Predictive value of peritumour radiomics in the diagnosis of benign and malignant pulmonary nodules with halo sign. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Tan, M.; Ma, W.; Yang, Y.; Duan, S.; Jin, L.; Wu, Y.; Li, M.
• Is Part Of: Clinical radiology. Volume 78:Issue 2(2023)
• Page Start: e52
• Page End: e62
• Publication Date: 2023-02
• 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.2022.09.130
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: To evaluate peritumour radiomics in predicting benign and malignant pulmonary nodules with halo sign. MATERIALS AND METHODS: In this retrospective study, 305 pulmonary nodules with halo sign (benign, 120; adenocarcinoma, 185) were collected. Manual segmentation was used to mark the gross tumour volume (GTV) and the peritumour volume (PTV) was established by uniform dilation (1 cm) of the tumour area in three dimensions. The GTV and PTV radiomic features were combined to produce the gross tumour and peritumour volume (GPTV). The minimum-redundancy maximum-relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) algorithm were used to eliminate redundant radiomic features. Predictive models combined with clinical features and radiomic signatures were established. Multivarible logistic regression analysis was used to establish the combined model and develop a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the model. RESULTS: In the testing cohort, the area under the ROC curve (AUC) of the GTV, PTV, and GPTV radiomic models was 0.701 (95% CI: 0.589–0.814), 0.674 (95% CI: 0.557–0.791) and 0.755 (95% CI: 0.643–0.867), respectively. The AUC of the nomogram model based on clinical and GPTV radiomic signatures was 0.804 (95% CI: 0.707–0.901). CONCLUSION: The nomogram model based on clinical and GPTV radiomic signatures can better predict benign and malignant pulmonary nodules with halo signs, demonstrating that the model has potential as a convenient and effective auxiliary diagnostic tool for radiologists. Highlights: Radiomic can help predict benign and malignant pulmonary nodule with the halo sign. The nomogram model is an effective tool for the diagnosis of pulmonary nodules. Clinical-radiomic nomogram may improve the diagnostic accuracy of pulmonary nodules.