Novel approach for predicting occult lymph node metastasis in peripheral clinical stage I lung adenocarcinoma

Song, Cheng‐Yang; Kimura, Daisuke; Sakai, Takehiro; Tsushima, Takao; Fukuda, Ikuo

doi:10.21037/jtd.2019.03.57

Cited by 20 publications

(15 citation statements)

References 25 publications

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“…Other factors should be considered. Similar to previous studies (19,22), a higher serum CEA (OR: 1.247, 95%CI: 1.110-1.402, P < 0.001) was also associated with a higher LNM rate in our study. Thus, the serum CEA level might be helpful for LNM prediction and should be listed as a routine test for NSCLC patients.…”

Section: Discussionsupporting

confidence: 91%

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An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

Han

Wang

et al. 2020

Front. Oncol.

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Background: Lymph node metastasis (LNM) status is of key importance for the decision-making on treatment and survival prediction. There is no reliable method to precisely evaluate the risk of LNM in NSCLC patients. This study aims to develop and validate a dynamic nomogram to evaluate the risk of LNM in small-size NSCLC. Methods: The NSCLC ≤ 2 cm patients who underwent initial pulmonary surgery were retrospectively reviewed and randomly divided into a training cohort and a validation cohort as a ratio of 7:3. The training cohort was used for the least absolute shrinkage and selection operator (LASSO) regression to select optimal variables. Based on variables selected, the logistic regression models were developed, and were compared by areas under the receiver operating characteristic curve (AUCs) and decision curve analysis (DCA). The optimal model was used to plot a dynamic nomogram for calculating the risk of LNM and was internally and externally well-validated by calibration curves. Results: LNM was observed in 12.0% (83/774) of the training cohort and 10.1% (33/328) of the validation cohort (P = 0.743). The optimal model was used to plot a nomogram with six variables incorporated, including tumor size, carcinoembryonic antigen, imaging density, pathological type (adenocarcinoma or non-adenocarcinoma), lymphovascular invasion, and pleural invasion. The nomogram model showed excellent discrimination (AUC = 0.895 vs. 0.931) and great calibration in both the training and validation cohorts. At the threshold probability of 0-0.8, our nomogram adds more net benefits than the treat-none and treat-all lines in the decision curve. Conclusions: This study firstly developed a cost-efficient dynamic nomogram to precisely and expediently evaluate the risk of LNM in small-size NSCLC and would be helpful for clinicians in decision-making.

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Section: Discussionsupporting

confidence: 91%

“…Previously, our (20) and other studies (19,(21)(22)(23) have found that several preoperative factors might be associated with the occurrence of LNM in small-size NSCLC, including serum CEA, tumor size and imaging features such as imaging density. Tumor size was identified as an important predictor for LNM.…”

Section: Discussionmentioning

confidence: 65%

An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

Han

Wang

et al. 2020

Front. Oncol.

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“…Using conventional univariate analysis, previous studies reported the risk factors associated with LNM in NSCLC ≤ 2 cm, including tumor size, serum CEA and imaging density (30,31). In addition, SUV max was also thought to be a risk factor in patients with cT1 NSCLC (32). Thus, the AUCs when using tumor size (AUC = 0.753), SUV max (AUC = 0.734), or CEA (AUC = 0.720) alone were also calculated, which were significantly lower than those of MLbased models ( Table 2).…”

Section: Discussionmentioning

confidence: 99%

Preoperative Prediction of Lymph Node Metastasis in Patients With Early-T-Stage Non-small Cell Lung Cancer by Machine Learning Algorithms

Liu

Han

et al. 2020

Front. Oncol.

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Background: Lymph node metastasis (LNM) is difficult to precisely predict before surgery in patients with early-T-stage non-small cell lung cancer (NSCLC). This study aimed to develop machine learning (ML)-based predictive models for LNM. Methods: Clinical characteristics and imaging features were retrospectively collected from 1,102 NSCLC ≤ 2 cm patients. A total of 23 variables were included to develop predictive models for LNM by multiple ML algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors. Results: The areas under the ROC curve (AUCs) of the 8 models ranged from 0.784 to 0.899. Some ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 9 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors were tumor size, imaging density, carcinoembryonic antigen (CEA), maximal standardized uptake value (SUV max), and age. Conclusions: By incorporating clinical characteristics and radiographical features, it is feasible to develop ML-based models for the preoperative prediction of LNM in early-T-stage NSCLC, and the RFC model performed best.

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“…Besides, another one of the valuable prognostic factors in primary pulmonary PA was lymph node involvement [14]. Ikuo et al revealed the higher occult nodal metastasis rate in PPA subtype (17/154, 11.0%) and SPA subtype (2/7, 28.6%) (both P=0.001) in comparison with additional subtypes through retrospectively analyzing 237 patients with peripheral clinical stage I lung adenocarcinoma [15]. In our current study, 38.4% of pulmonary PA exhibited positive lymph node invasion.…”

Section: Discussionmentioning

confidence: 99%

“…In the light of current literature review, our study developed the first nomogram to predict the survival and prognosis of patients with pulmonary PA based on SEER database. Nevertheless, our nomogram did not include other clinical information, including pivotal clinical symptoms, imaging presentations, and comprehensive laboratory indicators such as serum tumor markers [15]. More importantly, accumulating clinical studies revealed the identification of such tumors with alterations in genes such as ALK [21], epidermal growth factor receptor (EGFR) [22][23][24][25], which was momentous to guide tyrosine kinase inhibitors (TKIs) therapy.…”

Section: Discussionmentioning

confidence: 99%

The characteristics and nomogram for primary lung papillary adenocarcinoma

et al. 2020

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AbstractBackgroundPrimary pulmonary papillary adenocarcinoma (PA) is a specific and rare subtype of invasive pulmonary adenocarcinoma (ADC). The knowledge concerning the clinicopathologic features and prognosis of patients with primary pulmonary PA has not been clarified because of its rarity.MethodsThe clinical data of a total of 3391 patients with primary pulmonary PA were retrospectively analyzed to confirm their clinical characteristics and factors influencing prognosis and were in comparison with 3236 patients with non- PA pulmonary adenocarcinoma. All patients were histologically diagnosed between 1988 and 2015 in The Surveillance Epidemiology and End Results (SEER) database. A nomogram with satisfactory predictive performance was established to visually predict long-term survival of these patients.Results and conclusionCollectively, primary pulmonary PA is a rare pathological cancer and its prognosis is analogous to that of non-PA pulmonary adenocarcinoma. Older age, larger lesions, distant metastases, lymph node invasion, and poor pathological differentiation are correlative with unacceptable prognosis. Surgical intervention is conducive to reaping favorable prognosis. Unfortunately, radiotherapy or chemotherapy results of no significant effects on patient survival. In our study, a nomogram with prognostic function is formulated to confer individual prediction of overall survival (OS).

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Novel approach for predicting occult lymph node metastasis in peripheral clinical stage I lung adenocarcinoma

Cited by 20 publications

References 25 publications

An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

An Externally-Validated Dynamic Nomogram Based on Clinicopathological Characteristics for Evaluating the Risk of Lymph Node Metastasis in Small-Size Non-small Cell Lung Cancer

Preoperative Prediction of Lymph Node Metastasis in Patients With Early-T-Stage Non-small Cell Lung Cancer by Machine Learning Algorithms

The characteristics and nomogram for primary lung papillary adenocarcinoma

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