Value of artificial intelligence quantitative parameters in predicting the infiltration of pulmonary nodules_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIANG Yun ¹ , XIE Ning ¹ , DIAO Jingyan ¹ , REN Mengmeng ² ,  LIU Shuliang ¹

1. Department of Thoracic Surgery, The Affiliated Yantaishan Hospital of Binzhou Medical University, Yantai, 264000, Shandong, P. R. China;
2. Department of Epidemiology, School of Public Health and Management, Binzhou Medical University, Yantai, 264000, Shandong, P. R. China;

Corresponding?author：

LIU Shuliang, Email: ytlsl66@126.com

Keywords：

Artificial intelligence; ground-glass nodule; quantitative analysis; lung cancer; high-resolution computed tomography

DOI：

10.7507/1007-4848.202112041

Video：

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Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters of pulmonary ground-glass nodules (GGN) in predicting the degree of infiltration. Methods A retrospective analysis of 168 consecutive patients with 178 GGNs in our hospital from October 2019 to May 2021 was performed, including 43 males and 125 females, aged 21-78 (55.76±10.88) years. Different lesions of the same patient were analyzed as independent samples. Totally, 178 GGNs were divided into two groups, a non-invasive group (24 adenocarcinoma in situ and 77 minimally invasive adenocarcinoma), and an invasive group (77 invasive adenocarcinoma). We compared the difference of AI quantitative parameters between the two groups, and evaluated predictive valve by receiver operating characteristic curve and binary logistic regression model. Results (1) Except for the gender (P=0.115), the other parameters, such as maximal diameter [15.10 (11.50, 21.60) mm vs. 8.90 (7.65, 11.15) mm], minimum diameter [10.80 (8.85, 15.20) mm vs. 7.40 (6.10, 8.95) mm], proportion of consolidation/tumor ratio [13.58% (1.61%, 63.76%) vs. 0.00% (0.00%, 0.67%)], mean CT value [–347.00 (–492.00, –101.50) Hu vs. –598.00 (–657.50, –510.00) Hu], CT maximum value [40.00 (–40.00, 94.50) Hu vs. –218.00 (–347.00, –66.50) Hu], CT minimum value [–584.00 (–690.50, –350.00) Hu vs. –753.00 (–786.00, –700.00) Hu], danger rating (proportion of high-risk nodules, 92.2% vs. 66.3%), malignant probability [91.66% (85.62%, 94.92%) vs. 81.81% (59.98%, 90.29%)] and age (59.93±8.53 years vs. 52.04±12.10 years) were statistically significant between the invasive group and the non-invasive group (all P<0.001). (2) The highest predictive value of a single quantitative parameter was the maximal diameter (area under the curve=0.843), the lowest one was the risk classification (area under the curve=0.627), the combination of two among the three parameters (maximal diameter, mean CT value, and consolidation/tumor ratio) improved the predictive value entirely. (3) Logistic regression analysis showed that maximal diameter and mean CT value both were the independent risk factor for predicting invasive adenocarcinoma. (4) When the threshold of v was 1.775%, the diagnostic sensitivity of invasive adenocarcinoma was 0.753 and the specificity was 0.851. Conclusion AI quantitative parameters can effectively predict the degree of infiltration of GGNs and provide a reliable reference basis for clinicians.

Citation： LIANG Yun, XIE Ning, DIAO Jingyan, REN Mengmeng, LIU Shuliang. Value of artificial intelligence quantitative parameters in predicting the infiltration of pulmonary nodules. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(7): 878-885. doi: 10.7507/1007-4848.202112041 Copy

