Clinicopathologic predictors of metastasis of different regional lymph nodes in patients intraoperatively diagnosed with stage-I non-small cell lung cancer

Zhao, Fei; Zhen, Fuxi; Zhou, Yue; Huang, Chenjun; Yu, Yue; Li, Jun; Li, Qifan; Zhu, Chengxiang; Yang, Xiaoyu; You, Shu-Hui; Wu, Qian-Ge; Qin, Xueyun; Liu, Yi; Chen, Liang; Wang, Wei

doi:10.1186/s12885-019-5632-2

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Historically, pulmonary resection with lymphadenectomy is the standard treatment for NSCLC patients ( 6 ). It is recommended even for the early stage of NSCLC patients—T1N0M0 patients, which may because occult lymph node metastasis and false-negative lymph nodes exist ( 7 , 8 ). However, the optimal number of regional lymph nodes removed (LNRs) during surgery has remained debated all the time for the N0 NSCLC patients.…”

Section: Introductionmentioning

confidence: 99%

Exploration of the optimal number of regional lymph nodes removed for resected N0 NSCLC patients: A population-based study

Yao

Liu²,

Rao³

et al. 2022

Front. Oncol.

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BackgroundThe aim of our study was to explore the optimal number of regional lymph nodes removed (LNRs) in resected N0 non-small cell lung cancer (NSCLC) patients and identify potential risk factors.MethodsIncluded in this study were 55,024 N0 NSCLC patients between 2004 and 2015 based on the Surveillance, Epidemiology, and End Results database (SEER). All the patients were divided into No LNR group (57.8%), 1-3 LNRs group (8.1%) and ≥4 LNRs group (31.4%). Relevant clinical and patient parameters including overall survival (OS), lung cancer-specific survival (LCSS), gender, race, year of diagnosis, primary site, T stage, AJCC stage, laterality, histological type, lymphadenectomy, radiation, chemotherapy, age at diagnosis, insurance status, marital status, family income.ResultsKaplan-Meier analysis demonstrated LNRs had significantly better OS and LCSS than No LNRs in all the N0 NSCLC patients with different T stages (Logrank p<.001). Univariate and multivariate analysis showed that both OS and LCSS in ≥ 4 LNRs group were better than those in <1-3 LNRs group (OS: ≥4 LNRs group: HR, 0.583; 95%CI, 0.556-0.610; P<.001 vs.1-3 LNRs group: HR, 0.726; 95%CI, 0.687-0.769; P<.001; LCSS: ≥4 LNRs group: HR, 0.514; 95%CI, 0.480-0.550; P<.001 vs.1-3 LNRs group: HR, 0.647; 95%CI, 0.597-0.702; P<.001). In addition, whites, males, not upper lobe, large cell carcinoma and others, advance T stage or AJCC stage, no surgery, no LNR, no radiation, no chemotherapy, elder age at diagnosis, singled marital status and low family income had negative impact on prognosis of N0 NSCLC patients.ConclusionsOur study suggests that ≥ 4 LNRs can yield better survival outcomes compared with 1-3 LNRs in N0 NSCLC patients.

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Section: Introductionmentioning

confidence: 99%

Exploration of the optimal number of regional lymph nodes removed for resected N0 NSCLC patients: A population-based study

Yao

Liu²,

Rao³

et al. 2022

Front. Oncol.

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“…Pani et al found that histologic subtypes could be related to lymph node status (4). Another similar study suggested different LND strategy for different combination of various clinicopathological features and CEA concentration and albumin level (30). These studies used uni-and multivariate analysis to reveal clinicopathological predictors for different LNM patterns.…”

Section: Discussionmentioning

confidence: 99%

Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms

Chong¹,

Wu²,

Liu³

et al. 2021

J Thorac Dis

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Background: Lymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma. Methods: A total of 709 lung adenocarcinoma patients with tumor size ≤2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model's predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors. Results: LNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) >0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of five variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves. Conclusions: The cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%). Incorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms.

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“…Pani et al found that histologic subtypes could be related to lymph node status [34]. Another similar study suggested different lymph node dissection strategy for different combination of various clinicopathological features and CEA concentration and albumin level [35]. These studies used uni-and multivariate analysis to reveal clinicopathological predictors for different LNM patterns.…”

Section: Discussionmentioning

confidence: 99%

Is it feasible to predict lymph node metastasis intraoperatively or postoperatively in early-stage lung adenocarcinoma: the application of machine learning algorithms?

Chong

Liu

et al. 2020

Preprint

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BackgroundLymph node metastasis (LNM) status can be a critical decisive factor for clinical management of lung cancer. Accurately evaluating the risk of LNM during or after the surgery can be helpful for making clinical decisions. This study aims to incorporate clinicopathological characteristics to develop reliable machine learning (ML)-based models for predicting LNM in patients with early-stage lung adenocarcinoma.MethodsA total of 709 lung adenocarcinoma patients with tumor size ≤ 2 cm were enrolled for analysis and modeling by multiple ML algorithms. The receiver operating characteristic (ROC) curve and decision curve were used for evaluating model’s predictive performance and clinical usefulness. Feature selection based on potential models was performed to identify most-contributed predictive factors.ResultsLNM occurred in 11.3% (80/709) of patients with lung adenocarcinoma. Most models reached high areas under the ROC curve (AUCs) > 0.9. In the decision curve, all models performed better than the treat-all and treat-none lines. The random forest classifier (RFC) model, with a minimal number of 5 variables introduced (including carcinoembryonic antigen, solid component, micropapillary component, lymphovascular invasion and pleural invasion), was identified as the optimal model for predicting LNM, because of its excellent performance in both ROC and decision curves. The cost-efficient application of RFC model could precisely predict LNM during or after the operation of early-stage adenocarcinomas (sensitivity: 87.5%; specificity: 82.2%).ConclusionsIncorporating clinicopathological characteristics, it is feasible to predict LNM intraoperatively or postoperatively by ML algorithms.Trial registration: NA

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Clinicopathologic predictors of metastasis of different regional lymph nodes in patients intraoperatively diagnosed with stage-I non-small cell lung cancer

Cited by 8 publications

References 22 publications

Exploration of the optimal number of regional lymph nodes removed for resected N0 NSCLC patients: A population-based study

Exploration of the optimal number of regional lymph nodes removed for resected N0 NSCLC patients: A population-based study

Clinicopathological models for predicting lymph node metastasis in patients with early-stage lung adenocarcinoma: the application of machine learning algorithms

Is it feasible to predict lymph node metastasis intraoperatively or postoperatively in early-stage lung adenocarcinoma: the application of machine learning algorithms?

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