[Distant Metastasis and Survival Outcomes after Computed Tomography-guided Needle Biopsy in Stage I-II Non-small Cell Lung Cancer]

Recurrence and disease-free survival outcomes after computed tomography-guided needle biopsy in stage IA non-small cell lung cancer patients in China: a propensity score matching analysis

Background

Whether preoperative biopsy before radical resection can lead to recurrence and impact patient survival in non-small cell lung cancer (NSCLC) remains controversial. In this study, we carried out a retrospective analysis to determine whether preoperative biopsy can cause disease recurrence and influence disease-free survival (DFS) in patients with stage IA NSCLC.

Methods

Patients diagnosed with stage IA NSCLC (solid nodule) between January 2010 and December 2014 were identified from the databases of 7 Chinese medical centers and divided into two groups: a preoperative computed tomography (CT)-guided needle biopsy (CTNB) plus radical resection group, and a non-CTNB group. The propensity score matching (PSM) method was adopted to balance the observed covariates, and Kaplan-Meier estimates were used for survival analysis. Cox regression was used in a single-factor analysis to identify the factors affecting DFS in stage IA NSCLC.

Results

After initial screening, 730 patients were enrolled in this study, with 186 and 544 patients in the CTNB group and the non-CTNB group, respectively. After PSM, 186 patients were eventually included in each group. No significant differences in basic clinical features were identified between the two groups (P>0.05). The rates of recurrence were 17.2% and 14.0% in the CTNB and non-CTNB groups (χ²=0.735, P=0.391), respectively. No notable differences in DFS (χ²=1.895, P=0.173) or overall survival (OS, χ²=1.785, P=0.182) were observed. Lung adenocarcinoma [hazard ratio (HR), 0.167, P=0.001] and lesion size (>2 cm) (HR, 2.712, P=0.000) were identified as risk factors for DFS in stage IA NSCLC.

Conclusions

CTNB does not increase the incidence of recurrence in stage IA NSCLC or affect patient survival; therefore, it is not a risk factor for DFS. Lung adenocarcinoma and lesion size are risk factors for DFS.

Application of radiography of computed tomography in non-small cell lung cancer using prognosis model

Objective

Studying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients.

Method

CT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared.

Results

30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher.

Conclusion

The prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC.