum of PET/CT-derived metabolic parameters including core, threshold-based, and peritumoral volumetrics in relation to both the presence and extent of STAS. Second, the use of a standardized imaging protocol and consistent pathological assessment, including subclassification into limited and extended STAS, enhances the internal validity of our findings. Finally, the incorporation of survival analysis adds prognostic context to the imaging metrics evaluated.\u003c/p\u003e\u003cp\u003eHowever, some limitations must be acknowledged. The retrospective and single-center nature of the study introduces potential selection and information bias. Additionally, the sample size, particularly in subgroup analyses of STAS extent, may have been underpowered to detect subtle differences. Moreover, our analysis focused exclusively on metabolic imaging features, without incorporating other potentially relevant variables such as tumor morphology, molecular mutations, or detailed radiologic features that may further enhance STAS prediction. Lastly, the absence of radiomic or deep learning-based analyses which have shown promise in recent literature limits the predictive depth of the current approach.\u003c/p\u003e\u003cp\u003eFuture multicenter prospective studies incorporating advanced quantitative imaging tools and external validation cohorts are needed to refine STAS prediction models and translate them into clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGiven the clinical significance of STAS as an aggressive invasion pattern influencing surgical strategy, \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT may offer valuable insights for its non-invasive preoperative detection. Among the metabolic parameters evaluated in our study, only SUVmax was found to be significantly associated with the presence of STAS, demonstrating high sensitivity but limited specificity. However, none of the volumetric measurements were able to differentiate between limited and extended STAS subtypes. These findings suggest that while metabolic imaging, particularly SUVmax, may serve as a preliminary screening tool for STAS, it remains insufficient as a standalone predictive marker. Future studies integrating radiomics, tumor morphology, and AI-assisted models may provide more powerful tools for preoperative STAS detection and personalized surgical planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAysegul Akgun (Corresponding author) and Batuhan Kocabeyoglu (Corresponding author) conceived and designed the study. Data were acquired by B.K., Goktug Aydogan, and Pinar Gursoy. Data and algorithm quality control were performed by A.A. and Deniz Nart. B.K., Recep Halit Tokac, and A.A. analyzed and interpreted the data. Statistical analyses were conducted by B.K. and R.H.T. B.K. prepared the main manuscript draft. Manuscript editing was performed by B.K., G.A., and R.H.T. A.A. and D.N. critically reviewed the manuscript. All authors approved the final version of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContact to corresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmail address of the corresponding authors are [email protected] and [email protected] .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that all necessary approvals were obtained prior to data collection and that participants rights, privacy, and confidentiality were fully protected throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Ege University Faculty of Medicine Hospital, but restrictions apply to the availability of these data, which were used under licence for the current study and are therefore not publicly available. The data can be made available upon reasonable request and with permission from Ege University Faculty of Medicine Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients whose data were utilized in this research were included in the study following the principles outlined in the Declaration of Helsinki. Prior to data collection, approval was obtained from the Ethics Committee for Medical Research at Ege University (Approval No: 25-2.1T/58). Additionally, written informed consent was secured from all patients for the use of their medical data within the scope of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTravis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, Geisinger K, Hirsch FR, Ishikawa Y, Kerr KM, Noguchi M, Pelosi G, Powell CA, Tsao MS, Wistuba I; WHO Panel. 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PMID: 34942493.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8065101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8065101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eIn patients with Non-Small Cell Lung Cancer (NSCLC), spread through air spaces (STAS) describes tumor cells within alveolar spaces beyond the main tumor margin and is considered an invasive pattern associated with recurrence. This study aimed to preoperatively predict STAS using metabolic parameters obtained from F-18 FDG PET/CT imaging.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective single-center study analyzed 108 patients who underwent surgical resection between 2019 and 2024. Histological assessment determined the presence and subtype of STAS as limited or extended. PET/CT parameters included SUVmax, MTV, and TLG, measured by both a fixed threshold (42% of SUVmax) and patient-specific adaptive thresholds. Peritumoral rim values were calculated volumetrically. Variables associated with STAS in univariate analysis were tested with ROC analysis to determine diagnostic performance, and survival outcomes were evaluated using the Kaplan\u0026ndash;Meier method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSTAS was detected in 56.5% (n\u0026thinsp;=\u0026thinsp;61) of patients, with the extended subtype observed in 67.2% (n\u0026thinsp;=\u0026thinsp;41) of these cases. SUVmax was significantly higher in STAS-positive tumors and demonstrated modest discriminative ability, with an AUC of 0.619. A cut-off value of 5.3 achieved very high sensitivity (0.97) but only moderate specificity (0.64). None of the volumetric parameters, including MTV and TLG, differentiated between limited and extended STAS. Survival was numerically worse in patients with STAS, and extended STAS tended to have shorter median overall survival compared with limited STAS, though these differences did not reach statistical significance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIt was concluded that STAS, which plays a crucial role in the management of lung cancer treatment and prognosis prediction, can be predicted by SUVmax and may serve as a useful non-invasive parameter in the preoperative evaluation. However, other volumetric parameters showed no significant predictive value, and the high sensitivity but moderate specificity of SUVmax limit its standalone clinical utility. Future studies combining metabolic, radiomic, and morphological features may yield more accurate tools for preoperative STAS detection and tailored surgical planning.\u003c/p\u003e","manuscriptTitle":"Can F-18 FDG PET/CT Metabolic Parameters Predict STAS in Non-Small Cell Lung Cancer Patients?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 06:53:31","doi":"10.21203/rs.3.rs-8065101/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":"4925209f-726e-4f50-99b6-5dee31d0d359","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-21T16:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 06:53:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8065101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8065101","identity":"rs-8065101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}