Imaging for local recurrence of breast cancer

PURPOSE

Isolated locoregional recurrence of breast cancer (ILRR) and contralateral breast cancer (CBC) affect up to 20% of all breast cancer (BC) patients in the first 20 years after primary diagnosis. Treatment options comprise surgical interventions and further systemic therapies depending on the histological subtype. Patients with hereditary breast or ovarian cancer (HBOC) undergo MRI, mammography, and ultrasound in the aftercare of BC, while non-HBOC (nHBOC) patients do not regularly receive MRI. Since early detection is crucial for morbidity and mortality, the evaluation and constant improvement of imaging methods of the breast is necessary.

METHODS

We retrospectively analyzed the data of 1499 former BC patients that received imaging of the breast at a tertiary-care university hospital between 2015 and 2020. The analysis comprised various patient characteristics, such as breast density, age, tumor size and subtype, and their influence on BC detection rates by the different imaging methods.

RESULTS

Within the patient sample, 176 individuals (11.7% of former BC patients) were diagnosed with either ILRR or CBC. CBC was observed in 32.4% of patients, while both ILRR and secondary breast cancer occurred in 20.5% and 23.9% of all patients. Sensitivity of MRI, mammography, and ultrasound for recurrent malignancy was 97.9%, 66.3%, and 67.8%, respectively. ILRR and CBC detection rates were similar for patients with and without HBOC history. Lower breast density and larger tumor size increased the detection rates of all imaging modalities.

CONCLUSION

In breast cancer survivors, MRI might improve the early detection of ILRR and CBC in both HBOC and nHBOC patients.

Breast lesion morphology assessment with high and standard b values in diffusion-weighted imaging at 3 Tesla

INTRODUCTION

With increasing spatial resolution, diffusion-weighted imaging (DWI) may be suitable for morphologic lesion characterization in breast MRI - an area that has traditionally been occupied by dynamic contrast-enhanced imaging (DCE). This investigation compared DWI with b values of 800 and 1600 s/mm2 to DCE for lesion morphology assessment in high-resolution breast MRI at 3 Tesla.

MATERIAL AND METHODS

Multiparametric breast MRI was performed in 91 patients with 93 histopathologically proven lesions (31 benign, 62 malignant). Two radiologists independently evaluated three datasets per patient (DWIb800; DWIb1600; DCE) and assessed lesion visibility and BIRADS morphology criteria. Diagnostic accuracy was compared among readers and datasets using Cochran's Q test and pairwise post-hoc McNemar tests. Bland-Altman analyses were conducted for lesion size comparisons.

RESULTS

Discrimination of carcinomas was superior compared to benign findings in both DWIb800 and DWIb1600 (p < 0.001) with no b value-dependent difference. Similarly, assessability of mass lesions was better than of non-mass lesions, irrespective of b value (p < 0.001). Intra-reader reliability for the analysis of morphologic BIRADS criteria among DCE and DWI datasets was at least moderate (Fleiss κ≥0.557), while at least substantial inter-reader agreement was ascertained over all assessed categories (κ≥0.776). In pairwise Bland-Altman analyses, the measurement bias between DCE and DWIb800 was 0.7 mm, whereas the difference between DCE and DWIb1600 was 2.8 mm. DWIb1600 allowed for higher specificity than DCE (p = 0.007/0.062).

CONCLUSIONS

DWI can be employed for reliable morphologic lesion characterization in high-resolution breast MRI. High b values increase diagnostic specificity, while lesion size assessment is more precise with standard 800 s/mm2 images.

Influence of spectral shaping and tube voltage modulation in ultralow-dose computed tomography of the abdomen

PURPOSE

Unenhanced abdominal CT constitutes the diagnostic standard of care in suspected urolithiasis. Aiming to identify potential for radiation dose reduction in this frequent imaging task, this experimental study compares the effect of spectral shaping and tube voltage modulation on image quality.

METHODS

Using a third-generation dual-source CT, eight cadaveric specimens were scanned with varying tube voltage settings with and without tin filter application (Sn 150, Sn 100, 120, 100, and 80 kVp) at three dose levels (3 mGy: standard; 1 mGy: low; 0.5 mGy: ultralow). Image quality was assessed quantitatively by calculation of signal-to-noise ratios (SNR) for various tissues (spleen, kidney, trabecular bone, fat) and subjectively by three independent radiologists based on a seven-point rating scale (7 = excellent; 1 = very poor).

RESULTS

Irrespective of dose level, Sn 100 kVp resulted in the highest SNR of all tube voltage settings. In direct comparison to Sn 150 kVp, superior SNR was ascertained for spleen (p ≤ 0.004) and kidney tissue (p ≤ 0.009). In ultralow-dose scans, subjective image quality of Sn 100 kVp (median score 3; interquartile range 3-3) was higher compared with conventional imaging at 120 kVp (2; 2-2), 100 kVp (1; 1-2), and 80 kVp (1; 1-1) (all p < 0.001). Indicated by an intraclass correlation coefficient of 0.945 (95% confidence interval: 0.927-0.960), interrater reliability was excellent.

