Clinical value of quantitative artificial intelligence imaging parameters for predicting the benign and malignant nature and the risk of recurrence of lung nodules ≤2 cm_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

MA Yujie , PIAO Zhe , YOU Yuhe , CAO Wenjun , XUE Hongsheng ,  ZHAO Zhilong

Department of Thoracic Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116601, Liaoning, P. R. China;

Corresponding?author：

ZHAO Zhilong, Email: zhilong509@126.com

Keywords：

Pulmonary nodules; artificial intelligence; quantitative parameters; pathologic subtypes; prediction model

DOI：

10.7507/1007-4848.202504013

Video：

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Objective To 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. Methods Lung 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. Results A 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%. Conclusion In clinical decision-making for lung nodules ≤2 cm, AI assisted diagnosis systems have high application value.

Citation： MA Yujie, PIAO Zhe, YOU Yuhe, CAO Wenjun, XUE Hongsheng, ZHAO Zhilong. Clinical value of quantitative artificial intelligence imaging parameters for predicting the benign and malignant nature and the risk of recurrence of lung nodules ≤2 cm. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(10): 1384-1392. doi: 10.7507/1007-4848.202504013 Copy

1.	Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin, 2023, 73(1): 17-48.
2.	Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature, 2018, 553(7689): 446-454.
3.	Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015. J Thorac Oncol, 2022, 17(3): 362-387.
4.	Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
5.	Zhang Y, Ma X, Shen X, et al. Surgery for pre- and minimally invasive lung adenocarcinoma. J Thorac Cardiovasc Surg, 2022, 163(2): 456-464.
6.	Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet, 2022, 399(10335): 1607-1617.
7.	Hung JJ, Yeh YC, Jeng WJ, et al. Predictive value of the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification of lung adenocarcinoma in tumor recurrence and patient survival. J Clin Oncol, 2014, 32(22): 2357-2364.
8.	Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
9.	Ettinger DS, Wood DE, Aisner DL, et al. NCCN guidelines insights: non-small cell lung cancer, version 2. 2023. J Natl Compr Canc Netw, 2023, 21(4): 340-350.
10.	梁云, 謝寧, 刁晶艷, 等. 人工智能量化參數預測肺結節浸潤程度的臨床價值. 中國胸心血管外科臨床雜志, 2022, 29(7): 878-885.Liang Y, Xie N, Diao JY, et al. Value of artificial intelligence quantitative parameters in predicting the infiltration of pulmonary nodules. Chin J Clin Thorac Cardiovasc Surg, 2022, 29(7): 878-885.
11.	Wang Memoli JS, Nietert PJ, Silvestri GA. Meta-analysis of guided bronchoscopy for the evaluation of the pulmonary nodule. Chest, 2012, 142(2): 385-393.
12.	Li Y, Yang CF, Peng J, et al. Small (≤20 mm) ground-glass opacity pulmonary lesions: which factors influence the diagnostic accuracy of CT-guided percutaneous core needle biopsy? BMC Pulm Med, 2022, 22(1): 265.
