Analysis of long-term outcomes and application of the tumor regression grading system in the therapeutic assessment of resectable limited-disease small cell lung cancer

Objective The present study attempted to evaluate the value of neoadjuvant chemotherapy in limiteddisease small cell lung cancer(LD-SCLC),and to identify the predictive value of the tumor regression grading(TRG) system in LD-SCLC treatment-response and prognosis.Methods The records of patients with LD-SCLC(p-Stage I–IIIa) who underwent definitive radical resection at Shaanxi Provincial People's Hospital between March 1,2000 and March 31,2014 were retrospectively analyzed.We compared the disease-free survival(DFS) and overall survival(OS) rates between Group A patients(patients who underwent surgery combined with pre-and post-operative chemotherapy) and Group B patients(patients who underwent surgery combined with adjuvant chemotherapy only) using the Kaplan-Meier method and the Mantel-Cox test.The specimens of patients who received neoadjuvant chemotherapy were reassessed according to the TRG system.Results The median DFS for 27 patients was 16.267 months and the median OS was 81.167 months(1-year OS,74.07%;3-year OS,22.22%;5-year OS,14.81%).Thirteen patients received neoadjuvant chemotherapy,and their specimens were reassessed by TRG(pathological complete remission,3/13,23.08%).Patients in group A had a longer OS than those in group B(mean,93.782 months versus 42.322 months,P = 0.025),although there was no significant difference in DFS between the two groups(median 20.100 months versus 14.667 months,P = 0.551).Statistical analysis revealed that TRG Grade(G) 0(mean,61.222 months) was associated with better OS than G1-2(mean,31.213 months)(P = 0.311).Conclusion Our study indicated that neoadjuvant chemotherapy combined with surgical resection may represent a feasible treatment method for patients with LD-SCLC.The TRG system may be a valuable prediction tool to assess neoadjuvant chemotherapeutic efficacy,especially in patients with G0 disease as determined by TRG;these patients may attain an improved survival benefit with neoadjuvant chemotherapy.

The applied value of modified POSSUM score in evaluating lung cancer surgery’s risk

Objective: The aim of this study was to explore the modified physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) scoring system and the relationship between predicted data and actual data of complication and surgical mortality of lung cancer radical surgery made by such score system. Methods: Retrospective analysis on the 86 cases of the clinical materials of patients with primary lung cancer radical surgery for thoracic surgery of line lung cancer in the 81st Hospital of PLA from October 2010 to October 2011 and using the POSSUM scoring system to predict the cases of postoperative complication and death toll, then making a comparison with the actual cases. Results: The POSSUM scoring system predicting 29 cases of postoperative complications, but 32 cases of practical complications, the difference between them has no statistical significance (P﹥0.05), 8 cases of predicted postoperative deaths, 2 cases of practical deaths, by comparison, there was statistical significance (P﹤0.05). Conclusion: The modified POSSUM scoring system can be used to predict the postoperative complication of lung surgery patients, but sometimes overestimates the postoperative death cases.

BATE curve in assessment of clinical utility of predictive biomarkers

In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient. This new casual framework is based on a new concept, called Biomarker Adjusted Treatment Effect （BATE） curve. The BATE curve can be used for assessing clinical utility of a predictive biomarker, for designing a subsequent confirmation trial, and for guiding clinical practice. We then propose semi-p^rametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrate the application of the proposed method in a real-world data set.

Application of a support vector machine for prediction of slope stability

Slope stability estimation is an engineering problem that involves several parameters. To address these problems, a hybrid model based on the combination of support vector machine(SVM) and particle swarm optimization(PSO) is proposed in this study to improve the forecasting performance. PSO was employed in selecting the appropriate SVM parameters to enhance the forecasting accuracy. Several important parameters, including the magnitude of unit weight, cohesion, angle of internal friction, slope angle, height, pore water pressure coefficient, were used as the input parameters, while the status of slope was the output parameter. The results show that the PSO-SVM is a powerful computational tool that can be used to predict the slope stability.