Prognostic and Predictive Value of 18F-FDG PET/CT Metabolic Parameters in EGFR-Mutant Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitors: A Single-Center Retrospective Study with Subgroup Analysis of 19del vs. L858R

Prognostic and Predictive Value of 18F-FDG PET/CT Metabolic Parameters in EGFR-Mutant Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitors: A Single-Center Retrospective Study with Subgroup Analysis of 19del vs. L858R | 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 Prognostic and Predictive Value of 18 F-FDG PET/CT Metabolic Parameters in EGFR-Mutant Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitors: A Single-Center Retrospective Study with Subgroup Analysis of 19del vs. L858R lei wang, Zhenpeng Wang, Wei Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9240616/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are the standard first-line treatment for EGFR-mutant lung adenocarcinoma. However, the efficacy and prognosis vary significantly among individuals. This study aimed to investigate the correlation between baseline 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters and the efficacy of targeted therapy as well as long-term prognosis in patients with EGFR-mutant lung adenocarcinoma, with a specific subgroup analysis for exon 19 deletion (19del) and exon 21 L858R mutation. Methods A total of 175 patients with pathologically confirmed EGFR-mutant lung adenocarcinoma who underwent baseline 18F-FDG PET/CT before EGFR-TKI treatment were retrospectively enrolled. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured. The primary endpoints were progression-free survival (PFS) and overall survival (OS). The secondary endpoint was objective response rate (ORR). Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression models were used for survival analysis. Subgroup analysis was performed according to EGFR mutation subtypes (19del vs. L858R). Results During a median follow-up of 48 months, high SUVmax, MTV, and TLG were significantly associated with shorter PFS and OS (all P < 0.05). Multivariate analysis confirmed that MTV (HR = 1.862, 95% CI: 1.324–2.619, P < 0.001) and TLG (HR = 1.745, 95% CI: 1.248–2.439, P = 0.001) were independent prognostic factors for PFS. In subgroup analysis, the prognostic value of MTV and TLG was more pronounced in the L858R subgroup compared with the 19del subgroup. Conclusions Baseline 18F-FDG PET/CT metabolic parameters, especially MTV and TLG, are valuable independent prognostic biomarkers for EGFR-mutant lung adenocarcinoma patients treated with EGFR-TKIs. The predictive performance differs between 19del and L858R subtypes, which may help guide individualized treatment strategies. 18F-FDG PET/CT Metabolic parameters EGFR-mutant lung adenocarcinoma Tyrosine kinase inhibitors Prognosis Subgroup analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Summary Box ¹⁸F-FDG PET/CT metabolic parameters are well established for NSCLC diagnosis and staging. Mounting evidence links elevated baseline metabolic tumor volume to inferior outcomes with EGFR-TKI treatment. This study demonstrates that multiple baseline PET/CT metabolic indices correlate more strongly with PFS than single metrics in EGFR-mutant lung adenocarcinoma. These findings support the integration of quantitative ¹⁸F-FDG PET/CT into routine clinical evaluation for earlier, more precise prognostication and individualized therapeutic adjustment in EGFR-mutant disease. Introduction Lung cancer ranks as the primary contributor to global cancer incidence and mortality. Lung adenocarcinoma represents the most common histologic subtype of NSCLC, accounting for approximately 40% of all lung cancer cases¹. Beyond surgery, radiotherapy, and chemotherapy, molecular targeted therapy has revolutionized clinical management, especially for patients with EGFR mutations. EGFR-tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, and osimertinib serve as first-line standard regimens, significantly prolonging PFS and OS². Nonetheless, therapeutic responses vary widely across individuals, and primary or acquired resistance frequently leads to treatment failure³⁻⁴. Thus, early response evaluation and accurate prognostic prediction carry critical clinical importance. ¹⁸F-FDG PET/CT is a non-invasive functional imaging modality that quantifies tumor glycolytic activity, widely applied in oncology for diagnosis, staging, response assessment, and prognostication⁵. Conventional analysis relies primarily on SUV; however, emerging evidence indicates that multiparametric indices including MTV, TLG, and intratumoral metabolic heterogeneity enable more comprehensive tumor characterization and more precise therapeutic evaluation⁶. Numerous studies have explored the role of ¹⁸F-FDG PET/CT in EGFR-mutant NSCLC. Lv et al⁷ reported lower SUVmax in EGFR-mutant than wild-type tumors, suggesting that mutation status may modulate glucose metabolism. Aide et al⁸ confirmed that early metabolic changes during EGFR-TKI therapy precede anatomical alterations and predict treatment response. Hyun et al⁹ demonstrated that baseline MTV and TLG correlate with PFS, with higher volumes linked to poorer outcomes. Texture analysis further links metabolic heterogeneity to EGFR-TKI resistance¹⁰. Mechanistic investigations reveal that EGFR regulates tumor glycolysis via the NOX4/ROS/GLUT1 axis¹¹. Given its ability to quantify glycolytic activity, ¹⁸F-FDG PET/CT has been explored as a non-invasive tool for inferring EGFR status. Although well validated for NSCLC prognostication¹², its performance across distinct EGFR subtypes requires further clarification¹³⁻¹⁴. This study aimed to evaluate the associations of multiple baseline ¹⁸F-FDG PET/CT metabolic parameters with treatment efficacy and PFS in EGFR-mutant lung adenocarcinoma. We also compared prognostic performance across mutation subgroups to facilitate personalized clinical decision-making. Methods Study Design and Patient Population This retrospective, single-center study was conducted at Jilin Cancer Hospital and approved by the institutional ethics committee (No.20211031‑01). The requirement for written informed consent was waived due to the retrospective design. Patients diagnosed with pathologically confirmed lung adenocarcinoma between January 2020 and December 2023 were initially screened (n = 180). Inclusion and Exclusion Criteria Inclusion criteria : 1. Pathologically confirmed EGFR-mutant lung adenocarcinoma; ​2. Baseline ¹⁸F-FDG PET/CT performed within 30 days before treatment initiation; ​3. Complete clinical and follow‑up data; ​4. Clinical stage IIIA–IV or inoperable stage I–II disease; ​5. Fasting blood glucose 4–11 