Epidermal growth factor receptor mutation analysis in cytological specimens and responsiveness to gefitinib in advanced non-small cell lung cancer patients

Background： Epidermal growth factor receptor（EGFR） mutation is the key predictor of EGFR tyrosine kinase inhibitors（TKIs） efficacy in non-small cell lung cancer（NSCLC）. We conducted this study to verify the feasibility of EGFR mutation analysis in cytological specimens and investigate the responsiveness to gefitinib treatment in patients carrying EGFR mutations.Methods： A total of 210 cytological specimens were collected for EGFR mutation detection by both direct sequencing and amplification refractory mutation system（ARMS）. We analyzed EGFR mutation status by both methods and evaluated the responsiveness to gefitinib treatment in patients harboring EGFR mutations by overall response rate（ORR）, disease control rate（DCR） and progression free survival（PFS）.Results： Of all patients, EGFR mutation rate was 28.6%（60/210） by direct sequencing and 45.2%（95/210） by ARMS（P〈0.001） respectively. Among the EGFR wild type patients tested by direct sequencing, 26.7% of them were positive by ARMS. For the 72 EGFR mutation positive patients treated with gefitinib, the ORR, DCR and median PFS were 69.4%, 90.2% and 9.3 months respectively. The patients whose EGFR mutation status was negative by direct sequencing but positive by ARMS had lower ORR（48.0% vs. 80.9%, P=0.004） and shorter median PFS（7.4 vs. 10.5 months, P=0.009） as compared with that of EGFR mutation positive patients by both detection methods. Conclusions： Our study verified the feasibility of EGFR analysis in cytological specimens in advanced NSCLC. ARMS is more sensitive than direct sequencing in EGFR mutation detection. EGFR Mutation status tested on cytological samples is applicable for predicting the response to gefitinib. Abundance of EGFR mutations might have an influence on TKIs efficacy.

Three-Dimensional Model Retrieval Using Dynamic Multi-Descriptor Fusion

In this paper, we propose a dynamic multi-descriptor fusion （DMDF） approach to improving the retrieval accuracy of 3-dimensional （3D） model retrieval systems. First, an independent retrieval list is generated by using each individual descriptor. Second, we propose an automatic relevant/irrelevant models selection （ARMS） approach to selecting the relevant and irrelevant 3D models automatically without any user interaction. A weighted distance, in which the weight associated with each individual descriptor is learnt by using the selected relevant and irrelevant models, is used to measure the similarity between two 3D models. Furthermore, a descriptor-dependent adaptive query point movement （AQPM） approach is employed to update every feature vector. This set of new feature vectors is used to index 3D models in the next search process. Four 3D model databases are used to compare the retrieval accuracy of our proposed DMDF approach with several descriptors as well as some well-known information fusion methods. Experimental results have shown that our proposed DMDF approach provides a promising retrieval result and always yields the best retrieval accuracy.