A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma

doi:10.21203/rs.3.rs-5267564/v1

Purpose

A precise assessment of lymph nodal status is essential for guiding an individualized treatment plan in lung adenocarcinoma patients. A novel nomogram using easily accessible indicators was developed and validated in this study to predict CT-negative lymph nodal metastasis.

Methods

Between September 2020 and December 2023, data from 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection with systemic lymph node dissection or sampling were retrospectively reviewed. Risk factors associated with lymph nodal metastasis were identified using univariable and multivariable logistic regression analyses. Subsequently, a nomogram was developed on basis of these identified parameters. The performance and validity of the nomogram were evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curve, and bootstrap resampling techniques.

Results

Four predictors (primary tumor location, primary tumor SUVmax value, N1 lymph node SUVmax, and N2 lymph node SUVmax) were identified and incorporated into the nomogram. The nomogram exhibited notable discrimination, evidenced by an area under the receiver operating characteristic (ROC) curve of 0.825 (95% CI: 0.749–0.886, P < 0.001). Excellent concordance between the predicted and observed probabilities of lymph nodal involvement was demonstrated by the calibration curve. Furthermore, decision curve analysis indicated a net benefit associated with the use of our nomogram.

Conclusion

The nomogram demonstrated efficacy and practicality in predicting CT-negative lymph node metastasis for lung adenocarcinoma patients. It holds potential to offer valuable treatment guidance for clinicians.

Positron emission tomography/computed tomography (PET/CT)

lung adenocarcinoma

lymph nodal metastasis

nomogram

Lung cancer represents a predominant cause of cancer-specific mortality globally.(Nie et al. 2021; Wang S. et al. 2021) Non-small cell lung cancer (NSCLC) constitutes approximately 80% of lung cancer cases, with adenocarcinoma (AC) being the most prevalent histological subtype within NSCLC.(Arbour and Riely 2019; Wang S. et al. 2021) Accurate lymph node staging is essential in the comprehensive management of lung cancer patients, influencing both surgical decision-making and the administration of adjuvant therapies.(Ran et al. 2021) Consequently, the development of an effective and practical method for predicting lymph nodal metastasis in patients with AC is of critical clinical significance.

Pathological biopsy, necessitating invasive techniques like endobronchial ultrasound-guided biopsies and mediastinoscopy, remains the gold-standard reference for determining lymph nodal status in the preoperative setting. However, the routine implementation of these procedures heightens the risk of overdiagnosis and appears to offer no additional benefits beyond confirming an N0 pathological state in patients without lymph nodal metastasis (LNM).(Zhong et al. 2023) Furthermore, the feasibility and precision of safely conducting an invasive procedure are constrained by the potential for additional costs, trauma, and complications, particularly in patients with substantial comorbidities and diminutive lymph nodes. Computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) serve as crucial non-invasive modalities for tumor detection, diagnosis, staging, and clinical decision-making in individuals with lung cancer. CT is capable of diagnosing lymph nodes based on their size; however, it lacks sufficient accuracy for evaluating lymph nodes with small lesions. PET/CT provides a concurrent anatomical and metabolic characterization of lesions, yet its accuracy is not entirely reliable to eliminate the need for pathological confirmation of nodal malignancy.(Mattes et al. 2015) Moreover, infections and inflammatory processes can lead to false-positive results.

Nomograms, which integrate multiple risk factors rather than depending on a single variable, have been demonstrated to be a reliable and effective method for aiding surgeons in the formulation of more precise diagnostic, therapeutic, and prognostic strategies.(Balachandran et al. 2015; Fu et al. 2021) Several models have been developed to predict lymph nodal metastasis in lung cancer patients using PET/CT-related variables.(Lv et al. 2021; Mattes et al. 2015; Wei et al. 2023) However, predictive models for CT-negative patients with AC remain limited. Hence, this study sought to develop and validate a practical nomogram on basis of PET/CT to enhance noninvasive nodal evaluation in CT-negative patients with AC.