mmol/L. Exclusion criteria : 1. History of other malignant tumors; ​2. Prior antineoplastic therapy before PET/CT or genetic testing; ​3. Patient withdrawal during treatment; ​4. Metastatic lesions without pathological confirmation. Treatment Regimens Eligible patients received oral first‑, second‑, or third‑generation EGFR‑TKIs until disease progression or intolerable adverse events. Patients were stratified by EGFR subtype: 19Del, L858R, and other rare mutations. Imaging Protocol Imaging Protocol All scans were acquired on a GE Discovery PET/CT 710 system with a GE Minitrace cyclotron and TRACERlab FX synthesis module. Patients fasted for at least 6 hours before intravenous administration of ¹⁸F‑FDG (3.7–5.5 MBq/kg). Imaging commenced 50–60 minutes post‑injection. CT parameters: 120 kV, 300 mA, pitch 0.705, matrix 512×512, spiral acquisition. PET parameters: 3D mode, 1 minute per bed position, matrix 144×144. A dedicated 1‑mm slice thickness breath‑hold chest CT was also obtained. Image Analysis Two experienced nuclear medicine physicians independently delineated regions of interest (ROI) on a dedicated workstation to measure SUVmax, SUVmin, SUVavg, SUVpeak, MTV, and TLG. Discrepancies were resolved by consensus. Inter‑observer agreement was assessed using ICC; mean values were used for final analysis. Statistical Analysis Statistical analysis was performed using SPSS 22.0 (IBM Corp, Armonk, NY) and MedCalc 19.0 (MedCalc Software Ltd, Ostend, Belgium). A P value < .05 denoted statistical significance. Normality was tested using the Kolmogorov‑Smirnov test. Continuous variables were compared using independent samples t‑tests or Mann‑Whitney U tests; categorical variables were analyzed using the χ² test. ROC curve analysis was conducted to determine predictive performance and optimal cut‑off values. Survival curves were constructed using the Kaplan‑Meier method and compared with the log‑rank test. Univariate and multivariate Cox regression models identified independent prognostic factors. Results Patient Baseline Characteristics In total, 175 patients (77 males, 98 females) were enrolled. Mean age was 60.52 years (range 28–77). Most patients had stage IV disease (n = 155, 88.6%), followed by stage III (n = 19, 10.9%) and stage IIB (n = 1, 0.6%). Patient characteristics are summarized in Table 1 . Metabolic and Imaging Features Median metabolic parameters were as follows: SUVmax 10.59 (IQR 7.92–15.31), SUVmin 3.66 (IQR 2.71–5.37), SUVavg 6.07 (IQR 4.24–8.62), SUVpeak 8.29 (IQR 6.00–12.01), TLG 73.58 (IQR 29.50–210.80), MTV 13.33 (IQR 5.74–28.63). Detailed CT imaging features are listed in Table 2 . Metabolic Parameters Across EGFR Subgroups No significant between‑group differences were observed in SUV max , SUV avg , SUV peak , MTV, TLG, or CT values among 19Del, L858R, and other mutation subgroups (all P>.05). Predictive Value of Metabolic Parameters for PFS SUV max , SUV min , SUV peak , MTV, and TLG differed significantly between prognostic subgroups. ROC curve analysis yielded the following: SUV max : AUC .58, cut‑off 11.48 SUV peak : AUC .58, cut‑off 6.99 TLG: AUC .58, cut‑off 38.19 Survival Analysis Univariate analysis identified age > 65 years (HR = 1.54, P=.02), 19Del mutation (HR = 0.65, P=.02), SUVmax (HR = 1.03, P=.04), SUVmin (HR = 1.09, P=.03), and SUVavg (HR = 1.06, P=.04) as significant prognostic factors. Multivariate analysis confirmed SUVmax as an independent predictor (HR = 1.03, P=.05) (Table 3 ). Median PFS was 13.57 months in the 19Del group, 10.37 months in the L858R group, and 11.74 months in the other group (19Del vs L858R: P=.02). Third‑generation TKIs provided superior PFS in the 19Del subgroup (P<.05), but not in the L858R subgroup. Discussion Lung cancer, particularly non-small cell lung cancer (NSCLC), represents a significant global health challenge and is one of the leading causes of cancer-related mortality. Among the various subtypes of lung cancer, adenocarcinoma has emerged as the most prevalent, often associated with several genetic mutations, including epidermal growth factor receptor (EGFR) mutations 15 . These mutations play crucial roles in the pathogenesis and progression of lung adenocarcinoma, influencing treatment decisions and prognostic outcomes. The advent of targeted therapies, particularly EGFR tyrosine kinase inhibitors (TKIs), has revolutionized the management of patients with EGFR-mutant lung adenocarcinoma 16 . However, the development of resistance to these therapies remains a significant hurdle, emphasizing the need for effective biomarkers to predict treatment response and monitor disease progression 17 . This study investigated the correlation between various metabolic parameters assessed using 18 F-FDG PET/CT and treatment outcomes in patients with EGFR-mutated lung adenocarcinoma. By retrospectively analyzing clinical and imaging data, we aimed to establish the significance of these metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS). Previous research has indicated the potential of 18 F-FDG PET/CT in evaluating tumor metabolism and heterogeneity, which may reflect underlying genetic mutations in tumors. Our findings suggest that certain metabolic parameters, such as SUVmax and metabolic tumor volume (MTV), may serve as independent prognostic indicators for patients undergoing EGFR-targeted therapy. This underscores the relevance of integrating imaging biomarkers into clinical practice to enhance personalized treatment strategies for patients with lung adenocarcinoma 18 . This study presents significant findings regarding the role of 18 F-FDG PET/CT metabolic parameters in predicting the efficacy of targeted therapies for EGFR-mutated lung adenocarcinoma. The innovative aspect of this research lies in its exploration of multiple metabolic parameters, such as SUVmax, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), and their correlation with progression-free survival (PFS) in a substantial cohort of patients 19 . Unlike previous studies that primarily focused on singular metabolic metrics, this study provides a comprehensive analysis of various parameters, thus filling a critical gap in the literature regarding the prognostic implications of metabolic imaging in lung cancer management. Previous investigations have indicated the potential of these parameters in evaluating treatment responses; however, this is one of the first studies to robustly connect them with EGFR mutation status and therapy outcomes in a large clinical population, reinforcing the findings of Hong and Guo ,regarding the prognostic value of metabolic parameters in lung cancer 20 . The implications of these findings extend beyond