Contrast-enhanced Spectral Mammography Research Articles

Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.

Neoadjuvant chemotherapy (NAC) has been regarded as one of the standard treatments for patients with locally advanced breast cancer. No previous study has investigated the feasibility of using a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict pathological complete response (pCR) after NAC. To develop and validate a CESM-based radiomics nomogram to predict pCR after NAC in breast cancer. A total of 118 patients were enrolled, which are divided into a training dataset including 82 patients (with 21 pCR and 61 non-pCR) and a testing dataset of 36 patients (with 9 pCR and 27 non-pCR). The tumor regions of interest (ROIs) were manually segmented by two radiologists on the low-energy and recombined images and radiomics features were extracted. Intraclass correlation coefficients (ICCs) were used to assess the intra- and inter-observer agreements of ROI features extraction. In the training set, the variance threshold, SelectKBest method, and least absolute shrinkage and selection operator regression were used to select the optimal radiomics features. Radiomics signature was calculated through a linear combination of selected features. A radiomics nomogram containing radiomics signature score (Rad-score) and clinical risk factors was developed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate prediction performance of the radiomics nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the radiomics nomogram. The intra- and inter- observer ICCs were 0.769-0.815 and 0.786-0.853, respectively. Thirteen radiomics features were selected to calculate Rad-score. The radiomics nomogram containing Rad-score and clinical risk factor showed an encouraging calibration and discrimination performance with area under the ROC curves of 0.906 (95% confidence interval (CI): 0.840-0.966) in the training dataset and 0.790 (95% CI: 0.554-0.952) in the test dataset. The CESM-based radiomics nomogram had good prediction performance for pCR after NAC in breast cancer; therefore, it has a good clinical application prospect.

Predicting the response to neoadjuvant chemotherapy. Can the addition of tomosynthesis improve the accuracy of contrast-enhanced spectral mammography? A comparison with breast MRI.

Image monitoring is essential to monitor response to neoadjuvant chemotherapy (NACT). Whilst breast MRI is the gold-standard technique, evidence suggests contrast-enhanced spectral mammography (CESM) is comparable. We investigate whether the addition of digital breast tomosynthesis (DBT) to CESM increases the accuracy of response prediction. Women receiving NACT for breast cancer were included. Imaging with CESM+DBT and MRI was performed post-NACT. Imaging appearance was compared with pathological specimens. Accuracy for predicting pathological complete response (pCR) and concordance with size of residual disease was calculated. Sixteen cancers in 14 patients were included, 10 demonstrated pCR. Greatest accuracy for predicting pCR was with CESM enhancement (accuracy: 81.3%, sensitivity: 100%, specificity: 57.1%), followed by MRI (accuracy: 62.5%, sensitivity: 44.4%, specificity: 85.7%). Concordance with invasive tumour size was greater for CESM enhancement than MRI, concordance-coefficients 0.70 vs 0.66 respectively. MRI demonstrated greatest concordance with whole tumour size followed by CESM+microcalcification, concordance coefficients 0.86 vs 0.69. DBT did not improve accuracy for prediction of pCR or residual disease size. CESM+DBT underestimated size of residual disease, MRI overestimated but no significant differences were seen (p>0.05). CESM is similar to MRI for predicting residual disease post-NACT. Size of enhancement alone demonstrates best concordance with invasive disease. Inclusion of residual microcalcification improves concordance with ductal carcinoma in situ. The addition of DBT to CESM does not improve accuracy. The addition ofDBT to CESM does not improve NACT response prediction. CESM enhancement has greatest accuracy for residual invasive disease, CESM+calcification has greater accuracy for residual in situ disease.