2.1 Patients

We conducted a retrospective enrollment of 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection accompanied by systemic lymph node dissection or sampling at the Thoracic Surgery Department of The First Affiliated Hospital of Soochow University (Suzhou, China) from September 2020 to December 2023. The inclusion criteria for this study were as follows: (1) Patients had undergone PET/CT imaging during their hospitalization prior to surgical operation, without evidence of distant metastasis detected; (2) No enlarged lymph nodes were identified on CT scans, with lymph nodes measuring less than 1 cm in the shortest dimension; (3) Patients were pathologically diagnosed with primary adenocarcinoma, including both classic adenocarcinoma and mucinous adenocarcinoma; (4) Necessary clinicopathological data of the patient were complete. The exclusion criteria were as follows: (1) PET/CT scanning was performed in other institution; (2) Pleural metastasis was revealed during surgery; (3) CT scan indicated that the short axis of the lymph node exceeded 1 cm; (4) Systematic dissection or sampling of hilar and mediastinal lymph nodes was not performed; (5) Final pathological diagnosis was not adenocarcinoma.

Baseline clinicopathological data, encompassing age, gender, smoking history, peripheral blood cell parameters, pathological findings, and initial PET/CT data, were extracted from the HaiTai electronic medical record system (Nanjing, Jiangsu, China). Primary tumors situated within the proximal third of the hemithorax were classified as central-type tumors, whereas those located beyond the proximal third of the hemithorax were categorized as peripheral-type tumors.(Jin et al. 2017)

This study was retrospectively conducted following the principles delineated in the Declaration of Helsinki (as revised in 2013) and received approval from the ethical committee and institutional review board of The First Affiliated Hospital of Soochow University (No. 2024349). Individual consent was waived for this retrospective analysis. All data were anonymized to ensure the protection of personal privacy.

2.2 ¹⁸F-FDG PET/CT examination

All patients underwent integrated FDG PET/CT imaging prior to surgical operation. Patients fasted for a minimum of 6 hours to ensure their blood glucose levels were below 11 mmol/L before receiving an 18F-FDG injection, with a dosage ranging from 4.07 to 5.55 MBq/kg. Approximately 40 to 60 minutes post-injection, PET/CT scans were conducted from the base of the skull to the mid-thigh region, with an acquisition time of 2 to 3 minutes per bed position. The ordered subset expectation-maximization algorithm was used to conduct image reconstruction. The imaging was carried out using a Discovery PET/CT system (General Electric Medical Systems, Milwaukee, WI, USA) with low-dose CT parameters set at 140 kV, 120 mA, a transaxial field of view of 70 cm, and a slice thickness of 3.75 mm. The maximum standardized uptake value (SUVmax) of the tumor and suspected lymph nodes was ascertained by delineating a region of interest around each respective area. For part-solid tumors, tumor size was determined by measuring the solid component of the lesion. Final pathological findings were utilized as the gold standard to compare with PET/CT results.

2.3 Blood parameters

Each patient received a routine blood test during the week preceding surgery. The quantities of monocytes, neutrophils, platelets, and lymphocytes were measured. Subsequently, the ratios of neutrophils to lymphocytes (NLR), lymphocytes to monocytes (LMR), and platelets to lymphocytes (PLR) were individually calculated.

2.4 Lymph nodal staging

Tumor staging was assessed following the 8th edition of the TNM classification for NSCLC.(Goldstraw et al. 2016) Lymph nodal metastasis was defined as pathologically confirmed pN1 or pN2 status, whereas those without such confirmation were categorized as pN0. Clinically, lymph nodes were deemed positive if they exceeded 1 cm in the shortest dimension on CT or exhibited an SUVmax greater than 2.5 PET/CT.

2.5 Surgical procedures

Generally, lung cancer is typically excised via wedge resection, segmentectomy, lobectomy, or bilobectomy, accompanied by systemic lymph node dissection or sampling. Complete resection is characterized by the achievement of both macroscopic and microscopic tumor-free margins. At least 1 N1 (hilar, interlobar, and intrapulmonary) station and 3 N2 (mediastinal) stations including the subcarinal station were excised.

