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已发表论文
临床与操作相关预测模型用于气胸风险后的 CT引导肺活检
Authors Qin Y, Zhao Z, Zhang L, Shao X , Wang Q
Received 6 November 2025
Accepted for publication 27 January 2026
Published 5 February 2026 Volume 2026:22 579698
DOI https://doi.org/10.2147/TCRM.S579698
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Garry Walsh
Yongchao Qin,1 Zhenyu Zhao,2 Lele Zhang,2 Xiaonan Shao,3– 5 Qianyun Wang5,6
1Department of Radiology, Western Beijing Cancer Hospital, Beijing, 100161, People’s Republic of China; 2Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, People’s Republic of China; 3Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China; 4Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, 213003, People’s Republic of China; 5Changzhou Clinical Medical Center, Changzhou, 213003, People’s Republic of China; 6Department of Thoracic Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China
Correspondence: Lele Zhang, Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, People’s Republic of China, Tel +86-0-18951670138, Fax +86-025-52254223, Email smilesmilezhang@163.com Xiaonan Shao Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China, Tel +86-0-13776831531, Fax +86-0519-86621235, Email scorey@sina.com
Purpose: To develop and validate a model integrating clinical/imaging and procedural variables to predict pneumothorax after CT-guided percutaneous lung nodule biopsy.
Methods: We retrospectively enrolled 395 patients and split them 7:3 into training (n=276) and validation (n=119) cohorts. Variables with P < 0.15 in group comparisons (combined with clinical relevance) were selected as candidates and analyzed using age- and sex-adjusted univariable logistic regression. Two prespecified logistic models were fitted: a baseline model (clinical/imaging variables only) and a full model (baseline plus procedural variables). Discrimination, calibration, and clinical utility were assessed using ROC curves, calibration plots, and decision curve analysis (DCA).
Results: Pneumothorax occurred in 22.1% (training) and 22.7% (validation). The baseline model contained BMI and emphysema grade; the full model additionally included patient position (prone or lateral vs supine) and the number of needle adjustments. In training, the full model outperformed the baseline (AUC 0.722 vs 0.630; P=0.018). In validation, AUC was 0.720 vs 0.670 (P=0.307), but the difference did not reach statistical significance. Sensitivity was higher with the full model (0.667 vs 0.593). Both models showed good calibration; the full model was closer to the ideal line across predicted probabilities of 0.1– 0.5. DCA indicated greater net benefit for the full model across most threshold probabilities.
Conclusion: A model combining clinical/imaging and procedural characteristics may facilitate peri-operative risk communication and support peri-procedural risk management for CT-guided lung nodule biopsy.
Keywords: CT-guided, lung nodule, percutaneous biopsy, pneumothorax, predictive model
1Department of Radiology, Western Beijing Cancer Hospital, Beijing, 100161, People’s Republic of China; 2Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, People’s Republic of China; 3Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China; 4Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, 213003, People’s Republic of China; 5Changzhou Clinical Medical Center, Changzhou, 213003, People’s Republic of China; 6Department of Thoracic Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China
Correspondence: Lele Zhang, Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, People’s Republic of China, Tel +86-0-18951670138, Fax +86-025-52254223, Email smilesmilezhang@163.com Xiaonan Shao Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People’s Republic of China, Tel +86-0-13776831531, Fax +86-0519-86621235, Email scorey@sina.com
Purpose: To develop and validate a model integrating clinical/imaging and procedural variables to predict pneumothorax after CT-guided percutaneous lung nodule biopsy.
Methods: We retrospectively enrolled 395 patients and split them 7:3 into training (n=276) and validation (n=119) cohorts. Variables with P < 0.15 in group comparisons (combined with clinical relevance) were selected as candidates and analyzed using age- and sex-adjusted univariable logistic regression. Two prespecified logistic models were fitted: a baseline model (clinical/imaging variables only) and a full model (baseline plus procedural variables). Discrimination, calibration, and clinical utility were assessed using ROC curves, calibration plots, and decision curve analysis (DCA).
Results: Pneumothorax occurred in 22.1% (training) and 22.7% (validation). The baseline model contained BMI and emphysema grade; the full model additionally included patient position (prone or lateral vs supine) and the number of needle adjustments. In training, the full model outperformed the baseline (AUC 0.722 vs 0.630; P=0.018). In validation, AUC was 0.720 vs 0.670 (P=0.307), but the difference did not reach statistical significance. Sensitivity was higher with the full model (0.667 vs 0.593). Both models showed good calibration; the full model was closer to the ideal line across predicted probabilities of 0.1– 0.5. DCA indicated greater net benefit for the full model across most threshold probabilities.
Conclusion: A model combining clinical/imaging and procedural characteristics may facilitate peri-operative risk communication and support peri-procedural risk management for CT-guided lung nodule biopsy.
Keywords: CT-guided, lung nodule, percutaneous biopsy, pneumothorax, predictive model