What’s beyond breast asymmetry? Comparative study between artificial intelligence and contrast-enhanced spectral mammography in the assessment of various types of breast asymmetries

BackgroundBreast asymmetries are prevalent findings in mammograms and are commonly caused by variations in normal breast tissue. However, they may imply significant underlying causes in some cases. Such cases necessitate further assessment by adding further mammography views, targeted ultrasound, and investigations to exclude underlying pathology.ObjectivesTo investigate the role of artificial intelligence (AI) compared to contrast-enhanced spectral mammography (CESM) in the assessment of breast asymmetries and their performance as diagnostic modality among different types of breast asymmetries as well as the additive value of AI software to mammography in these cases.MethodsSixty-four female patients were diagnosed with breast asymmetries by standard mammography (MMG) on both craniocaudal (CC) and mediolateral oblique (MLO). Digital breast tomosynthesis (DBT) may have been added. After evaluating the breast asymmetry by MMG and complementary breast ultrasound (US), both CESM and AI were performed for all cases and analyzed, then the interpreted results were compared accordingly either by histopathology from suspected lesions scored as BI-RADS 4 or 5 or through further close follow-up by single-view mammography in benign cases scored as BI-RADS 2 or 3.ResultsThe sensitivity and specificity of CESM in the assessment of breast asymmetry in correlation with pathological data/follow-up results were 100% and 60% (p < 0.001). The corresponding values for AI were 70.83% and 75%; however, the estimated overall accuracy for both CESM and AI was close to each other measuring 75% and 73.44%, respectively (p < 0.001). The diagnostic accuracy of CESM to detect malignant causes of breast asymmetry was 100%; however, the detection of benign causes of breast asymmetry was 40%. The corresponding values for AI were 70.83% and 25%, respectively, with significant p-value (p < 0.001).ConclusionsThe CESM was more sensitive; however, the AI was more specific in the assessment of different breast asymmetries. Although the diagnostic accuracy of both is close to each other. Therefore, AI-aided reading can replace CESM in most cases, especially for those contraindicated to do CESM. AI also can reduce the radiation exposure hazards of a second dose of radiation for CESM and its financial cost as well. AI-aided reading in breast screening programs can reduce the recall of patients, unnecessary biopsies, and short-interval follow-up exams.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women.

To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM). Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk. During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001). Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females. • Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

Preoperative staging by multimodal imaging in newly diagnosed breast cancer: Diagnostic performance of contrast-enhanced spectral mammography compared to conventional mammography, ultrasound, and MRI

PurposeTo compare contrast-enhanced spectral mammography (CESM) with mammography (Mx), ultrasound (US), and magnetic resonance imaging (MRI) regarding breast cancer detection rate and preoperative local staging. Material and methodsThis prospective observational, single-centre study included 128 female patients (mean age 55.8 ± 11.5 years) with a newly diagnosed malignant breast tumour during routine US and Mx were prospectively enrolled. CESM and MRI examinations were performed within the study. Analysis included interreader agreement, tumour type and grade distribution, detection rates (DR), imaging morphology, contrast-enhancement and was performed by two independent readers blinded to patient history and histopathological diagnosis. Assessment of local disease extent was compared between modalities via Bland-Altman plots. ResultsOne-hundred-and-ten tumours were classified as NST (85.9%), 4 as ILC (3.1%) and 10 as DCIS (7.8%). DR was highest for MRI (128/128, 100.0%), followed by US (124/128, 96.9%) and CESM (123/128, 96.1%) and lowest for conventional Mx (106/128, 82.8%) (p = 0.0002). Higher breast density did not negatively affect DR of US, CESM or MRI. Local tumour extent measurements based on CESM (Bland-Altman bias 6.6, standard deviation 30.2) showed comparable estimation results to MRI, surpassing Mx (23.4/43.7) and US (35.4/40.5). Even though detection of multifocality and multicentricity was highest for CESM and MRI (p < 0.0001), second-look rates, i.e., targeted US examinations after MRI or CESM, were significantly lower for CESM (10.2% of cases) compared to MRI (16.2%) with a significantly higher true positive rate for CESM (72.0%) vs. MRI (42.5%). ConclusionCESM is a viable alternative to MRI for lesion detection and local staging in newly diagnosed malignant breast cancer and provides higher specificity in regard to second-look examinations.