2.6 Statistical analysis

The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of PET/CT for the evaluation of lymph nodal metastasis were assessed using histological results as the reference standard. Continuous variables with non-normal distributions were reported as medians (range) and analyzed using the Mann–Whitney U test. Categorical variables were expressed as numbers (rates) and analyzed using the χ² test or Fisher's exact test. Univariable and multivariable binary logistic regression analyses were conducted to identify independent risk factors for lymph nodal metastasis, which were subsequently used to construct a nomogram. Receiver operating characteristic (ROC) curves were generated using MedCalc, and the area under the curves (AUC) was calculated to demonstrate the predictive accuracy of the nomogram. Internal validation of the nomogram was performed with 1000 bootstrap resamples. Harrell's C-index was employed to evaluate the discrimination performance. Calibration plots and decision curve analysis (DCA) were utilized to assess the calibration and clinical utility of the nomogram. Clinical baseline data analysis was conducted using the SPSS statistical package (version 26.0, IBM Corp., Armonk, NY, USA). The nomogram, calibration plot, and DCA curve were generated using R software version 4.4.1. Two-sided p < 0.05 was considered statistically significant.

From September 2020 to December 2023, a total of 132 lung adenocarcinoma patients consisting of 54 males and 78 females were enrolled in the study. All patients underwent integrated FDG PET/CT before pulmonary resection and were at least diagnosed with CT negative (the shortest dimension of lymph nodes less than 1 cm). Table 1 presents the diagnostic performance of PET-CT lymph nodal staging in lung adenocarcinoma patients. The sensitivity and specificity for N1 lymph nodes were 34.6% and 91.5%, respectively; for N2 lymph nodes, 50% and 99.1%, respectively; and for the overall patient cohort, 45.2% and 90.1%, respectively. The overall diagnostic accuracy was 79.5%. In all patients, the incidence of lymph nodal metastasis was 23.5% (31/132). Among these patients, 26 patients had N1 LNM and 18 patients had N2 LNM. Of all the patients, 6 underwent wedge resection, 3 underwent segmentectomy, 120 underwent lobectomy and 3 underwent bilobectomy.

Table 1

Overview of FDG-PET/CT in diagnosing LNM (n = 132)
	Sensitivity % (n)	Specificity % (n)	Positive predictive value % (n)	Negative predictive value % (n)	Accuracy % (n)
N1 LNM	34.6(9/26)	91.5(97/106)	50(9/18)	85.1(97/114)	80.3(106/132)
N2 LNM	50(9/18)	99.1(113/114)	90(9/10)	92.6(113/122)	92.4(122/132)
Overall LNM	45.2(14/31)	90.1(91/101)	58.3(14/24)	84.3(91/108)	79.5(105/132)
Abbreviations: LNM, lymph nodal metastasis

3.1 Clinicopathological features and nomogram construction

All patients’ data including demographic, imaging, and pathological characteristics are summarized in Table 2. The distribution of these characteristics was compared between the lymph nodes negative (LN-) group and the lymph nodes positive (LN+) group. Compared to LN- patients, LN + patients exhibited a larger tumor size (29 vs 23; P = 0.018) and higher tumor SUVmax value (7.3 vs 2.9; P = 0.000). More patients in the LN + group had a central-type tumor (35.5% vs 13.9%; P = 0.007), a pure solid nodule (90.3% vs 71.3%; P = 0.031), and were with N1 LN SUVmax value ≥ 2.5 (29% vs 8.9%; P = 0.011), N2 LN SUVmax value ≥ 2.5 (29% vs 1%; P = 0.000), poor tumor grade (29% vs 11.9%; P = 0.045), solid pattern (58.1% vs 30.7%; P = 0.006). However, no statistically significant differences were observed in peripheral blood cell parameters. Independent risk factors for LNM were identified through univariable and multivariable binary logistic regression analyses (Table 3). Ultimately, tumor location (OR, 3.90; 95% CI, 1.21–12.53; P = 0.022), tumor SUVmax value (OR, 1.16; 95% CI, 1.0-1.35; P = 0.049), N1 LN SUVmax value (OR, 5.25; 95% CI, 1.37–20.04; P = 0.015) and N2 LN SUVmax value (OR, 21.89; 95% CI, 2.27-210.86; P = 0.008) were identified for the construction of the nomogram (Fig. 1).

