AI-based CT quantitative analysis was used for efficacy analysis in the evaluation of pulmonary nodules and degree of infiltration

doi:10.3877/cma.j.issn.1674-6902.2025.03.013

Abstract

Abstract:

Objective

To evaluate the value of AI-based CT quantitative analysis on pulmonary nodules.

Methods

A total of 182 patients with lung cancer confirmed by surgery and pathology in Xinqiao Hospital from September 2021 to May 2022 were retrospectively analyzed，including 63 cases of invasive adenocarcinoma （MIC），104 cases of minimally invasive adenocarcinoma （MIA） and 15 cases of carcinoma in situ （AIS）. All patients underwent CT quantitative analysis of AI. The diagnostic efficacy of pathology and AI-assisted CT examination for the nature and infiltration degree of pulmonary nodules was compared. At the same time，the AI analysis parameters of solid pulmonary nodules and ground glass pulmonary nodules and different infiltration degree of pulmonary nodules were compared.

Results

Surgical pathology showed that 95 cases were solid nodules，87 were ground glass nodules；15 cases were in situ pulmonary nodules，106 were microinvasive pulmonary nodules and 63 were invasive pulmonary nodules. The sensitivity，specificity，accuracy，positive predictive value and negative predictive value of the gold standard，No statistical difference （P＞0.05）；Comparison of CT parameters between solid nodules and ground glass nodules showed that：The mass of solid nodules was （448.34±60.05） g，the 3D long diameter was （16.88±0.80） mm，the average length and short diameter was （12.99±0.63）mm，the average CT value was （-453.29±15.17）Hu，and the CT variance was（107 460.12±17 115.26） Hu. The maximum value of CT was （401.03±32.37） Hu，and the entropy value was（9.09±0.88），which were higher than those of ground glass nodules，and there were differences between the groups （P＜0.05）. The CT parameters of lung nodules with different degrees of invasion showed that the weight and volume of lung nodules in situ were （238.04±163.23） g and （716.66±479.08） cm³，which were higher than those of invasive carcinoma，but lower than those of minimally invasive carcinoma，and the 3D long diameter and CT average were lower than those of minimally invasive carcinoma and invasive carcinoma （P＜0.05）. Compared with carcinoma in situ，microinvasive carcinoma had a higher mean length of meridian and invasive carcinoma had a lower mean length of meridian （P ＜0.05）.

Conclusion

The AI-assisted CT examination can accurately diagnose the nature and infiltration degree of pulmonary nodules，and the quantitative analysis of relevant parameters can provide a basis for disease evaluation.

Key words: Artificial intelligence, Quantitative analysis of CT, Pulmonary nodules, Nature, Degree of infiltration, Iconography

Yanyun Zhang, Qizhi Bai, Jianwei Zhang, Dazhi Guo, Dan Ma, Mingfu Gong. AI-based CT quantitative analysis was used for efficacy analysis in the evaluation of pulmonary nodules and degree of infiltration[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2025, 18(03): 411-415.

