Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformerbased neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909±0.069 versus 0.916±0.067, P<0.001) and on the external testset (0.824±0.144 versus 0.864±0.081, P=0.004). Moreover, the average symmetric surface distance was higher (=worse) for nnUNet than for TraBS on the internal (0.657±2.856 versus 0.548±2.195, P=0.001) and on the external testset (0.727±0.620 versus 0.584±0.413, P=0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening.

The primary objective of the study was to compare a spiral breast computed tomography system (SBCT) to digital breast tomosynthesis (DBT) for the detection of microcalcifications (MCs) in breast specimens. The secondary objective was to compare various reconstruction modes in SBCT. In total, 54 breast biopsy specimens were examined with mammography as a standard reference, with DBT, and with a dedicated SBCT containing a photon-counting detector. Three different reconstruction modes were applied for SBCT datasets (Recon1 = voxel size (0.15 mm)3, smooth kernel; Recon2 = voxel size (0.05 mm)3, smooth kernel; Recon3 = voxel size (0.05 mm)3, sharp kernel). Sensitivity and specificity of DBT and SBCT for the detection of suspicious MCs were analyzed, and the McNemar test was used for comparisons. Diagnostic confidence of the two readers (Likert Scale 1 = not confident; 5 = completely confident) was analyzed with ANOVA. Regarding detection of MCs, reader 1 had a higher sensitivity for DBT (94.3%) and Recon2 (94.9%) compared to Recon1 (88.5%; p < 0.05), while sensitivity for Recon3 was 92.4%. Respectively, reader 2 had a higher sensitivity for DBT (93.0%), Recon2 (92.4%), and Recon3 (93.0%) compared to Recon1 (86.0%; p < 0.05). Specificities ranged from 84.7–94.9% for both readers (p > 0.05). The diagnostic confidence of reader 1 was better with SBCT than with DBT (DBT 4.48 ± 0.88, Recon1 4.77 ± 0.66, Recon2 4.89 ± 0.44, and Recon3 4.75 ± 0.72; DBT vs. Recon1/2/3: p < 0.05), while reader 2 found no differences. Sensitivity and specificity for the detection of MCs in breast specimens is equal for DBT and SBCT when a small voxel size of (0.05 mm)3 is used with an equal or better diagnostic confidence for SBCT compared to DBT.

This study was performed to assess the prognostic relevance of genomic aberrations at chromosome 4q in NSCLC patients. We have previously identified copy number changes at 4q12-q32 to be significantly associated with the early hematogenous dissemination of non-small cell lung cancer (NSCLC), and now aim to narrow down potential hot-spots within this 107 Mb spanning region. Using eight microsatellite markers at position 4q12-35, allelic imbalance (AI) analyses were performed on a preliminary study cohort (n = 86). Positions indicating clinicopathological and prognostic associations in AI analyses were further validated in a larger study cohort using fluorescence in situ hybridization (FISH) in 209 NSCLC patients. Losses at positions 4q21.23 and 4q22.1 were shown to be associated with advanced clinicopathological characteristics as well as with shortened disease free (DFS) and overall survival (OS) (DFS: P = 0.019; OS: P = 0.002). Multivariate analyses identified the losses of 4q21.23-22.1 to be an independent prognostic marker for both DFS and OS in NSCLC (HR 1.64–2.20, all P<0.04), and especially in squamous cell lung cancer (P<0.05). A case report study of a lung cancer patient further revealed a loss of 4q21.23 in disseminated tumor cells (DTCs). Neither gains at the latter positions, nor genomic aberrations at 4q12, 4q31.2 and 4q35.1, indicated a prognostic relevance. In conclusion, our data indicate that loss at 4q21.23-22.1 in NSCLC is of prognostic relevance in NSCLC patients and thus, includes potential new tumor suppressor genes with clinical relevance.

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