Table 2

Clinicopathological variables in lung adenocarcinoma patients with (LN+) and without (LN-) lymph nodal metastasis
Variables	LN(-) cohort (n = 101)	LN(+) cohort (n = 31)	P value
Age, years (Median, range)	64(37–85)	66(34–77)	0.711
Gender, n (%)
Female/Male	61(60.4)/40(39.6)	17(54.8)/14(45.2)	0.582
Smoking history, n (%)
Never/Ever	83(82.2)/18(17.8)	29(93.5)/2(6.5)	0.208
PLR (Median, range)	109.3(8.7-318.8)	119.6(43.1-293.5)	0.673
NLR (Median, range)	1.8(0.1–9.5)	1.8(0.5–4.8)	0.741
LMR (Median, range)	4(0.5–33.7)	3.5(1.6–8.9)	0.492
Tumor size, mm (Median, range)	23(10–84)	29(13–61)	0.018*
Tumor side, n (%)
Left/Right	45(44.6)/56(55.4)	16(51.6)/15(48.4)	0.490
Tumor location, n (%)
Central/Peripheral	14(13.9)/87(86.1)	11(35.5)/20(64.5)	0.007*
Pure solid nodule, n (%)
Yes/No	72(71.3)/29(28.7)	28(90.3)/3(9.7)	0.031*
Tumor SUVmax value (Median, range)	2.9(0-17.8)	7.3(1.7–18.5)	0.000*
N1 LN SUVmax value, n (%)
≥ 2.5/<2.5	9(8.9)/92(91.1)	9(29)/22(71)	0.011*
N2 LN SUVmax value, n (%)
≥ 2.5/<2.5	1(1)/100(99)	9(29)/22(71)	0.000*
Tumor grade, n (%)
Well or Moderate/Poor	89(88.1)/12(11.9)	22(71)/9(29)	0.045*
Pathological subtype
Absent/Present, n (%)
Lepidic pattern	83(82.2)/18(17.8)	28(90.3)/3(9.7)	0.422
Acinar pattern	9(8.9)/92(91.1)	7(22.6)/24(77.4)	0.084
Papillary pattern	27(26.7)/74(73.3)	9(29)/22(71)	0.801
Micropapillary pattern	45(44.6)/56(55.4)	12(38.7)/19(61.3)	0.566
Solid pattern	70(69.3)/31(30.7)	13(41.9)/18(58.1)	0.006*
Mucinous pattern	98(97)/3(3)	30(96.8)/1(3.2)	1.000
Abbreviations: LN lymph node; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; SUVmax: maximum standardized uptake value. *p <0.05

Table 3

Univariable and multivariable analysis of predictors of lymph nodal metastasis in 132 patients with lung adenocarcinoma
Variables	Univariable			Multivariable
	OR	95%CI	P value	OR	95%CI	P value
Tumor size, mm	1.03	1.0-1.07	0.051
Tumor location
Peripheral vs Central	3.42	1.35–8.64	0.009*	3.90	1.21–12.53	0.022*
Pure solid nodule
No vs Yes	3.76	1.06–13.33	0.04*	1.06	0.24–4.61	0.94
Tumor SUVmax value	1.26	1.12–1.41	0.000*	1.16	1.0-1.35	0.049*
N1 LN SUVmax value
<2.5 vs ≥ 2.5	4.182	1.49–11.77	0.007*	5.25	1.37–20.04	0.015*
N2 LN SUVmax value
<2.5 vs ≥ 2.5	40.91	4.93-339.77	0.001*	21.89	2.27-210.86	0.008*
Tumor grade
Well or Moderate vs Poor	3.03	1.14–8.10	0.027*	1.512	0.34–6.74	0.59
Solid pattern
Absent vs Present	3.13	1.34–7.17	0.007*	2.29	0.71–7.37	0.16
Abbreviations: CI: confidence interval; OR: odds ratio. *p <0.05

3.2 Nomogram evaluation

Our model demonstrated robust discrimination, evidenced by a C-index of 0.810 for LNM when evaluated using 1000 bootstrap resamples. Internal validation further corroborated this performance, yielding a high C-index value of 0.825. Calibration curves shown in Fig. 2 indicated strong concordance between the predicted and actual probabilities of LNM. Decision curve analysis revealed that, for patients with a threshold probability exceeding 10%, the nomogram provided greater clinical benefit compared to both the treat-all and treat-none strategies (Fig. 3). Additionally, the area under the ROC curve was 0.825 (95% CI 0.749–0.886, P < 0.001) (Fig. 4), signifying a notable discrimination capability.

