Dataset Information

Anatomical location and number of metastatic lymph nodes for prognosis of non-small cell lung cancer.

ABSTRACT:

Background

The nodal classification of lung cancer is determined by the anatomical location of metastatic lymph nodes (mLNs). However, prognosis can be heterogeneous at the same nodal stage, and the current classification system requires improvement. Therefore, we investigated the correlation between the number of mLNs and prognosis in patients with non-small cell lung cancer.

Methods

Using a multicenter database in Japan, we retrospectively reviewed the records of patients who underwent complete resection for lung cancer between 2010 and 2016. Kaplan-Meier curves were used to determine recurrence-free and overall survival. Multivariate analyses were performed using the Cox proportional hazards model.

Results

We included 1,567 patients in this study. We could show a statistically significant difference in recurrence-free survival between pN2 patients with 1 mLN and pN2 patients with ≥2 mLNs (P=0.016). Patients with a combination of pN1 (≥4 mLNs) plus pN2 (1 mLN) had a poorer prognosis than pN1 patients (1-3 mLNs) (P=0.061) and a better prognosis than pN2 patients (≥2 mLNs) patients (P=0.007). Multivariate analysis showed that the number of mLNs was independently associated with cancer recurrence in patients with pN1 and pN2 disease (P=0.034 and 0.018, respectively).

Conclusions

Nodal classification that combines anatomical location and the number of mLNs may predict prognosis more accurately than the current classification system. Our study provides the concept that supports the subdivision of nodal classification in the upcoming revision of the tumor, node, and metastasis staging system.

SUBMITTER: Samejima J

PROVIDER: S-EPMC8339744 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Anatomical location and number of metastatic lymph nodes for prognosis of non-small cell lung cancer.

Samejima Joji J Ito Hiroyuki H Nagashima Takuya T Nemoto Daiji D Eriguchi Daisuke D Nakayama Haruhiko H Ikeda Norihiko N Okada Morihito M

Journal of thoracic disease 20210701 7

<h4>Background</h4>The nodal classification of lung cancer is determined by the anatomical location of metastatic lymph nodes (mLNs). However, prognosis can be heterogeneous at the same nodal stage, and the current classification system requires improvement. Therefore, we investigated the correlation between the number of mLNs and prognosis in patients with non-small cell lung cancer.<h4>Methods</h4>Using a multicenter database in Japan, we retrospectively reviewed the records of patients who un ...[more]

PMID: 34422338

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Project description:BackgroundThe current N classification, which is determined by the anatomical location of positive lymph nodes, does not effectively stratify N1 and N2 non-small cell lung cancer (NSCLC) patients into prognostically significant subgroups.MethodsWe acquired the clinical data of 3,234 N1 and N2 NSCLC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015). We eliminated patients undergoing chemotherapy or radiation because chemotherapy and radiotherapy might lower lymph node stage, and the SEER database does not distinguish between therapy administered before and after surgery. We developed the N-new classification based on the former N stage, the number and ratio of lymph nodes. Patients were finally classified into four categories (N1a, N1b, N2a, N2b). Then, the N-new classification was validated in subgroups based on a variety of clinical characteristics, such as tumor size. The multivariable Cox regression analysis, the decision curve analysis (DCA) and the time-dependent receiver operating characteristic (ROC) analysis were conducted to compare the performance of the N-new classification and the current N classification.ResultsThe cancer-specific survival (CSS) and overall survival were significantly different among each pair of N-new classification. And the same results were shown in the majority of the subgroups determined by various clinical characteristics. Compared with the current N classification (C-index, 0.639), the N-new classification (C-index, 0.652) performed better in classifying N1 and N2 NSCLC patients into subgroups with distinctive clinical outcomes. The 5-year CSS rates were 49.7%, 41.4%, 30.4% and 20.4% for N1a, N1b, N2a and N2b, respectively.ConclusionsWhen compared to the current N classification, the N-new classification could be a more reliable and accurate prognostic determinant, which is worth considering in the revision of the 9th edition of the tumor, node, metastasis (TNM) staging system.

Project description:Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used in tumor diagnosis, staging, and response evaluation. To determine an optimal therapeutic strategy for lung cancer patients, accurate staging is essential. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may fail to differentiate between benign and malignant lesions. Lymph nodes (LNs) in the mediastinal and pulmonary hilar regions with high FDG uptake due to granulomatous lesions such as tuberculosis, which has a high prevalence in China, pose a diagnostic challenge. This study aims to evaluate the diagnostic value of the quantitative metabolic parameters derived from dynamic 18F-FDG PET/CT in differentiating metastatic and non-metastatic LNs in lung cancer.MethodsOne hundred and eight patients with pulmonary nodules were enrolled to perform 18F-FDG PET/CT dynamic + static imaging with informed consent. One hundred and thirty-five LNs in 29 lung cancer patients were confirmed by pathology. Static image analysis parameters including LN-SUVmax, LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax), mediastinal blood pool SUVmax (MBP-SUVmax), LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter. Quantitative parameters including K1, k2, k3 and Ki and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was computed subsequently as a separate marker. We further divided the LNs into mediastinal LNs (N=82) and pulmonary hilar LNs (N=53). Wilcoxon rank-sum test or Independent-samples T-test and receiver-operating characteristic (ROC) analysis was performed on each parameter to compare the diagnostic efficacy in differentiating lymph node metastases from inflammatory uptake. P<0.05 were considered statistically significant.ResultsAmong the 135 FDG-avid LNs confirmed by pathology, 49 LNs were non-metastatic, and 86 LNs were metastatic. LN-SUVmax, MBP-SUVmax, LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter couldn't well differentiate metastatic from non-metastatic LNs (P>0.05). However, LN-SUVmax/PT-SUVmax have good performance in the differential diagnosis of non-metastatic and metastatic LNs (P=0.039). Dynamic metabolic parameters in addition to k3, the parameters including K1, k2, Ki, and Ki/K1, on the other hand, have good performance in the differential diagnosis of metastatic and non-metastatic LNs (P=0.045, P=0.001, P=0.001, P=0.001, respectively). For ROC analysis, the metabolic parameters Ki (AUC of 0.672 [0.579-0.765], sensitivity 0.395, specificity 0.918) and Ki/K1 (AUC of 0.673 [0.580-0.767], sensitivity 0.570, specificity 0.776) have good performance in the differential diagnosis of metastatic from non-metastatic LNs than SUVmax (AUC of 0.596 [0.498-0.696], sensitivity 0.826, specificity 0.388), included the mediastinal region and pulmonary hilar region.ConclusionCompared with SUVmax, quantitative parameters such as K1, k2, Ki and Ki/K1 showed promising results for differentiation of metastatic and non-metastatic LNs with high uptake. The Ki and Ki/K1 had a high differential diagnostic value both in the mediastinal region and pulmonary hilar region.

Dataset Information

Anatomical location and number of metastatic lymph nodes for prognosis of non-small cell lung cancer.

Background

Methods

Results

Conclusions

Publications

Anatomical location and number of metastatic lymph nodes for prognosis of non-small cell lung cancer.

Similar Datasets

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