AUTHOR=Gao Jian , Qi Qingyi , Li Hao , Wang Zhenfan , Sun Zewen , Cheng Sida , Yu Jie , Zeng Yaqi , Hong Nan , Wang Dawei , Wang Huiyang , Yang Feng , Li Xiao , Li Yun 

TITLE=Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1096453

DOI=10.3389/fonc.2023.1096453

ISSN=2234-943X

ABSTRACT=Background: Tumor 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.

Methods: We 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.

Results: In total, 299 AIS/MIAs and 889 IACs were included. In the training cohort, ,multivariate logistic regression analysis demonstrated that age (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<0.001) were independent predictor for IAC. The AUCs in the training cohort and validation cohort indicated a better discriminative ability of histogram model (AUC=0.892) compared with the clinical model (AUC=0.852) and integrated model (AUC=0.886).

Conclusion: We developed an AI-based histogram model which could reliably predict tumor invasiveness in lung adenocarcinoma manifesting as PSNs. This finding would provide promising value in guiding the precision management of PSNs in the daily practice.