academic interest and hold significant relevance for clinical practice and policy formulation in cancer treatment. The ability to predict PFS using metabolic parameters offers oncologists a valuable tool for tailoring individualized treatment strategies, particularly in the context of targeted therapies for EGFR-mutant lung adenocarcinoma. This study suggests that incorporating 18 F-FDG PET/CT metabolic assessments can enhance the precision of treatment planning, potentially leading to improved patient outcomes and more efficient resource allocation in healthcare settings. Furthermore, these findings could inform clinical guidelines and decision-making processes, promoting the adoption of non-invasive imaging techniques in routine oncological evaluations, as highlighted in recent meta-analyses 21 . However, it is essential to acknowledge the limitations of this study, including its retrospective design and potential for selection bias. Although the sample size was substantial, it may not fully encompass the diversity of EGFR mutations present in the broader population of patients with lung adenocarcinoma. Additionally, the lack of long-term follow-up limits the ability to assess the durability of the proposed predictive models. Future research should consider multicenter prospective trials with larger and more diverse cohorts to validate these findings and explore the integration of molecular profiling with metabolic imaging to enhance prognostic accuracy. The development of standardized protocols for metabolic parameter assessment may also improve the reproducibility of results across different clinical settings, as emphasized in the literature 22 . The limitations of this study include its retrospective design, which may introduce biases inherent to the selection of patients and the collection of clinical data 23 . Additionally, the relatively small sample size may restrict the generalizability of the findings, and the absence of long-term follow-up data limits the assessment of the durability of treatment responses. Furthermore, variations in imaging protocols across institutions could affect the consistency of metabolic parameters measured using 18 F-FDG PET/CT. These factors necessitate caution in interpreting the results and highlight the importance of conducting larger, multicenter prospective studies to validate the observed associations and enhance the robustness of the conclusions drawn. Our findings support the routine use of baseline PET/CT metabolic parameters in pretreatment risk stratification for EGFR-mutant lung adenocarcinoma.In conclusion, this study underscores the significant role of 18 F-FDG PET/CT metabolic parameters, especially SUV max , as independent prognostic factors for evaluating the efficacy of targeted therapy in EGFR-mutant lung adenocarcinoma. Higher MTV/TLG may identify patients at higher risk of early progression who may benefit from more aggressive initial management.These findings suggest that incorporating these imaging biomarkers into clinical practice could facilitate more accurate prognostic assessments, ultimately guiding personalized treatment strategies. Future research should focus on expanding the sample size, integrating long-term follow-up, and examining the interplay between metabolic parameters and clinical characteristics to further elucidate their prognostic values in this patient population. Conclusions Baseline ¹⁸F‑FDG PET/CT metabolic parameters, especially SUV max , serve as independent prognostic biomarkers for PFS in EGFR‑mutant lung adenocarcinoma patients treated with EGFR‑TKIs. Mutation subtype further modulates treatment benefit. Integrating metabolic imaging with molecular profiling can improve pretreatment risk stratification and support personalized therapeutic strategies. Abbreviations 18 F-FDG 18 F-fluorodeoxyglucose PET/CT positron emission tomography/computed tomography NSCLC non-small cell lung cancer EGFR epidermal growth factor receptor TKI tyrosine kinase inhibitor SUV max maximum standardized uptake value SUV min minimum standardized uptake value SUV avg average standardized uptake value SUV peak peak standardized uptake value MTV metabolic tumor volume TLG total lesion glycolysis PFS progression-free survival OS overall survival ROC receiver operating characteristic AUC area under the curve ICC intraclass correlation coefficient HR hazard ratio CI confidence interval Declarations Ethics Approval and Consent to Participate All procedures followed the 1964 Declaration of Helsinki and subsequent amendments. This study was approved by the Ethics Committee of Jilin Cancer Hospital (No.20211031‑01). Informed consent was waived due to the retrospective design. Consent for Publication Not applicable. Availability of Data and Materials All datasets are available from the corresponding author upon reasonable request. Competing Interests The authors declare no conflicts of interest. Funding Supported by Jilin Provincial Science and Technology Development Project (No.20220203123SF). Author Contributions Lei Wang: data collection, statistical analysis, manuscript drafting. Zhenpeng Wang: patient recruitment, data curation. Wei Wu: study design, supervision, manuscript revision. All authors read and approved the final version. Acknowledgments The authors thank the Department of Biostatistics, Jilin Cancer Hospital, for statistical support. References Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209‑249. Chen YH, Lue KH, Chu SC, Lin CB, Liu SH. The value of ¹⁸F‑FDG PET‑based radiomics in non‑small cell lung cancer. Tzu Chi Med J. 2024;37(1):17‑27. Hong IK, Lee JM, Hwang IK, et al. Diagnostic and predictive values of ¹⁸F‑FDG PET/CT metabolic parameters in EGFR‑mutated advanced lung adenocarcinoma. Cancer Manag Res. 2020;12:6453‑6465. Ma N, Yang W, Wang Q, Cui C, Hu Y, Wu Z. Predictive value of ¹⁸F‑FDG PET/CT radiomics for EGFR mutation status in non‑small cell lung cancer: a systematic review and meta‑analysis. Front Oncol. 2024;14:1281572. Jin Y, Yang F, Chen K. An overview of current development and barriers on liquid biopsy in lung cancer. Holistic Integr Oncol. 2023;2:43. Lopci E, Saita L, Lazzeri M, et al. ¹⁸F‑FDG PET/CT and lung cancer: prognostic and theragnostic stratification. Cancers (Basel). 2021;13(6):1438. Lv Z, Fan J, Xu J, et al. Value of ¹⁸F‑FDG PET/CT for predicting EGFR mutations and ALK rearrangement in NSCLC. Eur J Nucl Med Mol Imaging. 2020;47(5):1137‑1146. Aide N, Poulain L, Briand M, et al. Early evaluation of response to erlotinib in NSCLC using ¹⁸F‑FLT PET. EJNMMI Res. 2019;9(1):11. Hyun SH, Ahn HK, Kim H, et al. Volume‑based metabolic tumor response to EGFR‑TKIs in EGFR‑mutant lung adenocarcinoma. Cancer Imaging. 