A Multicenter Prediction Model for False-Positive Clinical Nodal Disease in <4-cm NSCLC: Clinical Implications for Upfront Surgery Versus Neoadjuvant Therapy

A Multicenter Prediction Model for False-Positive Clinical Nodal Disease in <4-cm NSCLC: Clinical Implications for Upfront Surgery Versus Neoadjuvant Therapy | 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 A Multicenter Prediction Model for False-Positive Clinical Nodal Disease in <4-cm NSCLC: Clinical Implications for Upfront Surgery Versus Neoadjuvant Therapy Tetsuya Isaka, Yui Kaburaki, Ikki Takada, Ryotaro Matsuyama, Chiaki Kanno, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9392177/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Perioperative immunochemotherapy is recommended for resectable non-small cell lung cancer (NSCLC) with ≥ 4-cm tumors or clinical nodal involvement (cN(+)). Therefore, accurate preoperative nodal assessment is crucial in < 4-cm tumors, where false-positive cN staging should be carefully considered. This multicenter study aimed to develop and validate a prediction model to identify patients at high risk of cN(+)pN(−) disease. Methods The development cohort included 251 patients with tumors < 4 cm who were diagnosed as cN(+) using positron emission tomography and underwent curative anatomical resection with mediastinal lymph node dissection between 2010 and 2020. The model predicting cN(+)pN(−) was developed and validated in an independent cohort of 108 patients treated during different periods from the development cohort. Results In the development cohort, 72 patients (28.7%) were cN(+)pN(−). In multivariable analysis, age ≥ 67 years (odds ratio [OR] 2.85, p = 0.004), right-sided tumor (OR 2.50, p = 0.006), carcinoembryonic antigen ≤ 12 ng/mL (OR 3.11, p = 0.048), cN1 (OR 2.37, p = 0.025), and tumor maximum standardized uptake value ≤ 6.5 (OR 2.03, p = 0.023) were independent predictors for cN(+)pN(−). The prediction model showed a C-index of 0.74 (95% confidence interval [CI] 0.67–0.81), specificity of 94.4%, and positive predictive value (PPV) of 66.7% in the development cohort. In the validation cohort, the C-index was 0.77 (95% CI 0.67–0.87) with good calibration (slope, 1.002; intercept, 0.001), specificity (96.3%), and PPV (70.0%). Conclusions In patients with < 4-cm cN(+) NSCLC, a simple model based on five readily available preoperative factors can identify individuals at high risk of false-positive nodal staging with high specificity. This model may help identify patients in whom upfront surgery for definitive pathological nodal evaluation may be preferable rather than immediate neoadjuvant therapy. clinical nodal involvement false-positive cN staging perioperative therapy positron emission tomography standardized uptake value Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Primary lung cancer remains a leading cause of cancer-related mortality worldwide [ 1 ]. Even in resectable non-small cell lung cancer (NSCLC), the outcomes remain suboptimal. According to the 8th edition of the TNM classification, the reported 5-year overall survival (OS) rates for clinical stages IIA, IIB, and IIIA are 15%–60%, 17%–53%, and 14%–36%, respectively [ 2 ]. Recently, the CheckMate 816 [ 3 ], KEYNOTE-671 [ 4 ], and AEGEAN trials [ 5 ] have demonstrated the efficacy of neoadjuvant or perioperative chemo-immunotherapy in clinical stage II–IIIA NSCLC without epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) fusions. Reflecting these data, the National Comprehensive Cancer Network (NCCN) guidelines [ 6 ], Japanese Lung Cancer Society guidelines [ 7 ], and National Institute for Health and Care Excellence guidance [ 8 ] recommend immunotherapy-based perioperative strategies for resectable stage II or higher disease—specifically ≥ 4-cm tumors or < 4-cm tumors with clinical nodal involvement (cN(+)) in EGFR/ALK-negative or unknown cases. In this context, accurate preoperative nodal assessment is particularly important in < 4-cm tumors. According to the European Society of Thoracic Surgeons (ESTS), American College of Chest Physicians (ACCP), and Japanese Lung Cancer Society guidelines, endobronchial ultrasound (EBUS)/endoscopic ultrasound (EUS)-guided sampling and/or mediastinoscopy is recommended not only for radiologically suspected mediastinal disease but also in high-risk patients, including those with central tumors, cN1 disease, or tumor size > 3 cm, even when mediastinal findings are absent on imaging [ 7 , 9 , 10 ]. However, accurate preoperative nodal staging remains challenging because EBUS-guided transbronchial needle aspiration (EBUS-TBNA) cannot access all nodal stations [ 11 – 13 ] and mediastinoscopy has similar anatomical limitations that preclude access to all mediastinal nodal stations, aside from requiring general anesthesia and carrying procedural risks [ 14 , 15 ]. Consequently, concordance between clinical and pathological staging remains suboptimal, with a reported clinical TNM–pathologic TNM (cTNM–pTNM) agreement of only 54.6% in a large cohort despite guideline-based staging [ 16 ] and clinical–pathological N discordance rates of 16.9%–38% in other studies [ 14 , 17 , 18 ]. Although neoadjuvant chemo-immunotherapy is increasingly being standardized, perioperative trials have reported grade ≥ 3 adverse events in 32.5%–44.9% of patients, grade 5 events in 0.9%–1.7% of patients, and failure to proceed to surgery in 17.9%–22.3% of patients [ 3 – 5 , 19 ]. Moreover, according to subgroup analyses from neoadjuvant and perioperative trials, the treatment effect is more pronounced in patients with more advanced stage disease, particularly those with nodal involvement, supporting the biological rationale that preoperative systemic therapy is particularly beneficial in patients with true pathological nodal metastasis or nodal involvement [ 3 – 5 ]. Therefore, in < 4-cm NSCLC, cN(+) should ideally be reserved for patients with a high probability of true pathological nodal involvement (pN+). Meanwhile, prioritizing surgical resection with definitive pathological nodal evaluation may represent a reasonable alternative strategy in patients at high risk of cN(+)pN(−) disease. Accordingly, this study aimed to identify the predictive factors for cN(+)pN(−) disease in < 4-cm NSCLC using a multicenter database, develop a high-specificity prediction model for false-positive cN staging, and validate the model in an independent cohort in order to facilitate more appropriate treatment selection. 2. Methods 2.1 Ethics statement This retrospective multicenter database study was approved by the institutional review boards of Kanagawa Cancer Center (24EKI54, approved June 14, 2021: 2025EKI-121 approved November 25, 2025), Tokyo Medical University Hospital (SH2969), and Hiroshima University Hospital (E-1216). The requirement for individual written informed consent was waived. 2.2 Patients The development cohort included patients who were diagnosed as cN(+) and underwent anatomical lung resection (segmentectomy or more extensive procedure) with mediastinal lymph node dissection at Kanagawa Cancer Center, Hiroshima University Hospital, or Tokyo Medical University Hospital between January 2010 and December 2020. Before surgery, all patients were treatment-naïve and underwent preoperative fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). Among the 443 eligible patients, 251 patients with tumor size < 4 cm on preoperative CT were included in the main analysis (Fig. 1 a). For external validation, we identified patients with NSCLC at Kanagawa Cancer Center who underwent preoperative FDG-PET/CT, were diagnosed as cN(+), and were treated with anatomical resection plus mediastinal lymph node dissection during two periods: January 2006–December 2009 and January 2021–December 2025. Among them, 108 patients with tumor size < 4 cm comprised the validation cohort (Fig. 1 b). A prespecified subgroup analysis was performed in patients who were EGFR/ALK–negative or had unknown mutation status (n = 84). 2.3 FDG-PET/CT assessment, cN staging, and pN evaluation All patients were evaluated using integrated three-dimensional PET/CT scanners. cN staging was performed in accordance with NCCN [ 6 ], Japanese Lung Cancer Society [ 7 ], ESTS [ 9 ], and ACCP guidelines [ 10 ] at each institution. In principle, mediastinal nodal evaluation by EBUS-TBNA and/or mediastinoscopy was performed in (i) patients with enlarged lymph nodes showing FDG uptake on PET/CT or contrast-enhanced CT or (ii) those with cN1 disease, centrally located tumors, or tumor size ≥ 3 cm, even in the absence of mediastinal FDG uptake whenever feasible [ 7 , 9 , 10 ]. By contrast, pathological mediastinal evaluation was considered optional for peripheral tumors located in the outer one-third of the lung with a diameter < 3 cm and no mediastinal FDG uptake on PET/CT [ 9 , 10 ]. Although current guidelines do not uniformly define the indications for hilar EBUS-TBNA, FDG-avid hilar nodes were sampled whenever feasible. Otherwise, the decision was left to each institution’s discretion. If EBUS-TBNA was technically infeasible, cN staging was determined based on imaging findings through multidisciplinary discussion. Nodal positivity on PET/CT was qualitatively defined as focal FDG uptake greater than background activity [ 6 , 7 , 9 , 10 ]. Tumor staging was performed in accordance with the 8th edition of the TNM classification for lung cancer [ 20 ]. Patients who underwent anatomical lung resection (segmentectomy, lobectomy, or pneumonectomy) with systematic or selective mediastinal lymph node dissection were included. Meanwhile, those who underwent lymph node sampling alone were excluded. 