Mammographic density to predict response to neoadjuvant systemic breast cancer therapy

Does Mammography Density Change the Response to Neoadjuvant Chemotherapy and Predict a Pathological Complete Response Rate?

BACKGROUND

Pathological complete response (PCR) is the surrogate marker of the outcome of a breast cancer patient. Breast cancer (BC) patients have variable responses to neoadjuvant chemotherapy (NACT). The effect of chemotherapy on mammographic density (MD) is unclear in the literature. Also, MD and PCR correlation is not extensively studied. The aim of the present study is to find MD's potential as a PCR predictor in a resource-constrained setting.

METHODS

The study included all patients of BC-related surgery from January 2018 to June 2021 with follow-up till June 2023. MD was classified by the American College of Radiology (ACR) (classes A-D) based on breast composition. The chi-square test and logistic regression analysis were used to calculate p-values.

RESULTS

Out of 557 patients, 554 were female with a mean age 46.8 years (premenopausal 54.5%). ACR grades of MD A, B, C, and D were 18.1% (n = 101), 56% (n = 312), 21.5% (n = 120), and 4.3% (n = 24), respectively. The odds of having PCR with MD B, C, and D were 0.51, 0.04, and 0.03, respectively, with respect to MD A. There was a significant inverse association of PCR and Ki-67 with MD on multivariate analysis. HER2 positive, TNBC, Ki 67 > 15%, and grade 3 had significantly high PCR.

CONCLUSION

MD had an inverse correlation with PCR and Ki-67. Low MD, HER2 positive, TNBC, high Ki-67 subtypes, and grade 3 were good predictors for PCR.

Association between mammographic breast density and outcome in patients with unilateral invasive breast cancer receiving neoadjuvant chemotherapy

PURPOSE

To analyze the relationship between mammographic breast density and tumor response and outcome at follow-up, in terms of overall survival (OS) and disease-free survival (DFS), in patients with unilateral invasive breast cancer receiving neoadjuvant chemotherapy (NACT).

METHODS

A total of 228 women (mean age, 47.6 years ± 10 [SD]; range: 24-74 years) with invasive breast cancer who underwent NACT were included in this observational retrospective study. Clinical, radiological and histopatological data were retrieved. Categorization of breast density was performed by two radiologists in consensus on mammography acquired at the time of diagnosis according to BI-RADS categories. Association between density categories and tumor response was analyzed in the overall population and in subgroups defined by menopausal status, tumor phenotype and stage at diagnosis. Kaplan-Meier (KM) curves were used to estimate the OS and DFS probabilities. Subgroup analyses based on menopausal status and tumor phenotype were performed.

RESULTS

A total of 30 patients (13.2%) achieved pathological complete response (pCR). No association between density categories and pCR was found (P = 0.973), even at subgroups analysis. The median follow-up time was 92 months. Patients with dense breast showed the longest DFS (P = 0.0094), results confirmed in premenopausal patients (P = 0.0024) and in triple negative breast cancers (P = 0.0292). Density category did not show a statistically significant association with OS.

CONCLUSION

Breast cancer patients receiving NACT with extremely dense breasts showed better DFS. No evidence of breast density as a predictive marker for complete pathological response or as a prognostic factor in terms of OS was found.

An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer

Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer. To enhance feasibility, MRP integrates cross-modal knowledge mining and temporal information embedding strategy to handle missing modalities and remain less affected by different NAT settings. We validated MRP through multi-center studies and multinational reader studies. MRP exhibited comparable robustness to breast radiologists, outperforming humans in predicting pathological complete response in the Pre-NAT phase (ΔAUROC 14% and 10% on in-house and external datasets, respectively). Furthermore, we assessed MRP's clinical utility impact on treatment decision-making. MRP may have profound implications for enrolment into NAT trials and determining surgery extensiveness.

Evaluation of tumor-infiltrating lymphocytes and mammographic density as predictors of response to neoadjuvant systemic therapy in breast cancer

BACKGROUND

Response rates vary among breast cancer patients treated with neoadjuvant systemic therapy (NAST). Thus, there is a need for reliable treatment predictors. Evidence suggests tumor-infiltrating lymphocytes (TILs) predict NAST response. Still, TILs are seldom used clinically as a treatment determinant. Mammographic density (MD) is another potential marker for NAST benefit and its relationship with TILs is unknown. Our aims were to investigate TILs and MD as predictors of NAST response and to study the unexplored relationship between TILs and MD.

MATERIAL AND METHODS

We studied 315 invasive breast carcinomas treated with NAST between 2013 and 2020. Clinicopathological data were retrieved from medical records. The endpoint was defined as pathological complete response (pCR) in the breast. TILs were evaluated in pre-treatment core biopsies and categorized as high (≥10%) or low (<10%). MD was scored (a-d) according to the breast imaging reporting and data system (BI-RADS) fifth edition. Binary logistic regression and Spearman's test of correlation were performed using SPSS.

