Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features

PurposeTo develop an MRI-based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space (STAS), and poorly differentiated patterns. Materials and MethodsAs a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18 F-fluorodeoxyglucose (FDG) positron emission tomography-CT (PET/CT) and chest CT from August 2018 to January 2020.We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT.If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images.Additionally, patient demographics, SUVmax (maximum standardized uptake value) on FDG PET/CT, and the mean ADC value on DWI, were considered together to build prediction models for high-risk pathologic features. ResultsAmong 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI.The MR-eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p<0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p<0.001),worse 4-year disease free survival (p<0.001) compared with non-MReligible group.The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets.Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC 0.860 and 0.907, respectively).