RESUMEN
According to global cancer data, lung cancer was the leading cause of cancer-related death in 2020. With the diversification of treatment strategies, the survival outcomes of patients with advanced lung cancer have improved significantly, but the 5-year overall survival rate remains <20%. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the preferred treatment for lung adenocarcinoma patients with EGFR-sensitive mutations; however, acquired drug resistance is inevitable. Osimertinib (a third-generation EGFR inhibitor) is the most commonly used drug for cancers with a secondary T790M mutation. Unfortunately, acquired drug resistance against third-generation drugs still emerges. The C797s mutation is the primary acquired resistance mechanism against Osimertinib. Research on fourth-generation EGFR-TKI drugs with a C797s mutation is currently at various experimental stages, and no drug has been approved for clinical use. In addition to the resistance mechanisms described above, HER2 amplification, MET amplification, PIK3A mutation, KRAS mutation, BRAF mutation, transformation to small cell lung cancer, transformation to lung squamous cell carcinoma, and EMT have been reported as mechanisms of acquired drug resistance to first-, second- and third-generation EGFR-TKIs. These mechanisms are noted in a relatively high proportion of tumors, but treatment options are limited. In recent years, immunotherapy has made progress in the treatment of several cancers, including advanced EGFR-mutated non-small cell lung cancer (NSCLC). Due to the relatively high frequency of EGFR mutation in patients with lung adenocarcinoma in China, an increased number of patients develop EGFR-TKI resistance, and subsequent treatment options are critical. This article reviews the mechanisms of drug resistance to different EGFR-TKIs and treatment progression, providing ideas for the follow-up treatment for EGFR-resistant patients.
RESUMEN
In estimating the two-dimensional (2D) direction-of-arrival (DOA) using a coprime planar array, the main issues are the high complexity of spectral peak search and the limited degree of freedom imposed by the number of sensors. In this paper, we present an algorithm based on the matrix completion theory in coprime planar array that reduces the computational complexity and obtains a high degree of freedom. The algorithm first analyzes the covariance matrix of received signals to estimate the covariance matrix of a virtual uniform rectangular array, which has the same aperture as the coprime planar array. Matrix completion theory is then applied to estimate the missing elements of the virtual array covariance matrix. Finally, a closed-form DOA solution is obtained using the unitary estimation signal parameters via rotational invariance techniques (Unitary-ESPRIT). Simulation results show that the proposed algorithm has a high degree of freedom, enabling the estimation of more signal DOAs than the number of sensors. The proposed algorithm has reduced computational complexity because the spectral peak search is replaced by Unitary-ESPRIT, but attains similarly high levels accuracy to those of the 2D multiple signal classification algorithm.
RESUMEN
Direct position determination (DPD) is currently a hot topic in wireless localization research as it is more accurate than traditional two-step positioning. However, current DPD algorithms are all based on uniform arrays, which have an insufficient degree of freedom and limited estimation accuracy. To improve the DPD accuracy, this paper introduces a coprime array to the position model of multiple non-circular sources with a moving array. To maximize the advantages of this coprime array, we reconstruct the covariance matrix by vectorization, apply a spatial smoothing technique, and converge the subspace data from each measuring position to establish the cost function. Finally, we obtain the position coordinates of the multiple non-circular sources. The complexity of the proposed method is computed and compared with that of other methods, and the Cramerâ»Rao lower bound of DPD for multiple sources with a moving coprime array, is derived. Theoretical analysis and simulation results show that the proposed algorithm is not only applicable to circular sources, but can also improve the positioning accuracy of non-circular sources. Compared with existing two-step positioning algorithms and DPD algorithms based on uniform linear arrays, the proposed technique offers a significant improvement in positioning accuracy with a slight increase in complexity.