Comprehensive analyses with radiological and biological markers of breast cancer on contrast-enhanced chest CT: a single center experience using dual-layer spectral detector CT.

OBJECTIVES

To evaluate the predictive value of virtual monoenergetic images (VMIs) by assessing tumor conspicuity on dual-layer spectral detector CT (SDCT) and correlate tumor conspicuity on VMI with prognostic biomarkers in patients with breast cancer.

METHODS

Sixty-four patients underwent arterial phase and 90-s delayed phase dual-layer SDCT. A retrospective tumor conspicuity analysis of 14 benign tumors and 65 breast cancers was performed using conventional images (CIs) and VMI at 40 keV (VMI40) on arterial and delayed phase scans (CI, VMI40, CI, VMI40). Mean Hounsfield units (HU) of tumors were measured on VMI40 and VMI40. A receiver operating characteristic (ROC) curve analysis was performed to compare diagnostic accuracy between image sets. Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 levels were evaluated using histopathology. Correlations between VMI analyses and histological characteristics of cancers were analyzed.

RESULTS

Cancers on VMI40 had a significantly higher conspicuity score and mean HU than benign tumors (p < 0.001). VMI40 showed the highest conspicuity for cancers (mean, 3.79) and the greatest area under the ROC curve (0.817; 95% confidence interval 0.745-0.889). VMI40 yielded significantly higher mean HU for cancers than VMI40 (p < 0.001). The conspicuity score and mean HU on VMI40 were significantly higher in cancers with ER negativity, PR negativity, and Ki67 positivity (p < 0.05).

CONCLUSIONS

VMI40 may be useful in the diagnosis of breast cancers due to higher tumor conspicuity and better enhancement than VMI40. VMI40 may be beneficial for the prediction of poor breast cancer prognoses.

KEY POINTS

• VMI40 improved conspicuity of breast cancer than CI. • VMI40yielded higher diagnostic performance of breast cancer than VMI40. • VMI40has an additional benefit in terms of prognosis prediction in patients with breast cancers.