Theoretical comparison and optimization of cadmium telluride and gallium arsenide photon-counting detectors for contrast-enhanced spectral mammography.

Most photon-counting detectors (PCDs) being developed use cadmium telluride (CdTe), which has nonoptimal characteristic x-ray emission with energies in the range used for breast imaging. New PCD using a gallium arsenide (GaAs) has been developed. Since GaAs has characteristic x-rays lower in energy than those of CdTe, it is hypothesized that this new PCD might be beneficial for spectral x-ray breast imaging. We performed simulations using realistic mammography x-ray spectra with both CdTe and GaAs PCDs. Five different experiments were conducted, each comparing the performance of CdTe and GaAs: (1) sensitivity of iodine quantification to charge cloud size and electronic noise, (2) effect of photon spectrum on iodine quantification, (3) effect of varying the number of energy bins, (4) a dose analysis to assess any possible dose reduction from using either detector, and (5) spectral performance of ideal CdTe and GaAs PCDs. For each study, 3 sets of 5000 noise realizations were used to calculate the Cramer-Rao lower bound (CRLB) of iodine quantification. For all spectra studied, GaAs gave a lower CRLB for iodine quantification, with 10 of the 12 spectra showing a statistically significant difference ( ). The photon energy spectrum that optimized iodine detection for both detector materials was the 40 kVp beam with 2-mm Al filtration, which produced CRLBs of and for CdTe and GaAs, respectively, when using five energy bins. GaAs is a promising detector material for contrast-enhanced spectral mammography that offers better spectral performance than CdTe.

An efficient hybrid computer-aided breast cancer diagnosis system with wavelet packet transform and synthetically-generated contrast-enhanced spectral mammography images

Contrast-enhanced spectral mammography (CESM) is an emerging modality for breast cancer diagnosis. This work investigates the feasibility of a computationally-efficient computer-aided diagnosis (CAD) system for breast lesion classification. Moreover, to avoid the need for intravenous contrast agents, we investigated the synthesis of contrast-enhanced (SynCESM) images. A total of 504 pairs of low-energy (LE) and CESM images were collected from 160 female subjects. A semi-automatic active-contour method was used for lesion segmentation. Then, 20 morphological and textural features were extracted. To improve the computational efficiency of the proposed system, the wavelet packet transform (WPT) was applied. Then, the same features were extracted from the WPT-approximated segmented lesions. Using LE images, a sigmoid-kernel SVM classifier exhibited a 90.20% accuracy, an 88.39% sensitivity, an 88.26% specificity, and a 0.92 AUC. The per-frame classification time was significantly reduced from 1.1046 s to 0.0734 s with WPT. Using CESM images, we achieved a 93.26% accuracy, a 95.94% sensitivity, a 93.37% specificity, a 0.94 AUC, and a 0.0657 s classification time. Interestingly, with SynCESM images, reasonable performance was still obtained with a 92.14% accuracy, a 93.87% sensitivity, a 91.58% specificity, a 0.93 AUC and a 0.0657 s classification time. Finally, with hybrid pairs of LE and CESM images, we got the best performance with a 96.87% accuracy, a 97.23% sensitivity, a 95.47% specificity, a 0.98 AUC, and a 0.0696 s classification time. The results demonstrate several advantages of the proposed system including its clinical feasibility, lower complexity, and reduced need for contrast agents via synthetic data generation.

Accuracy and precision of contrast enhanced mammography versus MRI for predicting breast cancer size: how "good" are they really?

To evaluate and compare the accuracy and precision of contrast-enhanced mammography (CEM) vs MRI to predict the size of biopsy-proven invasive breast cancer. Prospective study, 59 women with invasive breast cancer on needle biopsy underwent CEM and breast MRI. Two breast radiologists read each patient's study, with access limited to one modality. CEM lesion size was measured using low-energy and recombined images and on MRI, the first post-contrast series. Extent of abnormality per quadrant was measured for multifocal lesions. Reference standards were size of largest invasive malignant lesion, invasive (PathInvasive) and whole (PathTotal). Pre-defined clinical concordance ±10 mm. Mean patient age 56 years, 42 (71%) asymptomatic. Lesions were invasive ductal carcinoma 40 (68%) with ductal carcinoma in situ (31/40) in 78%, multifocal in 12 (20%). Median lesion size was 17 mm (invasive) and 27 mm (total), range (5-125 mm). Lin's concordance correlation coefficients for PathTotal 0.75 (95% CI 0.6, 0.84) and 0.71 (95% CI 0.56, 0.82) for MRI and contrast-enhanced spectral mammography (CESM) respectively. Mean difference for total size, 3% underestimated and 4% overestimated, and for invasive 41% and 50% overestimate on MRI and CESM respectively. LOAs for PathTotal varied from 60% under to a 2.4 or almost threefold over estimation. MRI was concordant with PathTotal in 36 (64%) cases compared with 32 (57%) for CESM. Both modalities concordant in 26 (46%) cases respectively. Neither CEM nor MRI have sufficient accuracy to direct changes in planned treatment without needle biopsy confirmation. Despite small mean differences in lesion size estimates using CEM or MRI, the 95% limits of agreement do not meet clinically acceptable levels.

Role of Contrast-Enhanced Spectral Mammography in Characterization of Suspicious Breast Microcalcification

Role of Contrast-Enhanced Spectral Mammography in Characterization of Suspicious Breast Microcalcification

Contrast-Enhanced Spectral Mammography in the Evaluation of Breast Microcalcifications: Controversies and Diagnostic Management

The aim of this study was to evaluate the diagnostic performance of contrast-enhanced spectral mammography (CESM) in predicting breast lesion malignancy due to microcalcifications compared to lesions that present with other radiological findings. Three hundred and twenty-one patients with 377 breast lesions that underwent CESM and histological assessment were included. All the lesions were scored using a 4-point qualitative scale according to the degree of contrast enhancement at the CESM examination. The histological results were considered the gold standard. In the first analysis, enhancement degree scores of 2 and 3 were considered predictive of malignity. The sensitivity (SE) and positive predictive value (PPV) were significative lower for patients with lesions with microcalcifications without other radiological findings (SE = 53.3% vs. 82.2%, p-value < 0.001 and PPV = 84.2% vs. 95.2%, p-value = 0.049, respectively). On the contrary, the specificity (SP) and negative predictive value (NPV) were significative higher among lesions with microcalcifications without other radiological findings (SP = 95.8% vs. 84.2%, p-value = 0.026 and NPV = 82.9% vs. 55.2%, p-value < 0.001, respectively). In a second analysis, degree scores of 1, 2, and 3 were considered predictive of malignity. The SE (80.0% vs. 96.8%, p-value < 0.001) and PPV (70.6% vs. 88.3%, p-value: 0.005) were significantly lower among lesions with microcalcifications without other radiological findings, while the SP (85.9% vs. 50.9%, p-value < 0.001) was higher. The enhancement of microcalcifications has low sensitivity in predicting malignancy. However, in certain controversial cases, the absence of CESM enhancement due to its high negative predictive value can help to reduce the number of biopsies for benign lesions.