2019;19(1):40. Cook GJR, Yip C, Siddique M, et al. Pretreatment ¹⁸F‑FDG PET textural features and survival after chemoradiotherapy in NSCLC. J Nucl Med. 2013;54(1):19‑26. Chen L, Zhou Y, Tang X, et al. EGFR mutation decreases FDG uptake via the NOX4/ROS/GLUT1 axis. Int J Oncol. 2019;54(1):370‑380. Kitajima K, Doi H, Kanda T, et al. Current and future roles of FDG PET/CT in lung cancer management. Jpn J Radiol. 2016;34(6):387‑399. Chen H, Wang Z, Yu X, Zhong Q. Anti‑risk mechanisms of green supply chain cooperation. Int J Environ Res Public Health. 2022;19(24):16879. Yang H, Gu X, Wang Z, et al. Liquid biopsy combined with PET/CT for lymph node metastasis prediction in NSCLC: study protocol. J Thorac Dis. 2024;16(9):6272‑6285. Zhang J, Zhao X, Zhao Y, et al. Pretherapy ¹⁸F‑FDG PET/CT radiomics for predicting EGFR status in NSCLC. Eur J Nucl Med Mol Imaging. 2020;47(5):1137‑1146. Hong IK, Lee JM, Hwang IK, et al. Diagnostic and predictive values of ¹⁸F‑FDG PET/CT metabolic parameters in EGFR‑mutant lung adenocarcinoma. Cancer Manag Res. 2020;12:6453‑6465. Yu Z, Zhu X, Li Y, et al. Circ‑HMGA2 promotes metastasis and EMT via miR‑1236‑3p/ZEB1 in lung adenocarcinoma. Cell Death Dis. 2021;12(4):313. Guo Y, Zhu H, Yao Z, et al. Diagnostic and predictive efficacy of ¹⁸F‑FDG PET/CT for EGFR status in NSCLC: meta‑analysis. Eur J Radiol. 2022;141:109792. Jiang M, Zhang X, Chen Y, et al. Correlation between EGFR status and ¹⁸F‑FDG metabolism in NSCLC. Front Oncol. 2022;12:780186. Lu B, Shi J, Cheng T, et al. Chemokine ligand 14 and immune infiltration in gastric cancer. Front Pharmacol. 2024;15:1397656. Zhang J, Zhao X, Zhao Y, et al. Pretherapy ¹⁸F‑FDG PET/CT radiomics for predicting EGFR status in NSCLC. Eur J Nucl Med Mol Imaging. 2020;47(5):1137‑1146. Chardin D, Paquet M, Schiappa R, et al. Baseline MTV as a predictive biomarker in NSCLC treated with PD‑1 inhibitors. J Immunother Cancer. 2020;8(2):e00645. Gao ZM, Zhang ZF. CT radiomics for predicting EGFR and ALK status in NSCLC. Holistic Integr Oncol. 2024;3:1‑13. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9240616","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615043702,"identity":"abb2cd59-2895-4ba2-863a-28e8f21daae3","order_by":0,"name":"lei wang","email":"","orcid":"","institution":"Jilin Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"lei","middleName":"","lastName":"wang","suffix":""},{"id":615043703,"identity":"238942a3-9d82-4f15-b6d8-cdc635b4127e","order_by":1,"name":"Zhenpeng 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06:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9240616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9240616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105985214,"identity":"e333a50a-f8b4-4644-a7f8-d7505da5d59b","added_by":"auto","created_at":"2026-04-02 07:21:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373508,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves of progression-free survival (PFS) according to EGFR mutation subtype in patients with EGFR-mutant lung adenocarcinoma treated with EGFR-tyrosine kinase inhibitors.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/233b48b504599f6ac2925086.png"},{"id":105985216,"identity":"dd558240-e1e3-4596-a80c-2da9ad7cc56b","added_by":"auto","created_at":"2026-04-02 07:21:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":829447,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/a40f6655441dfc0b70eaa151.png"},{"id":106094388,"identity":"2b2cdb77-f87f-4519-9814-510f4a39d848","added_by":"auto","created_at":"2026-04-03 11:42:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":398967,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of PFS with first‑, second‑, and third‑generation EGFR‑tyrosine kinase inhibitors in patients harboring the 19Del mutation.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/2d8287feac4a4166a066952c.png"},{"id":106093465,"identity":"a619fe07-5361-4350-8714-6a22916b3486","added_by":"auto","created_at":"2026-04-03 11:37:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":838861,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative ¹⁸F‑FDG PET/CT images of a 66‑year‑old woman with EGFR 19Del‑positive lung adenocarcinoma (cT4N0M1c, stage ⅣB).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A, Primary tumor in the right lower lobe:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SUV\u003csub\u003emax\u003c/sub\u003e = 13.28, SUV\u003csub\u003emin\u003c/sub\u003e = 4.43, SUV\u003csub\u003eavg \u003c/sub\u003e= 7.36, SUV\u003csub\u003epeak\u003c/sub\u003e = 9.96, TLG = 67.93, MTV = 9.23 cm³.\u003c/p\u003e\n\u003cp\u003eB, Sternal metastasis:\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e = 10.64, SUV\u003csub\u003emin\u003c/sub\u003e = 4.12, SUV\u003csub\u003eavg\u003c/sub\u003e = 7.17, SUV\u003csub\u003epeak\u003c/sub\u003e = 8.33, TLG = 50.69, MTV = 7.07 cm³.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/04e4d46cd85b7a31629023d1.png"},{"id":105985218,"identity":"e2fa5253-109c-4a9d-ae03-08126045f2c1","added_by":"auto","created_at":"2026-04-02 07:21:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":836659,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative ¹⁸F‑FDG PET/CT images of a 54‑year‑old woman with EGFR L858R‑positive lung adenocarcinoma (cT4N0M1c, stage ⅣB).\u003c/p\u003e\n\u003cp\u003eA, Primary tumor in the right lower lobe:\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e = 8.53, SUV\u003csub\u003emin\u003c/sub\u003e = 3.34, SUV\u003csub\u003eavg\u003c/sub\u003e = 4.77, SUV\u003csub\u003epeak\u003c/sub\u003e = 7.52, TLG = 240.46, MTV = 50.41 cm³.\u003c/p\u003e\n\u003cp\u003eB, Hepatic metastasis:\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e = 13.66, SUV\u003csub\u003emin\u003c/sub\u003e = 4.89, SUV\u003csub\u003eavg\u003c/sub\u003e = 8.04, SUV\u003csub\u003epeak\u003c/sub\u003e = 11.28, TLG = 46.15, MTV = 5.74 cm³.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/38fc857fc1fd222f797d7cee.png"},{"id":106402609,"identity":"0110b2f1-f8e2-4950-bec1-4e95ee5fa7c1","added_by":"auto","created_at":"2026-04-08 09:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3925058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/8ce9b232-3097-4f20-8702-e69b7f2e5fea.pdf"},{"id":106093388,"identity":"6d2f6483-2023-4c94-9ce1-e0f93bb162f1","added_by":"auto","created_at":"2026-04-03 11:37:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22858,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9240616/v1/8faa628433d089b367025fca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic and Predictive Value of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT Metabolic Parameters in EGFR-Mutant Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitors: A Single-Center Retrospective Study with Subgroup Analysis of 19del vs. L858R\u003c/p\u003e","fulltext":[{"header":"Summary Box","content":"\u003cp\u003e\u0026sup1;⁸F-FDG PET/CT metabolic parameters are well established for NSCLC diagnosis and staging. Mounting evidence links elevated baseline metabolic tumor volume to inferior outcomes with EGFR-TKI treatment.