2.4 Primary/secondary outcomes and definitions The primary objective of this study was to identify predictive factors associated with cN(+)pN(−) disease and the secondary objective was to validate the corresponding prediction model. The representative imaging examples of cN(+)pN(−) cases are shown in Supplementary Fig. 1. The site of recurrence was defined as the metastatic organ detected at the time of the first recurrence during follow-up. Intrathoracic recurrence included the hilar and mediastinal lymph nodes, lung, and pleura. Meanwhile, distant recurrence included the central nervous system, bone, and abdominal organs (e.g., liver and adrenal glands). Recurrence-free survival (RFS) was defined as the interval from the date of surgery to recurrence or death from any cause. Patients without recurrence were censored at the last follow-up date. OS was defined as the interval from surgery to death from any cause. Patients who were alive at the last follow-up were censored at that time. 2.5 Statistical analysis Group comparisons were performed using the Mann–Whitney U test for continuous variables and Fisher’s exact test for categorical variables. Univariable logistic regression was used to assess associations between cN(+)pN(−) disease and age, sex, Brinkman index, tumor size, radiologic appearance (pure solid vs part-solid), tumor laterality (right-sided), blood serum carcinoembryonic antigen (CEA) value, cN category (cN1 vs. cN2), tumor maximum standardized uptake value (SUVmax), and histology (squamous cell carcinoma). The cutoff values for age, Brinkman index, tumor size, serum CEA level, and SUVmax were determined using receiver operating characteristic (ROC) curve analysis, with cN(+)pN(−) disease as the endpoint. Variables with p < 0.10 in univariable analyses were entered into a multivariable logistic regression model. Statistical significance was considered at p < 0.05. Model discrimination was evaluated using the C-index. Calibration was assessed in the validation cohort (both overall validation cohort and in the EGFR/ALK-negative subset). To evaluate overfitting and internal validity, bootstrap internal validation with 1,000 resamples was performed in the development and validation cohorts, and optimism-corrected measures of discrimination and calibration were obtained. Sensitivity analyses were used to vary the cutoff for the number of predictive factors met (≥ 3, ≥ 4, or all 5). For each cutoff, the positive predictive value (PPV), specificity, and accuracy for cN(+)pN(−) disease were calculated in the development cohort, overall validation cohort, and EGFR/ALK-negative or unknown validation cohort. All statistical analyses were performed using EZR (R Commander version 1.30; Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). 3. Results 3.1 Development cohort In the development cohort, 72 patients (28.7%) were classified as cN(+)pN(−), whereas 179 (71.3%) were classified as cN(+)pN(+). The baseline clinicopathological characteristics of patients are summarized in Table 1. The patients in the cN(+)pN(−) group were older (median, 73 vs. 70 years; p = 0.003), had higher frequency of right-sided tumors (76.4% vs. 55.9%; p = 0.003), were more often staged as cN1 (84.7% vs. 67.6%; p = 0.007), and had lower tumor PET SUVmax (median, 7.5 vs. 9.8; p = 0.039) compared with those in the cN(+)pN(+) group. No significant differences were observed between the two groups with regard to institution (p = 0.367) or operative time (p = 0.403). Among patients with cN(+)pN(−) disease, 61 patients (84.7%) were classified as cN1–pN0, and 11 patients (15.3%) were classified as cN2–pN0. ROC curve analyses were used to determine cutoff values for continuous variables associated with cN(+)pN(−) (Supplementary Fig. 2). The optimal cutoffs were identified for age (67 years), tumor size (3.0 cm), Brinkman index (1000), tumor PET SUVmax (6.5), and serum CEA (12 ng/mL). On multivariable logistic regression analysis, age ≥ 67 years (odds ratio [OR], 2.85; 95% confidence interval [CI], 1.40–5.79; p = 0.004), right-sided tumor (OR, 2.50; 95% CI, 1.30–4.82; p = 0.006), CEA ≤ 12 ng/mL (OR, 3.11; 95% CI, 1.01–9.53; p = 0.048), cN1 category (OR, 2.37; 95% CI, 1.12–5.04; p = 0.025), and tumor PET SUVmax ≤ 6.5 (OR, 2.03; 95% CI, 1.10–3.73; p = 0.023) were independently associated with cN(+)pN(−) disease (Table 2). The cN(+)pN(−) group had significantly better RFS compared with the cN(+)pN(+) group (5-year RFS: 67.1% vs. 35.3%; p < 0.001). Moreover, the OS tended to be better in the cN(+)pN(−) group than in the cN(+)pN(+) group, although the difference did not reach statistical significance (5-year OS: 79.0% vs. 63.9%; p = 0.086) (Fig. 2). ROC curve analysis of the model incorporating the five predictive factors yielded a C-index of 0.74 (95% CI, 0.67–0.81) (Fig. 3a). The calibration showed good agreement between the predicted and observed probabilities (Fig. 3b). Among the 251 patients, 30 patients (12.0%) met all five predictive factors (age ≥ 67 years, right-sided tumor, CEA ≤ 12 ng/mL, cN1, and tumor SUVmax ≤ 6.5), of whom 20 (66.7%) were pN0. Using this cutoff, the PPV was 66.7% (95% CI, 47.2%–82.7%), the specificity was 94.4% (95% CI, 90.0%–97.3%), and the accuracy was 75.3% (95% CI, 69.5%–80.5%) (Table 3). Table 3 Positive predictive value, specificity, and accuracy for detecting cN(+)pN(−) among cN(+) patients at each risk score cutoff in the development and validation cohorts Cutoff of the number of risks Positive predictive value (%) Specificity (%) Accuracy (%) Development cohort, n = 251 3 ≦ 35.9 (28.9–43.3) 34.1 (27.2–41.5) 50.6 (44.2–56.9) 4 ≦ 47.1 (37.2–57.2) 69.3 (62.0–75.9) 68.9 (62.8–74.6) 5 66.7 (47.2–82.7) 94.4 (90.0–97.3) 75.3 (69.5–80.5) Validation cohort (overall), n = 108 3 ≦ 31.8 (22.1–42.8) 27.5 (18.1–38.6) 45.4 (35.8–55.2) 4 ≦ 43.2 (28.3–59.0) 68.8 (57.4–78.7) 68.5 (58.9–77.1) 5 70.0 (34.8–93.3) 96.3 (89.4–99.2) 77.8 (68.8–85.2) Validation cohort (EGFR/ALK-negative or unknown subset), n = 84 3 ≦ 36.8 (25.4–49.3) 25.9 (15.3–39.0) 47.6 (36.6–58.8) 4 ≦ 47.2 (30.4–64.5) 67.2 (53.7–79.0) 66.7 (55.5–76.6) 5 66.7 (29.9–92.5) 94.8 (85.6–98.9) 72.6 (61.8–82.9) ALK, anaplastic lymphoma kinase; cN, clinical nodal status; EGFR, epidermal growth factor receptor; pN, pathological nodal status 3.2 Validation in the overall validation cohort In the validation cohort (n = 108), 28 patients (25.9%) were classified as cN(+)pN(−). The baseline clinicopathological characteristics of patients are summarized in Table 4. Similar trends were observed in the validation cohort: patients with cN(+)pN(−) disease were older, had higher frequency of right-sided tumors, more often had CEA ≤ 12 ng/mL and cN1 disease, and tended to have lower tumor SUVmax (Table 4). ROC curve analysis demonstrated a C-index of 0.77 (95% CI, 0.67–0.86) (Fig. 3c). The calibration showed good agreement between the predicted and observed probabilities (slope, 1.002; intercept, 0.001) (Fig. 3d). Ten patients (9.3%) fulfilled all five predictive factors, of whom seven were pN0. Using this cutoff, the PPV was 70.0% (95% CI, 34.8%–93.3%), the specificity was 96.3% (95% CI, 89.4%–99.2%), and the accuracy was 77.8% (95% CI, 68.8%–85.2%) (Table 3). 3.3 Validation in the EGFR/ALK-negative or unknown subset Among patients without known EGFR mutations or ALK fusions in the validation cohort (n = 84), 26 patients (30.9%) were classified as cN(+)pN(−). The model achieved a C-index of 0.76 (95% CI, 0.66–0.86) (Fig. 4a) and demonstrated excellent calibration (slope, 1.001; intercept, 0.001) (Fig. 4b). Nine patients (10.7%) met all five predictive factors, of whom six were pN0. The PPV was 66.7% (95% CI, 29.9%–92.5%), the specificity was 94.8% (95% CI, 85.6%–98.9%), and the accuracy was 72.6% (95% CI, 61.8%–82.9%) (Table 3). 