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References 30

1	刘少博，黄波（指导）. 基于生物信息学方法识别肺腺癌预后相关基因及预后风险模型的构建［J］. 中国免疫学杂志，2021，37（23）：2880-2892.
2	马国玉，熊庆，蒋国庆，等. 基于生物信息学方法识别肺腺癌预后相关基因［J］. 昆明医科大学学报，2020，41（7）：30-37.
3	毛旭，刘刚，黄山. 能谱CT 定量在肺腺癌与鳞癌鉴别诊断中的价值［J］. 中国CT 和MRI 杂志，2023，21（6）：56-57，66.
4	朱北川，史芸芳，李丽香. 高分辨率CT 靶扫描在早期肺腺癌诊断中的应用价值［J］. 影像研究与医学应用，2020，4（22）：70-72.
5	老桂红，李萌. 薄壁空洞型肺结核和肺癌CT 诊断与鉴别诊断［J］. 中国CT 和MRI 杂志，2023，21（11）：60-63.
6	顾吾立，周卫军，王海亮，等. 原发性囊腔型肺癌的CT 诊断13例诊断分析［J］. 临床放射学杂志，2020，39（1）：62-65.
7	Jungblut L，Euler A，Landsmann A，et al.Pulmonary nodule visualization and evaluation of AI-based detection at various ultra-low-dose levels using photon-counting detector CT［J］. Acta Radiol，2024，65（10）：1238-1245.
8	Jungblut L，Blüthgen C，Polacin M，et al. First performance evaluation of an artificial intelligence-based computer-aided detection system for pulmonary nodule evaluation in dual-source photon-counting detector CT at different low-dose levels［J］. Invest Radiol，2022，57（2）：108-114.
9	梅举. 肺部结节诊疗手册［M］. 上海：第二军医大学出版社，2016：11-13.
10	章歌雅，李传传，王欢，等. 不同大小恶性实性肺结节CT 影像学特征及变化规律［J］. 中华航海医学与高气压医学杂志，2022，29（2）：211-215.
11	周新军，符伟平. 肺磨玻璃样结节的高分辨率CT 诊断分析［J］.影像研究与医学应用，2022，6（5）：65-67.
12	龙浩，张力. 现代肺癌诊断与治疗：临床实践与临床研究［M］. 广州：广东科技出版社，2020：44-45.
13	王俊，曾庆华，李永红，等. 高分辨CT 对肺部结节病患者原位癌和浸润性癌的判断价值［J］. 中国CT 和MRI 杂志，2021，19（8）：63-65.
14	赵文，钱伟军，李立，等. 浸润腺癌和微浸润腺癌的CT 征象鉴别［J］. 临床肺科杂志，2023，28（4）：517-522.
15	张力，肖丹丹. 胸部CT 双影像组学模型评估肺结节良恶性及浸润性［J］. 中国医学影像学杂志，2021，29（5）：514-518.
16	唐慧，时宏，刘婷，等. ASIR-V 重建算法在胸部低剂量CT 诊断肺结节中的临床价值［J］. 现代肿瘤医学，2021，29（13）：2329-2333.
17	何健. 螺旋CT 诊断肺结节的价值研究［J］. 中国实用医药，2023，18（12）：85-87.
18	雷哲锋，王黄震. 人工智能阅片技术在老年早期肺癌辅助诊断中的应用价值［J］. 医学临床研究，2022，39（10）：1590-1592.
19	杨锋，樊军，田周俊逸，等. 人群肺亚实性结节CT 筛查及人工智能应用研究初探［J］. 中华胸心血管外科杂志，2020，36（3）：145-150.
20	袁飞，钟临锋，陈兴，等. 肺结节人工智能评估危险度辅助人工阅片的应用价值［J］. 分子影像学杂志，2024，47（2）：132-137.
21	赵超. 人工智能识别软件辅助阅片在胸部CT 肺结节检出中的应用价值［J］. 医疗装备，2020，33（22）：23-24.
22	李甜，李晓东，刘敬禹. 人工智能辅助诊断肺结节的临床价值研究［J］. 中国全科医学，2020，23（7）：828-831，836.
23	马宁强，赵子光，樊玮，等. 人工智能与人工阅片不同联合方法在肺结节CT 筛查中的比较［J］. 实用放射学杂志，2020，36（8）：1317-1321.
24	王前，何金戈，李玉红，等. 人工智能自动阅片技术用于HIV/AIDS 人群结核病主动筛查效果的研究［J］. 中国防痨杂志，2021，43（6）：557-561.
25	张正华，蔡雅倩，韩丹，等. 基于深度学习的肺结节筛检和定性诊断分析［J］. 肿瘤防治研究，2020，47（4）：283-287.
26	邓琦，潘爱珍，徐志锋，等. 基于AI 技术CT 直方图参数模型预测微小磨玻璃结节样肺腺癌浸润性［J］. 放射学实践，2022，37（8）：977-981.
27	Diao K，Chen Y，Liu Y，et al. Diagnostic study on clinical feasibility of an AI-based diagnostic system as a second reader on mobile CT images：a preliminary result［J］. Ann Transl Med，2022，10（12）：668.
28	汪芳，杨利莉，许菲菲，等. 人工智能辅助阅片模式对血管粘连型肺结节在低剂量胸部CT 检出效能的影响［J］. 实用放射学杂志，2022，38（2）：213-216，231.
29	刘红敏，何艳枚，钱斌燕，等. 人工智能阅片对早期肺癌的诊断效果［J］. 深圳中西医结合杂志，2023，33（6）：49-51.
30	耿然. 基于深度学习的人工智能影像辅助诊断系统对肺结节的诊断效能评价［J］. 新乡医学院学报，2022，39（11）：1031-1035.

