The role of low keV virtual monochromatic imaging in increasing the conspicuity of primary breast cancer in dual-energy spectral thoracic CT examination for staging purposes

Assignment of the biological value of solid breast masses based on quantitative evaluations of spectral CT examinations using electron density mapping, Zeffective mapping and iodine mapping

OBJECTIVE

We aimed to asses, in a clinical setting, whether the newly available quantitative evaluation of electron density (ED) in spectral CT examinations of the breast provide information on the biological identity of solid breast masses and whether ED maps yield added value to the diagnostic information of iodine maps and Zeff maps calculated from the same CT image datasets.

METHODS

All patients at the University Breast Cancer Center who underwent a clinically indicated Dual Layer Computed Tomography (DLCT) examination for staging of invasive breast cancer from 2018 to 2020 were prospectively included. Iodine concentration maps, Zeff maps and ED maps were automatically reconstructed from the DLCT datasets. Region of interest (ROI) based evaluations in the breast target lesions and in the aorta were performed semi-automatically in identical anatomical positions using dedicated evaluation software. Case-by-case evaluations were carried independently by 2 of 4 radiologists for each examination, respectively. Statistical analysis derived from the ROIs was done by calculating ROC/AUC curves and Youden indices.

RESULTS

The evaluations comprised 166 DLCT examinations. In the ED maps the measurements in the breast target lesions yielded Youden cutpoints of 104.0% (reader 1) and 103.8% (reader 2) resulting in AUCs of 0.63 and 0.67 at the empirical cutpoints. The variables "Zeff" and "iodine content" derived from the target lesions showed superior diagnostical results, with a Youden cutpoint of 8.0 mg/ml in the iodine maps and cutpoints of 1.1/1.2 in the Zeff maps the AUCs ranging from 0.84 to 0.85 (p = 0.023 to <0.000). The computational combination of Zeff and ED measurements in the target lesions yielded a slight AUC increase (readers 1: 0.85-0.87; readers 2: 0.84-0.94). The ratios of the measured values in the target lesions normalized to the values measured in the aorta showed comparable results. The AUCs of ED derived from the cutpoints showed inferior results to those derived from the Zeff maps and iodine maps (ED: 0.64 and 0.66 for reader 1 and 2; Zeff: 0.86 for both readers; iodine content: 0.89 and 0.86 for reader 1 and 2, respectively). The computational combination of the ED results and the Zeff measurements did not lead to a clinically relevant diagnostic gain with AUCs ranging from 0.86 to 0.88.

CONCLUSIONS

Quantitative assessments of Zeff, iodine content and ED all targeting the physical and chemical aspects of iodine uptake in solid breast masses confirmed diagnostically robust cutpoints for the differentiation of benign and malignant findings (Zeff < 7.7, iodine content of <0.8 mg/ml). The evaluations of the ED did not indicate any added diagnostic value beyond the quantitative assessments of Zeff and iodine content. Further research is warranted to develop suitable clinical indications for the use of ED maps.

Correlation Between Quantitative Parameters Obtained by Dual Energy Spectral CT and Prognostic Histopathological Factors and Biomarkers in Breast Cancer

RATIONALE AND OBJECTIVES

To investigate the correlation between quantitative parameters obtained by dual energy spectral computed tomography (DESCT) and various histopathological factors and biomarkers associated with the prognosis of breast cancer.

MATERIALS AND METHODS

Quantitative parameters such as iodine content (IC), normalized IC (nIC), iodine enhancement (IE) and normalized IE (nIE) were measured on virtual monochromatic images and iodine mapping images obtained from DESCT in 116 female breast cancer patients. The relationship between these parameters and prognostic biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status and Ki67 levels, as well as the correlation with histological grade (HG), lymphovascular invasion (LVI), and metastatic axillary lymphadenopathy (LAP) were evaluated.

RESULTS

ER-negative tumors had significantly higher values of IC, nIC, IE, and nIE compared to ER-positive tumors. PR-negative tumors had significantly higher values of IE and nIEc compared to PR-positive tumors. HER2 overexpressed and Ki-67 high proliferation tumors showed significantly higher values of all quantitative parameters compared to HER2 negative and Ki-67 low proliferation tumors. All quantitative parameters were significantly higher in HG 3 tumors, tumors with detected LVI, and tumors with metastatic axillary LAP compared to low-grade tumors, LVI-negative tumors and tumors without metastatic axillary lymph nodes, respectively.

