Evaluation of log odds of positive lymph nodes in predicting the survival of patients with non-small cell lung cancer treated with neoadjuvant therapy and surgery: a SEER cohort-based study

All patients were selected from the SEER database (http://​seer.​cancer.​gov/​). Eighteen population-based cancer registries were selected from the SEER database, and the SEER*Stat program (v. 8.3.9) was used to extract information of patients with lung cancer. The extraction terms were as follows: “the location of the disease: lung and bronchus” and “diagnosis year: 2004–2015.” In this study, we enrolled patients with primary lung cancer who received neoadjuvant therapy and lung surgery between 2004 and 2015 (only neoadjuvant chemotherapy or radiotherapy, without any patients receiving immune checkpoint inhibitors and tyrosine kinase inhibitors). Figure 1 shows a flowchart of patient selection. The following variables were extracted: “Age recode with <1 year olds,” “Race recode (White, Black, Other),” “Sex,” “Marital status,” “Derived AJCC T, 6th ed (2004-2015),” “Derived AJCC M, 6th ed (2004-2015),” “Primary Site – labeled,” “Histologic Type ICD-O-3,” “RX Summ--Surg Prim Site (1998+),” “CS tumor size (2004-2015),” “CS Tumor Size/Ext Eval (2004-2015),” “Grade (thru 2017),” “Survival months,” “Vital status recode (study cut-off used),” “Regional nodes positive (1988+),” “Regional nodes examined (1988+),” “CS Reg Node Eval (2004-2015),” “First malignant primary indicator.” The AJCC TNM staging system was updated to the 8th version. Variables of “CS Tumor Size/Ext Eval (2004-2015)” and “CS Reg Node Eval (2004-2015)” were used to identify patients who underwent neoadjuvant therapy. The following patients were excluded: (a) patients with metastatic disease; (b) patients who did not undergo lung surgery; (c) patients in whom lung cancer was not the only primary tumor; (d) patients not receiving neoadjuvant therapy; (e) patients without information about the number of retrieved and positive LNs; and (f) patients with unknown race, marital status, tumor site, laterality, grade, T stage, and N stage.

Informed consent was waived, and ethical approval from the institutional review board was not needed because SEER is a public database and the SEER data contained no personal identifying information. This study was conducted according to the Declaration of Helsinki and the Harmonized Tripartite Guideline for Good Clinical Practice of the International Conference on Harmonization.

LODDS was calculated using the following formula: lg([NPLN + 0.5]/[NDLN-NPLN + 0.5]), where NPLN is the number of positive LNs and NDLN is the number of dissected LNs. X-Tile software (version 3.6.1; Yale University School of Medicine, New Haven, CT, USA) was used to identify the optimal LODDS cutoff value with the maximal survival difference or highest log-rank χ² value among the three groups [16]. As the X-tile software presented − 1.07 and − 0.27 as the LODDS cutoff value for the included patients, LODDS was divided into three ranges: LODDS<− 1.07, − 1.07 ≤ LODDS<-0.27, and LODDS≥-0.27.

R software (version 4.0.2) was used for statistical analysis. Statistical significance was set at p values < 0.05. Categorical variables are presented as proportions. Chi-square tests or Fisher’s precision probability tests were performed for different evaluations of categorical variables. Univariate and multivariate Cox regression analyses were conducted to screen risk factors for OS when variables with P values < 0.05 were finally incorporated into the risk model.

Kaplan–Meier survival curves and log-rank tests were used to compare the OS of patients with different LODDS ranges and N classifications. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the N classification, LODDS, and multivariate model for patients’ long-term outcomes. Weighted mean rank statistics were used to compare the area under the curve (AUC) of the N classification, LODDS, and multivariate model [17]. To better balance the baseline of patients with different LODDS ranges, propensity scores were determined using generalized boosted models, and inverse probability of treatment weighting (IPTW) was used to adjust the Cox regression analyses [18]. In addition, prediction accuracy was compared by calculating the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) between the LODDS, N classification, and multivariate model.