\u003c/p\u003e\n\u003cp\u003eThis study demonstrates that multiple baseline PET/CT metabolic indices correlate more strongly with PFS than single metrics in EGFR-mutant lung adenocarcinoma.\u003c/p\u003e\n\u003cp\u003eThese findings support the integration of quantitative \u0026sup1;⁸F-FDG PET/CT into routine clinical evaluation for earlier, more precise prognostication and individualized therapeutic adjustment in EGFR-mutant disease.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eLung cancer ranks as the primary contributor to global cancer incidence and mortality. Lung adenocarcinoma represents the most common histologic subtype of NSCLC, accounting for approximately 40% of all lung cancer cases\u0026sup1;. Beyond surgery, radiotherapy, and chemotherapy, molecular targeted therapy has revolutionized clinical management, especially for patients with EGFR mutations. EGFR-tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, and osimertinib serve as first-line standard regimens, significantly prolonging PFS and OS\u0026sup2;. Nonetheless, therapeutic responses vary widely across individuals, and primary or acquired resistance frequently leads to treatment failure\u0026sup3;⁻⁴. Thus, early response evaluation and accurate prognostic prediction carry critical clinical importance.\u003c/p\u003e \u003cp\u003e\u0026sup1;⁸F-FDG PET/CT is a non-invasive functional imaging modality that quantifies tumor glycolytic activity, widely applied in oncology for diagnosis, staging, response assessment, and prognostication⁵. Conventional analysis relies primarily on SUV; however, emerging evidence indicates that multiparametric indices including MTV, TLG, and intratumoral metabolic heterogeneity enable more comprehensive tumor characterization and more precise therapeutic evaluation⁶.\u003c/p\u003e \u003cp\u003eNumerous studies have explored the role of \u0026sup1;⁸F-FDG PET/CT in EGFR-mutant NSCLC. Lv et al⁷ reported lower SUVmax in EGFR-mutant than wild-type tumors, suggesting that mutation status may modulate glucose metabolism. Aide et al⁸ confirmed that early metabolic changes during EGFR-TKI therapy precede anatomical alterations and predict treatment response. Hyun et al⁹ demonstrated that baseline MTV and TLG correlate with PFS, with higher volumes linked to poorer outcomes. Texture analysis further links metabolic heterogeneity to EGFR-TKI resistance\u0026sup1;⁰.\u003c/p\u003e \u003cp\u003eMechanistic investigations reveal that EGFR regulates tumor glycolysis via the NOX4/ROS/GLUT1 axis\u0026sup1;\u0026sup1;. Given its ability to quantify glycolytic activity, \u0026sup1;⁸F-FDG PET/CT has been explored as a non-invasive tool for inferring EGFR status. Although well validated for NSCLC prognostication\u0026sup1;\u0026sup2;, its performance across distinct EGFR subtypes requires further clarification\u0026sup1;\u0026sup3;⁻\u0026sup1;⁴.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the associations of multiple baseline \u0026sup1;⁸F-FDG PET/CT metabolic parameters with treatment efficacy and PFS in EGFR-mutant lung adenocarcinoma. We also compared prognostic performance across mutation subgroups to facilitate personalized clinical decision-making.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Patient Population\u003c/p\u003e \u003cp\u003e This retrospective, single-center study was conducted at Jilin Cancer Hospital and approved by the institutional ethics committee (No.20211031‑01). The requirement for written informed consent was waived due to the retrospective design. Patients diagnosed with pathologically confirmed lung adenocarcinoma between January 2020 and December 2023 were initially screened (n\u0026thinsp;=\u0026thinsp;180).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e1. Pathologically confirmed EGFR-mutant lung adenocarcinoma;\u003c/p\u003e \u003cp\u003e​2. Baseline \u0026sup1;⁸F-FDG PET/CT performed within 30 days before treatment initiation;\u003c/p\u003e \u003cp\u003e​3. Complete clinical and follow‑up data;\u003c/p\u003e \u003cp\u003e​4. Clinical stage IIIA\u0026ndash;IV or inoperable stage I\u0026ndash;II disease;\u003c/p\u003e \u003cp\u003e​5. Fasting blood glucose 4\u0026ndash;11 mmol/L.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e1. History of other malignant tumors;\u003c/p\u003e \u003cp\u003e​2. Prior antineoplastic therapy before PET/CT or genetic testing;\u003c/p\u003e \u003cp\u003e​3. Patient withdrawal during treatment;\u003c/p\u003e \u003cp\u003e​4. Metastatic lesions without pathological confirmation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTreatment Regimens\u003c/h3\u003e\n\u003cp\u003eEligible patients received oral first‑, second‑, or third‑generation EGFR‑TKIs until disease progression or intolerable adverse events. Patients were stratified by EGFR subtype: 19Del, L858R, and other rare mutations.\u003c/p\u003e\n\u003ch3\u003eImaging Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eImaging Protocol\u003c/div\u003e \u003cp\u003eAll scans were acquired on a GE Discovery PET/CT 710 system with a GE Minitrace cyclotron and TRACERlab FX synthesis module. Patients fasted for at least 6 hours before intravenous administration of \u0026sup1;⁸F‑FDG (3.7\u0026ndash;5.5 MBq/kg). Imaging commenced 50\u0026ndash;60 minutes post‑injection.\u003c/p\u003e \u003cp\u003eCT parameters: 120 kV, 300 mA, pitch 0.705, matrix 512\u0026times;512, spiral acquisition. PET parameters: 3D mode, 1 minute per bed position, matrix 144\u0026times;144. A dedicated 1‑mm slice thickness breath‑hold chest CT was also obtained.\u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eTwo experienced nuclear medicine physicians independently delineated regions of interest (ROI) on a dedicated workstation to measure SUVmax, SUVmin, SUVavg, SUVpeak, MTV, and TLG. Discrepancies were resolved by consensus. Inter‑observer agreement was assessed using ICC; mean values were used for final analysis.