Costing analysis to introduce a contrast-enhanced mammography service to replace an existing breast MRI service for local staging of breast cancer

To assess the cost impact of switching from contrast-enhanced magnetic resonance imaging (CE-MRI) to contrast-enhanced spectral mammography (CESM) for loco-regional staging of breast cancer from a public healthcare perspective. The CE-MRI cost was obtained from the NHS reference cost. The CESM cost was calculated using a bottom-up approach including use of the machine, pump injector, contrast medium, image storage, and time allocation for staff reporting and cannulation. The cost of upgrading existing machines to CESM or purchasing new mammographic machines was obtained via national procurement. Other costs were obtained from local pharmacy, published unit cost data, or estimated based on surveys. For large health boards in Scotland (≥500 cancers diagnosed per annum), the cost savings of switching from CE-MRI to CESM range from £64,069 to £81,570. For small health boards (<500 cancers diagnosed per annum), the cost savings of switching from CE-MRI to CESM range from £6,453 to £23,953. The cost savings are most sensitive to the number of tests conducted per year, and whether the existing mammography machine can be upgraded to CESM or not. Switching from CE-MRI to CESM for loco-regional staging of breast cancer is likely to be cost saving for both large and small health boards in Scotland. Further research is urgently needed to confirm the non-inferiority of CESM to CE-MRI as a locoregional staging technique. The input data of this analysis can be updated when such results become available.

Contrast-enhanced spectral mammography: are kinetic patterns useful for differential diagnoses of enhanced lesions?

To investigate the diagnostic efficiency of the kinetic curves of enhanced lesions on contrast-en-hanced spectral mammography (CESM) and whether they were similar to those of magnetic resonance imaging (MRI). Two hundred and twelve patients with 222 enhanced lesions were included in this prospective study. Single-view craniocaudal of an affected breast was acquired at 3, 5, and 7 min after contrast media injection. The kinetic patterns of each lesion were evaluated and classified as elevated (type I), steady (type II), and depressed (type III). Statistical comparison used the chi-squared test, the receiver operating characteristic (ROC) curve, and Cohen's kappa. Of 222 enhanced lesions, 140 were breast cancers, and 82 were benign lesions. The distribution of the kinetic curves for breast cancer was type I, 3.57%, type II, 35.71%, and type III, 60.72%. As for benign lesions, the distribution was type I, 43.90%, type II, 45.12%, and type III, 10.98%. The difference in the enhancement patterns between benign lesions and breast cancers was significant (P < 0.001). The likelihood of breast cancer related to a type I, II, and III curve was 12.20%, 57.47%, and 90.43%, respectively. For the enhancement intensity, the area under curve (AUC) of the ROC curves was 0.702 ± 0.036; for enhancement patterns, the AUC increased to 0.819 ± 0.030. Cohen's kappa coefficient was 0.752 (P < 0.001) regarding the kinetic curves for CESM and MRI. The kinetic patterns on CESM show promise in differentiating between benign lesions and breast cancers, with good agreement, when compared with MRI.

Сontrast enhanced spectral mammography as a tool for accurate diagnosis of cancer on the background of the dense breast tissue

Introduction. Differential diagnosis of some pathological processes in the breast is difficult on the background of dense breast tissue. This often leads to false conclusions and to late diagnosis of breast cancer (BC) or unreasonable biopsy in a benign process. 50% of breast cancers detected less than 12 months after elective mammography were associated with high density of breast tissue. An important advantage of contrast enhanced spectral mammography (CESM) is that it does not depend on the size of the lesions and the X-ray density of the breast tissue. Objective. To compare the diagnostic performance of CESM and digital mammography (DM) in detection of breast cancer in a group of women with dense breast tissue.Materials and methods. The data of 438 patients with suspected breast cancer examined from August 2018 to January 2021 were analyzed in the study. The mean age of women was 50 ± 11 years (from 21 to 86 years). In the study group 154 (35%) malignant and 284 (65%) benign lesions were identified. All lesions were histologically verified. Breast tissue density corresponded to types A and B in 161 patients and corresponded to C and D types in 277 patients according to the ACR classification. 154 cases of breast cancer were identified, including 49 patients with density A and B and 105 patients with density C and D types.Results. Sensitivity, specificity and overall accuracy of DM were 85.7%, 87.3%, 86.8%, respectively. Diagnostic performance of CESM significantly higher than of DM with sensitivity, specificity and accuracy of 96.8% (p &lt; 0.001), 93.3% (p = 0.015), 94.5% (p &lt; 0.001), respectively. CESM had high positive and negative predictive values of 88.7% (p = 0.012) and 98.1% (p &lt; 0.001), which exceeded those of DM – 78.6% and 91.9%, respectively. The diagnostic performance of DM and CESM were comparable in women with normal breast density (types A and B according to ACR), but in patients with high breast density (types C and D according to ACR), CESM was significantly more sensitive in detecting breast cancer.Conclusion. Thus, diagnostic efficiency of CESM in detecting breast cancer significantly higher in comparison with digital mammography.