CONCLUSION

Quantitative parameters of IC and IE obtained from DESCT have shown potential for predicting prognosis in breast cancer patients. Higher values of these parameters have been found to correlate with poor prognostic biomarkers and histopathological features. These results suggest that quantitative DESCT imaging may offer an additional benefit in the noninvasive prediction of breast cancer prognosis.

Thoracic Diseases: Technique and Applications of Dual-Energy CT

Dual-energy computed tomography (DECT) is one of the most promising technological innovations made in the field of imaging in recent years. Thanks to its ability to provide quantitative and reproducible data, and to improve radiologists' confidence, especially in the less experienced, its applications are increasing in number and variety. In thoracic diseases, DECT is able to provide well-known benefits, although many recent articles have sought to investigate new perspectives. This narrative review aims to provide the reader with an overview of the applications and advantages of DECT in thoracic diseases, focusing on the most recent innovations. The research process was conducted on the databases of Pubmed and Cochrane. The article is organized according to the anatomical district: the review will focus on pleural, lung parenchymal, breast, mediastinal, lymph nodes, vascular and skeletal applications of DECT. In conclusion, considering the new potential applications and the evidence reported in the latest papers, DECT is progressively entering the daily practice of radiologists, and by reading this simple narrative review, every radiologist will know the state of the art of DECT in thoracic diseases.

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

BACKGROUND

Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with early-stage breast cancer.

METHODS

Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test. Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.

RESULTS

This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).

CONCLUSIONS

Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

Correlation of CT-data derived from multiparametric dual-layer CT-maps with immunohistochemical biomarkers in invasive breast carcinomas

OBJECTIVE

To examine the correlation of quantitative measurements from material decomposition maps calculated from dual-layer CT (DLCT)-image datasets with immunohistochemical biomarkers of invasive breast carcinomas.

MATERIAL AND METHODS

All patients at the University Breast Cancer Center who underwent a clinically indicated dual-layer CT-scan for staging of invasive ductal breast carcinoma from 01/2016 to 07/2020 were prospectively included. Iodine concentration maps and maps of the effective atomic numbers (Zeffective) were reconstructed from the image datasets. ROI-based evaluations of the index tumors and predefined references tissues for normalization were performed semi-automatically in identical anatomical positions using dedicated evaluation software. Statistical analysis was essentially descriptive using Spearmańs rank correlation and (multivariable) partial correlation.

RESULTS

Bivariate showed statistically significant correlations of iodine contents (r = -0.154/-0.202/0.180, p = 0.039/0.006/0.015), and Zeffective-values (r = -0.158/-0.199/0.179, p = 0.034/0.007/0.016) for all 184 carcinomas and the subgroup of 168 invasive ductal carcinomas. The results were confirmed by multivariate analyses with "age", "diameter" and "ACR-grade" as possible confounders. Normalization of the measured target values with those in the aorta confirmed significant correlations of iodine content and Zeffective compared to Estrogen (r = 0.174, p = 0.019), Progesteron (r = 0.168/0.177, p = 0.024/0.017), and HER2 receptor expression (r = -0.222/-0.184, p = 0.003/0.013). All CT-parameters showed significant correlations with immunohistochemical subtyping (r = 0.191/0.192, p = 0.010).

CONCLUSIONS

Our preliminary results indicate that iodine content and Zeffective-values derived from DLCT-examinations correlate with hormone receptor expression in invasive breast carcinomas. Assignments to benign entities already seam feasible in clinical routine CT-diagnostics. After further investigations iodine content and Zeffective may be translated as diagnostical and prognostical biomarkers into clinical routine in the long term.

Functionalized Au@Cu-Sb-S Nanoparticles for Spectral CT/Photoacoustic Imaging-Guided Synergetic Photo-Radiotherapy in Breast Cancer

Background

Radiotherapy (RT) is clinically well-established cancer treatment. However, radioresistance remains a significant issue associated with failure of RT. Phototherapy-induced radiosensitization has recently attracted attention in translational cancer research.

Methods

Cu-Sb-S nanoparticles (NPs) coated with ultra-small Au nanocrystals (Au@Cu-Sb-S) were synthesized and characterized. The biosafety profiles, absorption of near-infrared (NIR) laser and radiation-enhancing effect of the NPs were evaluated. In vitro and in vivo spectral computed tomography (CT) imaging and photoacoustic (PA) imaging were performed in 4T1 breast cancer-bearing mice. The synergetic radio-phototherapy was assessed by in vivo tumor inhibition studies.

