ObjectivesContrast-enhanced spectral mammography (CESM) is a novel method for breast cancer detection. The aim of this study is to check if there is a possibility of quantitative assessment of contrast enhancement in CESM and if there is any correlation between quantitative assessment of contrast enhancement in CESM and histopathology.MethodsA total of 167 female patients underwent CESM. All subjects previously had suspicious lesions found on mammography, breast ultrasound, or both. After imaging, the following parameters were evaluated: number of enhancing lesions in each breast and size and degree of enhancement of each lesion. Based on the collected data, the percentage signal difference between enhancing lesion and background (%RS) and signal-difference-to-noise ratio (SDNR) were measured for each lesion.ResultsThe number of lesions detected in the study population was 195. Among all diagnosed lesions, 120 (62%) were assessed to be infiltrating cancers, 16 (8%) non-infiltrating cancers, and 59 (30%) were benign. Thirteen (7%) lesions did not enhance in CESM; all non-enhancing lesions were confirmed to be benign under histopathological examination. Analysis of enhancement indices showed that signal values within lesions and signal values within background ROIs (regions of interest) were similar in CC (craniocaudal) and MLO (mediolateral) projections. Mean %RS values were correlated with the type of enhancing lesion, infiltrating cancers having the highest values, benign lesions the lowest.ConclusionsThis work has demonstrated a significant correlation between the degree of lesion enhancement in CESM and malignancy. Quantitative analysis of enhancement levels in CESM can distinguish between invasive cancers and benign or in situ lesions.Key Points • There is a possibility of quantitative assessment of contrast enhancement in CESM. • Correlation between quantitative assessment of contrast enhancement in CESM and histopathology was observed.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set ( n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference ( P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists’ performance improved when their predictions were averaged with DL’s predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.

Purpose: Breast cancer is the most common cause of death from neoplastic disease in women. Among all breast anatomy types, glandular type is the most problematic concerning evaluation. While digital mammography still remains the basic diagnostic tool, one must be aware of its limitations in dense breasts. Although magnetic resonance imaging (MRI) has greatly improved sensitivity, its specificity is low. Moreover, there are contraindications for MRI for some patients, so a substitute has been searched for. This study was performed to check if contrast-enhanced spectral mammography (CESM) can be a viable option for patients with dense breasts. Material and methods:The study involved 121 patients with abnormalities detected on base-line diagnostic imaging (ultrasound or mammography). The patients had subsequent examinations, both CESM and MRI performed within a maximum 2-month time interval. The sensitivity and specificity of both methods in the whole group as well as in specific breast structure types were measured and compared.Results: Contrast enhancement was visible in all 121 cases on MRI, while on CESM lack of enhancement was noted in 13 cases. All of those 13 lesions turned out to be benign. There were 40 (33%) benign and 81 (69%) malignant tumours. The analysed group included 53 (44%) glandular type breast patients, 39 (32%) mixed type, and 29 (23%) fatty type. Although MRI proved to be slightly more effective in dense breasts, both methods showed similar results in the whole study group. Conclusion:CESM can be used with confidence in patients with glandular breast type when MRI is not available or there are reported contraindications to MRI.

Breast cancer, which is the most common cancer in women, is a major problem both in Poland and worldwide. Mammography remains the primary screening method. However, the sensitivity of mammographic screening is lower in women with dense glandular breasts due to tissue overlap and the effect of the glandular tissue obscuring the tumor and the fact that tumors and glandular tissue show similar X-ray absorption. Consequently, other methods are being sought to increase breast cancer detection rates. Currently, the most common and used methods are ultrasonography, magnetic resonance imaging and advanced mammographic methods (digital breast tomosynthesis and contrast-enhanced spectral mammography). Despite many advantages and superiority over mammography in dense breasts, they also have many disadvantages. Ultrasound is operator-dependent and the other techniques are expensive or not widely available. The Automated Breast Ultrasound Service (ABUS) technique appears to be a good option in terms of both effectiveness and lower cost.

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