Predictive Value of Breast MRI Background Parenchymal Enhancement for Neoadjuvant Treatment Response among HER2- Patients

Advances in analytical approaches for background parenchymal enhancement in predicting breast tumor response to neoadjuvant chemotherapy: A systematic review

BACKGROUND

Breast cancer (BC) continues to pose a substantial global health concern, necessitating continuous advancements in therapeutic approaches. Neoadjuvant chemotherapy (NAC) has gained prominence as a key therapeutic strategy, and there is growing interest in the predictive utility of Background Parenchymal Enhancement (BPE) in evaluating the response of breast tumors to NAC. However, the analysis of BPE as a predictive biomarker, along with the techniques used to model BPE changes for accurate and timely predictions of treatment response presents several obstacles. This systematic review aims to thoroughly investigate recent advancements in the analytical methodologies for BPE analysis, and to evaluate their reliability and effectiveness in predicting breast tumor response to NAC, ultimately contributing to the development of personalized and effective therapeutic strategies.

METHODS

A comprehensive and structured literature search was conducted across key electronic databases, including Cochrane Database of Systematic Reviews, Google Scholar, PubMed, and IEEE Xplore covering articles published up to May 10, 2024. The inclusion criteria targeted studies focusing on breast cancer cohorts treated with NAC, involving both pre-treatment and at least one post-treatment breast dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) scan, and analyzing BPE utility in predicting breast tumor response to NAC. Methodological quality assessment and data extraction were performed to synthesize findings and identify commonalities and differences among various BPE analytical approaches.

RESULTS

The search yielded a total of 882 records. After meticulous screening, 78 eligible records were identified, with 13 studies ultimately meeting the inclusion criteria for the systematic review. Analysis of the literature revealed a significant evolution in BPE analysis, from early studies focusing on single time-point BPE analysis to more recent studies adopting longitudinal BPE analysis. The review uncovered several gaps that compromise the accuracy and timeliness of existing longitudinal BPE analysis methods, such as missing data across multiple imaging time points, manual segmentation of the whole-breast region of interest, and over reliance on traditional statistical methods like logistic regression for modeling BPE and pathological complete response (pCR).

CONCLUSION

This review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of deep learning-based approaches for automated tumor segmentation, and implementation of advanced AI technique that can better accommodate varied breast tumor responses, non-linear relationships and complex temporal dynamics in BPE datasets, while also handling missing data more effectively. Such integration could lead to more precise and timely predictions of breast tumor responses to NAC, thereby enhancing personalized and effective breast cancer treatment strategies.

Assessment of breast composition in MRI using artificial intelligence - A systematic review

INTRODUCTION

Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family history. MRI could take advantage of the implementation of artificial intelligence (AI). AI assessment of breast composition factors is less studied than those of lesion detection and classification. These factors are breast density, background parenchymal enhancement (BPE) and fibroglandular tissue (FGT), which are recognized breast cancer phenotypes.

METHODS

Following PRISMA guidelines, the PROSPERO registered review examined the role of AI in assessing breast composition in MRI. A search of articles from Pubmed, Ovid, Embase, Web of Science, Cochrane, and Google scholar from 2010 to 2022 was conducted. Peer-reviewed, in-vivo studies were included based on defined search categories. Adapted QUADAS-2, CASP and Covidence tools were utilized for quality assessment.

RESULTS

Seven studies were identified as being of sufficiently high quality. The studies showed that AI has the potential to provide a comparable level of accuracy against the relevant reference standard. There were limited performance results when delineating BPE and FGT BI-RADs categories. The review highlighted the variability in AI models while the range of statistical methods and small cohort sizes limited cross study compatibility.

CONCLUSIONS

AI has potential in assessing breast composition in MRI. However, variability in AI systems deployed and statistical measurements alongside limited validation across diverse populations remain an issue. AI systems may perform better with binary categorizations rather than the quaternary spectrum of BI-RADS.

IMPLICATIONS FOR PRACTICE

AI could assist in developing personalized breast composition assessments. Future developments could focus on better delineation of breast composition categories. AI models that have trained on more diverse and larger populations should result in more robust and effective clinical applications.

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms

OBJECTIVE

To construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT.

METHODS

A total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR.

RESULTS

For signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model.

CONCLUSION

The addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.

Predictive value of background parenchymal enhancement on breast magnetic resonance imaging for pathological tumor response to neoadjuvant chemotherapy in breast cancers: a systematic review

OBJECTIVES

This review aimed to assess the predictive value of background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) as an imaging biomarker for pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT).

METHODS

Two reviewers independently performed a systemic literature search using the PubMed, MEDLINE, and Embase databases for studies published up to 11 June 2022. Data from relevant articles were extracted to assess the relationship between BPE and pCR.

RESULTS

This systematic review included 13 studies with extensive heterogeneity in population characteristics, MRI follow-up points, MRI protocol, NACT protocol, pCR definition, and BPE assessment. Baseline BPE levels were not associated with pCR, except in 1 study that reported higher baseline BPE of the younger participants (< 55 years) in the pCR group than the non-pCR group. A total of 5 studies qualitatively assessed BPE levels and indicated a correlation between reduced BPE after NACT and pCR; however, among the studies that quantitatively measured BPE, the same association was observed only in the subgroup analysis of 2 articles that assessed the status of hormone receptor and human epidermal growth factor receptor 2. In addition, the predictive ability of early BPE changes for pCR was reported in several articles and remains controversial.

CONCLUSIONS

Changes in BPE may be a promising imaging biomarker for predicting pCR in breast cancer. Because current studies remain insufficient, particularly those that quantitatively measure BPE, prospective and multicenter large-sample studies are needed to confirm this relationship.

Magnetic Resonance Perfusion Imaging for Breast Cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, carrying a significant socioeconomic burden. Breast cancer is a heterogeneous disease with 4 major subtypes identified. Each subtype has unique prognostic factors, risks, treatment responses, and survival rates. Advances in targeted therapies have considerably improved the 5-year survival rates for primary breast cancer patients largely due to widespread screening programs that enable early detection and timely treatment. Imaging techniques are indispensable in diagnosing and managing breast cancer. While mammography is the primary screening tool, MRI plays a significant role when mammography results are inconclusive or in patients with dense breast tissue. MRI has become standard in breast cancer imaging, providing detailed anatomic and functional data, including tumor perfusion and cellularity. A key characteristic of breast tumors is angiogenesis, a biological process that promotes tumor development and growth. Increased angiogenesis in tumors generally indicates poor prognosis and increased risk of metastasis. Dynamic contrast-enhanced (DCE) MRI measures tumor perfusion and serves as an in vivo metric for angiogenesis. DCE-MRI has become the cornerstone of breast MRI, boasting a high negative-predictive value of 89% to 99%, although its specificity can vary. This review presents a thorough overview of magnetic resonance (MR) perfusion imaging in breast cancer, focusing on the role of DCE-MRI in clinical applications and exploring emerging MR perfusion imaging techniques.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population

BACKGROUND

Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.

METHODS

Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation.

RESULTS

pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05).

CONCLUSION

Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.

A Diagnostic Dilemma: New Enhancing Suspicious Findings on Breast MRI Following Neoadjuvant Chemotherapy

OBJECTIVE

Evaluate the incidence and outcome of new enhancing findings on breast MRI after neoadjuvant chemotherapy (NAC).

METHODS

This IRB-approved retrospective review included women with breast cancer undergoing MRI to evaluate NAC response at our institution from January 1, 1998 to March 3, 2021. Post-NAC MRIs given BI-RADS 4 or 5 with new enhancing findings were identified. Patients were excluded if they lacked pretreatment MRI or insufficient follow-up, or if the finding was a satellite of the primary tumor. Medical records and imaging studies were reviewed to identify patients and to find characteristics and outcomes.

RESULTS

Over the study period, 2880 post-NAC breast MRIs were performed. Of 128 post-NAC MRIs given BI-RADS 4 or 5 (4.4%), 35 new suspicious findings were found on 32 MRIs, incidence rate 1.1% (32/2880). Most were characterized as nonmass enhancement (17/35, 49%), followed by mass (11/35, 31%), and then focus (7/35, 20%), with an average maximum dimension of 1.3 cm (range 0.3-7.1 cm). New findings were ipsilateral to the index cancer in 20/35 (57%) of cases. Of the 35 suspicious findings, 22 underwent image-guided biopsy (62%), 1 was surgically excised (3%), 7 underwent mastectomy (20%), 5 were stable or resolved on follow-up (8%), and none were malignant. Thirty-three were benign (94%), and two were benign high-risk lesions (atypical ductal hyperplasia, radial scar) (6%).

CONCLUSION

New suspicious breast MRI findings after NAC are uncommon with a low likelihood of malignancy. Further study is warranted using multi-institutional data for this low incidence finding.

Background Parenchymal Enhancement on Breast MRI: Assessment and Clinical Implications

Purpose of Review

To present recent literature regarding the assessment and clinical implications of background parenchymal enhancement on breast MRI.

Recent Findings

The qualitative assessment of BPE remains variable within the literature, as well as in clinical practice. Several different quantitative approaches have been investigated in recent years, most commonly region of interest-based and segmentation-based assessments. However, quantitative assessment has not become standard in clinical practice to date. Numerous studies have demonstrated a clear association between higher BPE and future breast cancer risk. While higher BPE does not appear to significantly impact cancer detection, it may result in a higher abnormal interpretation rate. BPE is also likely a marker of pathologic complete response after neoadjuvant chemotherapy, with decreases in BPE during and after neoadjuvant chemotherapy correlated with pCR. In contrast, pre-treatment BPE does not appear to be predictive of pCR. The association between BPE and prognosis is less clear, with heterogeneous results in the literature.

Summary

Assessment of BPE continues to evolve, with heterogeneity in approaches to both qualitative and quantitative assessment. The level of BPE has important clinical implications, with associations with future breast cancer risk and treatment response. BPE may also be an imaging marker of prognosis, but future research is needed on this topic.

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Background: For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. Method: Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). Results: We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. Conclusions: The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics.