Abstract
ObjectiveWe aim to investigate radiomic imaging features extracted in computed tomography (CT) images to differentiate invasive pulmonary adenocarcinomas (IPAs) from non-IPAs appearing as part-solid ground-glass nodules (GGNs), and to incorporate significant radiomic features with other clinically-assessed features to develop a diagnostic nomogram model for IPAs.MethodsThis retrospective study was performed, with Institutional Review Board approval, on 88 patients with a total of 100 part-solid nodules (56 IPAs and 44 non-IPAs) that were surgically confirmed between February 2014 and November 2016 in the First Affiliated Hospital of China Medical University. Quantitative radiomic features were computed automatically on 3D nodule volume segmented from arterial-phase contrast-enhanced CT images. A set of regular risk factors and visually-assessed qualitative CT imaging features were compared with the radiomic features using logistic regression analysis. Three diagnostic models, i.e., a basis model using the clinical factors and qualitative CT features, a radiomics model using significant radiomic features, and a nomogram model combining all significant features, were built and compared in terms of receiver operating characteristic (ROC) curves. Decision curve analysis was performed for the nomogram model to explore its potential clinical benefit.ResultsIn addition to three visually-assessed qualitative imaging features, another three quantitative features selected from hundreds of radiomic features were found to be significantly (all P<0.05) associated with IPAs. The diagnostic nomogram model showed a significantly higher performance [area under the ROC curve (AUC) =0.903] in differentiating IPAs from non-IPAs than either the basis model (AUC=0.853, P=0.0009) or the radiomics model (AUC=0.769, P<0.0001). Decision curve analysis indicates a potential benefit of using such a nomogram model in clinical diagnosis.ConclusionsQuantitative radiomic features provide additional information over clinically-assessed qualitative features for differentiating IPAs from non-IPAs appearing as GGNs, and a diagnostic nomogram model including all these significant features may be clinically useful in preoperative strategy planning.
Highlights
In the multidisciplinary classification of lung adenocarcinomas in 2011 [1], adenocarcinoma in situ and minimally invasive adenocarcinoma were considered for sublobar resection due to their good prognosis, with a very high 5-year disease-free survival (DFS)
Radiomics was introduced as the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography (CT), positron emission tomography (PET) or magnetic resonance imaging (MRI) [7]
We investigated radiomics imaging features, clinical risk factors and visually-assessed qualitative CT imaging features in differentiating invasive pulmonary adenocarcinomas (IPAs) from non-IPAs appearing as ground-glass nodules (GGNs) and developed a diagnostic nomogram model based on statistically significant features identified from these different sources of features
Summary
In the multidisciplinary classification of lung adenocarcinomas in 2011 [1], adenocarcinoma in situ and minimally invasive adenocarcinoma were considered for sublobar resection due to their good prognosis, with a very high 5-year disease-free survival (DFS). Lobectomy is still the standard surgical treatment for invasive pulmonary adenocarcinomas (IPAs) with worse prognosis (5-year DFS is 74.6%, even in stage IA). A number of studies have demonstrated correlations between computed tomography (CT) findings of part-solid ground-glass nodules (GGNs) with histopathology [4]. Recent radiomics studies on investigating pulmonary nodules have shown promising performance for histological subtyping [8], gene expression [9], malignancy prediction [10], posttreatment prognosis [11] and so on. Little work was reported on studying GGNs using radomics-based methods for outcome prediction [10]
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