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Preliminary exploration of the correlation between spectral computed tomography quantitative parameters and spread
Hongzheng Song1, Shiyu Cui1, Liang Zhang1
1Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Background:
The invasive pattern called spread through air spaces (STAS) is linked to an unfavorable prognosis in patients with lung adenocarcinoma (LUAD). Using computed tomography (CT) signs alone to assess STAS is subjective and lacks quantitative evaluation, whereas spectral CT can provide quantitative analysis of tumors. The aim of this study was to investigate the association between spectral CT quantitative parameters and STAS in LUAD.
Methods:
We retrospectively collected consecutive patients with LUAD who underwent surgical resection and preoperative spectral CT scan at our institution. The quantitative parameters included CT values at 40, 70, and 100 keV [CT40keVa/v, CT70keVa/v, and CT100keVa/v (a: arterial; v: venous)]; iodine concentration (ICa/ICv); normalized iodine concentration (NICa/NICv); and slope λHU of the spectral curve (λHUa/λHUv). Clinical and CT features of the patients were also collected. Statistical analysis was performed to identify the quantitative parameters, clinical and CT features that were significantly correlated with STAS status. We evaluated the diagnostic performance of significant factors or models which combined quantitative parameters and CT features, using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
Results:
We enrolled a total of 47 patients, with 32 positive and 15 negative for STAS. The results revealed that CT100keVa (P=0.002), CT100keVv (P=0.007), pathologic stage (P=0.040), tumor density (P<0.001), spiculation (P=0.003), maximum solid component diameter (P=0.008), and the consolidation/tumor ratio (CTR) (P=0.001) were significantly correlated with STAS status. The tumor density demonstrated a superior diagnostic capability [AUC =0.824, 95% confidence interval (CI): 0.709-0.939, sensitivity =59.4%, specificity =100.0%] compared to other variables. CT100keVa exhibited the best diagnostic performance (AUC =0.779, 95% CI: 0.633-0.925, sensitivity =78.1%, specificity =80.0%) among the quantitative parameters. Combination models were then constructed by combining the quantitative parameters with CT features. The total combined model showed the highest diagnostic efficiency (AUC =0.952, 95% CI: 0.894-1.000, sensitivity =90.6%, specificity =86.7%).
Conclusions:
Spectral CT quantitative parameters CT100keVa and CT100keVv may be potentially useful parameters in distinguishing the STAS status in LUAD.
