Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule. Issue 128 (July 2020)

• Record Type: Journal Article
• Authors: Feng, Bao; Chen, Xiangmeng; Chen, Yehang; Liu, Kunfeng; Li, Kunwei; Liu, Xueguo; Yao, Nan; Li, Zhi; Li, Ronggang; Zhang, Chaotong; Ji, Jianbo; Long, Wansheng
• Is Part Of: European journal of radiology. Issue 128(2020)
• Publication Date: 2020-07
• Subjects: Tuberculosis granuloma -- lung adenocarcinoma -- solitary pulmonary solid nodule radiomics; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.109022
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Radiomics nomogram was used to preoperatively differentiate the TBG and LAC in patients with SPSN. Deep learning-based VOI segmentation and quantitative 3D radiomics features were extracted and analyzed. Radiomics nomogram achieved superior performance than either the radiomics signature or the clinical model alone. Abstract: Purpose: To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). Method: We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features. We used the least absolute shrinkage and selection operator (LASSO) logistic regression to build a radiomics signature. A clinical model was developed with clinical factors, including age, gender, and CT-based subjective findings (eg, lesion size, lesion location, lesion margin, lobulated sharp, and spiculation sign). We constructed individualized radiomics nomograms incorporating the radiomics signature and clinical factors to validate the diagnostic ability. Results: Three factors – radiomics signature, age, and spiculation sign – were found to be independent predictors and were used to build the radiomics nomogram, which showed better diagnostic accuracy than any single model (all net reclassification improvement p < 0.05). The area under curve yielded was 0.9660 (95% confidence interval [CI], 0.9390–0.9931), 0.9342 (95% CI, 0.8944–0.9739), and 0.9064 (95% CI, 0.8639–0.9490) for the training, internal validation, and external validation cohorts, respectively. Decision curve analysis (DCA) and stratification analysis showed the nomogram has potential for generalizability. Conclusion: The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.