Taichun Qiu1,2, Xiaoshuang Ru3, Ke Yin2, Jing Yu2, Yang Song3, Jianlin Wu4. 1. Rodiology Department, People's Hospital of Deyang City, No. 173, Taishan North Rd, Deyang, Sichuan, China. 2. Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China. 3. Radiology Department, Dalian Municipal Central Hospital Affiliated of Dalian Medical University, Dalian Medical University, No. 42, Xuegong Rd, Shahekou District, Dalian, China. 4. Radiology Department, The Affiliated ZhongShan Hospital of Dalian University, Dalian University, No. 6, Jiefang Rd, Zhongshan District, Dalian, 116001, China. 3623023912@qq.com.
Abstract
PURPOSE: The aim of the study is to construct two nomograms for predicting the invasive extent of pulmonary adenocarcinoma and nodule growth in patients with pulmonary pure ground-glass nodules (pGGN). METHOD: Consecutive patients with pGGNs (n = 172) were retrospectively studied at one institution, formed the development cohort in predicting IPAs' nomogram. A separate cohort of patients with pGGNs (n = 116) from another institution was used for validation. For the predicting growth nomogram, the primary cohort of patients with pGGNs (n = 80) was from the former institution. We developed the nomogram for predicting IPA using binary logistic regression model, and a Cox multivariable model for the growth nomogram. We assessed nomogram model performance by calibration and discrimination (C-index). RESULTS: The variables selected in binary logistic regression model (lesion size and shape) had a significant effect on identifying IPA from preinvasive lesion. The C-index of the development and validation cohort were 0.819 (95% CI 0.753-0.874) and 0.811 (95% CI 0.728-0.878), respectively. The risk variables (lesion size, blood vessel types) were selected in the multivariable Cox model. The C-index was 0.880 in the development cohort. CONCLUSION: Our nomograms are reliable prognostic methods that can predict the invasiveness of pulmonary adenocarcinomas and the growth of pure GGN in preoperative.
PURPOSE: The aim of the study is to construct two nomograms for predicting the invasive extent of pulmonary adenocarcinoma and nodule growth in patients with pulmonary pure ground-glass nodules (pGGN). METHOD: Consecutive patients with pGGNs (n = 172) were retrospectively studied at one institution, formed the development cohort in predicting IPAs' nomogram. A separate cohort of patients with pGGNs (n = 116) from another institution was used for validation. For the predicting growth nomogram, the primary cohort of patients with pGGNs (n = 80) was from the former institution. We developed the nomogram for predicting IPA using binary logistic regression model, and a Cox multivariable model for the growth nomogram. We assessed nomogram model performance by calibration and discrimination (C-index). RESULTS: The variables selected in binary logistic regression model (lesion size and shape) had a significant effect on identifying IPA from preinvasive lesion. The C-index of the development and validation cohort were 0.819 (95% CI 0.753-0.874) and 0.811 (95% CI 0.728-0.878), respectively. The risk variables (lesion size, blood vessel types) were selected in the multivariable Cox model. The C-index was 0.880 in the development cohort. CONCLUSION: Our nomograms are reliable prognostic methods that can predict the invasiveness of pulmonary adenocarcinomas and the growth of pure GGN in preoperative.