Dataset Information

Tumor Spread Through Air Spaces Is a Predictor of Occult Lymph Node Metastasis in Clinical Stage IA Lung Adenocarcinoma.

ABSTRACT:

Introduction

In patients with stage IA lung adenocarcinoma (ADC), sublobar resection and tumor spread through air spaces (STAS) are associated with high rates of locoregional recurrence, half of which occur within the regional lymph nodes (LNs). Our objectives were to investigate the association between occult LN metastasis (ONM) and STAS and to assess their prognostic value in patients with clinical stage IA lung ADC.

Methods

The association between STAS and ONM was analyzed in patients who underwent lobectomy and LN dissection for clinical stage IA lung ADC (n = 809). Multivariable logistic regression analysis was carried out to identify predictors of ONM. Site-specific recurrence by surgical procedure was investigated in patients with pathologic node-negative disease (n = 1055) using a competing risk approach.

Results

ONM was identified in 129 patients (16%)-one-third of ONMs were located only in intrapulmonary nodes. STAS was more common in patients with ONM than in those without ONM (67% versus 39%; p < 0.001) and in patients with multiple ONMs than in those with a single ONM (86%-89% versus 60%-67%). STAS was a significant predictor of ONM (p = 0.004) on multivariable analysis, independent of tumor size, maximum standardized uptake value, and lymphovascular invasion. In patients with STAS-positive ADC (high ONM risk), the risk of recurrence in the treated lobe and regional LNs increased as the extent of resection decreased (recurrence risk: lobectomy < segmentectomy < wedge resection). In patients with STAS-negative ADC, the risk of locoregional recurrence did not differ by procedure type.

Conclusions

Presence of STAS predicts ONM in patients with clinical stage IA lung ADC and can help stratify risk of recurrence by extent and type of resection.

SUBMITTER: Vaghjiani RG

PROVIDER: S-EPMC7199202 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAccurate prediction of occult lymph node metastasis (ONM) is an important basis for determining whether lymph node (LN) dissection is necessary in clinical stage IA lung adenocarcinoma patients. The aim of this study is to determine the best machine learning algorithm for radiomics modeling and to compare the performances of the radiomics model, the clinical-radilogical model and the combined model incorporate both radiomics features and clinical-radilogical features in preoperatively predicting ONM in clinical stage IA lung adenocarcinoma patients.MethodsPatients with clinical stage IA lung adenocarcinoma undergoing curative surgery from one institution were retrospectively recruited and assigned to training and test cohorts. Radiomics features were extracted from the preoperative computed tomography (CT) images of the primary tumor. Seven machine learning algorithms were used to construct radiomics models, and the model with the best performance, evaluated using the area under the curve (AUC), was selected. Univariate and multivariate logistic regression analyses were performed on the clinical-radiological features to identify statistically significant features and to develop a clinical model. The optimal radiomics and clinical models were integrated to build a combined model, and its predictive performance was assessed using receiver operating characteristic curves, Brier score, and decision curve analysis (DCA).ResultsThis study included 258 patients who underwent resection (training cohort, n=182; test cohort, n=76). Six radiomics features were identified. Among the seven machine learning algorithms, extreme gradient boosting (XGB) demonstrated the highest performance for radiomics modeling, with an AUC of 0.917. The combined model improved the AUC to 0.933 and achieved a Brier score of 0.092. DCA revealed that the combined model had optimal clinical efficacy.ConclusionsThe superior performance of the combined model, based on XGB algorithm in predicting ONM in patients with clinical stage IA lung adenocarcinoma, might aid surgeons in deciding whether to conduct mediastinal LN dissection and contribute to improve patients' prognosis.