Development and validation of a clinical decision tool for preoperative micropapillary and solid pattern lung adenocarcinoma of CT≤2cm.

Abstract

Micropapillary (MP) and solid(S) pattern adenocarcinoma are highly malignant subtypes of lung adenocarcinoma. In today's era of increasingly conservative surgery for small lung cancer, effective preoperative identification of these subtypes is greatly important for surgical planning and long term survival of patients. For this retrospective study, the presence of MP and/or S was evaluated in 2167 consecutive patients who underwent surgical resection for clinical stage IA1-2 lung adenocarcinoma. MP and/or S pattern-positive patients and negative-pattern patients were matched at a ratio of 1:3. The Lasso regression model was used for data dimension reduction and imaging signature building. Multivariate logistic regression was used to establish the predictive model, presented as an imaging nomogram. The performance of the nomogram was assessed based on calibration, identification, and clinical usefulness, and internal and external validation of the model was conducted. The proportion of solid components (PSC), Sphericity, entropy, Shape, bronchial honeycomb, nodule shape, sex, and smoking were independent factors in the prediction model of MP and/or S lung adenocarcinoma. The model showed good discrimination with an area under the ROC curve of 0.85. DCA demonstrated that the model could achieve good benefits for patients. RCS analysis suggested a significant increase in the proportion of MP and/or S from 11% to 48% when the PSC value was 68%. Small MP and/or S adenocarcinoma can be effectively identified preoperatively by their typical 3D and 2D imaging features.