A prediction model for N2 disease in T1 non–small cell lung cancer

Abstract

We developed a 4-predictor model that can estimate the probability of N2 disease in computed tomography-defined T1N0 non-small cell lung cancer. This prediction model can help to determine the cost-effective use of mediastinal staging procedures.

Clinicopathological Characteristics of Lung Adenocarcinoma with Unexpected Lymph Node Metastasis

“…Our results showed that there were also significant differences in age and tumor size between unexpected and expected groups, and younger age, left-sided primary tumor, and larger tumor size were significantly associated with unexpected diseases in multivariate analysis. Zhang et al 20) suggested that larger tumor size and younger age at diagnosis were both predictive factors for N2 disease in CT-defined T1N0 NSCLC. In addition, Chen et al 21) demonstrated development and validation of a clinical prediction model for N2 lymph node metastasis in NSCLC, and they identified that younger age was one of the independent predictive factors for unexpected mediastinal lymph node metastasis as well as larger tumor size, central tumor location, and adenocarcinoma pathology.…”

Statistical modelling for thoracic surgery using a nomogram based on logistic regression

“…However, one must give first priority to the most valuable questions encountered in daily practice; e.g., predicting brain metastasis as the relapse in curatively resected non-small cell lung cancer (NSCLC) for the revision of the postoperative follow-up scheme (4) or predicting N2 involvement in T1 NSCLC that may influence the extent of lymphadenectomy (5). Therefore, identifying a good clinical topic is a vital step in the construction of the nomogram.…”

“…Bootstrapping is a nonparametric data generating method in which new sub-samples are generated repeatedly from the original data via repeated estimation of the statistics (stated later in this paper). For example, the nomogram was subjected to 1,000 bootstrap resamples for internal validation in the Zhang et al study (5).…”

Optimal Lung Cancer Candidates for Sublobar Resection: Prediction of Pathologic Node-Negative Clinical Stage IA NSCLC

“…According to the data of Ye and colleagues, factors such as ground-glass opacity status, serum carcinoembryonic antigen level, histologic subtype, and maximum standardized uptake value have potential for the prediction of lymph node metastasis. A prediction model based on some of these factors has been established to assess the possibility of lymph node metastasis in early-stage lung cancer [2]. Some issues are worth noting here.…”

“…However, frozen section analysis is poorly reliable in SRC tumors. To overcome this limitation, a total gastrectomy with an extended esophagectomy and intrathoracic esojejunal anastomosis to maximize the chance of a disease-free surgical margin may be proposed [2].Second, based on the absence of statistical differences, the authors concluded that there was no survival benefit in downstaged SRC patients. However, the survival curves appear different, and a type II error linked to small numbers may explain the results.…”