13.	Walts AE, Marchevsky AM. Root cause analysis of problems in the frozen section diagnosis of in situ, minimally invasive, and invasive adenocarcinoma of the lung. Arch Pathol Lab Med, 2012, 136(12): 1515-1521.
14.	Yeh YC, Nitadori J, Kadota K, et al. Using frozen section to identify histological patterns in stageⅠlung adenocarcinoma of ≤3 cm: accuracy and interobserver agreement. Histopathology, 2015, 66(7): 922-938.
15.	馬鈺杰, 游雨禾, 樸哲, 等. 人工智能在肺結節預測模型中應用的研究現狀. 中華胸部外科電子雜志, 2025, 12(1): 39-48.Ma YJ, You YH, Piao Z, et al. Current status of research on the application of artificial intelligence in lung nodule prediction modeling. Chin J Thorac Surg (Electron Ed), 2025, 12(1): 39-48.
16.	Eguchi T, Kameda K, Lu S, et al. Lobectomy is associated with better outcomes than sublobar resection in spread through air spaces (STAS)-positive T1 lung adenocarcinoma: a propensity score-matched analysis. J Thorac Oncol, 2019, 14(1): 87-98.
17.	Liu W, Chen C, Zhang Q, et al. Histopathologic pattern and molecular risk stratification are associated with prognosis in patients with stage ⅠB lung adenocarcinoma. Transl Lung Cancer Res, 2024, 13(9): 2424-2434.
18.	Chan EG, Chan PG, Mazur SN, et al. Outcomes with segmentectomy versus lobectomy in patients with clinical T1cN0M0 non-small cell lung cancer. J Thorac Cardiovasc Surg, 2021, 161(5): 1639-1648.
19.	Bankier AA, MacMahon H, Colby T, et al. Fleischner Society: glossary of terms for thoracic imaging. Radiology, 2024, 310(2): e232558.
20.	Li M, Zhu L, Lv Y, et al. Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study. Sci Rep, 2023, 13(1): 6999.
21.	Hu X, Yang L, Kang T, et al. Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence. Heliyon, 2024, 10(15): e34863.
22.	Mao R, She Y, Zhu E, et al. A proposal for restaging of invasive lung adenocarcinoma manifesting as pure ground glass opacity. Ann Thorac Surg, 2019, 107(5): 1523-1531.
23.	Detterbeck FC, Boffa DJ, Kim AW, et al. The eighth edition lung cancer stage classification. Chest, 2017, 151(1): 193-203.
24.	Hattori A, Suzuki K, Takamochi K, et al. Prognostic impact of a ground-glass opacity component in clinical stage ⅠA non-small cell lung cancer. J Thorac Cardiovasc Surg, 2021, 161(4): 1469-1480.
25.	Heidinger BH, Anderson KR, Nemec U, et al. Lung adenocarcinoma manifesting as pure ground-glass nodules: correlating CT size, volume, density, and roundness with histopathologic invasion and size. J Thorac Oncol, 2017, 12(8): 1288-1298.
26.	Dong H, Yin LK, Qiu YG, et al. Prediction of high-grade patterns of stage ⅠA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study. Eur Radiol, 2023, 33(6): 3931-3940.
27.	Ding H, Shi J, Zhou X, et al. Value of CT characteristics in predicting invasiveness of adenocarcinoma presented as pulmonary ground-glass nodules. Thorac Cardiovasc Surg, 2017, 65(2): 136-141.
28.	萬光藝, 孔杰俊, 張璐. 基于CT影像學特征的肺腺癌組織分化程度分析及患者預后預測價值. 腫瘤影像學, 2024, 33(4): 388-394.Wan GY, Kong JJ, Zhang L, et al. The degree of tissue differentiation and prognostic significance of lung adenocarcinoma based on CT imaging features. Oncoradiology, 2024, 33(4): 388-394.
