MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions

Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron

The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spectral analysis of surface-enhanced Raman scattering (SERS). By performing radical SERS scanning through the coffee-ring area and sampling across the coffee ring, complicated chemical information was spatially gathered for further spectra analysis. The corresponding application in classification of serum samples from breast cancer patients was also proposed. A simulated serum environment was constructed by mixing phenylalanine, hypoxanthine, and bovine serum albumin (BSA), yielding the coffee-ring patterns along with gold nanoparticles. Distinct divergence in the distributions between hypoxanthine and phenylalanine within the rings were characterized, which is attributed to the inherent electrostatic properties of the noble metal colloid and the interactions among different solvents. Subsequently, this method was applied to serum samples from patients diagnosed with the four breast cancer subtypes. By preparing serum with SERS substrates and forming the coffee-ring patterns, radial SERS scanning was conducted across the rings. The acquired spectra were spatially segmented and processed by employing a multilayer perceptron for learning and prediction. The classification results demonstrated a predictive accuracy of 85.7% in distinguishing among the four breast cancer subtypes, highlighting the feasibility and effectiveness of the coffee-ring assisted radial SERS analysis.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer

OBJECTIVE

To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features.

METHODS

We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves.

RESULTS

The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration.

CONCLUSION

This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Avoiding unnecessary sentinel lymph node biopsy with the use of superparamagnetic iron oxide mapping agents (Magtrace®) in breast surgery

BACKGROUND

Superparamagnetic iron oxide (SPIO) (Magtrace®) is a non-radioactive liquid tracer that can stay in the sentinel lymph nodes for 30 days. Injection of SPIO at time of primary breast surgery where upfront sentinel lymph node biopsy (SLNB) is not immediately indicated allows for a return to theatre if pathology then identifies invasive disease. SLNB is associated with paraesthesia, pain, seroma formation and lymphoedema risk. Hence, our study aims to assess the use of SPIO to avoid upfront SLNB in breast surgery for ductal carcinoma in situ (DCIS) and prophylaxis.

METHODS

Retrospective single-centre study of consecutive patients who underwent injection of SPIO tracer at time of primary breast surgery to avoid upfront SLNB at Chris O'Brien Lifehouse, Sydney, NSW, Australia over a 10-month period.

RESULTS

SPIO was injected 38 times, with 34 at time of mastectomy and four cases at time of wide local excision. The indication for surgery was DCIS in 18 cases, risk reduction in 17 cases and other indications in three patients. Six cases (15.8%) required delayed SLNB (D-SLNB) due to the finding of invasive disease on post-operative histopathology. All patients who underwent D-SLNB had nodes successfully localized with SPIO.

CONCLUSION

In our cohort, 84.2% of cases were able to avoid upfront SLNB, and hence avoid the associated complications of SLNB. SPIO injection was successful in localizing the SLN in all cases at time of surgery for D-SLNB. This technique was safe with few associated complications.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions

OBJECTIVES

To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications.

MATERIALS AND METHODS

From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes.

RESULTS

The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970).

CONCLUSION

In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity.

CLINICAL RELEVANCE STATEMENT

Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions.

KEY POINTS

• Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Estimating disparities in breast cancer screening programs towards mortality, case fatality, and DALYs across BRICS-plus

BACKGROUND

Numerous studies over the past four decades have revealed that breast cancer screening (BCS) significantly reduces breast cancer (BC) mortality. However, in BRICS-plus countries, the association between BCS and BC case fatality and disability are unknown. This study examines the association of different BCS approaches with age-standardized mortality, case-fatality, and disability-adjusted life years (DALYs) rates, as well as with other biological and sociodemographic risk variables, across BRICS-plus from a national and economic perspective.

METHODS

In this ecological study applying mixed-effect multilevel regression models, a country-specific dataset was analyzed by combining data from the Global Burden of Disease study 2019 on female age-standardized BC mortality, incidence, and DALYs rates with information on national/regional BCS availability (against no such program or only a pilot program) and BCS type (only self-breast examination (SBE) and/or clinical breast examination (CBE) [SBE/CBE] versus SBE/CBE with mammographic screening availability [MM and/or SBE/CBE] versus SBE/CBE/mammographic with digital mammography and/or ultrasound (US) [DMM/US and/or previous tests] in BRICS-plus countries.

RESULTS

Compared to self/clinical breast examinations (SBE/CBE) across BRICS-plus, more complex BCS program availability was the most significant predictor of decreased mortality [MM and/or SBE/CBE: - 2.64, p < 0.001; DMM/US and/or previous tests: - 1.40, p < 0.001]. In the BRICS-plus, CVD presence, high BMI, second-hand smoke, and active smoking all contributed to an increase in BC mortality and DALY rate. High-income and middle-income regions in BRICS-plus had significantly lower age-standardized BC mortality, case-fatality, and DALYs rates than low-income regions when nationwide BC screening programs were implemented.

CONCLUSIONS

The availability of mammography (digital or traditional) and BCS is associated with breast cancer burden in BRICS-plus countries, with regional variations. In light of high-quality evidence from previous causal studies, these findings further support the preventive role of mammography screening for BCS at the national level. Intervening on BCS related risk factors may further reduce the disease burden associated with BC.

History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century

Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized.