Stage I lung adenocarcinoma: the value of quantitative CT in differentiating pathological subtypes and predicting growth of subsolid nodules

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The aim of this study was to investigate feasibility of quantitative computed tomography (CT) measurements in predicting invasiveness and growth of nodular ground glass opacities (nGGOs).

A set of 203 patients (group A) with nGGOs that were confirmed stage-I adenocarcinomas and 79 patients (group B) with nGGOs that were completely followed up were included. Lesions diameters, volume (VOL), maximum (MAX), mean (MEN), and standard deviation (STD) of CT attenuation were measured. P53 labeling index (LI) was evaluated through immunohistochemistry in group-A patients. Multivariate linear stepwise regressions were performed based on group-A lesions to calculate P53-LI prediction from CT measurements. The receiver operating characteristic (ROC) curve analyses were performed to assess the performance of P53-LI prediction in predicting invasiveness and growth of nGGOs. The Cox regression analysis was conducted to identify correlation between P53-LI Prediction and volume doubling time (VDT) of lesions in group B.

Diameter, VOL, MEN, STD, and the P53 LI showed significant differences between lesions of different pathological invasiveness (P < .01). By multivariate linear regressions, MEN and STD were identified as independent variables indicating P53 LI (P < .001); thus, an equation was established to calculate P53-LI Prediction as: P53LI Prediction = 0.013 ×  MEN + 0.024 × STD + 9.741 (R square = 0.411, P < .001). The P53-LI Prediction showed good performance, similar as the actual one, in differentiating pathological invasiveness of nGGOs. In addition, the P53-LI Prediction demonstrated excellent performance in predicting growth of nGGOs (AUC = 0.833, P < .001) and independently forecasted VDT of nGGOs (β = 1.773, P < .001).

The P53-LI Prediction that was calculated from preoperative quantitative CT measurements of nGGOs indicates lesions’ invasiveness and allows for predicting growth of nGGOs.

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