Background
Tumor size and consolidation‐to‐tumor ratio (CTR) are crucial for non–small cell lung cancer (NSCLC) prognosis. However, the optimal CTR cutoff remains unclear. Whether tumor size and CTR are independent prognostic factors for part‐solid NSCLC is under debate. Here, we aimed to evaluate the prognostic impacts of CTR and tumor size on NSCLC, especially on part‐solid NSCLC.
Methods
We reviewed 1366 clinical T1 NSCLC patients who underwent surgical treatment. Log‐rank test and Cox regression analyses were adopted for prognostic evaluation. The “surv_cutpoint” function was used to identify the optimal CTR and tumor size cutoff values.
Results
There were 416, 510, and 440 subjects with pure ground‐glass opacity (pGGO), part‐solid, and pure solid nodules. The 5‐year overall survival (disease‐free survival) for patients with pGGO, part‐solid, and pure solid nodules were 99.5% (99.5%), 97.3% (95.8%), and 90.4% (78.9%), respectively. Multivariate Cox regression analysis indicated that CTR was an independent prognostic factor for the whole patients, and the optimal CTR cutoff was 0.99. However, for part‐solid NSCLC, CTR was not independently associated with survival, even if categorized by the optimal cutoffs. The predicted optimal cutoffs of total tumor size and solid component size were 2.4 and 1.4 cm for part‐solid NSCLC. Total tumor size (HR = 6.21, 95% CI: 1.58–24.34,
p
= 0.009) and solid component size (HR = 2.27, 95% CI: 1.04–5.92,
p
= 0.045) grouped by the cutoffs were significantly associated with part‐solid NSCLC prognosis.
Conclusions
CTR was an independent prognostic factor for the whole NSCLC, but not for the part‐solid NSCLC. Tumor size was still meaningful for part‐solid NSCLC.
According to the problem of autonomous optical navigation, this paper presents an easy and high-precision algorithm to estimate the attitude and position of a lander by using at least three extracted marginal elliptic curves of craters. The geometric and algebraic constraints between the marginal elliptic curves of craters and its 2D images are derived, and then the linear equations about lander’s motion are established by using Kronecker product. Consequently, the laner’s attitude and position relative to targeted features are uniquely acquired from the linear equations. In particular, the algorithm is easy to carry out because all computations involved in this algorithm are linear matrix operations. The extensive experiments over simulated images and parameters demonstrate the robustness, accuracy and effectiveness of our method.
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