BACKGROUND AND PURPOSE: Normative age-related decline in paravertebral muscle quality is important for reference to disease and risk identification in patients. We aimed to establish age- and vertebral level–dependence of paravertebral (multifidus and erector spinae) muscle volume and fat content in healthy adult volunteers. MATERIALS AND METHODS: In this prospective study multifidus and erector spinae fat signal fraction and volume at lumbar levels L1–L5 were measured in 80 healthy volunteers (10 women and men per decade, 20–62 years of age) by 2-point Dixon 3T MR imaging. ANOVA with post hoc Bonferroni correction compared fat signal fraction and volume among subgroups. Pearson and Spearman analysis were used for correlations (P < .05). RESULTS: Fat signal fraction was higher in women (17.8% ± 10.7%) than men (14.7% ± 7.8%; P < .001) and increased with age. Multifidus and erector spinae volume was lower in women (565.4 ± 83.8 cm³) than in men (811.6 ± 98.9 cm³; P < .001) and was age-independent. No differences in fat signal fraction were shown between the right and left paravertebral muscles or among the L1, L2, and L3 lumbar levels. The fat signal fraction was highest at L5 (women, 31.9% ± 9.3%; men, 25.7% ± 8.0%; P < .001). The fat signal fraction at L4 correlated best with total lumbar fat signal fraction (women, r = 0.95; men, r = 0.92, P < .001). Total fat signal fraction was higher in the multifidus compared with erector spinae muscles at L1–L4 for both sexes (P < .001). CONCLUSIONS: Lumbar paravertebral muscle fat content increases with aging, independent of volume, in healthy volunteers 20–62 years of age. Women, low lumbar levels, and the multifidus muscle are most affected. Further study examining younger and older subjects and the functional impact of fatty infiltrated paravertebral muscles are warranted.

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a “one-size-fits-all” approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. Key Points: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. Key recommendations: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35–40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians.

Objectives: The aim of the article was to evaluate the lesion detectability, image quality, and radiation dose of a dedicated clinical spiral breast computed tomography (CT) system equipped with a photon-counting detector, and to propose optimal scan parameter settings to achieve low patient dose levels and optimal image quality. Methods: A breast phantom containing inserts mimicking microcalcifications (diameters 196, 290, and 400 μm) and masses (diameters 1.8, 3.18, 4.76, and 6.32 mm) was examined in a spiral breast CT system with systematic variations of x-ray tube currents between 5 and 125 mA, using 2 slabs of 100 and 160 mm. Signal-to-noise ratio and contrast-to-noise ratio measurements were performed by region of interest analysis. Two experienced radiologists assessed the detectability of the inserts. The average absorbed dose was calculated in Monte Carlo simulations. Results: Microcalcifications in diameters of 290 and 400 μm and masses in diameters of 3.18, 4.76, and 6.32 mm were visible for all tube currents between 5 and 125 mA. Soft tissue masses in a diameter of 1.8 mm were visible at tube currents of 25 mA and higher. Microcalcifications with a diameter of 196 μm were detectable at a tube current of 25 mA and higher in the small, and at a tube current of 40 mA and higher in the large slab. For the small and large breast, at a tube current of 25 and 40 mA, an average dose value of 4.30 ± 0.01 and 5.70 ± 0.02 mGy was calculated, respectively. Conclusions: Optimizing tube current of spiral breast CT according to the breast size enables the visualization of microcalcifications as small as 196 μm while keeping dose values in the range of conventional mammography.

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling. The icons were sorted into three categories: “no opacities” (BI-RADS 1), “probably benign opacities” (BI-RADS 2/3) and “suspicious opacities” (BI-RADS 4/5). A dCNN was trained (70% of data), validated (20%) and finally tested (10%). A sliding window approach was applied to create colored probability maps for visual impression. Diagnostic performance of the dCNN was compared to human readout by experienced radiologists on a “real-world” dataset. The accuracies of the models on the test dataset ranged between 73.8% and 89.8%. Compared to human readout, our dCNN achieved a higher specificity (100%, 95% CI: 85.4–100%; reader 1: 86.2%, 95% CI: 67.4–95.5%; reader 2: 79.3%, 95% CI: 59.7–91.3%), and the sensitivity (84.0%, 95% CI: 63.9–95.5%) was lower than that of human readers (reader 1:88.0%, 95% CI: 67.4–95.4%; reader 2:88.0%, 95% CI: 67.7–96.8%). In conclusion, a dCNN can be used for the automatic detection as well as the standardized and observer-independent classification of soft tissue opacities in mammograms independent of the presence of microcalcifications. Human decision making in accordance with the BI-RADS classification can be mimicked by artificial intelligence.