4. Discussion In this multicenter study of < 4-cm cN(+) NSCLC, 28.7% of patients in the development cohort were classified as cN(+)pN(−), indicating a substantial rate of false-positive cN staging. These patients had a more favorable prognosis than those with cN(+)pN(+) disease. Five independent preoperative factors associated with cN(+)pN(−) disease were identified, including age ≥ 67 years, right-sided tumor, CEA ≤ 12 ng/mL, cN1, and tumor SUVmax ≤ 6.5. The resulting model achieved a PPVs of 70.0% and 66.7% and high specificity (96.3% and 94.8%) in the overall validation cohort and the EGFR/ALK-negative or unknown subset, suggesting that it can identify patients likely to be cN(+)pN(−) while minimizing misclassification of true pN(+) disease. Several randomized trials have established the benefit of neoadjuvant chemo-immunotherapy and perioperative immunotherapy in resectable NSCLC [ 3 – 5 , 19 ]. According to subgroup analyses from the CheckMate 816, KEYNOTE-671, and AEGEAN trials, the treatment effect was more pronounced in patients with more advanced stage disease, particularly those with stage III or nodal involvement (e.g., hazard ratio 0.54 in stage III vs. 0.87 in stage IB–II in the CheckMate 816 trial; hazard ratio 0.57 in stage IIIA vs. 0.76 in stage II in the AEGEAN trial) [ 3 – 5 ]. Moreover, in the CheckMate 816 trial, patients with higher tumor mutational burden (≥ 12.3 mut/Mb) derived greater benefit, further supporting the concept that preoperative immune checkpoint inhibition is particularly advantageous in patients with true nodal metastasis and higher systemic disease burden [ 3 ]. These findings support the biological rationale that neoadjuvant immune checkpoint inhibition is particularly beneficial in patients with true nodal metastasis and a higher burden of micrometastatic disease. In these patients, early systemic therapy may eradicate occult distant disease and improve long-term outcomes. By contrast, patients with cN(+)pN(−) disease lack pathological nodal involvement and likely have a lower systemic disease burden, potentially deriving limited benefit from preoperative systemic therapy while remaining exposed to its toxicities, ultimately resulting in overtreatment [ 3 – 5 , 19 ]. Most previous studies that evaluated the accuracy of preoperative nodal staging included heterogeneous populations across tumor sizes and disease stages. Conversely, the present study specifically focused on tumors < 4 cm with cN(+) disease, a clinically important subgroup that is a candidate for neoadjuvant chemo-immunotherapy. Despite evaluation at three high-volume institutions, cN staging remained suboptimal, underscoring the need for improved strategies to refine preoperative nodal assessment. The accuracy of cN staging based on CT and PET/CT remains limited, particularly because of suboptimal specificity and PPV reported across studies [ 21 – 26 ]. Therefore, guidelines recommend pathological confirmation using EBUS/EUS with mediastinoscopy in selected cases [ 6 , 7 , 9 , 10 , 27 ]. Although EBUS-TBNA provides high diagnostic accuracy for accessible stations [ 13 ], accurate staging remains challenging for anatomically difficult stations, including several N1 nodes (e.g., #10, #12–14) [ 11 , 12 , 28 ]. Consistent with these limitations, most false-positive cases in the present study were cN1 (84.7% in the development cohort and 85.7% in the validation cohort), leaving clinicians to primarily rely on imaging when deciding on neoadjuvant therapy. False positivity of cN staging is a well-recognized limitation of PET/CT-based nodal assessment. Previous studies have reported false-positive rates of approximately 20%–50% for hilar and mediastinal lymph nodes when PET/CT is used alone [ 29 , 30 ]. Lower nodal SUVmax, older age, and non-adenocarcinoma histology have been associated with false-positive findings [ 29 , 31 ], and inflammatory or granulomatous conditions may further contribute to increased FDG uptake and false-positive interpretations [ 30 , 32 – 34 ]. These observations are consistent with our finding that false-positive cN staging occurred more frequently among older patients. Inaccurate preoperative nodal staging has been consistently reported. A meta-analysis showed low concordance between cTNM and pTNM staging, with overstaging in approximately 14% of cases [ 14 ]. Solberg et al. reported a cTNM–pTNM concordance of 48.1%, with cN(+)pN(–) disease being observed in 22.8% of patients [ 17 ]. Meanwhile, Macia et al. found an overall concordance of only 58%, which was particularly low in stages IB–III [ 18 ]. Together with the present findings, these data highlight the inherent limitations of preoperative nodal staging and the clinical importance of accounting for false-positive cN disease. The prediction model identified in this study, based on five readily available preoperative factors, enables high-specificity identification of patients with < 4-cm cN(+) NSCLC who are unlikely to have pathological nodal metastasis. This is particularly relevant for patients with cN1 disease, in whom pathological confirmation by EBUS/EUS is often difficult and treatment decisions frequently rely on imaging alone. The model supports a treatment strategy that prioritizes upfront surgery for definitive pathological staging by identifying patients at high risk of cN(+)pN(–) disease, thereby helping to avoid unnecessary neoadjuvant immunotherapy while preserving appropriate treatment for true pN(+) disease. This study has several limitations. First, its retrospective design may have introduced selection bias and bias in treatment decision-making, which cannot be fully eliminated. Second, FDG-PET/CT scanners, imaging protocols, and methods for measuring SUV were not fully standardized across institutions, potentially limiting the reproducibility of PET SUVmax. Third, the indications for and technical performance of EBUS/EUS and mediastinoscopy may have varied among institutions and operators, which could have influenced the cN staging accuracy despite adherence to guideline-based cN staging. Future prospective studies are needed to incorporate this model into a cN staging algorithm and validate its external applicability and clinical utility. 5. Conclusion Among patients with < 4-cm cN(+) NSCLC, approximately 30% had false-positive cN staging and demonstrated favorable outcomes. The prediction model based on the five preoperative factors identified in this study enabled high-specificity identification of cN(+)pN(–) cases, and its validity was confirmed in an independent validation cohort. In the contemporary treatment landscape where neoadjuvant-immunotherapy-based regimens are increasingly being standardized, this model may serve as a practical risk stratification tool to identify patients in whom upfront surgery for definitive pathological nodal evaluation may be considered as an alternative to immediate neoadjuvant immunotherapy. Abbreviations ACCP American College of Chest Physicians ALK anaplastic lymphoma kinase CEA carcinoembryonic antigen CI confidence interval cN clinical nodal status CT computed tomography cTNM–pTNM clinical TNM–pathologic TNM EBUS endobronchial ultrasound EBUS-TBNA endobronchial ultrasound-guided transbronchial needle aspiration EGFR epidermal growth factor receptor EUS endoscopic ultrasound ESTS European Society of Thoracic Surgeons FDG fluorodeoxyglucose NCCN National Comprehensive Cancer Network NSCLC non-small cell lung cancer OR odds ratio OS overall survival pN pathological nodal status PET/CT positron emission tomography/computed tomography PPV positive predictive value RFS recurrence-free survival ROC receiver operating characteristic SUVmax maximum standardized uptake value TNM tumor,node,metastasis Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. This retrospective multicenter study was approved by the Institutional Review Board of Kanagawa Cancer Center (approval numbers: 24EKI54, approved June 14, 2021; 2025EKI-121, approved November 25, 2025), the Institutional Review Board of Tokyo Medical University Hospital (SH2969), and the Institutional Review Board of Hiroshima University Hospital (E-1216). The requirement for individual written informed consent was waived by these institutional review boards owing to the retrospective nature of the study. Consent for publication Not applicable. Availability of data and material The datasets used and/or analyzed during the current study are available from corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions TI : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing –original draft, Writing – review & editing. YK : Data curation, Writing – review & editing. IT : Data curation, Writing – review & editing. RM : Data curation, Writing – review & editing. CK : Data curation, Writing – review & editing. TN : Conceptualization, Data curation, Writing – review & editing. YK : Conceptualization, Data curation, Writing – review & editing. YM : Conceptualization, Data curation, Writing – review & editing. MO : Conceptualization, Data curation, Writing – review & editing, Supervision. NI : Conceptualization, Data curation, Writing – review & editing, Supervision. HI : Conceptualization, Data curation, Writing – review & editing, Supervision. Acknowledgments: We thank Enago for English language editing. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. 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Cascone T, Awad MM, Spicer JD, He J, Lu S, Sepesi B, Tanaka F, Taube JM, Cornelissen R, Havel L, Karaseva N, Kuzdzal J, Petruzelka LB, Wu L, Pujol J-L, Ito H, Ciuleanu T-E, de Oliveira L, Koch M, Janssens A, Alexandru A, Bohnet S, Moiseyenko FV, Gao Y, Watanabe Y, Erdmann CC, Sathyanarayana P, Meadows-Shropshire S, Blum SI. Pulla, for the CheckMate 77T Investigators, Perioperative nivolumab in resectable lung cancer. N Engl J Med. 2024;390:1756–69. https://doi.org/10.1056/NEJMoa2401135 . Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WEE, Nicholson AG, Groome P, Mitchell A, Bolejack V, Goldstraw P, Rami-Porta R, Asamura H, Ball D, Beer DG, Beyruti R, Bolejack V, Chansky K, Crowley J, Detterbeck F, Eberhardt WEE, Edwards J, Galateau-Sallé F, Giroux D, Gleeson F, Groome P, Huang J, Kennedy C, Kim J, Kim YT, Kingsbury L, Kondo H, Krasnik M, Kubota K, Lerut A, Lyons G, Marino M, Marom EM, van Meerbeeck J, Mitchell A, Nakano T, Nicholson AG, Nowak A, Peake M, Rice T, Rosenzweig K, Ruffini E, Rusch V, Saijo N, Van Schil P, Sculier J-P, Shemanski L, Stratton K, Suzuki K, Tachimori Y, Thomas CF Jr., Travis W, Tsao MS, Turrisi A, Vansteenkiste J, Watanabe H, Wu Y-L, Baas P, Erasmus J, Hasegawa S, Inai K, Kernstine K, Kindler H, Krug L, Nackaerts K, Pass H, Rice D, Falkson C, Filosso PL, Giaccone G, Kondo K, Lucchi M, Okumura M, Blackstone E, Cavaco FA, Barrera EA, Arca JA, Lamelas IP, Obrer AA, Jorge RG, Ball D, Bascom GK, Blanco Orozco AI, González MA, Castro MG, Blum D, Chimondeguy V, Cvijanovic S, Defranchi B, de Olaiz Navarro I, Escobar Campuzano I, Macía Vidueira E, Fernández Araujo F, Andreo García KM, Fong G, Francisco Corral S, Iglesias Heras M, Izquierdo Elena JM, Jakobsen E, Kostas S, León P, Atance A, Núñez Ares M, Liao M, Losanovscky G, Lyons R, Magaroles L, De Esteban Júlvez M, Mariñán Gorospe B, McCaughan C, Kennedy R, Melchor Íñiguez L, Miravet Sorribes S, Naranjo Gozalo C, Álvarez de Arriba M, Núñez Delgado JP, Alarcón JC, Peñalver Cuesta JS, Park H, Pass MJ, Pavón Fernández M, Rosenberg E, Ruffini V, Rusch, Strand D, Subotic S, Swisher R, Terra C, Thomas K, Tournoy P, Van Schil M, Velasquez. Cerezo González, J. Freixinet Gilart, L. García Arangüena, S. García Barajas, P. Girard, T. Goksel, M.T. González Budiño, G. González Casaurrán, J.A. Gullón Blanco, J. Hernández Hernández, H. Hernández Rodríguez, J. Herrero Collantes, Y.L. Wu, K. Yokoi, The IASLC Lung Cancer Staging Project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition, J. Thorac. Oncol. 11 (2016) 39–51. https://doi.org/10.1016/j.jtho.2015.09.009 Birim O, Kappetein AP, Stijnen T, Bogers EW. Meta-analysis of positron emission tomographic and computed tomographic imaging in detecting mediastinal lymph node metastases in nonsmall cell lung cancer. Ann Thorac Surg. 2005;79:375–82. https://doi.org/10.1016/j.athoracsur.2004.06.072 . Schmidt-Hansen M, Baldwin DR, Hasler E, Zamora P, Abraira S. PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable NSCLC. Cochrane Database Syst Rev. 2014;2014:CD009519. https://doi.org/10.1002/14651858.CD009519.pub2 . Zhai X, Guo Y, Qian X. Combination of 18F-FDG PET/CT and tumor markers to diagnose lymph node metastasis in NSCLC. Med Sci Monit. 2020;26:e922675. https://doi.org/10.12659/MSM.922675 . Gould MK, Kuschner WG, Rydzak CE, Maclean CC, Demas AN, Shigemitsu H, Chan JK, Owens DK. Test performance of positron emission tomography and computed tomography for mediastinal staging in patients with non-small-cell lung cancer: a meta-analysis. Ann Intern Med. 2003;139:879–92. https://doi.org/10.7326/0003-4819-139-11-200312020-00013 . Hellwig D, Graeter TP, Ukena D, Groeschel H, Sybrecht R. 18F-FDG PET for mediastinal staging of lung cancer: which SUV threshold makes sense? J Nucl Med. 2007;48:1761–6. https://doi.org/10.2967/jnumed.107.043307 . Bryant AS, Cerfolio RJ, Klemm KM, Ojha B. Maximum SUV of mediastinal lymph nodes on integrated FDG-PET/CT predicts pathology in NSCLC. Ann Thorac Surg. 2006;82:417–22. https://doi.org/10.1016/j.athoracsur.2006.02.069 . Remon J, Soria JC, Peters S. Early and locally advanced NSCLC: update of the ESMO clinical practice guidelines. Ann Oncol. 2021;32:1637–42. https://doi.org/10.1016/j.annonc.2021.09.005 . Wi S, Kim BG, Shin SH, Kim H, Lee J. Clinical utility of EBUS-TBNA of hilar/interlobar/lobar lymph nodes in primary lung cancer. Thorac Cancer. 2022;13:2507–14. https://doi.org/10.1111/1759-7714.14573 . Damirov F, Büsing K, Yavuz G, Korkmaz M, Hekimoglu M. Accuracy of 18F-FDG-PET/CT for preoperative hilar/mediastinal staging: retrospective cohort study. Diagnostics (Basel). 2023;13:403. https://doi.org/10.3390/diagnostics13030403 . Al-Ibraheem A, Hirmas N, Fanti S, Alzoubi A, Alkhateeb H. Impact of 18F-FDG PET/CT, CT and EBUS/TBNA on preoperative mediastinal nodal staging of NSCLC. BMC Med Imaging. 2021;21:49. https://doi.org/10.1186/s12880-021-00584-4 . Li S, Zheng Q, Ma Y, Sun X. Implications of false negative and false positive diagnosis in lymph node staging of NSCLC by 18F-FDG PET/CT. PLoS ONE. 2013;8:e78552. https://doi.org/10.1371/journal.pone.0078552 . Silvestri GA, Gonzalez AV. M. A. Jantz Methods for staging non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American college of chest physicians evidence-based clinical practice guidelines. Chest. 143(2013). Shim SS, Lee KS, Kim BT, Chung H, Lee H. NSCLC: prospective comparison of integrated FDG PET/CT and CT alone for preoperative staging. Radiology. 2005;236:1011–9. https://doi.org/10.1148/radiol.2363041430 . Kim YK, Lee KS, Kim BT, Kim TS, Kim SW. Mediastinal nodal staging using integrated 18F-FDG PET/CT in a tuberculosis-endemic country. Cancer. 2007;109:1068–77. https://doi.org/10.1002/cncr.22532 . Table 1,2,4 Table 1,2,4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.jpg Supplementary Figure 1. Positron emission tomography/computed tomography (PET/CT) demonstrated high fluorodeoxyglucose (FDG) accumulation in hilar lymph node station #11s, leading to a clinical diagnosis of nodal metastasis. However, pathological examination of the dissected lymph nodes revealed no malignant cells, resulting in a final classification of cN(+)pN(−). SupplementaryFigure2.jpg Supplementary Figure 2. Receiver operating characteristic (ROC) analyses for determining optimal cutoff values for continuous variables associated with cN(+)pN(−) disease. Table124.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 30 Apr, 2026 Editor invited by journal 20 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9392177","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634083116,"identity":"c4ea15e4-0206-4f03-b538-ec8b972956b4","order_by":0,"name":"Tetsuya 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Center","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Nagashima","suffix":""},{"id":634083124,"identity":"8306f7d4-a6e7-48cf-9a96-23ac9decbcf3","order_by":6,"name":"Yujin Kudo","email":"","orcid":"","institution":"Tokyo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujin","middleName":"","lastName":"Kudo","suffix":""},{"id":634083130,"identity":"9f46b2df-3972-4e17-8ceb-bf1f2c47561f","order_by":7,"name":"Yoshihiro Miyata","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Miyata","suffix":""},{"id":634083135,"identity":"e48cd632-dce5-4cfd-b386-cae60208a225","order_by":8,"name":"Morihito Okada","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Morihito","middleName":"","lastName":"Okada","suffix":""},{"id":634083137,"identity":"893fb978-abdf-416c-815d-f8d8fd5cb4e9","order_by":9,"name":"Norihiko Ikeda","email":"","orcid":"","institution":"Tokyo Medical University","correspondingAuthor":false,"prefix":"","firstName":"Norihiko","middleName":"","lastName":"Ikeda","suffix":""},{"id":634083138,"identity":"4f0668d5-ebc7-43d4-aedd-e31c00de504a","order_by":10,"name":"Hiroyuki Ito","email":"","orcid":"","institution":"Kanagawa Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Ito","suffix":""}],"badges":[],"createdAt":"2026-04-12 06:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9392177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9392177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108973843,"identity":"50340d3c-1a56-483c-a54a-ef9bf2d13d3f","added_by":"auto","created_at":"2026-05-11 10:44:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165445,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT diagram of the development and validation cohorts\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/cb551df6cabc86c1f6c013a0.jpg"},{"id":108973671,"identity":"8176970e-1ddd-450a-84bb-e265e3ae9c62","added_by":"auto","created_at":"2026-05-11 10:43:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88940,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival outcomes according to pathological nodal status in clinically node-positive non-small cell lung cancer (\u0026lt;4 cm).\u003c/p\u003e\n\u003cp\u003e(a) Recurrence-free survival (RFS) and (b) overall survival (OS) in the development cohort stratified by pathological nodal status. Patients with cN(+)pN(−) disease (n = 72) demonstrated significantly better RFS than those with cN(+)pN(+) disease (n = 179) (5-year RFS: 67.1% vs. 35.3%, p \u0026lt; 0.001). The OS also tended to be better in the cN(+)pN(−) group than in the cN(+)pN(+) group (5-year OS: 79.0% vs. 63.9%, p = 0.086).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/60ac6d93409767fb3b030472.jpg"},{"id":108973795,"identity":"38a6d859-49d6-477e-a87f-14ffb943657e","added_by":"auto","created_at":"2026-05-11 10:44:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202778,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction model’s performance in the development cohort and the external cohort.\u003c/p\u003e\n\u003cp\u003e(a) Receiver operating characteristic (ROC) curve of the five-factor model for predicting cN(+)pN(−) disease in the development cohort (n = 251). The model demonstrated good discrimination, with a C-index of 0.74 (95% confidence interval: 0.67–0.81). (b) Internal validation using bootstrap resampling (1,000 repetitions), showing stable model performance with low prediction error. (c) Receiver operating characteristic (ROC) curve of the five-factor model in the validation cohort, demonstrating good discriminative performance (C-index = 0.77; 95% confidence interval, 0.67–0.86). (d) External validation using bootstrap resampling (1,000 repetitions), showing stable model performance with low prediction error.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/e1b7ff3d19160d7bdadbf5ea.jpg"},{"id":108973790,"identity":"3dbb807e-adc0-4692-9b73-d6505ad9c59f","added_by":"auto","created_at":"2026-05-11 10:44:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103791,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the prediction model in the epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK)–negative or unknown subset (n = 84).