ObjectiveTo evaluate the value of imaging quantification parameters in artificial intelligence (AI) assisted diagnosis systems in clinical decision-making for lung nodules≤2 cm and the diagnostic efficacy of AI. MethodsLung nodule patients admitted to Affiliated Zhongshan Hospital of Dalian University from 2020 to 2023 were included. Imaging parameters of lung nodules were extracted using AI assisted diagnosis systems. Multifactor analysis was used to screen predictors for distinguishing benign and malignant nodules and high-risk predictors for recurrent invasive adenocarcinoma, and a diagnostic model was established and its performance evaluated. The diagnostic efficacy of the AI system was judged according to pathological results. ResultsA total of 594 patients with lung nodules were included, including 202 males and 392 females, with an average age of (58.75±11.55) years. Volume, average CT value, and 3D maximum diameter of non-solid nodules were independent predictors of malignant nodules, with thresholds of 287.4 mm3, ?491 HU, and 12.0 mm, respectively. The area under the curve (AUC) for diagnostic efficacy was ranked from high to low as combined model (0.802), volume (0.783), average CT value (0.749), and 3D maximum diameter (0.714). The average CT value and 3D long diameter of solid nodules were independent predictors of malignant nodules, with thresholds of ?81 HU and 17.5 mm, respectively, and AUC values of 0.874 and 0.686, respectively, with the combined prediction AUC of 0.957. The mass of cystic nodules was an independent predictor of malignancy when the mass>180.7 mg. Independent predictors of high recurrence risk of invasive adenocarcinoma in non-solid nodules were consolidation-tumor ratio (CTR), average CT value, 3D long diameter, and volume, with thresholds of 0.14, ?386 HU, 15.6 mm, and 1018.9 mm3, respectively, and diagnostic efficacy was ranked from high to low as combined model (0.788), 3D long diameter (0.735), volume (0.725), average CT value (0.720), and CTR (0.697). The accuracy of AI in predicting benign and malignant target nodules was 87.4%, with positive predictive value of 96.6% and negative predictive value of 58.9%. ConclusionIn clinical decision-making for lung nodules ≤2 cm, AI assisted diagnosis systems have high application value.
The detection rates of pulmonary nodules and early-stage lung cancer are rising year on year, which underscores the clinical value of sublobar resections including lung segmentectomy. CT-based three-dimensional reconstruction can intuitively visualize the anatomical courses and variants of bronchial and pulmonary vascular structures (arteries and veins), thereby providing accurate guidance for preoperative localization, surgical planning and intraoperative identification. This article systematically reviews the anatomical classification patterns of the bronchi and pulmonary vessels in the right upper, middle, and lower lobes at the segmental and subsegmental levels, and summarizes clinically significant variations, including tracheal bronchus, common arterial trunk, right top pulmonary vein, lingular-like bronchial configuration of the middle lobe, ectopic venous drainage, the subsuperior segment ("star segment") variation, and the relationship of B7/A7 to the inferior pulmonary vein, analyzing their impact on intraoperative anatomical judgment and treatment strategies for bronchial and vessels. This review aims to provide an anatomical basis and clinical reference for individualized precise segmentectomy and complex sublobar resection, so as to maximize preservation of pulmonary function while ensuring oncologic radicality and reducing the risk of surgical complications.