Cancer

Internal medicine

Radiology

Oncology

Odds ratio

Retrospective cohort study

Perineural invasion

Lung

Lymphovascular invasion

Metastasis

Adenocarcinoma of the lung

Adenocarcinoma

Proportional hazards model

Medicine

Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.

Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma