Predicting malignant probability of subsolid nodules with artificial intelligence-assisted pulmonary nodule diagnosis system
10.13929/j.issn.1003-3289.2020.04.013
- VernacularTitle: 人工智能肺结节辅助诊断系统预测亚实性肺结节恶性概率
- Author:
Jianghong CHEN
1
Author Information
1. Department of Radiology, Beijing Friendship Hospital, Capital Medical University
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Diagnosis;
Lung neoplasms;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2020;36(4):535-539
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To evaluate the efficacy of artificial intelligence (AI)-assisted pulmonary nodule diagnosis system in predicting the malignant probability of pulmonary subsolid nodule (SN). Methods: Pulmonary SN from 86 patients who underwent surgical resection for pulmonary space-occupying lesions were enrolled and divided into 3 groups according to post operation pathological results, i.e. preinvasive lesions (including atypical adenomatous hyperplasia[AAH]and adenocarcinoma in situ [AIS]) in group 1, microinvasive adenocarcinoma in group 2 and invasive adenocarcinoma in group 3, respectively. Preoperative chest CT data were imported into AI pulmonary nodule diagnosis system to measure CT value and volume, also malignant probability prediction value of each SN. The differences of volume, CT value and malignant probability of SN based on plain and enhanced CT were compared among 3 groups, while the volume, CT value and malignant probability of SN were compared between plain CT and enhanced CT in each group, respectively. The correlations of the predicted malignant probability of all SN according to 3 phase CT images and nodule density and volume were analyzed, respectively. Results: A total of 88 SN were enrolled, including 27 in group 1, 28 in group 2 and 33 in group 3. The sensitivity of all SN detected by AI system was 100% (88/88). The malignant probability of SN based on plain CT, arterial phase and delayed phase of enhanced CT was (85.18 [56.64, 92.08])%, (67.15 [58.99,90.30])% and (89.82 [56.64, 92.23])% in group 1, (93.10 [85.72, 95.75])%, (89.61 [74.44,95.35])% and (92.21 [86.74, 95.59])% in group 2, (97.05 [92.81, 98.74])%, (96.89 [90.40, 98.60])% and (96.49 [89.89, 98.69])% in group 3, respectively. Statistical differences of nodule volume, CT value and the malignant probability of 3 phases CT images were found among 3 groups (all P<0.01), while no statistically difference of malignant probability of SN between plain and enhanced CT was found in any group (all P>0.05). The nodule CT values of arterial phase and delayed phase in each group were significantly higher than that of plain CT (all P<0.01).The predicted malignant probabilities according to plain CT, arterial phase and delayed phase enhanced CT were all positively correlated with CT value and volume of SN (all P<0.01). Conclusion: The deep learning-based AI-assisted pulmonary nodule diagnosis system can assist in evaluation on the invasiveness of SN of pulmonary adenocarcinoma based on plain CT data, while enhanced CT has no significant effect on the efficiency of predicting malignant probability.