Results

Au@Cu-Sb-S NPs were prepared by in situ growth of Au NCs on the surface of Cu-Sb-S NPs. The cell viability experiments showed that the combination of Au@Cu-Sb-S+NIR+RT was significantly more cytotoxic to tumor cells than the other treatments at concentrations above 25 ppm Sb. In vitro and in vivo spectral CT imaging demonstrated that the X-ray attenuation ability of Au@Cu-Sb-S NPs was superior to that of the clinically used Iodine, particularly at lower KeV levels. Au@Cu-Sb-S NPs showed a concentration-dependent and remarkable PA signal brightening effect. In vivo tumor inhibition studies showed that the prepared Au@Cu-Sb-S NPs significantly suppressed tumor growth in 4T1 breast cancer-bearing mice treated with NIR laser irradiation and an intermediate X-ray dose (4 Gy).

Conclusion

These results indicate that Au@Cu-Sb-S integrated with spectral CT, PA imaging, and phototherapy-enhanced radiosensitization is a promising multifunctional theranostic nanoplatform for clinical applications.

Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions

BACKGROUND

Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions.

METHODS

In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZ_eff), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λ_Hu). Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test.

RESULTS

The univariate analysis showed that the venous phase λ_Hu had the highest AUROC (0.88). Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88-0.99) and testing (AUROC, 0.83-0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001).

CONCLUSIONS

The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.

Possibility to discriminate benign from malignant breast lesions detected on dual-layer spectral CT-evaluation

OBJECTIVES

Intramammary mass lesions are reportedly present in up to 5.8% of all contrast enhanced CT-examinations of the female chest. We aimed to assess whether their biological relevance can be estimated using spectral CT-datasets.

METHODS

In this bicentric retrospective study patients with breast masses visualized on spectral CT-examinations from 07/2017 to 06/2019 were included. Lesions were characterized as malignant or benign based on histology and/or a stable follow-up of >2 years. Conventional CT-images, iodine density-maps, virtual monoenergetic-images (40 keV, 100 keV) and Zeffective-maps were evaluated by two independent readers. Statistical analysis derived from the Regions of interest (ROIs) was done by calculating the Areas under the Receiver operating characteristic (ROC) curve (AUC) and Youden-indices.

RESULTS

106 breast masses (malignant/benign: 81/25, 76.4%/23.6%) were included. The mean AUCs of the variables "iodine content" (reader 1/2:0.97;0.98), "monoenergetic curve-slope" (0.97;0.96) and "Zeffective" (0.98;0.98) measured in the target lesions (TL) showed superior results compared to those derived from the variable "density" (0.92;0.93) (p < 0.001). The ratios "TL to aorta" calculated for the variables "iodine content", "monoenergetic curve-slope" and "Zeffective" showed superior results compared to normal breast tissue and muscle (p < 0.001). The optimal cutpoint for the "iodine content" in the TL was 0.7-0.9 mg/ml (sensitivity 96.6%, specificity 91.7%). The best diagnostic results were achieved by normalizing the iodine content in the TL to that in the aorta (optimal cutpoint 0.1, sensitivity 95.5%, 98.9%, specificity 91.7%).

CONCLUSIONS

Our preliminary results suggest that spectral CT-datasets might allow to estimate the biological dignity of breast masses detected on clinically indicated chest-examinations.

Dual-energy CT quantitative parameters for the differentiation of benign from malignant lesions and the prediction of histopathological and molecular subtypes in breast cancer

Background

Dual-energy computed tomography (DECT) is widely used to characterize and differentiate tumors. However, data regarding its diagnostic performance for the characterization of breast tumors are limited. In this study, we assessed the diagnostic performance of quantitative parameters derived from DECT in differentiating benign from malignant lesions and predicting histopathological and molecular subtypes in patients with breast cancer.

Methods

Dual-phase contrast-enhanced DECT of the thorax was performed on participants with breast tumors. Conventional CT attenuation and DECT quantitative parameters, including normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λ_Hu), and normalized effective atomic number (nZ_eff), were obtained and compared between benign and malignant lesions, invasive non-special carcinoma, and ductal carcinoma in situ (DCIS), and among the four molecular subtypes of breast cancer. The diagnostic performance of the quantitative parameters was analyzed using receiver operating characteristic (ROC) curves.

