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Background <p>Visceral pleural invasion (VPI) is an adverse prognostic factor in lung adenocarcinoma. Accurate preoperative estimation of VPI risk in solid tumors with pleural contact may provide useful supplementary information during preoperative assessment.</p> Methods <p>This was a retrospective study of 162 patients. All patients had surgically resected, pathologically confirmed solid lung adenocarcinoma in pleural contact between November 2018 and August 2025. Patients were classified into VPI-positive and VPI-negative groups based on postoperative pathology. The patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3 for internal validation. Multivariable logistic regression was performed to identify independent risk factors for VPI. A nomogram prediction model was developed based on the multivariable analysis. Its predictive performance was then evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).</p> Results <p>Of the 162 patients with solid lung adenocarcinoma nodules, 58 had VPI confirmed by pathology. Multivariate logistic regression analysis identified spiculation, pleural indentation, and pleural contact length as independent risk factors for predicting VPI. A nomogram based on these three CT features showed good discriminative performance in both the training and testing cohorts, with AUCs of 0.901 (95% CI: 0.846–0.956) and 0.864 (95% CI: 0.737–0.937), respectively. Calibration curves showed the predictions matched observations well. Decision curve analysis suggested potential utility in preoperative risk assessment.</p> Conclusion <p>Among patients with solid lung adenocarcinoma nodules with pleural contact, we developed a nomogram based on routinely assessed CT semantic features to estimate the preoperative probability of VPI. The model is intended as a practical supplementary tool for preoperative VPI risk estimation.</p>

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CT-based nomogram for estimating visceral pleural invasion in solid pulmonary adenocarcinoma nodules with pleural contact: a retrospective single-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Rui Xu,
  • Jianing Tang,
  • Qianyao Yuan,
  • Mengjiao Ding,
  • Jingjing Zhang,
  • Wenjun Yao,
  • Dai Zhang,
  • Hong Zhao

摘要

Background

Visceral pleural invasion (VPI) is an adverse prognostic factor in lung adenocarcinoma. Accurate preoperative estimation of VPI risk in solid tumors with pleural contact may provide useful supplementary information during preoperative assessment.

Methods

This was a retrospective study of 162 patients. All patients had surgically resected, pathologically confirmed solid lung adenocarcinoma in pleural contact between November 2018 and August 2025. Patients were classified into VPI-positive and VPI-negative groups based on postoperative pathology. The patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3 for internal validation. Multivariable logistic regression was performed to identify independent risk factors for VPI. A nomogram prediction model was developed based on the multivariable analysis. Its predictive performance was then evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results

Of the 162 patients with solid lung adenocarcinoma nodules, 58 had VPI confirmed by pathology. Multivariate logistic regression analysis identified spiculation, pleural indentation, and pleural contact length as independent risk factors for predicting VPI. A nomogram based on these three CT features showed good discriminative performance in both the training and testing cohorts, with AUCs of 0.901 (95% CI: 0.846–0.956) and 0.864 (95% CI: 0.737–0.937), respectively. Calibration curves showed the predictions matched observations well. Decision curve analysis suggested potential utility in preoperative risk assessment.

Conclusion

Among patients with solid lung adenocarcinoma nodules with pleural contact, we developed a nomogram based on routinely assessed CT semantic features to estimate the preoperative probability of VPI. The model is intended as a practical supplementary tool for preoperative VPI risk estimation.