The clinical value of artificial intelligence quantitative parameters in distinguishing pathological grades of stage Ⅰ invasive pulmonary adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIANG Yun ^1,2 , REN Mengmeng ³ , HUANG Delong ⁴ , DIAO Jingyan ¹ , MU Xuri ¹ , ZHANG Guowei ⁴ , LIU Shuliang ¹ , FEI Xiuqu ¹ ,  DI Dongmei ² ,  XIE Ning ¹

1. Department of Thoracic Surgery, Yantaishan Hospital Affiliated to Binzhou Medical University, Yantai, 264000, Shandong, P. R. China;
2. Department of Cardiothoracic Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, 213000, Jiangsu, P. R. China;
3. Department of Cardiology, Yantai Yuhuangding Hospital, Yantai, 264000, Shandong, P. R. China;
4. Department of Radiology, Yantaishan Hospital Affiliated to Binzhou Medical University, Yantai, 264000, Shandong, P. R. China;

Corresponding author：

DI Dongmei, Email: sunnyvvv@163.com; XIE Ning, Email: ddm5122@163.com

Keywords：

Artificial intelligence; adenocarcinoma; pulmonary nodules; quantitative analysis; pathological grading

DOI：

10.7507/1007-4848.202408028

Video：

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Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. (1) Comparison among the three groups: the differences in parameters such as consolidation/tumor ratio (CTR), long diameter, short diameter, malignancy probability, CT average value, CT maximum value, CT minimum value, CT median value, CT standard deviation, kurtosis, skewness, and entropy were statistically significant (P<0.05). (2) Comparison between two groups: gradeⅠ and grade Ⅱ were combined and compared with grade Ⅲ, and univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05). Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. (3) Pathological comparisons: no lymph node metastasis was found in gradeⅠpatients, two gradeⅡ patients were of lymph node metastasis with micro-papillary components, and 19 grade Ⅲ patients were of lymph node metastasis. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. (4) Correlation analysis: CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. (5) CTR and CT median value were selected by using LASSO regression, and logistic regression, random forest, and XGBoost models were constructed and validated. Among them, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