1.	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.
2.	呂文暉, 周長圣, 李新宇, 等. 利用深度學習模型判斷基線胸部平掃CT肺結節的良惡性. 中華放射學雜志, 2019, 53(11): 957-962.
3.	Suzuki K, Watanabe SI, Wakabayashi M, et al. A single-arm study of sublobar resection for ground-glass opacity dominant peripheral lung cancer. J Thorac Cardiovasc Surg, 2022, 163(1): 289-301.
4.	Wan YL, Wu PW, Huang PC, et al. The use of artificial intelligence in the differentiation of malignant and benign lung nodules on computed tomograms proven by surgical pathology. Cancers (Basel), 2020, 12(8): 2211.
5.	李大勝, 王大為, 黃宇清, 等. 多原發肺癌病理結果與AI輔助CT診斷的相關性研究. 中國醫療設備, 2021, 36(2): 77-80, 95.
6.	Chen L, Gu D, Chen Y, et al. An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans. Comput Med Imaging Graph, 2021, 89: 101899.
7.	Ginsberg RJ, Rubinstein LV. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. Ann Thorac Surg, 1995, 60(3): 615-622.
8.	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.
9.	Dembitzer FR, Flores RM, Parides MK, et al. Impact of histologic subtyping on outcome in lobar vs sublobar resections for lung cancer: A pilot study. Chest, 2014, 146(1): 175-181.
10.	Bedetti B, Bertolaccini L, Rocco R, et al. Segmentectomy versus lobectomy for stageⅠ non-small cell lung cancer: A systematic review and meta-analysis. J Thorac Dis, 2017, 9(6): 1615-1623.
11.	Qu X, Wang K, Zhang T, et al. Long-term outcomes of stage Ⅰ NSCLC (≤3 cm) patients following segmentectomy are equivalent to lobectomy under analogous extent of lymph node removal: A PSM based analysis. J Thorac Dis, 2017, 9(11): 4561-4573.
12.	Sakurai H, Asamura H. Sublobar resection for early-stage lung cancer. Transl Lung Cancer Res, 2014, 3(3): 164-172.
13.	Tane S, Nishio W, Nishioka Y, et al. Evaluation of the residual lung function after thoracoscopic segmentectomy compared with lobectomy. Ann Thorac Surg, 2019, 108(5): 1543-1550.
14.	Gu Z, Wang H, Mao T, et al. Pulmonary function changes after different extent of pulmonary resection under video-assisted thoracic surgery. J Thorac Dis, 2018, 10(4): 2331-2337.
15.	Chen L, Gu Z, Lin B, et al. Pulmonary function changes after thoracoscopic lobectomy versus intentional thoracoscopic segmentectomy for early-stage non-small cell lung cancer. Transl Lung Cancer Res, 2021, 10(11): 4141-4151.
16.	陳琦, 朱全新, 郁義星, 等. 肺部單發微小磨玻璃結節 (<10 mm) MSCT特征對肺腺癌病理亞型的診斷價值. 放射學實踐, 2019, 34(7): 778-783.
17.	蔡雅倩, 張正華, 韓丹, 等. AI對肺磨玻璃結節篩查及定性的臨床應用研究. 放射學實踐, 2019, 34(9): 958-962.
18.	周小君, 馬玲, 盛林麗, 等. 人工智能量化參數預測磨玻璃結節早期肺癌浸潤性的臨床初探. 實用放射學雜志, 2021, 37(3): 388-391.
19.	Singh R, Kalra MK, Homayounieh F, et al. Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography. Quant Imaging Med Surg, 2021, 11(4): 1134-1143.
20.	孫炎冰, 陶廣昱, 陳群慧, 等. 人工智能CT定量分析肺磨玻璃密度結節初探. 中國醫學計算機成像雜志, 2018, 24(5): 383-387.
21.	Zhang Y, Qiang JW, Ye JD, et al. High resolution CT in differentiating minimally invasive component in early lung adenocarcinoma. Lung Cancer, 2014, 84(3): 236-241.

