1. Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules
- Author
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Jian Gao, Qingyi Qi, Hao Li, Zhenfan Wang, Zewen Sun, Sida Cheng, Jie Yu, Yaqi Zeng, Nan Hong, Dawei Wang, Huiyang Wang, Feng Yang, Xiao Li, and Yun Li
- Subjects
lung adenocarcinoma ,CT histogram ,part-solid nodule ,tumor invasiveness ,three-dimensional index ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
BackgroundTumor invasiveness plays a key role in determining surgical strategy and patient prognosis in clinical practice. The study aimed to explore artificial-intelligence-based computed tomography (CT) histogram indicators significantly related to the invasion status of lung adenocarcinoma appearing as part-solid nodules (PSNs), and to construct radiomics models for prediction of tumor invasiveness.MethodsWe identified surgically resected lung adenocarcinomas manifesting as PSNs in Peking University People’s Hospital from January 2014 to October 2019. Tumors were categorized as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) by comprehensive pathological assessment. The whole cohort was randomly assigned into a training (70%, n=832) and a validation cohort (30%, n=356) to establish and validate the prediction model. An artificial-intelligence-based algorithm (InferRead CT Lung) was applied to extract CT histogram parameters for each pulmonary nodule. For feature selection, multivariate regression models were built to identify factors associated with tumor invasiveness. Logistic regression classifier was used for radiomics model building. The predictive performance of the model was then evaluated by ROC and calibration curves.ResultsIn total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, multivariate logistic regression analysis demonstrated that age [odds ratio (OR), 1.020; 95% CI, 1.004–1.037; p=0.017], smoking history (OR, 1.846; 95% CI, 1.058–3.221; p=0.031), solid mean density (OR, 1.014; 95% CI, 1.004–1.024; p=0.008], solid volume (OR, 5.858; 95% CI, 1.259–27.247; p = 0.037), pleural retraction sign (OR, 3.179; 95% CI, 1.057–9.559; p = 0.039), variance (OR, 0.570; 95% CI, 0.399–0.813; p=0.002), and entropy (OR, 4.606; 95% CI, 2.750–7.717; p
- Published
- 2023
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