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Article type: Research Article
Authors: Wei, Shu-Huaa; 1 | Zhang, Jin-Meia; 1 | Shi, Bina | Gao, Feia | Zhang, Zhao-Xuanb | Qian, Li-Tingc; *
Affiliations: [a] Department of Radiology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC West District, Hefei, China | [b] Department of Pathology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC West District, Hefei, China | [c] Department of Radiotherapy, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC West District, Hefei, China
Correspondence: [*] Corresponding author: Li-Ting Qian, Department of Radiotherapy, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC West District, 107 Huanhu East Road, Hefei, AnHui 230031, China. Tel.: +86 13966716720; Fax: +86 551 65327751; E-mail: [email protected].
Note:  These authors contributed equally to this study.
Abstract: OBJECTIVE:To investigate predictive value of CT-based radiomics features on visceral pleural invasion (VPI) in ≤3.0 cm peripheral type early non-small cell lung cancer (NSCLC). METHODS:A total of 221 NSCLC cases were collected. Among them, 115 are VPI-positive and 106 are VPI-negative. Using a stratified random sampling method, 70% cases were assigned to training dataset (n = 155) and 30% cases (n = 66) were assigned to validation dataset. First, CT findings, imaging features, clinical data and pathological findings were retrospectively analyzed, the size, location and density characteristics of nodules and lymph node status, the relationship between lesions and pleura (RAP) were assessed, and their mean CT value and the shortest distance between lesions and pleura (DLP) were measured. Next, the minimum redundancy-maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) features were extracted from the imaging features. Then, CT imaging prediction model, texture feature prediction model and joint prediction model were built using multifactorial logistic regression analysis method, and the area under the ROC curve (AUC) was applied to evaluate model performance in predicting VPI. RESULTS:Mean diameter, density, fractal relationship with pleura, and presence of lymph node metastasis were all independent predictors of VPI. When applying to the validation dataset, the CT imaging model, texture feature model, and joint prediction model yielded AUC = 0.882, 0.824 and 0.894, respectively, indicating that AUC of the joint prediction model was the highest (p < 0.05). CONCLUSION:The study demonstrates that the joint prediction model containing CT morphological features and texture features enables to predict the presence of VPI in early NSCLC preoperatively at the highest level.
Keywords: Non-small cell lung cancer (NSCLC), Visceral pleural invasion (VPI), Computed tomography (CT), radiomics, predictive models
Journal: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1115-1126, 2022
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