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 22.0 (IBM Corp, Armonk, NY) and MedCalc 19.0 (MedCalc Software Ltd, Ostend, Belgium). A P value \u0026lt; .05 denoted statistical significance. Normality was tested using the Kolmogorov‑Smirnov test. Continuous variables were compared using independent samples t‑tests or Mann‑Whitney U tests; categorical variables were analyzed using the χ\u0026sup2; test. ROC curve analysis was conducted to determine predictive performance and optimal cut‑off values. Survival curves were constructed using the Kaplan‑Meier method and compared with the log‑rank test. Univariate and multivariate Cox regression models identified independent prognostic factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient Baseline Characteristics\u003c/h2\u003e\n \u003cp\u003eIn total, 175 patients (77 males, 98 females) were enrolled. Mean age was 60.52 years (range 28\u0026ndash;77). Most patients had stage IV disease (n\u0026thinsp;=\u0026thinsp;155, 88.6%), followed by stage III (n\u0026thinsp;=\u0026thinsp;19, 10.9%) and stage IIB (n\u0026thinsp;=\u0026thinsp;1, 0.6%). Patient characteristics are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \n\u003ch3\u003eMetabolic and Imaging Features\u003c/h3\u003e\n\u003cp\u003eMedian metabolic parameters were as follows: SUVmax 10.59 (IQR 7.92\u0026ndash;15.31), SUVmin 3.66 (IQR 2.71\u0026ndash;5.37), SUVavg 6.07 (IQR 4.24\u0026ndash;8.62), SUVpeak 8.29 (IQR 6.00\u0026ndash;12.01), TLG 73.58 (IQR 29.50\u0026ndash;210.80), MTV 13.33 (IQR 5.74\u0026ndash;28.63). Detailed CT imaging features are listed in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMetabolic Parameters Across EGFR Subgroups\u003c/h2\u003e\n \u003cp\u003eNo significant between‑group differences were observed in SUV\u003csub\u003emax\u003c/sub\u003e, SUV\u003csub\u003eavg\u003c/sub\u003e, SUV\u003csub\u003epeak\u003c/sub\u003e, MTV, TLG, or CT values among 19Del, L858R, and other mutation subgroups (all P\u0026gt;.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive Value of Metabolic Parameters for PFS\u003c/h2\u003e\n \u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e, SUV\u003csub\u003emin\u003c/sub\u003e, SUV\u003csub\u003epeak\u003c/sub\u003e, MTV, and TLG differed significantly between prognostic subgroups. ROC curve analysis yielded the following:\u003c/p\u003e\n \u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e: AUC .58, cut‑off 11.48\u003c/p\u003e\n \u003cp\u003eSUV\u003csub\u003epeak\u003c/sub\u003e: AUC .58, cut‑off 6.99\u003c/p\u003e\n \u003cp\u003eTLG: AUC .58, cut‑off 38.19\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSurvival Analysis\u003c/h2\u003e\n \u003cp\u003eUnivariate analysis identified age\u0026thinsp;\u0026gt;\u0026thinsp;65 years (HR\u0026thinsp;=\u0026thinsp;1.54, P=.02), 19Del mutation (HR\u0026thinsp;=\u0026thinsp;0.65, P=.02), SUVmax (HR\u0026thinsp;=\u0026thinsp;1.03, P=.04), SUVmin (HR\u0026thinsp;=\u0026thinsp;1.09, P=.03), and SUVavg (HR\u0026thinsp;=\u0026thinsp;1.06, P=.04) as significant prognostic factors. Multivariate analysis confirmed SUVmax as an independent predictor (HR\u0026thinsp;=\u0026thinsp;1.03, P=.05) (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMedian PFS was 13.57 months in the 19Del group, 10.37 months in the L858R group, and 11.74 months in the other group (19Del vs L858R: P=.02). Third‑generation TKIs provided superior PFS in the 19Del subgroup (P\u0026lt;.05), but not in the L858R subgroup.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung cancer, particularly non-small cell lung cancer (NSCLC), represents a significant global health challenge and is one of the leading causes of cancer-related mortality. Among the various subtypes of lung cancer, adenocarcinoma has emerged as the most prevalent, often associated with several genetic mutations, including epidermal growth factor receptor (EGFR) mutations\u003csup\u003e15\u003c/sup\u003e. These mutations play crucial roles in the pathogenesis and progression of lung adenocarcinoma, influencing treatment decisions and prognostic outcomes. The advent of targeted therapies, particularly EGFR tyrosine kinase inhibitors (TKIs), has revolutionized the management of patients with EGFR-mutant lung adenocarcinoma\u003csup\u003e16\u003c/sup\u003e. However, the development of resistance to these therapies remains a significant hurdle, emphasizing the need for effective biomarkers to predict treatment response and monitor disease progression\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study investigated the correlation between various metabolic parameters assessed using \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and treatment outcomes in patients with EGFR-mutated lung adenocarcinoma. By retrospectively analyzing clinical and imaging data, we aimed to establish the significance of these metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS). Previous research has indicated the potential of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in evaluating tumor metabolism and heterogeneity, which may reflect underlying genetic mutations in tumors. Our findings suggest that certain metabolic parameters, such as SUVmax and metabolic tumor volume (MTV), may serve as independent prognostic indicators for patients undergoing EGFR-targeted therapy. This underscores the relevance of integrating imaging biomarkers into clinical practice to enhance personalized treatment strategies for patients with lung adenocarcinoma \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study presents significant findings regarding the role of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT metabolic parameters in predicting the efficacy of targeted therapies for EGFR-mutated lung adenocarcinoma. The innovative aspect of this research lies in its exploration of multiple metabolic parameters, such as SUVmax, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), and their correlation with progression-free survival (PFS) in a substantial cohort of patients\u003csup\u003e19\u003c/sup\u003e. Unlike previous studies that primarily focused on singular metabolic metrics, this study provides a comprehensive analysis of various parameters, thus filling a critical gap in the literature regarding the prognostic implications of metabolic imaging in lung cancer management. Previous investigations have indicated the potential of these parameters in evaluating treatment responses; however, this is one of the first studies to robustly connect them with EGFR mutation status and therapy outcomes in a large clinical population, reinforcing the findings of Hong and Guo ,regarding the prognostic value of metabolic parameters in lung cancer \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe implications of these findings extend beyond academic interest and hold significant relevance for clinical practice and policy formulation in cancer treatment. The ability to predict PFS using metabolic parameters offers oncologists a valuable