PurposeWe investigated the correlation between the number of examined lymph nodes (ELNs) and correct staging and long-term survival in non–small-cell lung cancer (NSCLC) by using large databases and determined the minimal threshold for the ELN count.MethodsData from a Chinese multi-institutional registry and the US SEER database on stage I to IIIA resected NSCLC (2001 to 2008) were analyzed for the relationship between the ELN count and stage migration and overall survival (OS) by using multivariable models. The series of the mean positive LNs, odds ratios (ORs), and hazard ratios (HRs) were fitted with a LOWESS smoother, and the structural break points were determined by Chow test. The selected cut point was validated with the SEER 2009 cohort.ResultsAlthough the distribution of ELN count differed between the Chinese registry (n = 5,706) and the SEER database (n = 38,806; median, 15 versus seven, respectively), both cohorts exhibited significantly proportional increases from N0 to N1 and N2 disease (SEER OR, 1.038; China OR, 1.012; both P < .001) and serial improvements in OS (N0 disease: SEER HR, 0.986; China HR, 0.981; both P < .001; N1 and N2 disease: SEER HR, 0.989; China HR, 0.984; both P < .001) as the ELN count increased after controlling for confounders. Cut point analysis showed a threshold ELN count of 16 in patients with declared node-negative disease, which were examined in the derivation cohorts (SEER 2001 to 2008 HR, 0.830; China HR, 0.738) and validated in the SEER 2009 cohort (HR, 0.837).ConclusionA greater number of ELNs is associated with more-accurate node staging and better long-term survival of resected NSCLC. We recommend 16 ELNs as the cut point for evaluating the quality of LN examination or prognostic stratification postoperatively for patients with declared node-negative disease.

Lobectomy showed better survival than sublobar resection for patients with NSCLC ≤ 1 cm and > 1 to 2 cm. For patients in whom lobectomy is unsuitable, segmentectomy should be recommended for NSCLC > 1 to 2 cm, whereas surgeons could rely on surgical skills and the patient profile to decide between segmentectomy and wedge resection for NSCLC ≤ 1 cm.

To investigate the universal structure of interactions in financial dynamics, we analyze the cross-correlation matrix C of price returns of the Chinese stock market, in comparison with those of the American and Indian stock markets. As an important emerging market, the Chinese market exhibits much stronger correlations than the developed markets. In the Chinese market, the interactions between the stocks in a same business sector are weak, while extra interactions in unusual sectors are detected. Using a variation of the two-factor model, we simulate the interactions in financial markets.In recent years, there has been a growing interest of physicists in economic systems. Concepts and methods in physics have been applied to the study of financial time series [1][2][3][4][5][6][7]. Different models and theoretical approaches have been developed to describe the features of the financial dynamics [8][9][10][11][12][13][14][15][16][17][18][19]. Statistical properties of price fluctuations and correlations between different stocks are topics of interest, not only scientifically for understanding the complex structure and dynamics of the economy, but also practically for the asset allocation and portfolio risk estimation [20][21][22]. The probability distributions of stock prices in different stock markets show a universal nature and follow the "inverse cubic law" [23][24][25]. However, the statistical properties of correlations between different stocks seem less universal across different stock markets [26].Unlike most traditional physical systems, where one derives correlations between subunits from their interactions, the underlying "interactions" for the stock markets are not known. Pioneering studies at the phenomenological level analyze cross-correlations between stocks by applying concepts and methods of the random matrix theory (RMT), which was developed in the context of complex quantum systems where the precise nature of the interactions between subunits is not known [27,28]. The properties of the empirical correlation matrix C of price returns are compared with those of a random matrix in which the price movements are uncorrelated [29,30]. This spectral property-focused method was first applied to developed markets such as the New York Stock Exchange (NYSE) in USA [29][30][31][32], and recently also to some emerging markets, e.g. the National Stock Exchange (NSE) in India [26].In general, the bulk of the eigenvalue spectrum of the correlation matrix C of price returns shares universal properties with the Gaussian orthogonal ensemble of random matrices, while the largest eigenvalue of C which deviates significantly from the bulk represents the influence of

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