1.	Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent, 2024, 4(1): 47-53.
2.	National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
3.	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.
4.	梁云, 谢宁, 刁晶艳, 等. 人工智能量化参数预测肺结节浸润程度的临床价值. 中国胸心血管外科临床杂志, 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.
5.	冉姗姗, 陈佳情, 罗朝芬, 等. 基于人工智能的影像组学在肺癌诊疗中的应用进展. 国际医学放射学杂志, 2024, 47(3): 294-299.Ran SS, Chen JQ, Luo CF, et al. Application progress of radiomics based on artificial intelligence in the diagnosis and treatment of lung cancer. J Int Med Radiol, 2024, 47(3): 294-299.
6.	Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
7.	单文莉, 孔丹, 张辉, 等. 浸润性肺腺癌分化程度预测模型的临床价值. 中华肿瘤杂志, 2022, 44(7): 767-775.Shan WL, Kong D, Zhang H, et al. Clinical value of prediction models for differentiation degree in invasive pulmonary adenocarcinoma. Chin J Oncol, 2022, 44(7): 767-775.
8.	何花, 杨德伦, 孙硕, 等. 基于影像组学的机器学习模型辅助肺磨玻璃结节浸润程度鉴别的应用价值. 中国胸心血管外科临床杂志, 2023, 30(4): 522-531.He H, Yang DL, Sun S, et al. Application value of radiomics-based machine learning models in differentiating the invasion degree of lung ground-glass nodules. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(4): 522-531.
9.	顾鑫蕾, 刘展, 邵为朋, 等. 肺结节CT特征对腺癌病理亚型的预测价值. 中国胸心血管外科临床杂志, 2022, 29(6): 684-692.Gu XL, Liu Z, Shao WP, et al. Predictive value of CT features of lung nodules for pathological subtypes of adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2022, 29(6): 684-692.
10.	梁演婷, 林欢, 李夙芸, 等. CT特征联合人工智能定量参数评估ⅠA 期肺腺癌高级别组织学亚型. 中国医学影像技术, 2023, 39(2): 199-203.Liang YT, Lin H, Li SY, et al. Assessment of high-grade histological subtypes of stage Ⅰa pulmonary adenocarcinoma using ct features combined with artificial intelligence quantitative parameters. Chin J Med Imaging Technol, 2023, 39(2): 199-203.
11.	黄汉清, 叶波. 肿瘤实性成分占比在早期周围型肺癌诊疗中的研究进展. 中国肺癌杂志, 2022, 25(10): 764-770.Huang HQ, Ye B. Research progress on the proportion of solid components in the diagnosis and treatment of early peripheral lung cancer. Chin J Lung Cancer, 2022, 25(10): 764-770.
12.	Suzuki K, Koike T, Asakawa T, et al. A prospective radiological study of thin-section computed tomography to predict pathological noninvasiveness in peripheral clinical ⅠA lung cancer (Japan Clinical Oncology Group 0201). J Thorac Oncol, 2011, 6(4): 751-756.
13.	Tsutani Y, Suzuki K, Koike T, et al. High-risk factors for recurrence of stageⅠ lung adenocarcinoma: Follow-up data from JCOG0201. Ann Thorac Surg, 2019, 108(5): 1484-1490.
14.	Kim SK, Kim TJ, Chung MJ, et al. Lung adenocarcinoma: CT features associated with spread through air spaces. Radiology, 2018, 289(3): 831-840.
15.	Zhang Y, Fu F, Wen Z, et al. Segment location and ground glass opacity ratio reliably predict node-negative status in lung cancer. Ann Thorac Surg, 2020, 109(4): 1061-1068.
16.	Nakahashi K, Tsunooka N, Hirayama K, et al. Preoperative predictors of lymph node metastasis in clinical T1 adenocarcinoma. J Thorac Dis, 2020, 12(5): 2352-2360.
17.	Xi J, Yin J, Liang J, et al. Prognostic impact of radiological consolidation tumor ratio in clinical stageⅠa pulmonary ground glass opacities. Front Oncol, 2021, 11: 616149.
18.	Ahn B, Yoon S, Kim D, et al. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma. Lung Cancer, 2022, 170: 176-184.
19.	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.
20.	Altorki N, Wang X, Kozono D, et al. Lobar or sublobar resection for peripheral stageⅠa non-small-cell lung cancer. N Engl J Med, 2023, 388(6): 489-498.
21.	Aokage K, Suzuki K, Saji H, et al. Segmentectomy for ground-glass-dominant lung cancer with a tumor diameter of 3 cm or less including ground-glass opacity (JCOG1211): A multicentre, single-arm, confirmatory, phase 3 trial. Lancet Respir Med, 2023, 11(6): 540-549.
22.	Jeon YJ, Lee J, Shin S, et al. Prognostic impact of micropapillary and solid histological subtype on patients undergoing curative resection for stageⅠ lung adenocarcinoma according to the extent of pulmonary resection and lymph node assessment. Lung Cancer, 2022, 168: 21-29.
23.	Chen T, Guestrin C. XGBoost: A scalable tree boosting system. DD'16: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Miningacm, 2016: 785-794.
24.	冷菲, 李巍. 基于XGBoost对肺鳞癌和肺腺癌的分类预测. 首都医科大学学报, 2019, 40(6): 889-893.Leng F, Li W. Classification prediction of lung squamous cell carcinoma and lung adenocarcinoma based on XGBoost. J Capital Med Univ, 2019, 40(6): 889-893.

1. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent, 2024, 4(1): 47-53.
2. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
3. 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.
4. 梁云, 谢宁, 刁晶艳, 等. 人工智能量化参数预测肺结节浸润程度的临床价值. 中国胸心血管外科临床杂志, 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.
5. 冉姗姗, 陈佳情, 罗朝芬, 等. 基于人工智能的影像组学在肺癌诊疗中的应用进展. 国际医学放射学杂志, 2024, 47(3): 294-299.Ran SS, Chen JQ, Luo CF, et al. Application progress of radiomics based on artificial intelligence in the diagnosis and treatment of lung cancer. J Int Med Radiol, 2024, 47(3): 294-299.
6. Bera K, Braman N, Gupta A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol, 2022, 19(2): 132-146.
7. 单文莉, 孔丹, 张辉, 等. 浸润性肺腺癌分化程度预测模型的临床价值. 中华肿瘤杂志, 2022, 44(7): 767-775.Shan WL, Kong D, Zhang H, et al. Clinical value of prediction models for differentiation degree in invasive pulmonary adenocarcinoma. Chin J Oncol, 2022, 44(7): 767-775.
8. 何花, 杨德伦, 孙硕, 等. 基于影像组学的机器学习模型辅助肺磨玻璃结节浸润程度鉴别的应用价值. 中国胸心血管外科临床杂志, 2023, 30(4): 522-531.He H, Yang DL, Sun S, et al. Application value of radiomics-based machine learning models in differentiating the invasion degree of lung ground-glass nodules. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(4): 522-531.
9. 顾鑫蕾, 刘展, 邵为朋, 等. 肺结节CT特征对腺癌病理亚型的预测价值. 中国胸心血管外科临床杂志, 2022, 29(6): 684-692.Gu XL, Liu Z, Shao WP, et al. Predictive value of CT features of lung nodules for pathological subtypes of adenocarcinoma. Chin J Clin Thorac Cardiovasc Surg, 2022, 29(6): 684-692.
10. 梁演婷, 林欢, 李夙芸, 等. CT特征联合人工智能定量参数评估ⅠA 期肺腺癌高级别组织学亚型. 中国医学影像技术, 2023, 39(2): 199-203.Liang YT, Lin H, Li SY, et al. Assessment of high-grade histological subtypes of stage Ⅰa pulmonary adenocarcinoma using ct features combined with artificial intelligence quantitative parameters. Chin J Med Imaging Technol, 2023, 39(2): 199-203.
11. 黄汉清, 叶波. 肿瘤实性成分占比在早期周围型肺癌诊疗中的研究进展. 中国肺癌杂志, 2022, 25(10): 764-770.Huang HQ, Ye B. Research progress on the proportion of solid components in the diagnosis and treatment of early peripheral lung cancer. Chin J Lung Cancer, 2022, 25(10): 764-770.
12. Suzuki K, Koike T, Asakawa T, et al. A prospective radiological study of thin-section computed tomography to predict pathological noninvasiveness in peripheral clinical ⅠA lung cancer (Japan Clinical Oncology Group 0201). J Thorac Oncol, 2011, 6(4): 751-756.
13. Tsutani Y, Suzuki K, Koike T, et al. High-risk factors for recurrence of stageⅠ lung adenocarcinoma: Follow-up data from JCOG0201. Ann Thorac Surg, 2019, 108(5): 1484-1490.
14. Kim SK, Kim TJ, Chung MJ, et al. Lung adenocarcinoma: CT features associated with spread through air spaces. Radiology, 2018, 289(3): 831-840.
15. Zhang Y, Fu F, Wen Z, et al. Segment location and ground glass opacity ratio reliably predict node-negative status in lung cancer. Ann Thorac Surg, 2020, 109(4): 1061-1068.
16. Nakahashi K, Tsunooka N, Hirayama K, et al. Preoperative predictors of lymph node metastasis in clinical T1 adenocarcinoma. J Thorac Dis, 2020, 12(5): 2352-2360.
17. Xi J, Yin J, Liang J, et al. Prognostic impact of radiological consolidation tumor ratio in clinical stageⅠa pulmonary ground glass opacities. Front Oncol, 2021, 11: 616149.
18. Ahn B, Yoon S, Kim D, et al. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma. Lung Cancer, 2022, 170: 176-184.
19. 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.
20. Altorki N, Wang X, Kozono D, et al. Lobar or sublobar resection for peripheral stageⅠa non-small-cell lung cancer. N Engl J Med, 2023, 388(6): 489-498.
21. Aokage K, Suzuki K, Saji H, et al. Segmentectomy for ground-glass-dominant lung cancer with a tumor diameter of 3 cm or less including ground-glass opacity (JCOG1211): A multicentre, single-arm, confirmatory, phase 3 trial. Lancet Respir Med, 2023, 11(6): 540-549.
22. Jeon YJ, Lee J, Shin S, et al. Prognostic impact of micropapillary and solid histological subtype on patients undergoing curative resection for stageⅠ lung adenocarcinoma according to the extent of pulmonary resection and lymph node assessment. Lung Cancer, 2022, 168: 21-29.
23. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. DD'16: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Miningacm, 2016: 785-794.
24. 冷菲, 李巍. 基于XGBoost对肺鳞癌和肺腺癌的分类预测. 首都医科大学学报, 2019, 40(6): 889-893.Leng F, Li W. Classification prediction of lung squamous cell carcinoma and lung adenocarcinoma based on XGBoost. J Capital Med Univ, 2019, 40(6): 889-893.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesThe clinical value of artificial intelligence quantitative parameters in distinguishing pathological grades of stage Ⅰ invasive pulmonary adenocarcinoma

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