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Biomechanical parameters quantified by MR elastography for predicting response to neoadjuvant chemotherapy and disease-free survival in breast cancer: a prospective longitudinal study

Breast Cancer Research volume 27, Article number: 72 (2025) Cite this article

Abstract

Background

Little is known regarding biomechanical properties derived from multifrequency MR elastography temporal changes during neoadjuvant chemotherapy (NAC) and associated with pathologic complete response (pCR) and disease-free survival (DFS) in breast cancer. We aimed to investigate temporal changes in NAC-associated biomechanical parameters and assess biomechanical parameters as a predictor of pCR and DFS in breast cancer.

Methods

In this prospective longitudinal study, participants with breast cancer who received NAC were enrolled from February 2021 to May 2023. All participants underwent multifrequency MR-elastography at four timepoints: before NAC (T1) and after 2 (T2), 4 (T3), and 6 (T4) cycles. Tomoelastography postprocessing provided biomechanical maps of shear-wave-speed (c) and loss-angle (φ) as proxies of stiffness and viscosity. The biomechanical parameters were validated by means of correlation with histopathologic measurements. Generalized estimating equations were used to compare temporal changes in biomechanical parameters at four time points. Logistic regression was used for pCR analysis and Cox proportional hazards regression was used for survival analysis. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) analysis.

Results

A total of 235 women (50.6 ± 7.9 years) with 964 scans were enrolled. Biomechanical parameters were supported by positive correlations with pathologic examination–based stroma fraction (c: r =.76, P <.001; φ: r =.49, P =.008) and cellularity (c: r =.58, P =.001; φ: r =.40, P =.035). Progesterone receptor, human epidermal growth factor receptor-2 (HER2), T2-c, and T2-φ were independently associated with pCR (all P <.05). Estrogen receptor, HER2, clinical stage, and change in φ at the early stage of NAC were associated with PFS (all P <.05). The predictive model, which incorporated biomechanical parameters and clinicopathologic characteristics significantly outperformed the clinicopathologic model in predicting pCR (AUC: 0.95, 95% confidence interval [CI]: 0.92, 0.98 vs. 0.79, 95%CI: 0.73, 0.84; P <.001). The predictive model also showed good discrimination ability for DFS (C-index = 0.82, 95%CI: 0.72, 0.90) and stratified prognosis into low-risk and high-risk groups (log-rank, P <.001).

Conclusions

During NAC, patients with higher tumor stiffness and viscosity are less likely to achieve DFS and pCR. The biomechanical parameters exhibit excellent biological interpretability and serve as valuable biomarkers for predicting pCR and DFS in patients with breast cancer.

Background

Neoadjuvant chemotherapy (NAC) is a standard treatment for locally advanced breast cancer [1] that offers the advantages of cancer downstaging, drug sensitivity detection, and increased potential for breast-conserving therapy [2]. However, tumor response to NAC varies greatly among patients, with an estimated 19–30% of patients experiencing pathologic complete response (pCR) and 5–20% exhibiting disease progression [3]. In addition, the prognostic significance of pCR remains somewhat controversial, and the true demonstration of treatment efficacy depends on its ability to predict long-term outcomes of recurrence and death [4]. In current clinical practice, there is a lack of standardized methods or imaging biomarkers that can reliably predict pCR and recurrence in patients with breast cancer treated with NAC.

There is increasing recognition of the influential role of the tumor’s physical microenvironment in treatment outcomes. Discoveries in this field are undergoing translation into new therapeutic strategies, and researchers are attempting to understand how biomechanical parameters and processes affect cancer treatment outcomes [5]. Increased deposition of stroma and extracellular matrix (ECM) molecules leads to increased tissue stiffness, which is significantly correlated with the tumor treatment response and survival among patients with breast cancer [6,7,8]. Thus, accurate quantitative measures of biomechanical parameters could serve as valuable biomarkers for the prediction of pCR and survival in patients with breast cancer treated with NAC.

Imaging plays a vital role in the management of patients receiving NAC because treatment decisions heavily rely on accurate assessments of therapeutic responses [9].