结节性质	例数	质量 (g)	体积 (cm³)	3D 长径 (mm)	长短径平均值 (mm)	CT 平均值 (Hu)
实性结节	95	448.34±60.05	2303.81±1132.84	16.88±0.80	12.99±0.63	-453.29±15.17
磨玻璃结节	87	245.95±41.97	847.24±151.90	12.77±0.62	10.00±0.52	-667.03±9.85
t 值		2.716	1.221	4.015	3.612	11.585
P 值		0.007	0.224	0.001	0.001	0.001
结节性质	例数	CT 方差 (Hu)	CT 最大值 (Hu)	CT 最小值 (Hu)	熵	偏度	紧凑度
实性结节	95	107460.12±17115.26	401.03±32.37	-1006.04±9.67	9.09±0.88	0.33±0.05	0.63±0.02
磨玻璃结节	87	33790.89±2983.35	95.43±43.50	-991.83±7.36	8.34±1.13	0.79±0.07	0.63±0.18
t 值		4.067	5.697	-1.154	5.056	-5.127	-0.284
P 值		0.001	0.001	0.250	0.001	0.001	0.777

结节性质	例数	质量 (g)	体积 (cm³)	3D 长径 (mm)	长短径平均值 (mm)	CT 平均值 (Hu)
实性结节	95	448.34±60.05	2303.81±1132.84	16.88±0.80	12.99±0.63	-453.29±15.17
磨玻璃结节	87	245.95±41.97	847.24±151.90	12.77±0.62	10.00±0.52	-667.03±9.85
t 值		2.716	1.221	4.015	3.612	11.585
P 值		0.007	0.224	0.001	0.001	0.001
结节性质	例数	CT 方差 (Hu)	CT 最大值 (Hu)	CT 最小值 (Hu)	熵	偏度	紧凑度
实性结节	95	107460.12±17115.26	401.03±32.37	-1006.04±9.67	9.09±0.88	0.33±0.05	0.63±0.02
磨玻璃结节	87	33790.89±2983.35	95.43±43.50	-991.83±7.36	8.34±1.13	0.79±0.07	0.63±0.18
t 值		4.067	5.697	-1.154	5.056	-5.127	-0.284
P 值		0.001	0.001	0.250	0.001	0.001	0.777

浸润程度	例数	质量 (g)	体积 (cm³)	3D 长径 (mm)	长短径平均值 (mm)	CT 平均值 (Hu)
原位	15	238.04±163.23	716.66±479.08	10.76±1.82	8.20±1.31	-652.15±28.63
微浸润	104	153.16±21.20	508.81±82.48	11.49±0.45	8.81±0.36	-604.14±11.55
浸润	63	706.19±79.39	3633.42±1694.24	21.57±0.76	0.69±0.02	-452.09±23.72
F 值		31.200	3.127	75.184	137.171	24.979
P 值		0.001	0.046	0.001	0.001	0.001
浸润程度	例数	CT 方差 (Hu)	CT 最大值 (Hu)	CT 最小值 (Hu)	熵	偏度	紧凑度
原位	15	32002.41±6946.55	-44.80±98.21	-996.13±15.73	8.12±0.25	0.62±0.15	0.59±0.03
微浸润	104	54039.74±9097.67	148.07±33.31	-993.32±7.31	8.40±0.10	0.63±0.05	0.60±0.18
浸润	63	111978.73±21906.51	502.74±432.65	-1009.78±12.55	9.42±0.09	0.39±0.10	0.69±0.24
F 值		5.094	26.840	0.780	27.137	2.662	5.619
P 值		0.007	0.001	0.460	0.001	0.073	0.004

浸润程度	例数	质量 (g)	体积 (cm³)	3D 长径 (mm)	长短径平均值 (mm)	CT 平均值 (Hu)
原位	15	238.04±163.23	716.66±479.08	10.76±1.82	8.20±1.31	-652.15±28.63
微浸润	104	153.16±21.20	508.81±82.48	11.49±0.45	8.81±0.36	-604.14±11.55
浸润	63	706.19±79.39	3633.42±1694.24	21.57±0.76	0.69±0.02	-452.09±23.72
F 值		31.200	3.127	75.184	137.171	24.979
P 值		0.001	0.046	0.001	0.001	0.001
浸润程度	例数	CT 方差 (Hu)	CT 最大值 (Hu)	CT 最小值 (Hu)	熵	偏度	紧凑度
原位	15	32002.41±6946.55	-44.80±98.21	-996.13±15.73	8.12±0.25	0.62±0.15	0.59±0.03
微浸润	104	54039.74±9097.67	148.07±33.31	-993.32±7.31	8.40±0.10	0.63±0.05	0.60±0.18
浸润	63	111978.73±21906.51	502.74±432.65	-1009.78±12.55	9.42±0.09	0.39±0.10	0.69±0.24
F 值		5.094	26.840	0.780	27.137	2.662	5.619
P 值		0.007	0.001	0.460	0.001	0.073	0.004

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