Contrast-enhanced Spectral Mammography Research Articles

Abstract PO3-20-08: Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation

Abstract Purpose: To introduce clinical cases of ductal carcinoma in situ (DCIS) underestimation focusing on additional benefits of contrast-enhanced spectral mammography (CESM). Background: DCIS, a noninvasive breast cancer, is often underestimated during diagnosis. Predicting which cases will upgrade to invasive cancer is crucial. CESM offers superior diagnostic performance compared to mammography and mammography with targeted ultrasound (US). Its low-energy images improve microcalcification conspicuity, enable accurate tumor extent measurement, and provide information on enhancements associated with nonmass microcalcifications. Enhanced areas on CESM can be easily compared with mammographic microcalcifications, for they are obtained during the same session. Indicators heralding DCIS underestimation 1. Detectability: underestimated DCIS is more likely to be detected on CESM and mammogram than pure DICS, whereas detection rates do not statistically differ on US and MRI. 2. Lesion type: underestimated DCIS mostly presents as enhancing mass or nonmass lesions on CESM and calcifications on low-energy conventional mammography. 3. Disease extent: on CESM, pure DCIS show smaller tumor extent than the underestimated ones. 4. CESM gray value (CGV) of the lesion on recombined image: the pure DCIS types show lower mean, standard deviation, maximum, and difference values, whereas the underestimated types demonstrate lower minimum values, and vice versa. Case 1: Screen detected pure DCIS without postsurgical upgrade in an asymptomatic 44-year-old woman. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 1.5 cm pure ductal carcinoma in situ, intermediate nuclear grade with necrosis. (A) US shows a 1.3 cm irregular hypoechoic mass in the right breast. (B) Routine mammography demonstrates no definite abnormality in the right breast. (C) CESM shows a 1.2 cm irregular enhancing mass in the upper central portion of right breast and CGV was 2034 AU. (D) Contrast-enhanced MR images show a 2.1 cm irregular enhancing mass in 12 o’clock direction of the right breast. Case 2: Screen detected DCIS with postsurgical upgrade to microinvasive ductal carcinoma in a 64-year-old woman with nonspecific discomfort in her right breast. US-guided core biopsy yielded DCIS, high nuclear grade with necrosis. Postsurgical pathology confirmed a 2.8 cm sized microinvasive ductal carcinoma, high nuclear grade, histologic grade 3 with necrosis. (A) US demonstrates a 1.9 cm irregular hypoechoic mass with calcifications in 9 o’clock direction 2.1 cm from the nipple in the right breast. (B) Right magnification view shows a 2.5 cm-extent segmentally distributed fine pleomorphic calcifications in the right breast. (C) CESM shows a 3 cm irregular enhancing mass with calcifications in the right breast, and CGV was 2045 AU. (D) Contrast-enhanced MR images shows a 2.3 cm irregular enhancing mass in the right breast. Case 3: Screen detected DCIS with postsurgical upgrade to invasive ductal carcinoma with DCIS in a 47-year-old woman who had a family history of breast cancer. US-guided core biopsy yielded DCIS, intermediate nuclear grade with necrosis. Postsurgical pathology confirmed a 0.2 cm invasive ductal carcinoma with 2.4 cm DCIS. (A) US demonstrates a 2.3 cm irregular hypoechoic mass in the left breast. (B) Routine mammography shows asymmetry in the upper portion of the left breast. (C) CESM shows a 3 cm enhancing mass in the left breast, and CGV of the lesion on MLO-recombined image was 2072 AU. (D) Contrast-enhanced MR image shows a 6 cm irregular enhancing mass in the upper portion of the left breast. Conclusion: CESM provides added advantages over mammography and breast US in detecting underestimated DCIS, and it is comparable to breast MRI. Key indicators to consider include detectability, enhancing lesion, larger tumor extent, higher mean CGV on CESM. Citation Format: Minji Song, Hee Jung Shin. Case Studies on Detecting Ductal Carcinoma in Situ Using Contrast-Enhanced Spectral Mammography and Predictive Indicators for Underestimation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-08.

