Predictive value of mutant p53 expression index obtained from nonenhanced computed tomography measurements for assessing invasiveness of ground-glass opacity nodules.

PURPOSE

To predict p53 expression index (p53-EI) based on measurements from computed tomography (CT) for preoperatively assessing pathologies of nodular ground-glass opacities (nGGOs).

METHODS

Information of 176 cases with nGGOs on high-resolution CT that were pathologically confirmed adenocarcinoma was collected. Diameters, total volumes (TVs), maximum (MAX), average (AVG), and standard deviation (STD) of CT attenuations within nGGOs were measured. p53-EI was evaluated through immunohistochemistry with Image-Pro Plus 6.0. A multiple linear stepwise regression model was established to calculate p53-EI prediction from CT measurements. Receiver-operating characteristic curve analysis was performed to compare the diagnostic performance of variables in differentiating preinvasive adenocarcinoma (PIA), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC).

RESULTS

Diameters, TVs, MAX, AVG, and STD showed significant differences among PIAs, MIAs, and IACs (all P-values <0.001), with only MAX being incapable to differentiate MIAs from IACs (P=0.106). The mean p53-EIs of PIAs, MIAs, and IACs were 3.4±2.0, 7.2±1.9, and 9.8±2.7, with significant intergroup differences (all P-values <0.001). An equation was established by multiple linear regression as: p53-EI prediction =0.001* TVs +0.012* AVG +0.022* STD +9.345, through which p53-EI predictions were calculated to be 4.4%±1.0%, 6.8%±1.3%, and 8.5%±1.4% for PIAs, MIAs, and IACs (Kruskal-Wallis test P<0.001; Tamhane's T2 test: PIA vs MIA P<0.001, MIA vs IAC P<0.001), respectively. Although not significant, p53-EI prediction has a little higher area under the curve (AUC) than the actual one both in differentiating MIAs from PIAs (AUC 0.938 vs 0.914, P=0.263) and in distinguishing IACs from MIAs (AUC 0.812 vs 0.786, P=0.718).

CONCLUSION

p53-EI prediction of nGGOs obtained from CT measurements allows accurately estimating lesions' pathology and invasiveness preoperatively not only from radiology but also from pathology.