\u003c/p\u003e\n\u003cp\u003e(a) Receiver operating characteristic (ROC) curve of the prediction model for cN(+)pN(−) disease in the EGFR/ALK-negative validation cohort, showing a C-index of 0.76 (95% confidence interval [CI], 0.66–0.86). (b) Calibration plot of the model in the same cohort, demonstrating good agreement between predicted and observed probabilities.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/e738df584c24ff6dde4c3aa9.jpg"},{"id":108973879,"identity":"6d222a0c-8b9f-4693-99d1-e07f74f757d6","added_by":"auto","created_at":"2026-05-11 10:44:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/ed01d120-f320-480c-b068-abdc0105fd4f.pdf"},{"id":108973683,"identity":"b2b5f6ce-9635-4a71-9417-bf3f64654f42","added_by":"auto","created_at":"2026-05-11 10:43:52","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":114320,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 1. Positron emission tomography/computed tomography (PET/CT) demonstrated high fluorodeoxyglucose (FDG) accumulation in hilar lymph node station #11s, leading to a clinical diagnosis of nodal metastasis. However, pathological examination of the dissected lymph nodes revealed no malignant cells, resulting in a final classification of cN(+)pN(−).\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/918a9add42e0f61f1943f938.jpg"},{"id":108973783,"identity":"d953f868-51db-4fd3-b94c-72dea9b90e47","added_by":"auto","created_at":"2026-05-11 10:43:59","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":116379,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 2. Receiver operating characteristic (ROC) analyses for determining optimal cutoff values for continuous variables associated with cN(+)pN(−) disease.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/0b603225d4f2ad7843854b1b.jpg"},{"id":108973842,"identity":"fac0b663-b9f6-4ff4-b1d0-1c78b1cfdb8a","added_by":"auto","created_at":"2026-05-11 10:44:16","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26938,"visible":true,"origin":"","legend":"","description":"","filename":"Table124.docx","url":"https://assets-eu.researchsquare.com/files/rs-9392177/v1/38e3814826d45899c8d07c55.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multicenter Prediction Model for False-Positive Clinical Nodal Disease in \u003c4-cm NSCLC: Clinical Implications for Upfront Surgery Versus Neoadjuvant Therapy","fulltext":[{"header":"1. Background","content":"\u003cp\u003ePrimary lung cancer remains a leading cause of cancer-related mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Even in resectable non-small cell lung cancer (NSCLC), the outcomes remain suboptimal. According to the 8th edition of the TNM classification, the reported 5-year overall survival (OS) rates for clinical stages IIA, IIB, and IIIA are 15%\u0026ndash;60%, 17%\u0026ndash;53%, and 14%\u0026ndash;36%, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, the CheckMate 816 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], KEYNOTE-671 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and AEGEAN trials [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] have demonstrated the efficacy of neoadjuvant or perioperative chemo-immunotherapy in clinical stage II\u0026ndash;IIIA NSCLC without epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) fusions. Reflecting these data, the National Comprehensive Cancer Network (NCCN) guidelines [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Japanese Lung Cancer Society guidelines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and National Institute for Health and Care Excellence guidance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] recommend immunotherapy-based perioperative strategies for resectable stage II or higher disease\u0026mdash;specifically\u0026thinsp;\u0026ge;\u0026thinsp;4-cm tumors or \u0026lt;\u0026thinsp;4-cm tumors with clinical nodal involvement (cN(+)) in EGFR/ALK-negative or unknown cases.\u003c/p\u003e \u003cp\u003eIn this context, accurate preoperative nodal assessment is particularly important in \u0026lt;\u0026thinsp;4-cm tumors. According to the European Society of Thoracic Surgeons (ESTS), American College of Chest Physicians (ACCP), and Japanese Lung Cancer Society guidelines, endobronchial ultrasound (EBUS)/endoscopic ultrasound (EUS)-guided sampling and/or mediastinoscopy is recommended not only for radiologically suspected mediastinal disease but also in high-risk patients, including those with central tumors, cN1 disease, or tumor size\u0026thinsp;\u0026gt;\u0026thinsp;3 cm, even when mediastinal findings are absent on imaging [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, accurate preoperative nodal staging remains challenging because EBUS-guided transbronchial needle aspiration (EBUS-TBNA) cannot access all nodal stations [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and mediastinoscopy has similar anatomical limitations that preclude access to all mediastinal nodal stations, aside from requiring general anesthesia and carrying procedural risks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consequently, concordance between clinical and pathological staging remains suboptimal, with a reported clinical TNM\u0026ndash;pathologic TNM (cTNM\u0026ndash;pTNM) agreement of only 54.6% in a large cohort despite guideline-based staging [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and clinical\u0026ndash;pathological N discordance rates of 16.9%\u0026ndash;38% in other studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough neoadjuvant chemo-immunotherapy is increasingly being standardized, perioperative trials have reported grade\u0026thinsp;\u0026ge;\u0026thinsp;3 adverse events in 32.5%\u0026ndash;44.9% of patients, grade 5 events in 0.9%\u0026ndash;1.7% of patients, and failure to proceed to surgery in 17.9%\u0026ndash;22.3% of patients [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, according to subgroup analyses from neoadjuvant and perioperative trials, the treatment effect is more pronounced in patients with more advanced stage disease, particularly those with nodal involvement, supporting the biological rationale that preoperative systemic therapy is particularly beneficial in patients with true pathological nodal metastasis or nodal involvement [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, in \u0026lt;\u0026thinsp;4-cm NSCLC, cN(+) should ideally be reserved for patients with a high probability of true pathological nodal involvement (pN+). Meanwhile, prioritizing surgical resection with definitive pathological nodal evaluation may represent a reasonable alternative strategy in patients at high risk of cN(+)pN(\u0026minus;) disease.\u003c/p\u003e \u003cp\u003eAccordingly, this study aimed to identify the predictive factors for cN(+)pN(\u0026minus;) disease in \u0026lt;\u0026thinsp;4-cm NSCLC using a multicenter database, develop a high-specificity prediction model for false-positive cN staging, and validate the model in an independent cohort in order to facilitate more appropriate treatment selection.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethics statement\u003c/h2\u003e \u003cp\u003e This retrospective multicenter database study was approved by the institutional review boards of Kanagawa Cancer Center (24EKI54, approved June 14, 2021: 2025EKI-121 approved November 25, 2025), Tokyo Medical University Hospital (SH2969), and Hiroshima University Hospital (E-1216). The requirement for individual written informed consent was waived.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Patients\u003c/h2\u003e \u003cp\u003eThe development cohort included patients who were diagnosed as cN(+) and underwent anatomical lung resection (segmentectomy or more extensive procedure) with mediastinal lymph node dissection at Kanagawa Cancer Center, Hiroshima University Hospital, or Tokyo Medical University Hospital between January 2010 and December 2020. Before surgery, all patients were treatment-na\u0026iuml;ve and underwent preoperative fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). Among the 443 eligible patients, 251 patients with tumor size\u0026thinsp;\u0026lt;\u0026thinsp;4 cm on preoperative CT were included in the main analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor external validation, we identified patients with NSCLC at Kanagawa Cancer Center who underwent preoperative FDG-PET/CT, were diagnosed as cN(+), and were treated with anatomical resection plus mediastinal lymph node dissection during two periods: January 2006\u0026ndash;December 2009 and January 2021\u0026ndash;December 2025. Among them, 108 patients with tumor size\u0026thinsp;\u0026lt;\u0026thinsp;4 cm comprised the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). A prespecified subgroup analysis was performed in patients who were EGFR/ALK\u0026ndash;negative or had unknown mutation status (n\u0026thinsp;=\u0026thinsp;84).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 FDG-PET/CT assessment, cN staging, and pN evaluation\u003c/h2\u003e \u003cp\u003eAll patients were evaluated using integrated three-dimensional PET/CT scanners. cN staging was performed in accordance with NCCN [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Japanese Lung Cancer Society [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], ESTS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and ACCP guidelines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] at each institution.