29.	Wang H, Weng Q, Hui J, et al. Value of TSCT features for differentiating preinvasive and minimally invasive adenocarcinoma from invasive adenocarcinoma presenting as subsolid nodules smaller than 3 cm. Acad Radiol, 2020, 27(3): 395-403.
30.	劉江江, 于曉軍, 黃海濤, 等. 表現為周圍型肺磨玻璃結節的浸潤性腺癌影像學高危因素分析. 中國胸心血管外科臨床雜志, 2024, 31(1): 85-91.Liu JJ, Yu XJ, Huang HT, et al. High-risk factors in images of infiltrating lung adenocarcinoma manifesting as peripheral ground-glass nodules. Chin J Clin Thorac Cardiovasc Surg, 2024, 31(1): 85-91.
31.	顧鑫蕾, 劉展, 邵為朋, 等. 肺結節CT特征對腺癌病理亞型的預測價值. 中國胸心血管外科臨床雜志, 2022, 29(6): 684-692.Gu XL, Liu Z, Shao WP, et al. CT features of pulmonary nodules predictive value of histological subtypes of adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2022, 29(6): 684-692.
32.	王璐, 劉杰克, 徐富陽, 等. 基于低劑量CT定量-定性特征預測低分化浸潤性非黏液肺腺癌. 臨床放射學雜志, 2023, 42(12): 1887-1894.Wang L, Liu JK, Xu FY, et al. Quantitative-semantic features of low-dose CT for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. J Clin Radiol, 2023, 42(12): 1887-1894.
33.	Nakagawa K, Watanabe SI, Kunitoh H, et al. The Lung Cancer Surgical Study Group of the Japan Clinical Oncology Group: past activities, current status and future direction. Jpn J Clin Oncol, 2017, 47(3): 194-199.
34.	張瀟文, 趙紫維, 劉經緯, 等. 混合磨玻璃結節的CT征象對肺腺癌病理亞型及分化程度的預測價值. 中國胸心血管外科臨床雜志, 2023, 30(2): 191-197.Zhang XW, Zhao ZW, Liu JW, et al. The predictive value of CT signs of mixed ground-glass nodules in pathological subtypes and differentiation of lung adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(2): 191-197.
35.	Chen J, Zeng X, Li F, et al. Study on the value of 3D visualization in differentiating ⅠA and non-ⅠA pulmonary ground-glass nodules. Clin Radiol, 2024, 79(12): e1433-e1442.
36.	中華醫學會呼吸病學分會, 中國肺癌防治聯盟專家組. 肺結節診治中國專家共識(2024年版). 中華結核和呼吸雜志, 2024, 47(8): 716-729.Chinese Thoracic Society, Chinese Medical Association, Chinese Alliance Against Lung Cancer Expert Group. Chinese expert consensus on diagnosis and treatment of pulmonary nodules (2024). Chin J Tuberc Respir Dis, 2024, 47(8): 716-729.
37.	梁云, 任蒙蒙, 黃德龍, 等. 人工智能量化參數鑒別Ⅰ期浸潤性肺腺癌病理分級的臨床價值. 中國胸心血管外科臨床雜志, 2025, 32(4): 1-12.Liang Y, Ren MM, Huang DL, et al. The clinical value of artificial intelligence quantitative parameters in distinguishing pathological grades of stageⅠ invasive pulmonary adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(4): 1-12.
38.	楊卓文, 鄭智中, 李斌, 等. 人工智能系統在預測肺結節良惡性和病理分型中的臨床應用. 中國胸心血管外科臨床雜志, 2025, 32(8): 1086-1095.Yang ZW, Zheng ZZ, Li B, et al. Clinical application of an artificial intelligence system in predicting benign or malignant pulmonary nodules and pathological subtypes. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(8): 1086-1095.
39.	容宇, 韓念樵, 郝雁冰, 等. 基于臨床-影像特征預測ⅠA期浸潤性肺腺癌高級別組織學亞型模型的外部驗證. 中國胸心血管外科臨床雜志, 2025, 32(8): 1096-1104.Rong Y, Han NQ, Hao YB, et al. External validation of the model for predicting high-grade patterns of stage IA invasive lung adenocarcinoma based on clinical and imaging features. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(8): 1096-1104.

1. Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin, 2023, 73(1): 17-48.
2. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature, 2018, 553(7689): 446-454.
3. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015. J Thorac Oncol, 2022, 17(3): 362-387.
4. Moreira AL, Ocampo PSS, Xia Y, et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610.
5. Zhang Y, Ma X, Shen X, et al. Surgery for pre- and minimally invasive lung adenocarcinoma. J Thorac Cardiovasc Surg, 2022, 163(2): 456-464.
6. Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet, 2022, 399(10335): 1607-1617.
7. Hung JJ, Yeh YC, Jeng WJ, et al. Predictive value of the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification of lung adenocarcinoma in tumor recurrence and patient survival. J Clin Oncol, 2014, 32(22): 2357-2364.
8. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
9. Ettinger DS, Wood DE, Aisner DL, et al. NCCN guidelines insights: non-small cell lung cancer, version 2. 2023. J Natl Compr Canc Netw, 2023, 21(4): 340-350.
10. 梁云, 謝寧, 刁晶艷, 等. 人工智能量化參數預測肺結節浸潤程度的臨床價值. 中國胸心血管外科臨床雜志, 2022, 29(7): 878-885.Liang Y, Xie N, Diao JY, et al. Value of artificial intelligence quantitative parameters in predicting the infiltration of pulmonary nodules. Chin J Clin Thorac Cardiovasc Surg, 2022, 29(7): 878-885.
11. Wang Memoli JS, Nietert PJ, Silvestri GA. Meta-analysis of guided bronchoscopy for the evaluation of the pulmonary nodule. Chest, 2012, 142(2): 385-393.
12. Li Y, Yang CF, Peng J, et al. Small (≤20 mm) ground-glass opacity pulmonary lesions: which factors influence the diagnostic accuracy of CT-guided percutaneous core needle biopsy? BMC Pulm Med, 2022, 22(1): 265.
13. Walts AE, Marchevsky AM. Root cause analysis of problems in the frozen section diagnosis of in situ, minimally invasive, and invasive adenocarcinoma of the lung. Arch Pathol Lab Med, 2012, 136(12): 1515-1521.
14. Yeh YC, Nitadori J, Kadota K, et al. Using frozen section to identify histological patterns in stageⅠlung adenocarcinoma of ≤3 cm: accuracy and interobserver agreement. Histopathology, 2015, 66(7): 922-938.
15. 馬鈺杰, 游雨禾, 樸哲, 等. 人工智能在肺結節預測模型中應用的研究現狀. 中華胸部外科電子雜志, 2025, 12(1): 39-48.Ma YJ, You YH, Piao Z, et al. Current status of research on the application of artificial intelligence in lung nodule prediction modeling. Chin J Thorac Surg (Electron Ed), 2025, 12(1): 39-48.
16. Eguchi T, Kameda K, Lu S, et al. Lobectomy is associated with better outcomes than sublobar resection in spread through air spaces (STAS)-positive T1 lung adenocarcinoma: a propensity score-matched analysis. J Thorac Oncol, 2019, 14(1): 87-98.
17. Liu W, Chen C, Zhang Q, et al. Histopathologic pattern and molecular risk stratification are associated with prognosis in patients with stage ⅠB lung adenocarcinoma. Transl Lung Cancer Res, 2024, 13(9): 2424-2434.
18. Chan EG, Chan PG, Mazur SN, et al. Outcomes with segmentectomy versus lobectomy in patients with clinical T1cN0M0 non-small cell lung cancer. J Thorac Cardiovasc Surg, 2021, 161(5): 1639-1648.
19. Bankier AA, MacMahon H, Colby T, et al. Fleischner Society: glossary of terms for thoracic imaging. Radiology, 2024, 310(2): e232558.
20. Li M, Zhu L, Lv Y, et al. Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study. Sci Rep, 2023, 13(1): 6999.
21. Hu X, Yang L, Kang T, et al. Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence. Heliyon, 2024, 10(15): e34863.
22. Mao R, She Y, Zhu E, et al. A proposal for restaging of invasive lung adenocarcinoma manifesting as pure ground glass opacity. Ann Thorac Surg, 2019, 107(5): 1523-1531.
23. Detterbeck FC, Boffa DJ, Kim AW, et al. The eighth edition lung cancer stage classification. Chest, 2017, 151(1): 193-203.
24. Hattori A, Suzuki K, Takamochi K, et al. Prognostic impact of a ground-glass opacity component in clinical stage ⅠA non-small cell lung cancer. J Thorac Cardiovasc Surg, 2021, 161(4): 1469-1480.
25. Heidinger BH, Anderson KR, Nemec U, et al. Lung adenocarcinoma manifesting as pure ground-glass nodules: correlating CT size, volume, density, and roundness with histopathologic invasion and size. J Thorac Oncol, 2017, 12(8): 1288-1298.