CONCLUSIONS

In abdominal CT with maximised dose reduction, tin prefiltration at 100 kVp allows for superior image quality over Sn 150 kVp and conventional imaging without spectral shaping.

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI

BACKGROUND

For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising.

PURPOSE

To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI.

STUDY TYPE

Retrospective.

POPULATION

133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2 ).

ASSESSMENT

DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale.

STATISTICAL TESTS

Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant.

RESULTS

Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error.

DATA CONCLUSION

Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

Diffusion-weighted Breast MRI at 3 Tesla: Improved Lesion Visibility and Image Quality with a Combination of Water-excitation and Spectral Fat Saturation

RATIONALE AND OBJECTIVES

In breast MRI with diffusion-weighted imaging (DWI), fat suppression is essential for eliminating the dominant lipid signal. This investigation evaluates a combined water-excitation-spectral-fatsat method (WEXfs) versus standard spectral attenuated inversion recovery (SPAIR) in high-resolution 3-Tesla breast MRI.

MATERIALS AND METHODS

Multiparametric breast MRI with 2 echo-planar DWI sequences was performed in 83 patients (50.1 ± 12.6 years) employing either WEXfs or SPAIR for fat signal suppression. Three radiologists assessed overall DWI quality and delineability of 88 focal lesions (28 malignant, 60 benign) on images with b values of 800 and 1600 s/mm2, as well as apparent diffusion coefficient (ADC) maps. For each fat suppression method and b value, the longest lesion diameter was determined in addition to measuring the signal intensity in DWI and ADC value in standardized regions of interest.

RESULTS

Regardless of b values, image quality (all p < 0.001) and lesion delineability (all p ≤ 0.003) with WEXfs-DWI were deemed superior compared to SPAIR-DWI in benign and malignant lesions. Irrespective of lesion characterization, WEXfs-DWI provided superior signal-to-noise, contrast-to-noise and signal-intensity ratios with 1600 s/mm2 (all p ≤ 0.05). The lesion size difference between contrast-enhanced T1 subtraction images and DWI was smaller for WEXfs compared to SPAIR fat suppression (all p ≤ 0.007). The mean ADC value in malignant lesions was lower for WEXfs-DWI (p < 0.001), while no significant ADC difference was ascertained between both techniques in benign lesions (p = 0.947).

CONCLUSION

WEXfs-DWI provides better subjective and objective image quality than standard SPAIR-DWI, resulting in a more accurate estimation of benign and malignant lesion size.

Whitepaper: Training in Diagnostic and Interventional Breast Radiology

PURPOSE

Breast imaging represents an integral part of radiology and is subject to strict quality controls. Regarding this, precise diagnostics including multimodal assessment by mammography, sonography, and MRI, including image-guided biopsy and localization procedures, is often decisive and must be performed by experts with profound knowledge and skills in all of these procedures.However, due to numerous restructurings, breast imaging has been shifted more and more towards large, specialized centers, resulting in less patient exposition and training opportunities for radiologists in smaller sites. The following whitepaper summarizes the current circumstances and discusses opinions of the participating societies.

MATERIALS

Under the leadership of the German Roentgen Society (DRG) and with the participation of the DRG's AG Mammadiagnostik, the CAFRAD (Chefarztforum Radiologie), the KLR (Konferenz der Lehrstuhlinhaber für Radiologie e. V.), the DRG's Forum Junge Radiologie (FJR) and the Berufsverband der Deutschen Radiologen e. V. (BDR), possible solutions were discussed and consented for a structured training in breast radiology in the future.

RESULTS

In addition to the teaching provided at the primary workplace, qualified training should be ensured through flexible, multi-institutional, interdisciplinary, and cross-sectoral collaboration. Furthermore, the integration of online case collections and close cooperation with certified breast cancer centers and mammography screening units is recommended. It is indispensible that online courses and case collections adhere to the standards of the national societies and include a maximum of one third of the required cases.

CONCLUSION

In order to provide training in breast radiology at a high professional level, a paradigm shift with closer cooperation of all participants is necessary. This includes close collaboration of the breast imaging societies with the federal medical associations to establish new teaching concepts like e-learning in the training schedule of radiologists.

KEY POINTS

· Breast diagnostics is an integral part of radiology training.. · Due to recent restructurings, smaller training centers have difficulties in meeting the case numbers demanded by the Specialist Training Regulations (WBO). Improved integration of the new structures and their adaptation to the needs of education are necessary to guarantee standardized high-quality training of young radiologists.. · The integration of certified case collections enables quality-assured training, even across regions in online-based formats. In accordance with the "blended learning principle", up to one-third of the required number of patient studies can be substituted with cases from a certified case collection.. · Legally secured short- and medium-term internships may complement training in radiology..