Contextual Features and Information Bottleneck-Based Multi-Input Network for Breast Cancer Classification from Contrast-Enhanced Spectral Mammography.

In computer-aided diagnosis methods for breast cancer, deep learning has been shown to be an effective method to distinguish whether lesions are present in tissues. However, traditional methods only classify masses as benign or malignant, according to their presence or absence, without considering the contextual features between them and their adjacent tissues. Furthermore, for contrast-enhanced spectral mammography, the existing studies have only performed feature extraction on a single image per breast. In this paper, we propose a multi-input deep learning network for automatic breast cancer classification. Specifically, we simultaneously input four images of each breast with different feature information into the network. Then, we processed the feature maps in both horizontal and vertical directions, preserving the pixel-level contextual information within the neighborhood of the tumor during the pooling operation. Furthermore, we designed a novel loss function according to the information bottleneck theory to optimize our multi-input network and ensure that the common information in the multiple input images could be fully utilized. Our experiments on 488 images (256 benign and 232 malignant images) from 122 patients show that the method's accuracy, precision, sensitivity, specificity, and f1-score values are 0.8806, 0.8803, 0.8810, 0.8801, and 0.8806, respectively. The qualitative, quantitative, and ablation experiment results show that our method significantly improves the accuracy of breast cancer classification and reduces the false positive rate of diagnosis. It can reduce misdiagnosis rates and unnecessary biopsies, helping doctors determine accurate clinical diagnoses of breast cancer from multiple CESM images.

Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems?

ObjectiveTo evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared.MethodsIn a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results.ResultsSignificant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases.ConclusionMammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible.Key Points• Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively.• Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement.• Lesion conspicuity is higher in cases of lower background enhancement on CESM.

Role of contrast-enhanced spectral mammography in the assessment of residual disease following neoadjuvant chemotherapy in patients with breast cancer

BackgroundNeoadjuvant chemotherapy (NAC) is currently one of the most commonly utilized treatment options for patients with breast cancer. Evaluating residual disease following NAC is vital in post-chemotherapy treatment planning. Underestimation or overestimation of the residual disease may result in incomplete surgical resection or unnecessary aggressive surgical treatment which can compromise patient survival or result in unwanted morbidity. Contrast-enhanced spectral mammography (CESM) is a new imaging modality that can provide both morphological and anatomical characterization of neoplastic breast lesions when MRI is unavailable or contraindicated. The aim of this prospective observational study was to evaluate the value of CESM in assessing the extent of residual disease following NAC and predicting complete response (CR) with histopathology as the gold standard.ResultsA total of 142 patients were included in our study with invasive duct carcinoma the most prevalent histopathological type seen in 81.7% of cases. The average tumor reduction in size was 40.8% by CESM and 35.8% by histopathology. Comparison between maximum tumor diameter after NAC by CESM and histopathology revealed a high degree of correlation (R = 0.918, p < 0.01). In 45.1% of cases, CESM underestimated the residual tumor size by an average of 1.1 cm. Comparison between CESM and histopathology in predicting CR yielded CESM sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of 96.7%, 84.6%, 97.2%, and 100%, respectively.ConclusionsCESM is a new effective imaging modality in assessing residual disease in breast cancer patients receiving NAC. Post-NAC CESM correlated well with residual tumor size by histopathology with a tendency for underestimation. Careful assessment of CESM images should be made to accurately measure the exact tumor size to avoid overestimation or underestimation potentially increasing the extent of the operation. Proper assessment of the true extent of residual disease can help the surgeon to select the optimal surgical technique ensuring radical treatment of the disease improving patient survival and reducing morbidity.

Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner

We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2-G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45-58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy.

Mammographic and contrast-enhanced spectral mammography imaging findings of HER2-positive cancers according to hormone receptor status

BackgroundBreast cancer is the leading cause of cancer-related mortality in women. Human epidermal growth factor receptor 2 (HER2) overexpression is seen in 20 out of 100 invasive breast cancers. Among HER2+ patients, two distinct hormone receptor (HR) subtypes can be defined: HR-positive (HR+) and HR-negative (HR−) each of which with unique therapeutic response and survival pattern. Contrast-enhanced spectral mammography (CESM) is an emerging novel imaging modality that offers diagnostic performance comparable to contrast-enhanced MRI. The purpose of this retrospective study was to describe the CESM features of HER2+ breast cancers according to hormone receptor status and to assess whether specific mammographic and CESM imaging features can differentiate between HER2+/HR+ and HER2+/HR− breast cancers potentially aiding treatment planning in HER2+ breast cancer patients.ResultsA total of 61 patients were included. Twenty-nine cases (47.5%) were HER2+/HR+ and 32 cases (52.5%) were HER2+/HR−. No statistically significant difference was found between mammographic imaging presentations and hormonal status. HR- were more likely to be multifocal (P 0.018), rounded or oval (P 0.008), circumscribed (P 0.004), and with associated non-mass enhancement (NME) (P < 0.001). HR+ cancers showed a tendency for irregular shape (P 0.008), spiculated outline (P 0.004), and heterogeneous (P 0.021) or ring (P 0.046) enhancement.ConclusionsHER2+ tumors have different demographic, pathologic and imaging features according to the hormone receptor status. Because the two subtypes of HER2 breast cancer have different clinical outcomes, CESM imaging features can potentially enhance patient outcome by accelerating the diagnosis and treatment.

Energy-sensitive scatter estimation and correction for spectral x-ray imaging with photon-counting detectors

As photon counting detectors are being explored for medical and industrial imaging applications, there is a critical need to understand spectral characteristics of scattered x-ray photons. Scattered radiation is detrimental to x-ray imaging by reducing image quality and quantitative accuracy. While various scatter correction techniques have been proposed for x-ray imaging with conventional energy-integrating detectors, additional efforts are required to develop approaches for spectral x-ray imaging with energy-resolving PCDs. We show the benefits of accurate scatter estimation and correction for each energy bin when using a photon counting detector. We propose a scatter estimation model that accounts for the energy-dependent scatter characteristics in projection imaging. This can then be used to restore quantitative accuracy for spectral x-ray imaging with PCDs. Results are shown in the context of contrast-enhanced spectral mammography using dual-energy subtraction to digitally isolate iodine targets (2.5–40 mg/ml). In the presence of scatter, the projected iodine densities are increasingly underestimated as the object thickness increases. The energy-sensitive scatter correction improves the iodine density estimation up to 46%. These results suggest that our scatter estimation model can accurately account for the energy-dependent scatter distribution, which can be an effective tool for scatter compensation in spectral x-ray imaging. Implementing this scatter estimation model does not require any modifications to the acquisition parameters and is transferable to other x-ray imaging applications such as tomosynthesis and CT.

Breast Asymmetry and Value of Contrast-Enhanced Spectral Mammography: Review Article

Breast Asymmetry and Value of Contrast-Enhanced Spectral Mammography: Review Article