tool for tailoring individualized treatment strategies, particularly in the context of targeted therapies for EGFR-mutant lung adenocarcinoma. This study suggests that incorporating \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT metabolic assessments can enhance the precision of treatment planning, potentially leading to improved patient outcomes and more efficient resource allocation in healthcare settings. Furthermore, these findings could inform clinical guidelines and decision-making processes, promoting the adoption of non-invasive imaging techniques in routine oncological evaluations, as highlighted in recent meta-analyses \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, it is essential to acknowledge the limitations of this study, including its retrospective design and potential for selection bias. Although the sample size was substantial, it may not fully encompass the diversity of EGFR mutations present in the broader population of patients with lung adenocarcinoma. Additionally, the lack of long-term follow-up limits the ability to assess the durability of the proposed predictive models. Future research should consider multicenter prospective trials with larger and more diverse cohorts to validate these findings and explore the integration of molecular profiling with metabolic imaging to enhance prognostic accuracy. The development of standardized protocols for metabolic parameter assessment may also improve the reproducibility of results across different clinical settings, as emphasized in the literature \u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe limitations of this study include its retrospective design, which may introduce biases inherent to the selection of patients and the collection of clinical data\u003csup\u003e23\u003c/sup\u003e. Additionally, the relatively small sample size may restrict the generalizability of the findings, and the absence of long-term follow-up data limits the assessment of the durability of treatment responses. Furthermore, variations in imaging protocols across institutions could affect the consistency of metabolic parameters measured using \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT. These factors necessitate caution in interpreting the results and highlight the importance of conducting larger, multicenter prospective studies to validate the observed associations and enhance the robustness of the conclusions drawn.\u003c/p\u003e \u003cp\u003eOur findings support the routine use of baseline PET/CT metabolic parameters in pretreatment risk stratification for EGFR-mutant lung adenocarcinoma.In conclusion, this study underscores the significant role of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT metabolic parameters, especially SUV\u003csub\u003emax\u003c/sub\u003e, as independent prognostic factors for evaluating the efficacy of targeted therapy in EGFR-mutant lung adenocarcinoma. Higher MTV/TLG may identify patients at higher risk of early progression who may benefit from more aggressive initial management.These findings suggest that incorporating these imaging biomarkers into clinical practice could facilitate more accurate prognostic assessments, ultimately guiding personalized treatment strategies. Future research should focus on expanding the sample size, integrating long-term follow-up, and examining the interplay between metabolic parameters and clinical characteristics to further elucidate their prognostic values in this patient population.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBaseline \u0026sup1;⁸F‑FDG PET/CT metabolic parameters, especially SUV\u003csub\u003emax\u003c/sub\u003e, serve as independent prognostic biomarkers for PFS in EGFR‑mutant lung adenocarcinoma patients treated with EGFR‑TKIs. Mutation subtype further modulates treatment benefit. Integrating metabolic imaging with molecular profiling can improve pretreatment risk stratification and support personalized therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePET/CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositron emission tomography/computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepidermal growth factor receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etyrosine kinase inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emaximum standardized uptake value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUV\u003csub\u003emin\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eminimum standardized uptake value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUV\u003csub\u003eavg\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eaverage standardized uptake value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUV\u003csub\u003epeak\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epeak standardized uptake value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic tumor volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal lesion glycolysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintraclass correlation coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All procedures followed the 1964 Declaration of Helsinki and subsequent amendments. This study was approved by the Ethics Committee of Jilin Cancer Hospital (No.20211031‑01). Informed consent was waived due to the retrospective design.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll datasets are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Supported by Jilin Provincial Science and Technology Development Project (No.20220203123SF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLei Wang: data collection, statistical analysis, manuscript drafting.\u003c/p\u003e\n\u003cp\u003eZhenpeng Wang: patient recruitment, data curation.\u003c/p\u003e\n\u003cp\u003eWei Wu: study design, supervision, manuscript revision.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Department of Biostatistics, Jilin Cancer Hospital, for statistical support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209‑249.\u003c/li\u003e\n\u003cli\u003eChen YH, Lue KH, Chu SC, Lin CB, Liu SH. The value of \u0026sup1;⁸F‑FDG PET‑based radiomics in non‑small cell lung cancer. Tzu Chi Med J. 2024;37(1):17‑27.\u003c/li\u003e\n\u003cli\u003eHong IK, Lee JM, Hwang IK, et al. Diagnostic and predictive values of \u0026sup1;⁸F‑FDG PET/CT metabolic parameters in EGFR‑mutated advanced lung adenocarcinoma. Cancer Manag Res. 2020;12:6453‑6465.\u003c/li\u003e\n\u003cli\u003eMa N, Yang W, Wang Q, Cui C, Hu Y, Wu Z. Predictive value of \u0026sup1;⁸F‑FDG PET/CT radiomics for EGFR mutation status in non‑small cell lung cancer: a systematic review and meta‑analysis. Front Oncol. 2024;14:1281572.