A novel nomogram was developed and validated in this retrospective study to predict the incidence of lymph node metastasis in patients with lung adenocarcinoma, utilizing readily accessible PET/CT indicators. The analysis identified primary tumor location, tumor SUVmax, N1 lymph node SUVmax ≥ 2.5, and N2 lymph node SUVmax ≥ 2.5 as independent risk factors. Our nomogram indicated that patients with central-type lung adenocarcinoma, elevated tumor SUVmax, and N1 and N2 lymph node SUVmax ≥ 2.5 are at a higher risk of lymph nodal involvement. The model demonstrated robust discrimination and calibration. Hence, it might possess potential clinical utility to evaluate preoperative lymph nodal status in lung adenocarcinoma patients and might provide clinicians with valuable treatment guidance.

Clinicians predominantly depend on specific clinical features, particularly imaging characteristics, to assess the risk of LNM in lung cancer during routine practice. Several studies have suggested that metabolic and morphologic parameters observed in PET/CT scans, such as tumor size, tumor location, consolidation ratio, and metabolic value, may offer valuable insights into the likelihood of LNM.(Kagimoto et al. 2020; Kameyama et al. 2022; Nie et al. 2021; Zhong et al. 2023) Nevertheless, this subjective evaluation is limited in its ability to comprehensively estimate the probability of LNM due to the variability in clinicians' experiences. This limitation has direct implications for the management strategies employed for individual patients. Therefore, various models utilizing PET/CT have been developed and validated to predict lymph nodal involvement in NSCLC.(Fu et al. 2021; Lv et al. 2021; Mattes et al. 2015; Wei et al. 2023) Whereas, to the best of our knowledge, there are limited studies specifically aimed at constructing prediction models for LNM in lung adenocarcinoma. Consistent with previous studies, factors such as tumor location, the SUVmax of the primary tumor, and suspicious lymph nodes have been identified as significant risk factors for lymph nodal metastasis.(Fu et al. 2021; Kawamoto et al. 2023; Lv et al. 2021; Wei et al. 2023) Notably, central lung cancer characterized by a high SUVmax value of both the primary tumor and suspicious lymph nodes exhibits a markedly higher prevalence of lymph nodal metastasis. Given that our model was designed for lung adenocarcinoma patients, which could be confirmed by preoperative biopsy or intraoperative frozen section, we incorporated the precise SUVmax of the primary tumor into our model to minimize measurement error. However, for the suspicious lymph nodes, 2.5 was used as the cut-off value associated with positive lymph nodes, which is more pragmatic and consistent with current clinical habits.(Kaseda et al. 2016; Miyasaka et al. 2013) We exclusively utilized the lymph node station with the highest SUVmax, despite the potential inaccuracy arising from the possibility that a single individual may present with multiple lymph nodes exhibiting abnormal SUVmax. This approach is justifiable for two reasons: firstly, our study aimed to evaluate overall lymph nodal involvement rather than the risk associated with specific lymph nodal stations; secondly, distinguishing between metastatic hilar and interlobar lymph nodes and those that are non-metastatic is challenging due to their proximity to the bronchus and similar soft tissue characteristics.(Dyas et al. 2018; Fu et al. 2021) Thus, both N1 and N2 lymph node SUVmax were included in our model, with the latter demonstrating a more substantial contribution to the risk of LNM. Fundamentally, the identification of LNM and improvement of staging accuracy is determined depending on complete and en bloc resection of each lymph node station.(Darling et al. 2011; Doddoli et al. 2005)

In consideration of the importance of inflammation in tumor initiation, progression, and metastasis, Wei, Chen, and Wang reported that peripheral blood cell parameters such as NLR and PLR were significantly elevated in LNM patients.(Chen et al. 2020; Wang Y. et al. 2021; Wei et al. 2023) However, no statistical difference was found in our cohort. This may be derived from the different biological characteristics of adenocarcinoma and other types of NSCLC. Additionally, micropapillary and/or solid subtypes of lung adenocarcinoma have been demonstrated to correlate with LNM and poor prognosis.(Hung et al. 2016; Zhao et al. 2016) While the pathological subtype significantly influenced the risk of lymph node metastasis in univariable analysis, it was not identified as an independent predictor in our multivariable analysis. This may be because previous studies did not incorporate both PET/CT-related parameters and clinicopathological features simultaneously. Thus, these intriguing findings require to be validated by further studies with a larger sample size. Notably, some clinicopathologic factors such as vascular invasion, pleural invasion, and the existence of tumor spread through air spaces (STAS) were not incorporated into our prediction model, despite numerous studies reporting the potential increase of LNM in lung adenocarcinoma patients presenting with these characteristics.(Hung et al. 2016; Moon et al. 2016; Vaghjiani et al. 2020) For the moment, the determination of these pathological characteristics by preoperative biopsy or intraoperative FS is challenging for most pathologists due to limited access to the tissue and detection techniques, causing relatively low accuracy.