Abstract PO1-07-03: An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography

Abstract Objectives: Our study aims to develop an accurate and comprehensive deep neural network capable of classifying CESM images to aid in the early detection and diagnosis of breast cancer in clinical settings. Methods: We enrolled diagnostic CESM examinations conducted between January 1, 2019 and January 17, 2021. We developed and tested the performance of a multi-feature fusion network for breast lesion classification by combining low-energy (LE) and dual-energy subtracted (DES) images, dual-view, and bilateral information using a large and diverse CESM dataset. We have evaluated the ability of the proposed network to generalize on external datasets and the results were reported using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: In the study period 1973CESMs were included. Mean age was 53 years ± 12 (standard deviation). In the internal test dataset, the model with CESM (LE +DES) inputs combined not only bilateral information (left and right breasts) but also dualview information (CC and MLO), achieved best diagnosis performance with AUC of 0.96 [95% confidence interval (CI): 0.95–0.97], accuracy of 0.9 [95% confidence interval (CI): 0.88–0.92], sensitivity of 0.84 [95% confidence interval (CI): 0.80–0.88] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95], while the model with LE inputs only got an AUC of 0.94 [95% (CI): 0.93–0.95], accuracy of 0.88 [95% confidence interval (CI): 0.86–0.90], sensitivity of 0.79 [95% confidence interval (CI): 0.74–0.83] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95] (external dataset: an AUC of 0.90 [95% (CI): 0.85–0.94], accuracy of 0.84 [95% confidence interval (CI): 0.78–0.89], sensitivity of 0.77 [95% confidence interval (CI): 0.61–0.88] and specificity of 0.87 [95% confidence interval (CI):0.80–0.92]). Conclusion: CESM is a promising technique in breast cancer diagnosis due to its high feasibility and potential. The results demonstrate that left-right breast fusion and dual-view fusion are helpful for accurate diagnosis on CESM images. Specifically, dual-energy subtracted (DES) images generated by CESM improve diagnostic accuracy when compared to low-energy (LE) images alone. Keywords: Contrast-enhanced spectral mammography; Deep neural network; Breast cancer diagnosis; Multi-feature fusion Citation Format: Yipeng Song, Wei Jiang. An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-03.

A deep learning approach for virtual contrast enhancement in Contrast Enhanced Spectral Mammography

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first requires intravenously administering an iodinated contrast medium. Then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations, this work proposes using deep generative models for virtual contrast enhancement on CESM, aiming to make CESM contrast-free and reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model’s performance, also exploiting radiologists’ assessments, on a novel CESM dataset that includes 1138 images. As a further contribution to this work, we make the dataset publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field.

DIAGNOSTIC PERFORMANCE OF CONTRAST-ENHANCED SPECTRAL MAMMOGRAPHY: COMPARISON WITH CONTRAST-ENHANCED DYNAMIC MR IMAGING IN PATIENTS WITH SUSPECTED BREAST CANCER

OBJECTIVE: To compare the diagnostic performance of contrast-enhanced spectral mammography (CESM) with dynamic contrast-enhanced magnetic resonance (MR) imaging in terms of the detection of BI-RADS 4 and 5 lesions suspected of breast cancer. MATERIAL AND METHODS: 92 patients with ACR BI-RADS 4 and 5 lesions underwent CESM, MR Imaging, and consequent core biopsy. Two readers assessed the index lesions which were classified as mass lesions, non-mass lesions, and microcalcifications, and scored using a 7-point scoring system. RESULTS: A total of 98 index lesions were detected, including bilateral lesions in six patients. In histopathological analysis, 56 of the lesions were benign (56/98, 57%,) and 42 of the lesions were malignant (42/98, 43%). 55 of the lesions were classified as mass lesions, 18 as non-mass lesions, and 25 as microcalcifications. CESM scored 28 of the lesions (28,6%) as benign, and 70 (71,4%) of the lesions were malignant whereas these results were 30 (30,6%) and 68 (69,4%) for MR Imaging examinations, respectively. The sensitivity of both CESM and MR imaging for depicting the index cancer was 95 % for both modalities. In ROC (Receiver Operating Characteristic) analysis, AUC (Area Under the Curve) was 0.93 (%95 CI:0.870-0.977) for CESM and 0.94 (%95 CI:0.882-0.982) for MR Imaging. There was no statistically significant difference in AUC values between CESM and MR Imaging (p=0.332; p>0.05). CONCLUSIONS: The diagnostic performance of CESM is similar when compared to MR imaging in the detection of index cancers in patients with BI-RADS 4 and 5 lesions. CESM may be used as a confidential diagnostic tool in this regard.

Contrast-Enhanced Spectral Mammography Versus Magnetic Resonance Imaging: Intra- and Inter-Observer Agreements in Tumour Size Assessment

Contrast-Enhanced Spectral Mammography Versus Magnetic Resonance Imaging: Intra- and Inter-Observer Agreements in Tumour Size Assessment

Postoperative breast cancer surveillance: Can contrast-enhanced spectral mammography solve the diagnostic dilemma?