RESULTS

Out of 315 carcinomas, 136 achieved pCR. 94 carcinomas had high TILs and 215 had low TILs. Six carcinomas had no available TIL data. The number of carcinomas in each BI-RADS category were 37, 122, 112, and 44 for a, b, c, and d, respectively. High TILs were independently associated with pCR (OR: 2.95; 95% CI: 1.59-5.46) compared to low TILs. In the univariable analysis, MD (BI-RADS d vs. a) showed a tendency of higher likelihood for pCR (OR: 2.43; 95% CI: 0.99-5.98). However, the association was non-significant, which is consistent with the result of the multivariable analysis (OR: 2.51; 95% CI: 0.78-8.04). We found no correlation between TILs and MD (0.02; p = .80).

CONCLUSION

TILs significantly predicted NAST response. We could not define MD as a significant predictor of NAST response. These findings should be further replicated.

Mammographic density in relation to breast cancer recurrence and survival in women receiving neoadjuvant chemotherapy

Objective

The association between mammographic density (MD) and breast cancer (BC) recurrence and survival remains unclear. Patients receiving neoadjuvant chemotherapy (NACT) are in a vulnerable situation with the tumor within the breast during treatment. This study evaluated the association between MD and recurrence/survival in BC patients treated with NACT.

Methods

Patients with BC treated with NACT in Sweden (2005-2016) were retrospectively included (N=302). Associations between MD (Breast Imaging-Reporting and Data System (BI-RADS) 5^th Edition) and recurrence-free/BC-specific survival at follow-up (Q1 2022) were addressed. Hazard ratios (HRs) for recurrence/BC-specific survival (BI-RADS a/b/c vs. d) were estimated using Cox regression analysis and adjusted for age, estrogen receptor status, human epidermal growth factor receptor 2 status, axillary lymph node status, tumor size, and complete pathological response.

Results

A total of 86 recurrences and 64 deaths were recorded. The adjusted models showed that patients with BI-RADS d vs. BI-RADS a/b/c had an increased risk of recurrence (HR 1.96 (95% confidence interval (CI) 0.98-3.92)) and an increased risk of BC-specific death (HR 2.94 (95% CI 1.43-6.06)).

Conclusion

These findings raise questions regarding personalized follow-up for BC patients with extremely dense breasts (BI-RADS d) pre-NACT. More extensive studies are required to confirm our findings.

The association between breast density and breast cancer pathological response to neoadjuvant chemotherapy

Purpose

Mammographic Density (MD) refers to the amount of fibroglandular breast tissue present in the breast and is an established risk factor for developing breast cancer. The ability to evaluate treatment response dynamically renders neoadjuvant chemotherapy (NACT) the preferred treatment option in many clinical scenarios. Previous studies have suggested that MD can predict patients likely to achieve a pathological complete response (pCR) to NACT. We aimed to determine whether there is a causal relationship between BI-RADS breast composition categories for breast density at diagnosis and the pCR rate and residual cancer burden score (RCB) by performing a retrospective review on consecutive breast cancer patients who received NACT in a tertiary referral centre from 2015 to 2021.

Methods

The Mann–Whitney U Test was used to test for differences between two independent groups (i.e. those who achieved pCR and those who did not). A binary logistic regression model was used to estimate odds ratios (OR) and corresponding 95% confidence intervals (CI) for an association between the independent variables of molecular subtype, MD, histological grade and FNA positivity and the dependant variable of pCR. Statistical analysis was conducted with SPSS (IBM SPSS for Mac, Version 26.0; IBM Corp).

Results

292 patients were included in the current study. There were 124, 155 and 13 patients in the BI-RADS MD category b, c and d, respectively. There were no patients in the BI-RADS MD category a. The patients with less dense breast composition (MD category b) were significantly older than patients with denser breast composition (MD category c, d) (p = 0.001) and patients who had a denser breast composition (MD category d) were more likely to have ER+ tumours. There was no significant difference in PgR status, HER2 status, pathological complete response (pCR), FNA positivity, or RCB class dependent upon the three MD categories. A binary logistic regression revealed that patients with HER2-enriched breast cancer and triple-negative breast cancer are more likely to achieve pCR with an OR of 3.630 (95% CI 1.360–9.691, p = 0.010) and 2.445 (95% CI 1.131–5.288, p = 0.023), respectively.

Conclusion

Whilst dense MD was associated with ER positivity and these women were less likely to achieve a pCR, MD did not appear to independently predict pCR post-NACT.