CITATION FORMAT

· Sauer ST, Bley TA, Wenkel E et al. Whitepaper: Training in Diagnostic and Interventional Breast Radiology. Fortschr Röntgenstr 2023; 195: 699 - 706.

Obesity-Related Pitfalls of Virtual versus True Non-Contrast Imaging-An Intraindividual Comparison in 253 Oncologic Patients

OBJECTIVES

Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients.

MATERIALS AND METHODS

A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m²) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m² (n = 110), pre-obese: 25-29.9 kg/m² (n = 73), and obese: >30 kg/m² (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view.

RESULTS

While arterial contrast phases in DECT were associated with a higher CTDI_vol than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R² = 0.738) and SECT (R² = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1%) compared with non-obese (0%) and pre-obese patients (4.1%).

CONCLUSION

DECT facilitates a 30.8% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.

Artificial-intelligence-enhanced synthetic thick slabs versus standard slices in digital breast tomosynthesis

Objectives:

Digital breast tomosynthesis (DBT)can provide additional information over mammography, albeit at the cost of prolonged reading time. Thisstudy retrospectively investigated the impact of reading enhanced synthetic 6 mm slabsinstead of standard 1 mm slices on interpretation time andreadersperformance in a diagnostic assessment centre.

Methods:

Three radiologists (R1-3; 6/4/2 years of breast imaging experience) reviewed 111 diagnostic DBT examinations. Two datasets were interpreted independently for each patient, with one set containing artificial-intelligence-enhanced synthetic 6 mm slabs with 3 mm overlap, while the other set comprised standard 1 mm slices. Blinded to histology and follow-up, readers noted individual BIRADS categoriesand diagnostic confidence while reading time was recorded. Among the 111 examinations, 70 findings were histopathologicallycorrelated including 56 malignancies.

Results:

No significant difference was found between BIRADS categories assigned based on6-mm vs 1 mm datasets (p ≥ 0.317). Diagnostic accuracy was comparable for 6 mm and 1 mm readings (R1: 87.0% vs 87.0%; R2: 86.1% vs 87.0%; R3: 80.0% vs 84.4%; p ≥ 0.125) with high interrater agreement (intraclass correlation coefficient 0.848 vs 0.865).Onereader reported higher confidence with 1 mm slices (R1: p = 0.033). Reading time was substantially shorter when interpreting 6 mm slabs compared to 1 mm slices (R1: 33.5 vs 46.2; R2: 49.1 vs 64.8; R3: 39.5 vs 67.2 sec; all p < 0.001).

Conclusions:

Artificial-intelligence-enhancedsynthetic 6 mm slabs allow for substantial interpretation time reduction in diagnostic DBT without a decrease inreader accuracy.

Advances in knowledge:

A simplified slab-only protocol instead of 1 mm slices may offset the higher reading time without a loss of diagnosis-relevant image information in first and second readings. Further evaluations are required regarding workflow implications,particularlyin screening settings.

Potential of employing a quantum iterative reconstruction algorithm for ultra-high-resolution photon-counting detector CT of the hip

INTRODUCTION

This study investigated the image quality of a new quantum iterative reconstruction algorithm (QIR) for high resolution photon-counting CT of the hip.

METHODS

Using a first-generation photon-counting CT scanner, five cadaveric specimens were examined with ultra-high-resolution protocols matched for radiation dose. Images were post-processed with a sharp convolution kernel and five different strength levels of iterative reconstruction (QIR 0 - QIR 4). Subjective image quality was rated independently by three radiologists on a five-point scale. Intraclass correlation coefficients (ICC) were computed for assessing interrater agreement. Objective image quality was evaluated by means of contrast-to-noise-ratios (CNR) in bone and muscle tissue.

RESULTS

For osseous tissue, subjective image quality was rated best for QIR 2 reformatting (median 5 [interquartile range 5-5]). Contrarily, for soft tissue, QIR 4 received the highest ratings among compared strength levels (3 [3-4]). Both ICC_bone (0.805; 95% confidence interval 0.711-0.877; p < 0.001) and ICC_muscle (0.885; 0.824-0.929; p < 0.001) suggested good interrater agreement. CNR in bone and muscle tissue increased with ascending strength levels of iterative reconstruction with the highest results recorded for QIR 4 (CNR_bone 29.43 ± 2.61; CNR_muscle 8.09 ± 0.77) and lowest results without QIR (CNR_bone 3.90 ± 0.29; CNR_muscle 1.07 ± 0.07) (all p < 0.001).

CONCLUSION

Reconstructing photon-counting CT data with an intermediate QIR strength level appears optimal for assessment of osseous tissue, whereas soft tissue analysis benefitted from applying the highest strength level available.

IMPLICATIONS FOR PRACTICE

Quantum iterative reconstruction technique can enhance image quality by significantly reducing noise and improving CNR in ultra-high resolution CT imaging of the hip.