BackgroundFor women worldwide, breast cancer is considered a significant public health concern. The development of enormous changes caused by surgery and irradiation makes diagnosing the postoperative breast a complicated procedure. The current study aimed to detect the additive role of contrast-enhanced spectral mammogram (CESM) to digital mammogram and US in the surveillance of postoperative breast cancer patients.MethodsThis research was conducted on 74 female patients with a prior history of surgery for the treatment of previous breast cancer. All patients had undergone sonomammography and CESM. Benign lesions were followed up, while suspicious lesions were biopsied.ResultsThe current study revealed that CESM can enhance the sensitivity, specificity our overall accuracy of sonomammography in the surveillance of breast cancer patients after surgery.ConclusionAdding CESM to properly selected patients in the surveillance of breast cancer patients after surgical treatment can enhance the diagnostic performance of conventional imaging modalities.

27 (PB-8) Poster Spotlight - Role of contrast enhanced spectral mammography (CESM) in the evaluation of breast lesions

27 (PB-8) Poster Spotlight - Role of contrast enhanced spectral mammography (CESM) in the evaluation of breast lesions

Contrast-enhanced spectral mammography.

Contrast-enhanced spectral mammography.

YOLO-based CAD framework with ViT transformer for breast mass detection and classification in CESM and FFDM images

Breast cancer detection is considered a challenging task for the average experienced radiologist due to the variation of the lesions’ size and shape, especially with the existence of high fibro-glandular tissues. The revolution of deep learning and computer vision contributes recently in introducing systems that can provide an automated diagnosis for breast cancer that can act as a second opinion for doctors/radiologists. The most of previously proposed deep learning-based Computer-Aided Diagnosis (CAD) systems mainly utilized Convolutional Neural Networks (CNN) that focuses on local features. Recently, vision transformers (ViT) have shown great potential in image classification tasks due to its ability in learning the local and global spatial features. This paper proposes a fully automated CAD framework based on YOLOv4 network and ViT transformers for mass detection and classification of Contrast Enhanced Spectral Mammography (CESM) images. CESM is an evolution type of Full Field Digital Mammography (FFDM) images that provides enhanced visualization for breast tissues. Different experiments were conducted to evaluate the proposed framework on two different datasets that are INbreast and CDD-CESM that provides both FFDM and CESM images. The model achieved at mass detection a mean Average Precision (mAP) score of 98.69%, 81.52%, and 71.65% and mass classification accuracy of 95.65%, 97.61%, and 80% for INbreast, CE-CESM, and DM-CESM, respectively. The proposed framework showed competitive results regarding the state-of-the-art models in INbreast. It outperformed the previous work in the literature in terms of the F1-score by almost 5% for mass detection in CESM. Moreover, the experiments showed that the CESM could provide more morphological features that can be more informative, especially with the highly dense breast tissues.

Estudio comparativo de la dosis de radiación de la mamografía espectral con contraste (CEM), mamografía digital y tomosíntesis mamaria

IntroductionThe use of contrast-enhanced spectral mammography (CESM) has increased in recent years, as has awareness of radiation dose safety among professionals and patients. The principal aim of this study was to compare radiation exposure measured using the entrance surface dose (ESD) and the average glandular dose (AGD) in CESM, full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Our second objective was to evaluate differences caused by compressed breast thickness, compression force and patient age. Material and methodsA retrospective observational study included all patients who had undergone a CESM between May 2021 and May 2022. Data was collected on ESD and AGD from the different CESM studies, and breast density and volume were determined by 2expert radiologists. The comparative analysis focused on the dose of radiation received during the craniocaudal (CC) projection of the right breast in CESM studies and FFDM or DBT, performed within a 12-month period. Lastly, a statistical analysis was performed to determine the influence of breast compression thickness, compression force and patient age. ResultsSeventy-seven patients participated in the comparative study and forty-four in the dosimetric study. Differences in radiation dose (ESD/AGD) were found among the 3breast imaging techniques. The dose in CESM (1.70/5.39 mGy) was lower than in DBT (2.19/6.79 mGy) and higher than in FFDM (1.26/4.06 mGy) for an average breast compression thickness of 59.64mm. A positive correlation was observed between the dose received in CESM and breast compression thickness (ρ=0.55), and a negative correlation was observed with patient age (ρ= –0.27). No differences in dosimetric variables were observed for different compression forces. ConclusionsThe ESD and AGD in the CC projection of the right breast in CESM are higher than in FFDM but lower than in DBT. The dose had a positive correlation with breast compression thickness and a negative correlation with patient age.

Comparative Evaluation of Contrast-Enhanced Spectral Mammography and Digital Breast Tomosynthesis for Diagnosing and Treating Breast Cancer.