1. 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.
2. 呂文暉, 周長圣, 李新宇, 等. 利用深度學習模型判斷基線胸部平掃CT肺結節的良惡性. 中華放射學雜志, 2019, 53(11): 957-962.
3. Suzuki K, Watanabe SI, Wakabayashi M, et al. A single-arm study of sublobar resection for ground-glass opacity dominant peripheral lung cancer. J Thorac Cardiovasc Surg, 2022, 163(1): 289-301.
4. Wan YL, Wu PW, Huang PC, et al. The use of artificial intelligence in the differentiation of malignant and benign lung nodules on computed tomograms proven by surgical pathology. Cancers (Basel), 2020, 12(8): 2211.
5. 李大勝, 王大為, 黃宇清, 等. 多原發肺癌病理結果與AI輔助CT診斷的相關性研究. 中國醫療設備, 2021, 36(2): 77-80, 95.
6. Chen L, Gu D, Chen Y, et al. An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans. Comput Med Imaging Graph, 2021, 89: 101899.
7. Ginsberg RJ, Rubinstein LV. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. Ann Thorac Surg, 1995, 60(3): 615-622.
8. 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.
9. Dembitzer FR, Flores RM, Parides MK, et al. Impact of histologic subtyping on outcome in lobar vs sublobar resections for lung cancer: A pilot study. Chest, 2014, 146(1): 175-181.
10. Bedetti B, Bertolaccini L, Rocco R, et al. Segmentectomy versus lobectomy for stageⅠ non-small cell lung cancer: A systematic review and meta-analysis. J Thorac Dis, 2017, 9(6): 1615-1623.
11. Qu X, Wang K, Zhang T, et al. Long-term outcomes of stage Ⅰ NSCLC (≤3 cm) patients following segmentectomy are equivalent to lobectomy under analogous extent of lymph node removal: A PSM based analysis. J Thorac Dis, 2017, 9(11): 4561-4573.
12. Sakurai H, Asamura H. Sublobar resection for early-stage lung cancer. Transl Lung Cancer Res, 2014, 3(3): 164-172.
13. Tane S, Nishio W, Nishioka Y, et al. Evaluation of the residual lung function after thoracoscopic segmentectomy compared with lobectomy. Ann Thorac Surg, 2019, 108(5): 1543-1550.
14. Gu Z, Wang H, Mao T, et al. Pulmonary function changes after different extent of pulmonary resection under video-assisted thoracic surgery. J Thorac Dis, 2018, 10(4): 2331-2337.
15. Chen L, Gu Z, Lin B, et al. Pulmonary function changes after thoracoscopic lobectomy versus intentional thoracoscopic segmentectomy for early-stage non-small cell lung cancer. Transl Lung Cancer Res, 2021, 10(11): 4141-4151.
16. 陳琦, 朱全新, 郁義星, 等. 肺部單發微小磨玻璃結節 (<10 mm) MSCT特征對肺腺癌病理亞型的診斷價值. 放射學實踐, 2019, 34(7): 778-783.
17. 蔡雅倩, 張正華, 韓丹, 等. AI對肺磨玻璃結節篩查及定性的臨床應用研究. 放射學實踐, 2019, 34(9): 958-962.
18. 周小君, 馬玲, 盛林麗, 等. 人工智能量化參數預測磨玻璃結節早期肺癌浸潤性的臨床初探. 實用放射學雜志, 2021, 37(3): 388-391.
19. Singh R, Kalra MK, Homayounieh F, et al. Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography. Quant Imaging Med Surg, 2021, 11(4): 1134-1143.
20. 孫炎冰, 陶廣昱, 陳群慧, 等. 人工智能CT定量分析肺磨玻璃密度結節初探. 中國醫學計算機成像雜志, 2018, 24(5): 383-387.
21. Zhang Y, Qiang JW, Ye JD, et al. High resolution CT in differentiating minimally invasive component in early lung adenocarcinoma. Lung Cancer, 2014, 84(3): 236-241.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Value of artificial intelligence quantitative parameters in predicting the infiltration of pulmonary nodules

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