Fully automated breast segmentation on spiral breast computed tomography images

Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code

Objective. To compare the dosimetric performance of three cone-beam breast computed tomography (BCT) scanners, using real-time Monte Carlo-based dose estimates obtained with the virtual clinical trials (VCT)-BREAST graphical processing unit (GPU)-accelerated platform dedicated to VCT in breast imaging. Approach. A GPU-based Monte Carlo (MC) code was developed for replicatingin silicothe geometric, x-ray spectra and detector setups adopted, respectively, in two research scanners and one commercial BCT scanner, adopting 80 kV, 60 kV and 49 kV tube voltage, respectively. Our cohort of virtual breasts included 16 anthropomorphic voxelized breast phantoms from a publicly available dataset. For each virtual patient, we simulated exams on the three scanners, up to a nominal simulated mean glandular dose of 5 mGy (primary photons launched, in the order of 1011-1012per scan). Simulated 3D dose maps (recorded for skin, adipose and glandular tissues) were compared for the same phantom, on the three scanners. MC simulations were implemented on a single NVIDIA GeForce RTX 3090 graphics card.Main results.Using the spread of the dose distribution as a figure of merit, we showed that, in the investigated phantoms, the glandular dose is more uniform within less dense breasts, and it is more uniformly distributed for scans at 80 kV and 60 kV, than at 49 kV. A realistic virtual study of each breast phantom was completed in about 3.0 h with less than 1% statistical uncertainty, with 109primary photons processed in 3.6 s computing time.Significance. We reported the first dosimetric study of the VCT-BREAST platform, a fast MC simulation tool for real-time virtual dosimetry and imaging trials in BCT, investigating the dose delivery performance of three clinical BCT scanners. This tool can be adopted to investigate also the effects on the 3D dose distribution produced by changes in the geometrical and spectrum characteristics of a cone-beam BCT scanner.

Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient's Age and Breast Quadrant

OBJECTIVES

Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age.

METHODS

The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method.

RESULTS

The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast.

CONCLUSIONS

The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features-BTV, MGV, and PBD, as a function of the patient's age.

Diagnostic value of mammography density of breast masses by using deep learning

Objective

In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density.

Methods

This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity.

Results

In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001).

Conclusions

Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.

Radiation dose estimates based on Monte Carlo simulation for spiral breast computed tomography imaging in a large cohort of patients

BACKGROUND

Spiral breast computed tomography (BCT) equipped with a photon-counting detector (PCD) is a new radiological modality allowing for the compression-free acquisition of high-resolution 3-D datasets of the breast. Optimized dose exposu04170/re setups according to breast size were previously proposed but could not effectively be applied in a clinical environment due to ambiguity in measuring breast size.

PURPOSE

This study aims to report the standard radiation dose values in a large cohort of patients examined with BCT, and to provide a mathematical model to estimate radiation dose based on morphological features of the breast.

METHODS

This retrospective study was conducted on 1657 BCT examinations acquired between 2018 and 2021 from 829 participants (57 ± 10 years, all female). Applying a dedicated breast tissue segmentation algorithm and Monte Carlo (MC) simulation, mean absorbed dose (MAD), mean glandular dose (MGD), mean skin dose (MSD), maximum glandular dose (maxGD), and maximum skin dose (maxSD) were calculated and related to morphological features such as breast volume, effective diameter, breast length, skin volume, and glandularity. Effective dose (ED) was calculated by applying the corresponding beam and tissue weighting factors, 1 Sv/Gy and 0.12 per breast. Relevant morphological features predicting dose values were identified based on the Spearman's rank correlation coefficient. Exponential or bi-exponential models predicting the dose values as a function of morphological features were fitted by using a non-linear least squares (LS) method. The models were validated by assessing R² and residual standard error (RSE).

RESULTS

The most relevant morphological features for radiation dose estimation were the breast volume (correlation coefficient: -0.8), diameter (-0.7), and length (-0.6). The glandularity presented a weak-positive correlation (0.4) with MGD and maxGD due to the inhomogeneous distribution of the glandularity and absorbed dose in the 3-D breast volume. The standard MGDs were calculated to be 7.3 ± 0.7, 6.5 ± 0.3, and 5.9 ± 0.3 mGy, MADs to 7.6 ± 0.8, 6.8 ± 0.3, and 6.2 ± 0.3 mGy, maxSDs to 19.9 ± 1.6, 19.5 ± 0.5, and 18.9 ± 0.5 mGy, and EDs to 0.88 ± 0.08, 0.78 ± 0.04, and 0.72 ± 0.04 mSv for small, medium, and large breasts with average breast lengths of 5.9 ± 1.6, 8.7 ± 1.3, and 12.2 ± 2.0 cm, respectively. The estimated glandularity - 23.1 ± 16.9, 12.5 ± 11.4, and 6.9 ± 7.3% from small to large breasts. The mathematical models were able to estimate the MAD, MGD, MSD, and maxSD as a function of each morphological feature with only upto 0.5 mGy RSE.

CONCLUSION

We presented the typical morphological features and standard dose values according to the breast size acquired from a large patient cohort. We established radiation dose estimation models allowing accurate estimation of dose values including MGD with an acceptable RSE based on each of the easily measured morphological features of the breast. Clinicians could use the breast length to operate as a dosimetric alert of the scanner prior to a BCT scan. Radiation exposure for BCT was lower than diagnostic mammography (MG) and cone-beam breast CT (BCT).