In Table 1, we compared demographic and clinicopathological characteristics of patients with different LODDS ranges. A total of 2059 patients from the SEER database were enrolled in this study and divided into three groups: LODDS<− 1.07, − 1.07 ≤ LODDS<-0.27, and LODDS≥-0.27. There was no significant difference among the groups in terms of age, marital status, surgery type, and radiotherapy (all P > 0.05). However, the variables of sex, race, laterality, primary site, histologic type, differentiation, and chemotherapy were significantly different among the three groups (all P < 0.05). Patients with LODDS≥-0.27 had higher proportions of females, right laterality, primary site of the lower lobe, adenocarcinoma, low differentiation grade, low T1 stage, and chemotherapy. Since LODDS was calculated using NDLN and NPLN, patients with LODDS≥-0.27 had a higher N stage, more regional nodes examined, and positive. We conducted IPTW to eliminate demographic and clinicopathological characteristics of patients with different LODDS ranges. As shown in Fig. S1, the absolute standardized differences in variables decreased under 0.2 and mostly under 0.1, indicating that the three groups were well matched after IPTW.

We conducted univariate and multivariate Cox regression analyses to confirm independent risk factors for patient survival, as shown in Tables 2 and 3. Before IPTW, univariate analysis demonstrated that LODDS, age, sex, T stage, N stage, and radiotherapy were significantly associated with OS (all P < 0.05). However, multivariate analysis showed that LODDS, age, sex, T stage, and radiotherapy were independent risk factors for patient survival (all P < 0.05), with N stage excluded.

After IPTW, the results of the univariate analysis were similar to previous results, showing that LODDS, age, sex, T stage, N stage, and radiotherapy were statistically significant variables, whereas race, marital status, primary site, histologic type, differentiation, and surgery type were newly added variables (all P < 0.05). Furthermore, multivariate regression analysis indicated that LODDS, age, sex, race, marital status, primary site, differentiation, and T stage were independent risk factors for patient survival (all P < 0.05), with N stage excluded. With or without IPTW, LODDS was an independent risk factor for the prognosis of patients receiving neoadjuvant therapy followed by lung surgery.

We also conducted subgroup analysis to further validate the significance of LODDS. We further compared the relative risks of different LODDS ranges by dividing patients into different subgroups based on the variable. We found that a higher LODDS was associated with a higher risk in most subgroups, as shown in Table 4. However, there was no statistical significance among the different LODDS ranges with respect to middle lobe, overlapping primary site, grade I differentiation, grade IV differentiation, and N3 stage, which could be because of the relatively small sample size.

We compared the long-term survival of patients with different N classifications (Fig. 2A). Although patients with different N stages presented different survival curves with P values of 0.036, the curve was not separate and mostly overlapped. Nevertheless, when we divided patients into three groups based on LODDS ranges, we found that the curve was much more distinct (Fig. 2B). Patients with LODDS<-1.07 had the best survival status compared to patients in the other two groups, while patients in the middle group (− 1.07 ≤ LODDS<-0.27) had better OS than those with LODDS≥-0.27 (P < 0.0001). Even after IPTW, the survival curve remained significant among the three groups (P < 0.0001), as shown in Fig. 3.

We compared the accuracy and prognostic value of the N classification, LODDS, and multivariate model using ROC curves and AUC comparisons. We used a multivariate model with five variables that were independent prognostic indicators in the multivariate analysis in Table 2: LODDS, age, sex, T stage, and radiotherapy. As shown in Fig. 4, LODDS had a significantly higher AUC than the N classification for 1-year (P = 0.008), 3-year (P = 0.007), and 5-year OS (P = 0.010) but not at 10-year OS (P = 0.228). However, the multivariate model had a significantly higher AUC than LODDS and N classification for 1-, 3-, 5-, and 10-year OS (all P < 0.001). We also compared the IDI and NRI of the N classification, LODDS, and multivariate model, as shown in Table 5. On considering LODDS as a reference, we found that the IDI and NRI of the N classification were negative. At the same time, those of the multivariate model were positive, suggesting that the LODDS had significantly higher predictive accuracy than the N classification but had lower predictive accuracy than the multivariate model (P < 0.05).