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.
Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods A retrospective analysis was conducted on clinical data of 261 patients with IAC treated at Yantaishan Hospital from October 2018 to May 2023. Among them, there were 101 males and 160 females, aged 27 to 88 years at a mean age of (61.96±9.17) years. Six patients had dual primary lesions, with each lesion analyzed as an independent sample. According to the 2021 WHO classification criteria for lung adenocarcinoma, 267 IACs were classified as gradeⅠ(48 patients), grade Ⅱ (89 patients), and grade Ⅲ (130 patients). Differences in parameters among groups were compared, and logistic regression analysis was used to evaluate the predictive value of AI quantitative parameters for grade Ⅲ IAC. LASSO regression analysis was employed to select parameters with non-zero coefficients, and three machine learning models were constructed and internally verified based on the joint parameters to predict grade Ⅲ IAC efficacy, which were visualized by the Nomogram. Results(1) There were statistical differences between the two groups in parameters such as solid component proportion, 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 (P<0.05). (2) Comparison between two groups: gradeⅠand gradeⅡwere combined for single-factor analysis against grade Ⅲ, indicating differences in all variables except age (P<0.05). Multi-factor analysis identified CTR and CT standard deviation as independent risk factors for distinguishing grade Ⅲ IAC, with a negative correlation between them. (3) Pathological comparisons: gradeⅠhad no lymph node metastasis, gradeⅡhad 2 patients of lymph node metastasis with micro-papillary components, and Grade Ⅲ had 19 patients 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 Ⅱ. (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. Conclusions Cautious consideration should be given to grade Ⅲ IAC when CTR is more 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.