Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

Breast percent density changes in digital mammography pre- and post-radiotherapy

INTRODUCTION

Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy.

METHODS

This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density.

CONCLUSIONS

This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.

Intrafraction motion monitoring to determine PTV margins in early stage breast cancer patients receiving neoadjuvant partial breast SABR

BACKGROUND AND PURPOSE

To quantify intra-fraction tumor motion using imageguidance and implanted fiducial markers to determine if a 5 mm planning-target-volume (PTV) margin is sufficient for early stage breast cancer patients receiving neoadjuvant stereotactic ablative radiotherapy (SABR).

MATERIALS AND METHODS

A HydroMark© (Mammotome) fiducial was implanted at the time of biopsy adjacent to the tumor. Sixty-one patients with 62 tumours were treated prone using a 5 mm PTV margin. Motion was quantified using two methods (separate patient groups): 1) difference in 3D fiducial position pre- and post-treatment cone-beam CTs (CBCTs) in 18 patients receiving 21 Gy/1fraction (fx); 2) acquiring 2D triggered-kVimages to quantify 3D intra-fraction motion using a 2D-to-3D estimation method for 44 tumours receiving 21 Gy/1fx (n = 22) or 30 Gy/3fx (n = 22). For 2), motion was quantified by calculating the magnitude of intra-fraction positional deviation from the pretreatment CBCT. PTV margins were derived using van Herkian analysis.

RESULTS

The average ± standard deviation magnitude of motion across patients was 1.3 ± 1.15 mm Left/Right (L/R), 1.0 ± 0.9 mm Inferiorly/Superiorly (I/S), and 1.8 ± 1.5 mm Anteriorly/Posteriorly (A/P). 85/105 (81%) treatment fractions had dominant anterior motion. 6/62patients (9.7%) had mean intra-fraction motion during any fraction > 5 mm in any direction, with 4 in the anterior direction. Estimated PTV margins for single and three-fx patients in the L/R, I/S, and A/P directions were 6.0x4.1x5.9 mm and 4.5x2.9x4.3 mm, respectively.

CONCLUSION

Our results suggest that a 5 mm PTV margin is sufficient for the I/S and A/P directions if a lateral kV image is acquired immediately before treatment. For the L/R direction, either further immobilization or a larger margin is required.

Does breast composition influence late adverse effects in breast radiotherapy?

BACKGROUND

Large breast size is associated with increased risk of late adverse effects after surgery and radiotherapy for early breast cancer. It is hypothesised that effects of radiotherapy on adipose tissue are responsible for some of the effects seen. In this study, the association of breast composition with late effects was investigated along with other breast features such as fibroglandular tissue distribution, seroma and scar.

METHODS

The patient dataset comprised of 18 cases with changes in breast appearance at 2 years follow-up post-radiotherapy and 36 controls with no changes, from patients entered into the FAST-Pilot and UK FAST trials at The Royal Marsden. Breast composition, fibroglandular tissue distribution, seroma and scar were assessed on planning CT scan images and compared using univariate analysis. The association of all features with late-adverse effect was tested using logistic regression (adjusting for confounding factors) and matched analysis was performed using conditional logistic regression.

RESULTS

In univariate analyses, no statistically significant differences were found between cases and controls in terms of breast features studied. A statistically significant association (p < 0.05) between amount of seroma and change in photographic breast appearance was found in unmatched and matched logistic regression analyses with odds ratio (95% CI) of 3.44 (1.28-9.21) and 2.57 (1.05-6.25), respectively.

CONCLUSIONS

A significant association was found between seroma and late-adverse effects after radiotherapy although no significant associations were noted with breast composition in this study. Therefore, the cause for large breast size as a risk factor for late effects after surgery and optimally planned radiotherapy remains unresolved.

Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT

BACKGROUND

Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breast.

METHODS

Planning CT data of 23 patients were evaluated in this study. Texture features were derived from the radial glandular fraction (RGF), which described the distribution of FT within three breast regions (posterior, middle, and anterior). Using visual assessment, experts grouped patients according to FT spatial distribution: sparse or non-sparse. Differences in the features between the two groups were investigated using the Wilcoxon rank test. Classification performance of the features was evaluated for a range of support vector machine (SVM) classifiers.

RESULTS

Experts found eight patients and 15 patients had sparse and non-sparse spatial distribution of FT, respectively. A large proportion of features (>9 of 13) from the individual breast regions had significant differences (p <0.05) between the sparse and non-sparse group. The features from middle region had most significant differences and gave the highest classification accuracy for all the SVM kernels investigated. Overall, the features from middle breast region achieved highest accuracy (91%) with the linear SVM kernel.

CONCLUSION

This study found that features based on radial glandular fraction provide a means for discriminating between fibroglandular tissue distributions and could achieve a classification accuracy of 91%.

Quantitative analysis for breast density estimation in low dose chest CT scans

A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %-8.52 %) and 3.09 % (0.20 %-6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r=0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods.

The validation index: a new metric for validation of segmentation algorithms using two or more expert outlines with application to radiotherapy planning

Validation is required to ensure automated segmentation algorithms are suitable for radiotherapy target definition. In the absence of true segmentation, algorithmic segmentation is validated against expert outlining of the region of interest. Multiple experts are used to overcome inter-expert variability. Several approaches have been studied in the literature, but the most appropriate approach to combine the information from multiple expert outlines, to give a single metric for validation, is unclear. None consider a metric that can be tailored to case-specific requirements in radiotherapy planning. Validation index (VI), a new validation metric which uses experts' level of agreement was developed. A control parameter was introduced for the validation of segmentations required for different radiotherapy scenarios: for targets close to organs-at-risk and for difficult to discern targets, where large variation between experts is expected. VI was evaluated using two simulated idealized cases and data from two clinical studies. VI was compared with the commonly used Dice similarity coefficient (DSCpair - wise) and found to be more sensitive than the DSCpair - wise to the changes in agreement between experts. VI was shown to be adaptable to specific radiotherapy planning scenarios.