A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules - Chen, Wufei; Wang, Ruizhi; Ma, Zhuangxuan; Hua, Yanqing; Mao, Dingbiao; Wu, Hao; Yang, Yuling; Li, Cheng; Li, Ming (Frontiers in Oncology, 2022)
Purpose: To explore the value of delta-radiomics(DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSN) . Methods: A total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analysed from January 2017 to December 2021. All patients underwent diagnostic non-contrast enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) algorithm based on the training cohort. The conventional radiomics model based on NCECT were also constructed following the same process for comparison purpose. The prediction performance was assessed using area under the ROC curve (AUC). To provide an easy-to-use tool, A radiomics-based Integrated nomogram was constructed and evaluated by integrated discrimination increment (IDI), calibration curves, decision curve analysis (DCA) and clinical impact plot. Results: The DelRADx signature, which consisted nine robust selected features, showed significant differences between the AIS/MIA group and IAC group (P < 0.05) in both training and validation sets. The DelRADx signature showed significantly higher AUC(0.902) compared to the conventional radiomics model based on NCECT(AUC=0.856) in the validation set. The IDI was significant at 0.0769 for the integrated nomogram compared with DelRADx signature. the calibration curve of the integrated nomogram demonstrated favorable agreement both in the training set and validation set with a mean absolute error of 0.001 and 0.019 , respectively. Decision curve analysis and clinical impact plot indicated that if the threshold probability was within 90%, the integrated nomogram showed a high clinical application value. Conclusion: The DelRADx method has the potential to assist doctors in predicting the invasiveness for patients with PSNs. The integrated nomogram incorporating the DelRADx signature with the radiographic features could facilitate the performance and serve as an alternative way for determining management.