Nodal biopsy is considered the gold standard for lymph node staging in the preoperative setting, but the potential risk of invasive procedures cannot be ignored. It is thus necessary to balance the advantages and disadvantages of this dual-nature procedure to individualize the lymph node staging for lung cancer patients.(Czarnecka-Kujawa and Yasufuku 2017; Detterbeck et al. 2007) Besides, in surgical decision-making, sublobar resection and lobe-specific lymph node dissection are increasingly utilized for the surgical treatment of early-stage NSCLC due to more lung parenchyma preservation and less surgical trauma. However, lobectomy with systematic lymph node dissection is still the more appropriate choice if LNM occurs.(Dezube et al. 2022; Zhao et al. 2017) Therefore, it is advisable to recommend more aggressive diagnostic and therapeutic strategies for patients predicted by the model to have a high incidence of lymph node metastasis. Ultimately, our model may aid in identifying patients at high risk for lymph node involvement, thereby preventing missed opportunities for perioperative adjuvant therapy.

Several limitations of this study warrant acknowledgment. Firstly, selection bias was unavoidable in this single-center retrospective study, raising questions about the generalizability of our findings to other populations. There still exists some unknown potential biases between the groups although multivariable analysis was conducted to balance the apparent biases. Secondly, all included cases were adenocarcinomas. Different histological types exhibit distinct radiological phenotypes and tumor aggressiveness, which contribute to heterogeneity in metastatic behavior. Consequently, patients were divided into two groups based solely on lymph node metastasis status, a constraint necessitated by the small sample size. Additionally, PET/CT imaging is not a mandatory preoperative procedure for every patient within our department. Consequently, our analysis was limited to the subset of patients who had undergone this imaging modality. Despite conducting internal validation to mitigate adverse influence and calibrate the model, external validation using data from other centers is necessary to ensure the generalizability of this nomogram.

Table 1 Overview of FDG-PET/CT in diagnosing LNM (n = 132)

Conflict of interest

The authors declare no conflicts of interest.

Funding information

This work was supported by the grants from National Natural Science Foundation of China

Author Contribution

Design: XY.Z., XY.J. and C.L.; Collection of statistics: SB.T.; Statistical analysis: JW.C. and K.F.; Manuscript writing: XY.Z., J.Z. and C.L. All authors reviewed the manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Table 1 Overview of FDG-PET/CT in diagnosing LNM (n = 132)

	Sensitivity % (n)	Specificity % (n)	Positive predictive value % (n)	Negative predictive value % (n)	Accuracy % (n)
N1 LNM	34.6(9/26)	91.5(97/106)	50(9/18)	85.1(97/114)	80.3(106/132)
N2 LNM	50(9/18)	99.1(113/114)	90(9/10)	92.6(113/122)	92.4(122/132)
Overall LNM	45.2(14/31)	90.1(91/101)	58.3(14/24)	84.3(91/108)	79.5(105/132)

Abbreviations: LNM, lymph nodal metastasis

Table 2 Clinicopathological variables in lung adenocarcinoma patients with (LN+) and without (LN-) lymph nodal metastasis