\u003c/p\u003e \u003cp\u003eIn principle, mediastinal nodal evaluation by EBUS-TBNA and/or mediastinoscopy was performed in (i) patients with enlarged lymph nodes showing FDG uptake on PET/CT or contrast-enhanced CT or (ii) those with cN1 disease, centrally located tumors, or tumor size\u0026thinsp;\u0026ge;\u0026thinsp;3 cm, even in the absence of mediastinal FDG uptake whenever feasible [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By contrast, pathological mediastinal evaluation was considered optional for peripheral tumors located in the outer one-third of the lung with a diameter\u0026thinsp;\u0026lt;\u0026thinsp;3 cm and no mediastinal FDG uptake on PET/CT [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Although current guidelines do not uniformly define the indications for hilar EBUS-TBNA, FDG-avid hilar nodes were sampled whenever feasible. Otherwise, the decision was left to each institution\u0026rsquo;s discretion. If EBUS-TBNA was technically infeasible, cN staging was determined based on imaging findings through multidisciplinary discussion. Nodal positivity on PET/CT was qualitatively defined as focal FDG uptake greater than background activity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Tumor staging was performed in accordance with the 8th edition of the TNM classification for lung cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients who underwent anatomical lung resection (segmentectomy, lobectomy, or pneumonectomy) with systematic or selective mediastinal lymph node dissection were included. Meanwhile, those who underwent lymph node sampling alone were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Primary/secondary outcomes and definitions\u003c/h2\u003e \u003cp\u003eThe primary objective of this study was to identify predictive factors associated with cN(+)pN(\u0026minus;) disease and the secondary objective was to validate the corresponding prediction model. The representative imaging examples of cN(+)pN(\u0026minus;) cases are shown in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe site of recurrence was defined as the metastatic organ detected at the time of the first recurrence during follow-up. Intrathoracic recurrence included the hilar and mediastinal lymph nodes, lung, and pleura. Meanwhile, distant recurrence included the central nervous system, bone, and abdominal organs (e.g., liver and adrenal glands).\u003c/p\u003e \u003cp\u003eRecurrence-free survival (RFS) was defined as the interval from the date of surgery to recurrence or death from any cause. Patients without recurrence were censored at the last follow-up date. OS was defined as the interval from surgery to death from any cause. Patients who were alive at the last follow-up were censored at that time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eGroup comparisons were performed using the Mann\u0026ndash;Whitney U test for continuous variables and Fisher\u0026rsquo;s exact test for categorical variables. Univariable logistic regression was used to assess associations between cN(+)pN(\u0026minus;) disease and age, sex, Brinkman index, tumor size, radiologic appearance (pure solid vs part-solid), tumor laterality (right-sided), blood serum carcinoembryonic antigen (CEA) value, cN category (cN1 vs. cN2), tumor maximum standardized uptake value (SUVmax), and histology (squamous cell carcinoma). The cutoff values for age, Brinkman index, tumor size, serum CEA level, and SUVmax were determined using receiver operating characteristic (ROC) curve analysis, with cN(+)pN(\u0026minus;) disease as the endpoint. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariable analyses were entered into a multivariable logistic regression model. Statistical significance was considered at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eModel discrimination was evaluated using the C-index. Calibration was assessed in the validation cohort (both overall validation cohort and in the EGFR/ALK-negative subset). To evaluate overfitting and internal validity, bootstrap internal validation with 1,000 resamples was performed in the development and validation cohorts, and optimism-corrected measures of discrimination and calibration were obtained.\u003c/p\u003e \u003cp\u003eSensitivity analyses were used to vary the cutoff for the number of predictive factors met (\u0026ge;\u0026thinsp;3, \u0026ge;\u0026thinsp;4, or all 5). For each cutoff, the positive predictive value (PPV), specificity, and accuracy for cN(+)pN(\u0026minus;) disease were calculated in the development cohort, overall validation cohort, and EGFR/ALK-negative or unknown validation cohort.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using EZR (R Commander version 1.30; Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1 Development cohort\u003c/h2\u003e\n \u003cp\u003eIn the development cohort, 72 patients (28.7%) were classified as cN(+)pN(−), whereas 179 (71.3%) were classified as cN(+)pN(+). The baseline clinicopathological characteristics of patients are summarized in Table 1. The patients in the cN(+)pN(−) group were older (median, 73 vs. 70 years; p = 0.003), had higher frequency of right-sided tumors (76.4% vs. 55.9%; p = 0.003), were more often staged as cN1 (84.7% vs. 67.6%; p = 0.007), and had lower tumor PET SUVmax (median, 7.5 vs. 9.8; p = 0.039) compared with those in the cN(+)pN(+) group. No significant differences were observed between the two groups with regard to institution (p = 0.367) or operative time (p = 0.403). Among patients with cN(+)pN(−) disease, 61 patients (84.7%) were classified as cN1–pN0, and 11 patients (15.3%) were classified as cN2–pN0.\u003c/p\u003e\n \u003cp\u003eROC curve analyses were used to determine cutoff values for continuous variables associated with cN(+)pN(−) (Supplementary Fig.\u0026nbsp;2). The optimal cutoffs were identified for age (67 years), tumor size (3.0 cm), Brinkman index (1000), tumor PET SUVmax (6.5), and serum CEA (12 ng/mL).\u003c/p\u003e\n \u003cp\u003eOn multivariable logistic regression analysis, age ≥ 67 years (odds ratio [OR], 2.85; 95% confidence interval [CI], 1.40–5.79; p = 0.004), right-sided tumor (OR, 2.50; 95% CI, 1.30–4.82; p = 0.006), CEA ≤ 12 ng/mL (OR, 3.11; 95% CI, 1.01–9.53; p = 0.048), cN1 category (OR, 2.37; 95% CI, 1.12–5.04; p = 0.025), and tumor PET SUVmax ≤ 6.5 (OR, 2.03; 95% CI, 1.10–3.73; p = 0.023) were independently associated with cN(+)pN(−) disease (Table 2).\u003c/p\u003e\n \u003cp\u003eThe cN(+)pN(−) group had significantly better RFS compared with the cN(+)pN(+) group (5-year RFS: 67.1% vs. 35.3%; p \u0026lt; 0.001). Moreover, the OS tended to be better in the cN(+)pN(−) group than in the cN(+)pN(+) group, although the difference did not reach statistical significance (5-year OS: 79.0% vs. 63.9%; p = 0.086) (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cp\u003eROC curve analysis of the model incorporating the five predictive factors yielded a C-index of 0.74 (95% CI, 0.67–0.81) (Fig.\u0026nbsp;3a). The calibration showed good agreement between the predicted and observed probabilities (Fig.\u0026nbsp;3b). Among the 251 patients, 30 patients (12.0%) met all five predictive factors (age ≥ 67 years, right-sided tumor, CEA ≤ 12 ng/mL, cN1, and tumor SUVmax ≤ 6.5), of whom 20 (66.7%) were pN0. Using this cutoff, the PPV was 66.7% (95% CI, 47.2%–82.7%), the specificity was 94.4% (95% CI, 90.0%–97.3%), and the accuracy was 75.3% (95% CI, 69.5%–80.5%) (Table\u0026nbsp;3).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePositive predictive value, specificity, and accuracy for detecting cN(+)pN(−) among cN(+) patients at each risk score cutoff in the development and validation cohorts\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCutoff of the number of risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePositive predictive value (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eAccuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eDevelopment cohort, n = 251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3 ≦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35.9 (28.9–43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e34.1 (27.2–41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e50.6 (44.2–56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4 ≦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e47.1 (37.2–57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e69.3 (62.0–75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e68.9 (62.8–74.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e66.7 (47.2–82.