BACKGROUND The aim of this study was to evaluate the efficacy of contrast-enhanced spectral mammography (CESM) and digital breast tomosynthesis (DBT) in the diagnosis and chemotherapy of breast cancer. MATERIAL AND METHODS We retrospectively analyzed data on 125 lesions of 115 patients with breast diseases in Lanzhou First People's Hospital from January 2020 to June 2022. Patients were examined by digital breast tomographic fusion and contrast-enhanced spectral mammography after chemotherapy. We compared the diagnostic accuracy of the 2 imaging techniques, and the diagnostic efficacy was evaluated with ROC curves. RESULTS There were significant differences in the type and degree of CESM enhancement between benign and malignant lesions. Malignant lesions mostly showed moderate to severe enhancement, while benign lesions mostly showed mild to moderate enhancement. There was no significant difference in DBT manifestations between benign and malignant lesions. After neoadjuvant chemotherapy, 88 patients had pathological remission, and the remission rate was 70.40%. Thirty-seven patients did not respond (nonresponse rate: 29.60%). The accuracy of CESM lesion size assessment was 84.00% (105/125), with high consistency. The accuracy of DBT lesion size assessment was 68.00% (85/125), and the consistency was poor. BI-RADS 4B was the truncation point. CESM had significantly higher sensitivity, specificity, accuracy, positive predictive value, and negative predictive value than DBT. In premenopausal patients and patients aged less than 50 years, the diagnostic efficacy of DBT and CESM was significantly different. CONCLUSIONS The diagnostic efficacy of CESM was significantly better than DBT in premenopausal women and patients under age 50. Diagnosis and treatment of breast diseases may be enhanced by the use of CESM.

Modern Methods of Imaging of Breast Neoplasms (Literature Review)

High incidence of breast cancer is unchanged; therefore, it is relevant to study the possibilities of modern radiological methods for the diagnostics of breast cancer at an early stage. The purpose of the study was to analyze current trends of breast cancer diagnostics at early stages by modern imaging methods according to the literature. The systematic search was performed for reviews and meta-analyses in RSCI, PubMed, Scopus, Web of Science databases, published from 2012 to 2022, on breast cancer diagnostics using the keywords: breast cancer, mammography, digital breast tomosynthesis, Contrast-Enhanced Spectral Mammography, positron emission computed tomography, computed tomography, single photon emission computed tomography, ultrasound, ultrasound tomosynthesis, magnetic resonance imaging. Out of 100 publications published over the past 10 years, 48 were included in the presented review. The existing methods of diagnosing breast cancer have certain advantages and disadvantages relative to each other, the consideration of which is necessary when drawing up a plan of clarifying diagnostic measures, including women with high breast density. Promising methods of diagnosing breast cancer at an early stage — digital tomosynthesis, ultrasound tomosynthesis, dual-energy spectral contrast mammography, short MRI protocol are new and could be optimize the approach to the diagnosis of breast lesions in women with high breast density. This poorly studied methods require further research to optimize the breast cancer-screening algorithm, improve the indicators of early detection of breast cancer and increase economic efficiency.

Determining the Differentiation of Benign and Malignant NME Lesions in Contrast-Enhanced Spectral Mammography Images Based on Convolutional Neural Networks

Determining the Differentiation of Benign and Malignant NME Lesions in Contrast-Enhanced Spectral Mammography Images Based on Convolutional Neural Networks

Role of Contrast-Enhanced Spectral Mammography in the detection of breast cancer

Objective: to assess the benefits of using contrast-enhancer spectral mammography in patients with an established category of BI-RADS 4a, 4b according to the results of a standard examination (digital mammography and ultrasound). Materials and methods. The date of 425 patients with a suspicious category of BI-RADS in the period from 2018 to 2021 were analyzed. As part of the additional examination, all patients underwent contrast-enhanced spectral mammography follow by histological verification of the identified changes. The sensitivity and specificity of the methods were evaluated. Results. The final diagnosis of breast cancer was established in 247 (58.1%) patients out of 425 women. Multicenter and multifocal growth was diagnosed in 17 (6.8%) patients out of 247, and primary multiple synchronous cancer was detected in 6 (2.4%) patients out of 247. False-positive cases when assessing CESM mainly consisted of: typical and atypical ductal hyperplasia, fibroadenoma, intraductal papilloma. Conclusion. Contrast-enhancer spectral mammography is a highly sensitive and highly specific method in the detection of malignant tumors of the breast. The method also allows minimizing the number of unwanted interventions in suspicious BI-RADS categories.

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.