26. Dong H, Yin LK, Qiu YG, et al. Prediction of high-grade patterns of stage ⅠA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study. Eur Radiol, 2023, 33(6): 3931-3940.
27. Ding H, Shi J, Zhou X, et al. Value of CT characteristics in predicting invasiveness of adenocarcinoma presented as pulmonary ground-glass nodules. Thorac Cardiovasc Surg, 2017, 65(2): 136-141.
28. 萬光藝, 孔杰俊, 張璐. 基于CT影像學特征的肺腺癌組織分化程度分析及患者預后預測價值. 腫瘤影像學, 2024, 33(4): 388-394.Wan GY, Kong JJ, Zhang L, et al. The degree of tissue differentiation and prognostic significance of lung adenocarcinoma based on CT imaging features. Oncoradiology, 2024, 33(4): 388-394.
29. Wang H, Weng Q, Hui J, et al. Value of TSCT features for differentiating preinvasive and minimally invasive adenocarcinoma from invasive adenocarcinoma presenting as subsolid nodules smaller than 3 cm. Acad Radiol, 2020, 27(3): 395-403.
30. 劉江江, 于曉軍, 黃海濤, 等. 表現為周圍型肺磨玻璃結節的浸潤性腺癌影像學高危因素分析. 中國胸心血管外科臨床雜志, 2024, 31(1): 85-91.Liu JJ, Yu XJ, Huang HT, et al. High-risk factors in images of infiltrating lung adenocarcinoma manifesting as peripheral ground-glass nodules. Chin J Clin Thorac Cardiovasc Surg, 2024, 31(1): 85-91.
31. 顧鑫蕾, 劉展, 邵為朋, 等. 肺結節CT特征對腺癌病理亞型的預測價值. 中國胸心血管外科臨床雜志, 2022, 29(6): 684-692.Gu XL, Liu Z, Shao WP, et al. CT features of pulmonary nodules predictive value of histological subtypes of adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2022, 29(6): 684-692.
32. 王璐, 劉杰克, 徐富陽, 等. 基于低劑量CT定量-定性特征預測低分化浸潤性非黏液肺腺癌. 臨床放射學雜志, 2023, 42(12): 1887-1894.Wang L, Liu JK, Xu FY, et al. Quantitative-semantic features of low-dose CT for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. J Clin Radiol, 2023, 42(12): 1887-1894.
33. Nakagawa K, Watanabe SI, Kunitoh H, et al. The Lung Cancer Surgical Study Group of the Japan Clinical Oncology Group: past activities, current status and future direction. Jpn J Clin Oncol, 2017, 47(3): 194-199.
34. 張瀟文, 趙紫維, 劉經緯, 等. 混合磨玻璃結節的CT征象對肺腺癌病理亞型及分化程度的預測價值. 中國胸心血管外科臨床雜志, 2023, 30(2): 191-197.Zhang XW, Zhao ZW, Liu JW, et al. The predictive value of CT signs of mixed ground-glass nodules in pathological subtypes and differentiation of lung adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(2): 191-197.
35. Chen J, Zeng X, Li F, et al. Study on the value of 3D visualization in differentiating ⅠA and non-ⅠA pulmonary ground-glass nodules. Clin Radiol, 2024, 79(12): e1433-e1442.
36. 中華醫學會呼吸病學分會, 中國肺癌防治聯盟專家組. 肺結節診治中國專家共識(2024年版). 中華結核和呼吸雜志, 2024, 47(8): 716-729.Chinese Thoracic Society, Chinese Medical Association, Chinese Alliance Against Lung Cancer Expert Group. Chinese expert consensus on diagnosis and treatment of pulmonary nodules (2024). Chin J Tuberc Respir Dis, 2024, 47(8): 716-729.
37. 梁云, 任蒙蒙, 黃德龍, 等. 人工智能量化參數鑒別Ⅰ期浸潤性肺腺癌病理分級的臨床價值. 中國胸心血管外科臨床雜志, 2025, 32(4): 1-12.Liang Y, Ren MM, Huang DL, et al. The clinical value of artificial intelligence quantitative parameters in distinguishing pathological grades of stageⅠ invasive pulmonary adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(4): 1-12.
38. 楊卓文, 鄭智中, 李斌, 等. 人工智能系統在預測肺結節良惡性和病理分型中的臨床應用. 中國胸心血管外科臨床雜志, 2025, 32(8): 1086-1095.Yang ZW, Zheng ZZ, Li B, et al. Clinical application of an artificial intelligence system in predicting benign or malignant pulmonary nodules and pathological subtypes. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(8): 1086-1095.
39. 容宇, 韓念樵, 郝雁冰, 等. 基于臨床-影像特征預測ⅠA期浸潤性肺腺癌高級別組織學亞型模型的外部驗證. 中國胸心血管外科臨床雜志, 2025, 32(8): 1096-1104.Rong Y, Han NQ, Hao YB, et al. External validation of the model for predicting high-grade patterns of stage IA invasive lung adenocarcinoma based on clinical and imaging features. Chin J Clin Thorac Cardiovasc Surg, 2025, 32(8): 1096-1104.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Clinical value of quantitative artificial intelligence imaging parameters for predicting the benign and malignant nature and the risk of recurrence of lung nodules ≤2 cm

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