Results

This study included 130 participants with 161 breast lesions (44 benign and 117 malignant). In the arterial and venous phase, NICs, λ_Hu, nZ_eff, and attenuation were higher in malignant lesions than benign lesions (all P<0.001). The venous phase λ_Hu had the best differential diagnostic capability, with an area under the curve (AUC) of 0.90, a sensitivity of 84.1% (37 of 44), a specificity of 86.3% (101 of 117), and an accuracy of 85.7% (138 of 161). The NICs in the arterial and venous phases were higher in invasive non-special carcinoma than DCIS (both P<0.001). In terms of diagnostic performance, NIC in the venous phase had an AUC of 0.77, a sensitivity of 75.0% (12 of 16), a specificity of 81.2% (82 of 101), and an accuracy of 80.3% (94 of 117). The luminal A subtype produced a lower venous phase NIC, and arterial and venous phase nZ_eff than the non-luminal A subtype (AUC of 0.91 for the combination of these three parameters).

Conclusions

Dual-energy CT quantitative parameters are a feasible and valuable noninvasive means of differentiating between benign and malignant lesions, and predicting histopathological and molecular subtypes in patients with breast cancer.

Subjective and Objective Assessment of Monoenergetic and Polyenergetic Images Acquired by Dual-Energy CT in Breast Cancer

Objective

To objectively and subjectively assess and compare the characteristics of monoenergetic images [MEI (+)] and polyenergetic images (PEI) acquired by dual-energy CT (DECT) of patients with breast cancer.

Materials and Methods

This retrospective study evaluated the images and data of 42 patients with breast cancer who had undergone dual-phase contrast-enhanced DECT from June to September 2019. One standard PEI, five MEI (+) in 10-kiloelectron volt (keV) intervals (range, 40–80 keV), iodine density (ID) maps, iodine overlay images, and Z effective (Z_eff) maps were reconstructed. The contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) were calculated. Multiple quantitative parameters of the malignant breast lesions were compared between the arterial and the venous phase images. Two readers independently assessed lesion conspicuity and performed a morphology analysis.

Results

Low keV MEI (+) at 40–50 keV showed increased CNR and SNR_{breast lesion} compared with PEI, especially in the venous phase ([CNR: 40 keV, 20.10; 50 keV, 14.45; vs. PEI, 7.27; p < 0.001], [SNR_{breast lesion}: 40 keV, 21.01; 50 keV, 16.28; vs. PEI, 10.77; p < 0.001]). Multiple quantitative DECT parameters of malignant breast lesions were higher in the venous phase images than in the arterial phase images (p < 0.001). MEI (+) at 40 keV, ID, and Z_eff reconstructions yielded the highest Likert scores for lesion conspicuity. The conspicuity of the mass margin and the visual enhancement were significantly better in 40-keV MEI (+) than in the PEI (p = 0.022, p = 0.033, respectively).

Conclusion

Compared with PEI, MEI (+) reconstructions at low keV in the venous phase acquired by DECT improved the objective and subjective assessment of lesion conspicuity in patients with malignant breast lesions. MEI (+) reconstruction acquired by DECT may be helpful for the preoperative evaluation of breast cancer.

Dual-energy CT quantitative parameters for evaluating Immunohistochemical biomarkers of invasive breast cancer

Background

Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and Ki67 are the most useful immunohistochemical biomarkers of invasive breast cancer. The purpose of this study is to investigate the possibility of quantitative parameters derived from dual-energy CT (DECT) to discriminate immunohistochemical biomarkers of invasive breast cancer.

Methods

This prospective study enrolled 120 patients with invasive breast cancer who underwent preoperative contrast-enhanced DECT for staging purposes from June 2019 to January 2020. DECT quantitative parameters, including normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λ_Hu), and the normalized effective atomic number (nZ_eff), were obtained from reconstructed images. DECT quantitative parameters were compared with the expression status, and the correlations with the value of immunohistochemical biomarkers were evaluated. Inter-observer reproducibility analysis was performed to assess the measurement reproducibility of quantitative parameters. The diagnostic performance of the quantitative parameters was analyzed by receiver operating characteristic curve.