Variables	LN(-) cohort (n=101)	LN(+) cohort (n=31)	P value
Age, years (Median, range)	64(37-85)	66(34-77)	0.711
Gender, n (%)
Female/Male	61(60.4)/40(39.6)	17(54.8)/14(45.2)	0.582
Smoking history, n (%)
Never/Ever	83(82.2)/18(17.8)	29(93.5)/2(6.5)	0.208
PLR (Median, range)	109.3(8.7-318.8)	119.6(43.1-293.5)	0.673
NLR (Median, range)	1.8(0.1-9.5)	1.8(0.5-4.8)	0.741
LMR (Median, range)	4(0.5-33.7)	3.5(1.6-8.9)	0.492
Tumor size, mm (Median, range)	23(10-84)	29(13-61)	0.018*
Tumor side, n (%)
Left/Right	45(44.6)/56(55.4)	16(51.6)/15(48.4)	0.490
Tumor location, n (%)
Central/Peripheral	14(13.9)/87(86.1)	11(35.5)/20(64.5)	0.007*
Pure solid nodule, n (%)
Yes/No	72(71.3)/29(28.7)	28(90.3)/3(9.7)	0.031*
Tumor SUVmax value (Median, range)	2.9(0-17.8)	7.3(1.7-18.5)	0.000*
N1 LN SUVmax value, n (%)
≥2.5/＜2.5	9(8.9)/92(91.1)	9(29)/22(71)	0.011*
N2 LN SUVmax value, n (%)
≥2.5/＜2.5	1(1)/100(99)	9(29)/22(71)	0.000*
Tumor grade, n (%)
Well or Moderate/Poor	89(88.1)/12(11.9)	22(71)/9(29)	0.045*
Pathological subtype
Absent/Present, n (%)
Lepidic pattern	83(82.2)/18(17.8)	28(90.3)/3(9.7)	0.422
Acinar pattern	9(8.9)/92(91.1)	7(22.6)/24(77.4)	0.084
Papillary pattern	27(26.7)/74(73.3)	9(29)/22(71)	0.801
Micropapillary pattern	45(44.6)/56(55.4)	12(38.7)/19(61.3)	0.566
Solid pattern	70(69.3)/31(30.7)	13(41.9)/18(58.1)	0.006*
Mucinous pattern	98(97)/3(3)	30(96.8)/1(3.2)	1.000

Abbreviations: LN lymph node; PLR, platelet to lymphocyte ratio; NLR, neutrophil to lymphocyte ratio; LMR, lymphocyte to monocyte ratio; SUVmax: maximum standardized uptake value. *p ＜0.05

Table 3 Univariable and multivariable analysis of predictors of lymph nodal metastasis in 132 patients with lung adenocarcinoma

Variables	Univariable			Multivariable
	OR	95%CI	P value	OR	95%CI	P value
Tumor size, mm	1.03	1.0-1.07	0.051
Tumor location
Peripheral vs Central	3.42	1.35-8.64	0.009*	3.90	1.21-12.53	0.022*
Pure solid nodule
No vs Yes	3.76	1.06-13.33	0.04*	1.06	0.24-4.61	0.94
Tumor SUVmax value	1.26	1.12-1.41	0.000*	1.16	1.0-1.35	0.049*
N1 LN SUVmax value
＜2.5 vs ≥2.5	4.182	1.49-11.77	0.007*	5.25	1.37-20.04	0.015*
N2 LN SUVmax value
＜2.5 vs ≥2.5	40.91	4.93-339.77	0.001*	21.89	2.27-210.86	0.008*
Tumor grade
Well or Moderate vs Poor	3.03	1.14-8.10	0.027*	1.512	0.34-6.74	0.59
Solid pattern
Absent vs Present	3.13	1.34-7.17	0.007*	2.29	0.71-7.37	0.16

Abbreviations: CI: confidence interval; OR: odds ratio. *p ＜0.05

No competing interests reported.

A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Patients

2.2 ¹⁸F-FDG PET/CT examination

2.3 Blood parameters

2.4 Lymph nodal staging

2.5 Surgical procedures

2.6 Statistical analysis

3. Results

3.1 Clinicopathological features and nomogram construction

3.2 Nomogram evaluation

4. Discussion

Declarations

Conflict of interest

Funding information

Author Contribution

Data Availability

References

Tables

Additional Declarations

Status:

Version 1

A novel nomogram based on PET/CT to predict CT-negative lymph nodal metastasis for patients with lung adenocarcinoma

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Patients

2.2 18F-FDG PET/CT examination

2.3 Blood parameters

2.4 Lymph nodal staging

2.5 Surgical procedures

2.6 Statistical analysis

3. Results

3.1 Clinicopathological features and nomogram construction

3.2 Nomogram evaluation

4. Discussion

Declarations

Conflict of interest

Funding information

Author Contribution

Data Availability

References

Tables

Additional Declarations

Status:

Version 1

2.2 ¹⁸F-FDG PET/CT examination