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Nomogram based on radiomics and CT features for predicting visceral pleural invasion of invasive adenocarcinoma ≤ 2
Rikun Huang1, Chunli Zhao2, Jinhan Yang1
1Guiping People's Hospital, Guiping, China.
Objective:
To explore the value of a nomogram based on radiomics and computed tomography (CT) features for preoperative prediction of visceral pleural invasion (VPI) of subpleural, small (≤2 cm) invasive adenocarcinoma (IAC) of the lung.
Methods:
For this retrospective study, 457 cases of invasive lung adenocarcinoma ≤ 2 cm were collected from three tertiary hospitals in Guangxi and used in a training group (n = 254), validation group (n = 112), and test group (n = 91). Risk factors for IAC VPI were screened by univariate and multivariate logistic regression analyses, and a CT model was constructed. Radiomics features of regions representing the gross tumor area (GTA), peritumor area (PTA), and gross peritumor area (GPTA) were extracted from CT images, and the optimal feature subsets based on radiomics score were selected to construct three radiomics models. A combination model was then constructed from the radiomics model with the optimal radiomics score and the CT model and visualized by nomogram. Model performance was analyzed by receiver operating characteristic curve analysis and DeLong test.
Results:
Pleural indentation (P < 0.05), pleural thickening (P < 1e-04), and tumor diameter (P < 0.001) were identified as risk factors of the CT model for predicting VPI of IAC. Among 1226 radiomics features, 5, 13, and 12 optimal features were selected for the GTA, PTA, and GPTA models, respectively, and the area under the curve (AUC) values did not differ among these models. Based on AUC values, the CT model and GPTA model features were combined to construct the predictive nomogram. Compared with the individual models, the nomogram exhibited better accuracy, specificity, and AUC values (AUC values for training, verification, and test groups were 0.86, 0.84, and 0.86, respectively). Calibration curve and decision curve analyses showed that the nomogram outperformed traditional CT features and radiomics studies, and could offer greater clinical benefit.
Conclusions:
The developed nomogram combining CT and radiomics features shows high diagnostic value for VPI prediction of IAC of the lung.