Results

The ER-negative group tended to display higher venous phase NIC and nZ_eff compared with the ER-positive group (individually, p = 0.003, 0.011; area under the curve [AUC] of 0.65, 0.60). The PR-negative group demonstrated higher arterial and venous phase NIC compared with the PR-positive group (individually, p = 0.022, 0.005; AUC of 0.63, 0.65). NIC was correlated negatively with the value of ER and PR expression (r = − 0.175 ~ − 0.265, p = 0.002 ~ 0.042). The HER2-positive group tended to display higher venous phase nZ_eff than the HER2-negative group (p = 0.022; AUC of 0.59). The Ki67 high-proliferation group demonstrated higher arterial phase, venous phase NIC and nZ_eff than the Ki67 low-proliferation group (p < 0.001 ~ 0.005; AUC of 0.67 ~ 0.75). Both the NIC and nZ_eff were correlated positively with the value of Ki67 (r = 0.240 ~ 0.490, p < 0.001 ~ 0.014).

Conclusions

NIC and nZ_eff derived from DECT could be used to discriminate expression status and may associate with the value of immunohistochemical biomarkers of invasive breast cancer.

Dual energy CT in gland tumors: a comprehensive narrative review and differential diagnosis

Dual energy CT (DECT)with image acquisition at two different photon X-ray levels allows the characterization of a specific tissue or material/elements, the extrapolation of virtual unenhanced and monoenergetic images, and the quantification of iodine uptake; such special capabilities make the DECT the perfect technique to support oncological imaging for tumor detection and characterization and treatment monitoring, while concurrently reducing the dose of radiation and iodine and improving the metal artifact reduction. Even though its potential in the field of oncology has not been fully explored yet, DECT is already widely used today thanks to the availability of different CT technologies, such as dual-source, single-source rapid-switching, single-source sequential, single-source twin-beam and dual-layer technologies. Moreover DECT technology represents the future of the imaging innovation and it is subject to ongoing development that increase according its clinical potentiality, in particular in the field of oncology. This review points out recent state-of-the-art in DECT applications in gland tumors, with special focus on its potential uses in the field of oncological imaging of endocrine and exocrine glands.

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_ART, VMI40_ART, CI_DE, VMI40_DE). Mean Hounsfield units (HU) of tumors were measured on VMI40_ART and VMI40_DE. 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_DE 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_DE yielded significantly higher mean HU for cancers than VMI40_ART (p < 0.001). The conspicuity score and mean HU on VMI40_ART were significantly higher in cancers with ER negativity, PR negativity, and Ki67 positivity (p < 0.05).

CONCLUSIONS

VMI40_DE may be useful in the diagnosis of breast cancers due to higher tumor conspicuity and better enhancement than VMI40_ART. VMI40_ART may be beneficial for the prediction of poor breast cancer prognoses.

KEY POINTS

• VMI40 improved conspicuity of breast cancer than CI. • VMI40_DEyielded higher diagnostic performance of breast cancer than VMI40_ART. • VMI40_ARThas an additional benefit in terms of prognosis prediction in patients with breast cancers.

Dual-energy computed tomography for evaluation of breast cancer: value of virtual monoenergetic images reconstructed with a noise-reduced monoenergetic reconstruction algorithm

PURPOSE

To evaluate the image quality and lesion visibility of virtual monoenergetic images (VMIs) reconstructed using a new monoenergetic reconstruction algorithm (nMERA) for evaluation of breast cancer.

MATERIALS AND METHODS

Forty-two patients with 46 breast cancers who underwent 4-phasic breast contrast-enhanced computed tomography (CT) using dual-energy CT (DECT) were enrolled. We selected the peak enhancement phase of the lesion in each patient. The selected phase images were generated by 120-kVp-equivalent linear blended (M120) and monoenergetic reconstructions from 40 to 80 keV using the standard reconstruction algorithm (sMERA: 40, 50, 60, 70, 80) and nMERA (40 +, 50 +, 60 +, 70 +, 80 +). The contrast-to-noise ratio (CNR) was calculated and objectively analyzed. Two independent readers subjectively scored tumor visibility and image quality each on a 5-point scale.

RESULTS

The CNR at 40 + and tumor visibility scores at 40 + and 50 + were significantly higher than those on M120. The CNR at 50 + was not significantly different from that on M120. However, the overall image quality score at 40 + was significantly lower than that at 50 + and on M120 (40 + vs M120, P < 0.0001 and 40 + vs 50 +, P = 0.0001).

CONCLUSIONS

VMI reconstructed with nMERA at 50 keV is preferable for evaluation of patients with breast cancer.