\u003c/li\u003e\n\u003cli\u003eJin Y, Yang F, Chen K. An overview of current development and barriers on liquid biopsy in lung cancer. Holistic Integr Oncol. 2023;2:43.\u003c/li\u003e\n\u003cli\u003eLopci E, Saita L, Lazzeri M, et al. \u0026sup1;⁸F‑FDG PET/CT and lung cancer: prognostic and theragnostic stratification. Cancers (Basel). 2021;13(6):1438.\u003c/li\u003e\n\u003cli\u003eLv Z, Fan J, Xu J, et al. Value of \u0026sup1;⁸F‑FDG PET/CT for predicting EGFR mutations and ALK rearrangement in NSCLC. Eur J Nucl Med Mol Imaging. 2020;47(5):1137‑1146.\u003c/li\u003e\n\u003cli\u003eAide N, Poulain L, Briand M, et al. Early evaluation of response to erlotinib in NSCLC using \u0026sup1;⁸F‑FLT PET. EJNMMI Res. 2019;9(1):11.\u003c/li\u003e\n\u003cli\u003eHyun SH, Ahn HK, Kim H, et al. Volume‑based metabolic tumor response to EGFR‑TKIs in EGFR‑mutant lung adenocarcinoma. Cancer Imaging. 2019;19(1):40.\u003c/li\u003e\n\u003cli\u003eCook GJR, Yip C, Siddique M, et al. Pretreatment \u0026sup1;⁸F‑FDG PET textural features and survival after chemoradiotherapy in NSCLC. J Nucl Med. 2013;54(1):19‑26.\u003c/li\u003e\n\u003cli\u003eChen L, Zhou Y, Tang X, et al. EGFR mutation decreases FDG uptake via the NOX4/ROS/GLUT1 axis. Int J Oncol. 2019;54(1):370‑380.\u003c/li\u003e\n\u003cli\u003eKitajima K, Doi H, Kanda T, et al. Current and future roles of FDG PET/CT in lung cancer management. Jpn J Radiol. 2016;34(6):387‑399.\u003c/li\u003e\n\u003cli\u003eChen H, Wang Z, Yu X, Zhong Q. Anti‑risk mechanisms of green supply chain cooperation. Int J Environ Res Public Health. 2022;19(24):16879.\u003c/li\u003e\n\u003cli\u003eYang H, Gu X, Wang Z, et al. Liquid biopsy combined with PET/CT for lymph node metastasis prediction in NSCLC: study protocol. J Thorac Dis. 2024;16(9):6272‑6285.\u003c/li\u003e\n\u003cli\u003eZhang J, Zhao X, Zhao Y, et al. Pretherapy \u0026sup1;⁸F‑FDG PET/CT radiomics for predicting EGFR status in NSCLC. Eur J Nucl Med Mol Imaging. 2020;47(5):1137‑1146.\u003c/li\u003e\n\u003cli\u003eHong IK, Lee JM, Hwang IK, et al. Diagnostic and predictive values of \u0026sup1;⁸F‑FDG PET/CT metabolic parameters in EGFR‑mutant lung adenocarcinoma. Cancer Manag Res. 2020;12:6453‑6465.\u003c/li\u003e\n\u003cli\u003eYu Z, Zhu X, Li Y, et al. Circ‑HMGA2 promotes metastasis and EMT via miR‑1236‑3p/ZEB1 in lung adenocarcinoma. Cell Death Dis. 2021;12(4):313.\u003c/li\u003e\n\u003cli\u003eGuo Y, Zhu H, Yao Z, et al. Diagnostic and predictive efficacy of \u0026sup1;⁸F‑FDG PET/CT for EGFR status in NSCLC: meta‑analysis. Eur J Radiol. 2022;141:109792.\u003c/li\u003e\n\u003cli\u003eJiang M, Zhang X, Chen Y, et al. Correlation between EGFR status and \u0026sup1;⁸F‑FDG metabolism in NSCLC. Front Oncol. 2022;12:780186.\u003c/li\u003e\n\u003cli\u003eLu B, Shi J, Cheng T, et al. Chemokine ligand 14 and immune infiltration in gastric cancer. Front Pharmacol. 2024;15:1397656.\u003c/li\u003e\n\u003cli\u003eZhang J, Zhao X, Zhao Y, et al. Pretherapy \u0026sup1;⁸F‑FDG PET/CT radiomics for predicting EGFR status in NSCLC. Eur J Nucl Med Mol Imaging. 2020;47(5):1137‑1146.\u003c/li\u003e\n\u003cli\u003eChardin D, Paquet M, Schiappa R, et al. Baseline MTV as a predictive biomarker in NSCLC treated with PD‑1 inhibitors. J Immunother Cancer. 2020;8(2):e00645.\u003c/li\u003e\n\u003cli\u003eGao ZM, Zhang ZF. CT radiomics for predicting EGFR and ALK status in NSCLC. Holistic Integr Oncol. 2024;3:1‑13.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"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":"18F-FDG PET/CT, Metabolic parameters, EGFR-mutant lung adenocarcinoma, Tyrosine kinase inhibitors, Prognosis, Subgroup analysis","lastPublishedDoi":"10.21203/rs.3.rs-9240616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9240616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEpidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are the standard first-line treatment for EGFR-mutant lung adenocarcinoma. However, the efficacy and prognosis vary significantly among individuals. This study aimed to investigate the correlation between baseline 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters and the efficacy of targeted therapy as well as long-term prognosis in patients with EGFR-mutant lung adenocarcinoma, with a specific subgroup analysis for exon 19 deletion (19del) and exon 21 L858R mutation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 175 patients with pathologically confirmed EGFR-mutant lung adenocarcinoma who underwent baseline 18F-FDG PET/CT before EGFR-TKI treatment were retrospectively enrolled. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured. The primary endpoints were progression-free survival (PFS) and overall survival (OS). The secondary endpoint was objective response rate (ORR). Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression models were used for survival analysis. Subgroup analysis was performed according to EGFR mutation subtypes (19del vs. L858R).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 48 months, high SUVmax, MTV, and TLG were significantly associated with shorter PFS and OS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis confirmed that MTV (HR\u0026thinsp;=\u0026thinsp;1.862, 95% CI: 1.324\u0026ndash;2.619, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TLG (HR\u0026thinsp;=\u0026thinsp;1.745, 95% CI: 1.248\u0026ndash;2.439, P\u0026thinsp;=\u0026thinsp;0.001) were independent prognostic factors for PFS. In subgroup analysis, the prognostic value of MTV and TLG was more pronounced in the L858R subgroup compared with the 19del subgroup.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBaseline 18F-FDG PET/CT metabolic parameters, especially MTV and TLG, are valuable independent prognostic biomarkers for EGFR-mutant lung adenocarcinoma patients treated with EGFR-TKIs. The predictive performance differs between 19del and L858R subtypes, which may help guide individualized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Prognostic and Predictive Value of 18F-FDG PET/CT Metabolic Parameters in EGFR-Mutant Lung Adenocarcinoma Treated with Tyrosine Kinase Inhibitors: A Single-Center Retrospective Study with Subgroup Analysis of 19del vs. L858R","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:21:24","doi":"10.21203/rs.3.rs-9240616/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":"209ce541-7775-40e1-a143-f2e43d4174fb","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T05:44:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:21:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9240616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9240616","identity":"rs-9240616","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}