Rationale and ObjectivesThis study aimed to investigate the association of clinical, imaging, and pathological-molecular characteristics with the prediction of patient prognosis with stage IA invasive lung adenocarcinoma (ILADC) after sub-lobar resection. Materials and MethodsThis study assessed 360 patients, including 91 and 269 with and without recurrence 3 years postoperativel, respectively, with stage IA ILADC undergoing preoperative chest computed tomography (CT) scans and subsequent sub-lobar resection at our institution. Their clinical and CT features and histological subtypes and gene mutation status were compared. Binary logistic regression analysis was conducted to identify the independent risk factors for recurrence. An external validation cohort included 113 patients, used to test the model’s efficiency. ResultsFor clinical features, old age, male gender, smokers, and high age-adjusted Charlson comorbidity index (ACCI) were frequently observed in patients with recurrence than those without (all p < 0.05). For CT features, large tumor size, solid-predominant density, spiculation, peripheral fibrosis, type II pleural tag, and pleural adhesion were more common in recurrent patients than non-recurrent ones (all p < 0.05). The regression model revealed old age, large tumor size, solid-predominant density, spiculation, type II pleural tag, and pleural adhesion as independent risk factors for recurrence, with an area under the curve (AUC) of 0.942. The external validation cohort obtained an AUC of 0.958. For phological-molecular features, miropapillary/solid-predominant growth pattern, KRAS, ALK, and NRAS mutation or fusion were more ccommon in the recurrent group, whereas EGFR mutation was more frequent in the non-recurrent group (all p < 0.05). ConclusionClinical and CT features help predict the prognosis of patients with stage IA ILADC after sub-lobar resection and decide for individualized treatment. Moreover, patients with different prognosis demonstrated different pathological-molecular features.
Read full abstract