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.4 (90.0–97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e75.3 (69.5–80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eValidation cohort (overall), n = 108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3 ≦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e31.8 (22.1–42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e27.5 (18.1–38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e45.4 (35.8–55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4 ≦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e43.2 (28.3–59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e68.8 (57.4–78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e68.5 (58.9–77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e70.0 (34.8–93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e96.3 (89.4–99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e77.8 (68.8–85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eValidation cohort\u003c/p\u003e\n \u003cp\u003e(EGFR/ALK-negative\u003c/p\u003e\n \u003cp\u003eor unknown subset), n = 84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3 ≦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e36.8 (25.4–49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e25.9 (15.3–39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e47.6 (36.6–58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e4 ≦\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e47.2 (30.4–64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e67.2 (53.7–79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e66.7 (55.5–76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e66.7 (29.9–92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e94.8 (85.6–98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e72.6 (61.8–82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eALK, anaplastic lymphoma kinase; cN, clinical nodal status; EGFR, epidermal growth factor receptor; pN, pathological nodal status\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.2 Validation in the overall validation cohort\u003c/h2\u003e\n \u003cp\u003eIn the validation cohort (n = 108), 28 patients (25.9%) were classified as cN(+)pN(−). The baseline clinicopathological characteristics of patients are summarized in Table\u0026nbsp;4. Similar trends were observed in the validation cohort: patients with cN(+)pN(−) disease were older, had higher frequency of right-sided tumors, more often had CEA ≤ 12 ng/mL and cN1 disease, and tended to have lower tumor SUVmax (Table\u0026nbsp;4).\u003c/p\u003e\n \u003cdiv\u003eROC curve analysis demonstrated a C-index of 0.77 (95% CI, 0.67–0.86) (Fig. 3c). The calibration showed good agreement between the predicted and observed probabilities (slope, 1.002; intercept, 0.001) (Fig. 3d). Ten patients (9.3%) fulfilled all five predictive factors, of whom seven were pN0. Using this cutoff, the PPV was 70.0% (95% CI, 34.8%–93.3%), the specificity was 96.3% (95% CI, 89.4%–99.2%), and the accuracy was 77.8% (95% CI, 68.8%–85.2%) (Table 3).\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.3 Validation in the EGFR/ALK-negative or unknown subset\u003c/h2\u003e\n \u003cp\u003eAmong patients without known EGFR mutations or ALK fusions in the validation cohort (n = 84), 26 patients (30.9%) were classified as cN(+)pN(−). The model achieved a C-index of 0.76 (95% CI, 0.66–0.86) (Fig.\u0026nbsp;4a) and demonstrated excellent calibration (slope, 1.001; intercept, 0.001) (Fig.\u0026nbsp;4b). Nine patients (10.7%) met all five predictive factors, of whom six were pN0. The PPV was 66.7% (95% CI, 29.9%–92.5%), the specificity was 94.8% (95% CI, 85.6%–98.9%), and the accuracy was 72.6% (95% CI, 61.8%–82.9%) (Table\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this multicenter study of \u0026lt;\u0026thinsp;4-cm cN(+) NSCLC, 28.7% of patients in the development cohort were classified as cN(+)pN(\u0026minus;), indicating a substantial rate of false-positive cN staging. These patients had a more favorable prognosis than those with cN(+)pN(+) disease. Five independent preoperative factors associated with cN(+)pN(\u0026minus;) disease were identified, including age\u0026thinsp;\u0026ge;\u0026thinsp;67 years, right-sided tumor, CEA\u0026thinsp;\u0026le;\u0026thinsp;12 ng/mL, cN1, and tumor SUVmax\u0026thinsp;\u0026le;\u0026thinsp;6.5. The resulting model achieved a PPVs of 70.0% and 66.7% and high specificity (96.3% and 94.8%) in the overall validation cohort and the EGFR/ALK-negative or unknown subset, suggesting that it can identify patients likely to be cN(+)pN(\u0026minus;) while minimizing misclassification of true pN(+) disease.\u003c/p\u003e \u003cp\u003eSeveral randomized trials have established the benefit of neoadjuvant chemo-immunotherapy and perioperative immunotherapy in resectable NSCLC [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to subgroup analyses from the CheckMate 816, KEYNOTE-671, and AEGEAN trials, the treatment effect was more pronounced in patients with more advanced stage disease, particularly those with stage III or nodal involvement (e.g., hazard ratio 0.54 in stage III vs. 0.87 in stage IB\u0026ndash;II in the CheckMate 816 trial; hazard ratio 0.57 in stage IIIA vs. 0.76 in stage II in the AEGEAN trial) [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, in the CheckMate 816 trial, patients with higher tumor mutational burden (\u0026ge;\u0026thinsp;12.3 mut/Mb) derived greater benefit, further supporting the concept that preoperative immune checkpoint inhibition is particularly advantageous in patients with true nodal metastasis and higher systemic disease burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These findings support the biological rationale that neoadjuvant immune checkpoint inhibition is particularly beneficial in patients with true nodal metastasis and a higher burden of micrometastatic disease. In these patients, early systemic therapy may eradicate occult distant disease and improve long-term outcomes. By contrast, patients with cN(+)pN(\u0026minus;) disease lack pathological nodal involvement and likely have a lower systemic disease burden, potentially deriving limited benefit from preoperative systemic therapy while remaining exposed to its toxicities, ultimately resulting in overtreatment [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost previous studies that evaluated the accuracy of preoperative nodal staging included heterogeneous populations across tumor sizes and disease stages. Conversely, the present study specifically focused on tumors\u0026thinsp;\u0026lt;\u0026thinsp;4 cm with cN(+) disease, a clinically important subgroup that is a candidate for neoadjuvant chemo-immunotherapy. Despite evaluation at three high-volume institutions, cN staging remained suboptimal, underscoring the need for improved strategies to refine preoperative nodal assessment.\u003c/p\u003e \u003cp\u003eThe accuracy of cN staging based on CT and PET/CT remains limited, particularly because of suboptimal specificity and PPV reported across studies [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, guidelines recommend pathological confirmation using EBUS/EUS with mediastinoscopy in selected cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although EBUS-TBNA provides high diagnostic accuracy for accessible stations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], accurate staging remains challenging for anatomically difficult stations, including several N1 nodes (e.g., #10, #12\u0026ndash;14) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consistent with these limitations, most false-positive cases in the present study were cN1 (84.7% in the development cohort and 85.7% in the validation cohort), leaving clinicians to primarily rely on imaging when deciding on neoadjuvant therapy.\u003c/p\u003e \u003cp\u003eFalse positivity of cN staging is a well-recognized limitation of PET/CT-based nodal assessment. Previous studies have reported false-positive rates of approximately 20%\u0026ndash;50% for hilar and mediastinal lymph nodes when PET/CT is used alone [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Lower nodal SUVmax, older age, and non-adenocarcinoma histology have been associated with false-positive findings [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and inflammatory or granulomatous conditions may further contribute to increased FDG uptake and false-positive interpretations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These observations are consistent with our finding that false-positive cN staging occurred more frequently among older patients.\u003c/p\u003e \u003cp\u003eInaccurate preoperative nodal staging has been consistently reported. A meta-analysis showed low concordance between cTNM and pTNM staging, with overstaging in approximately 14% of cases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Solberg et al. reported a cTNM\u0026ndash;pTNM concordance of 48.1%, with cN(+)pN(\u0026ndash;) disease being observed in 22.8% of patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Meanwhile, Macia et al. found an overall concordance of only 58%, which was particularly low in stages IB\u0026ndash;III [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Together with the present findings, these data highlight the inherent limitations of preoperative nodal staging and the clinical importance of accounting for false-positive cN disease.\u003c/p\u003e \u003cp\u003eThe prediction model identified in this study, based on five readily available preoperative factors, enables high-specificity identification of patients with \u0026lt;\u0026thinsp;4-cm cN(+) NSCLC who are unlikely to have pathological nodal metastasis. This is particularly relevant for patients with cN1 disease, in whom pathological confirmation by EBUS/EUS is often difficult and treatment decisions frequently rely on imaging alone. The model supports a treatment strategy that prioritizes upfront surgery for definitive pathological staging by identifying patients at high risk of cN(+)pN(\u0026ndash;) disease, thereby helping to avoid unnecessary neoadjuvant immunotherapy while preserving appropriate treatment for true pN(+) disease.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, its retrospective design may have introduced selection bias and bias in treatment decision-making, which cannot be fully eliminated. Second, FDG-PET/CT scanners, imaging protocols, and methods for measuring SUV were not fully standardized across institutions, potentially limiting the reproducibility of PET SUVmax. Third, the indications for and technical performance of EBUS/EUS and mediastinoscopy may have varied among institutions and operators, which could have influenced the cN staging accuracy despite adherence to guideline-based cN staging. Future prospective studies are needed to incorporate this model into a cN staging algorithm and validate its external applicability and clinical utility.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAmong patients with \u0026lt;\u0026thinsp;4-cm cN(+) NSCLC, approximately 30% had false-positive cN staging and demonstrated favorable outcomes. The prediction model based on the five preoperative factors identified in this study enabled high-specificity identification of cN(+)pN(\u0026ndash;) cases, and its validity was confirmed in an independent validation cohort. In the contemporary treatment landscape where neoadjuvant-immunotherapy-based regimens are increasingly being standardized, this model may serve as a practical risk stratification tool to identify patients in whom upfront surgery for definitive pathological nodal evaluation may be considered as an alternative to immediate neoadjuvant immunotherapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican College of Chest Physicians\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanaplastic lymphoma kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecarcinoembryonic antigen\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 \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclinical nodal status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecTNM\u0026ndash;pTNM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclinical TNM\u0026ndash;pathologic TNM\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendobronchial ultrasound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBUS-TBNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendobronchial ultrasound-guided transbronchial needle aspiration\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\"\u003eEUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendoscopic ultrasound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Society of Thoracic Surgeons\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efluorodeoxyglucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCCN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Comprehensive Cancer Network\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\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\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\"\u003epN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epathological nodal status\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\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erecurrence-free 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\"\u003eSUVmax\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\"\u003eTNM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor,node,metastasis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. This retrospective multicenter study was approved by the Institutional Review Board of Kanagawa Cancer Center (approval numbers: 24EKI54, approved June 14, 2021; 2025EKI-121, approved November 25, 2025), the Institutional Review Board of Tokyo Medical University Hospital (SH2969), and the Institutional Review Board of Hiroshima University Hospital (E-1216). The requirement for individual written informed consent was waived by these institutional review boards owing to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from corresponding author on reasonable request. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTI\u003c/strong\u003e: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing \u0026ndash;original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYK\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eIT\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eRM\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eCK\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eTN\u003c/strong\u003e: Conceptualization, Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYK\u003c/strong\u003e: Conceptualization, Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYM\u003c/strong\u003e: Conceptualization, Data curation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eMO\u003c/strong\u003e: Conceptualization, Data curation, Writing \u0026ndash; review \u0026amp; editing, Supervision. \u003cstrong\u003eNI\u003c/strong\u003e: Conceptualization, Data curation, Writing \u0026ndash; review \u0026amp; editing, Supervision. \u003cstrong\u003eHI\u003c/strong\u003e: Conceptualization, Data curation, Writing \u0026ndash; review \u0026amp; editing, Supervision.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Enago for English language editing.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. 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Cancer. 2007;109:1068\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cncr.22532\u003c/span\u003e\u003cspan address=\"10.1002/cncr.22532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1,2,4","content":"\u003cp\u003eTable 1,2,4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"clinical nodal involvement, false-positive cN staging, perioperative therapy, positron emission tomography, standardized uptake value","lastPublishedDoi":"10.21203/rs.3.rs-9392177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9392177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePerioperative immunochemotherapy is recommended for resectable non-small cell lung cancer (NSCLC) with \u0026ge;\u0026thinsp;4-cm tumors or clinical nodal involvement (cN(+)). Therefore, accurate preoperative nodal assessment is crucial in \u0026lt;\u0026thinsp;4-cm tumors, where false-positive cN staging should be carefully considered. This multicenter study aimed to develop and validate a prediction model to identify patients at high risk of cN(+)pN(\u0026minus;) disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe development cohort included 251 patients with tumors\u0026thinsp;\u0026lt;\u0026thinsp;4 cm who were diagnosed as cN(+) using positron emission tomography and underwent curative anatomical resection with mediastinal lymph node dissection between 2010 and 2020. The model predicting cN(+)pN(\u0026minus;) was developed and validated in an independent cohort of 108 patients treated during different periods from the development cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the development cohort, 72 patients (28.7%) were cN(+)pN(\u0026minus;). In multivariable analysis, age\u0026thinsp;\u0026ge;\u0026thinsp;67 years (odds ratio [OR] 2.85, p\u0026thinsp;=\u0026thinsp;0.004), right-sided tumor (OR 2.50, p\u0026thinsp;=\u0026thinsp;0.006), carcinoembryonic antigen\u0026thinsp;\u0026le;\u0026thinsp;12 ng/mL (OR 3.11, p\u0026thinsp;=\u0026thinsp;0.048), cN1 (OR 2.37, p\u0026thinsp;=\u0026thinsp;0.025), and tumor maximum standardized uptake value\u0026thinsp;\u0026le;\u0026thinsp;6.5 (OR 2.03, p\u0026thinsp;=\u0026thinsp;0.023) were independent predictors for cN(+)pN(\u0026minus;). The prediction model showed a C-index of 0.74 (95% confidence interval [CI] 0.67\u0026ndash;0.81), specificity of 94.4%, and positive predictive value (PPV) of 66.7% in the development cohort. In the validation cohort, the C-index was 0.77 (95% CI 0.67\u0026ndash;0.87) with good calibration (slope, 1.002; intercept, 0.001), specificity (96.3%), and PPV (70.0%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn patients with \u0026lt;\u0026thinsp;4-cm cN(+) NSCLC, a simple model based on five readily available preoperative factors can identify individuals at high risk of false-positive nodal staging with high specificity. This model may help identify patients in whom upfront surgery for definitive pathological nodal evaluation may be preferable rather than immediate neoadjuvant therapy.\u003c/p\u003e","manuscriptTitle":"A Multicenter Prediction Model for False-Positive Clinical Nodal Disease in \u0026lt;4-cm NSCLC: Clinical Implications for Upfront Surgery Versus Neoadjuvant Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:40:35","doi":"10.21203/rs.3.rs-9392177/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-30T19:17:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T19:05:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T11:53:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T13:36:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-04-17T11:18:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea2a441e-3870-4dcd-a32f-9357604a5964","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"42","date":"2026-04-30T19:17:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T19:05:10+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:40:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:40:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9392177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9392177","identity":"rs-9392177","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}