ArticleQuantitative imaging in medicine and surgery2025
Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Development and validation of a nomogram for preoperative prediction of the invasiveness of stage T1 lung adenocarcinoma utilizing AI-driven radiomics.BMC cancer · 2026Article
- The value of relative CT attenuation in predicting invasiveness in patients with T1-stage lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Threshold optimization in AI chest radiography analysis: integrating real-world data and clinical subgroups.European radiology experimental · 2025Article
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Abstract
Background: The accuracy of intraoperative rapid frozen pathology is suboptimal, and the assessment of invasiveness in malignant pulmonary nodules significantly influences surgical resection strategies. Predicting the invasiveness of lung adenocarcinoma based on preoperative imaging is a clinical challenge, and there are no established standards for the optimal threshold value using the threshold segmentation method to predict the invasiveness of stage T1 lung adenocarcinoma. This study aimed to explore the efficacy of three-dimensional solid component volume (3D SCV) [calculated by artificial intelligence (AI) threshold segmentation method] in predicting the aggressiveness of T1 lung adenocarcinoma and to determine its optimal threshold and cut-off point. Methods: A retrospective case-control analysis was conducted on 1,179 confirmed T1 lung adenocarcinoma nodules from two centers. AI-based threshold segmentation was used to calculate seven sets of solid component volume data (V Results: The solid component volume was a stable predictive factor for the invasiveness of T1 lung adenocarcinoma. The optimal threshold value for predicting the invasiveness of T1 lung adenocarcinoma using AI-based 3D SCV segmentation was -350 HU, with an area under the curve (AUC) and 95% confidence interval (CI) of 0.930 (0.915-0.945), a cut-off value of 106.5 mm Conclusions: The -350 HU threshold is reasonable for predicting the invasiveness of T1 lung adenocarcinoma based on solid component volume using the threshold segmentation method. However, there are certain differences in the threshold values depending on the diameter of the pulmonary nodule.
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