A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Issue 12 (December 2019)

• Record Type: Journal Article
• Authors: Weng, Q.; Zhou, L.; Wang, H.; Hui, J.; Chen, M.; Pang, P.; Zheng, L.; Xu, M.; Wang, Z.; Ji, J.
• Is Part Of: Clinical radiology. Volume 74:Issue 12(2019)
• Page Start: 933
• Page End: 943
• Publication Date: 2019-12
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2019.07.026
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: A nomogram model was developed to predict the histological subtypes of lung invasive adenocarcinomas (IAs) and minimally invasive adenocarcinomas (MIAs) that manifest as part-solid ground-glass nodules (GGNs). MATERIALS AND METHODS: This retrospective study enrolled 119 patients with histopathologically confirmed part-solid GGNs assigned to the training ( n= 83) or testing cohorts ( n= 36). Radiomic features were extracted based on the unenhanced computed tomography (CT) images. R software was applied to process the qualitative and quantitative data. The CT features model, radiomic signature model, and combined prediction model were constructed and compared. RESULTS: A total of 396 radiomic features were extracted from the preoperative CT images, four features including MaxIntensity, RMS, ZonePercentage, and LongRunEmphasis_angle0_offset7 were indicated to be the best discriminators to establish the radiomic signature model. The performance of the model was satisfactory in both the training and testing set with areas under the curve (AUCs) of 0.854 (95% confidence interval [CI]: 0.774 to 0.934) and 0.813 (95% CI: 0.670 to 0.955), respectively. The CT morphology of the lesion shape and diameter of the solid component were confirmed to be a significant feature for building the CT features model, which had an AUC of 0.755 (95% CI: 0.648 to 0.843). A nomogram that integrated lesion shape and radiomic signature was constructed, which contributed an AUC of 0.888 (95% CI: 0.82 to 0.955). CONCLUSIONS: The radiomic signature could provide an important reference for differentiating IAs from MIAs, and could be significantly enhanced by the addition of CT morphology. The nomogram may be highly informative for making clinical decisions. Highlights: Radiomics can improve and refine the pre-operative diagnosis of part-solid nodules. Radiomic signatures combined with CT features can significantly help in differential diagnosis of mixed attenuation ground glass nodules and aid in differentiation of IAs from MIAs. The quantitative nomogram prediction model based on the radiomic score and shape could be a step towards precision medicine and provide critical information for clinical decision making and guiding further management.