MRI is regarded as the most sensitive imaging modality for assessments of treatment response in breast cancer. MR elastography has the potential to inform noninvasively on prognosis and risk stratification for patients with breast cancer [10]. A recent study [11] reported that MR elastography derived parameters demonstrated an association with pCR in breast cancer patients. Moreover, a combined approach using dynamic contrast-enhanced MRI and MR elastography notably improved the specificity for identifying complete responders following NAC, while maintaining high sensitivity. Multifrequency MR elastography can reveal cancer-associated microstructural alterations from a biomechanical perspective and has excellent diagnostic power in clinical cancer imaging [12, 13]. Considering the potential for tumor biomarkers to exhibit changes during NAC [14], longitudinal monitoring of evolving tumor biomechanical properties is necessary to predict pCR and recurrence.

In our study, we aimed to (I) validate multifrequency MR elastography–derived biomechanical parameters via correlation with pathological findings, (II) develop a model for the early prediction of pCR in breast cancer, (III) investigate temporal changes in NAC-associated biomechanical parameters and access them as a predictor of disease-free survival (DFS) in patients with breast cancer treated with NAC.

Methods

Study sample

This prospective study protocol was approved by the Institutional Review Board of our hospital, and written informed consent was obtained from all participants. Consecutive participants with suspected breast cancer were recruited from February 2021 to May 2023. Inclusion criteria were biopsy-proven primary breast cancer without distant metastasis; receipt of complete NAC without prior antitumor treatment; MRI examinations including multifrequency MR elastography performed before NAC (T1) and after 2 (T2), 4 (T3), and 6 cycles (T4); and surgery performed after NAC completion, with pathological confirmation of pCR status. Exclusion criteria were surgery performed at an external institution or lack of pCR assessment; insufficient MRI quality for measurements (e.g., motion artifacts); and history of breast surgery (e.g., benign/malignant breast lesions and implants) or chest radiation. The study schematic and participant inclusion/exclusion flowchart are provided in Fig. 1.

Fig. 1
figure 1

(A) Study schematic and (B) participant inclusion and exclusion flowchart. NAC = neoadjuvant chemotherapy, pCR = pathologic complete response, TNBC = triple-negative breast cancer, HER2 = human epidermal growth factor receptor-2

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MRI protocol

MRI examinations were performed on a 3-T system (MAGNETOM Prisma, Siemens Healthineers) using a dedicated breast coil with participants in the prone position. MRI examinations included T1-weighted, T2-weighted with fat suppression, and diffusion-weighted imaging. Multifrequency MR elastography was performed via mechanical excitations at vibration frequencies of 30, 40, 50, and 60 Hz, achieved using two anterior pressure pads (0.8 bar) placed on the lateral sides of the breasts. A three-dimensional wave field was acquired with a single-shot spin-echo echo-planar imaging sequence at eight different time points along three motion-encoding directions [12]. The MR elastography scanning parameters were as follows: resolution, 2.0 × 2.0 × 3.0 mm3; FOV, 360 × 170 mm2; number of slices, 15. Dynamic contrast-enhanced MRI was performed after intravenous injection of gadolinium contrast agent (Jia Di Xian, Heng Rui). Detailed acquisition parameters are provided in Table S1.

Imaging analyses

Two radiologists (with 11 and 12 years of experience, respectively) were involved in image analysis. Multifrequency MR elastography data were processed using the tools at bioqic-apps.com. Maps of shear wave speed (c in m/s, representing stiffness) and loss angle (φ in rad, representing viscosity) were generated using the multifrequency wave number–based processing algorithm (k-MDEV) [15] and Laplacian operators-based processing method (MDEV) [16], respectively.

Radiologists manually delineated regions of interest within breast tumors. First, c and φ maps were carefully matched with the peak tumor enhancement phase at dynamic contrast-enhanced MRI to guide lesion delineation. Second, one main section showing the largest cross-sectional extent of the breast lesion was selected from the c and φ maps. Third, due to the higher resolution of the c map compared with the φ map, the ROI was manually drawn on the c map. During this process, areas of necrosis, hemorrhagic components, and calcification were excluded. Finally, to guarantee the measurement of stiffness and viscosity within the identical area of the tumor, the ROI was then copied from c map to corresponding φ map. Following the same procedure, ROI measurements were repeated with an interval of at least 1 month by the more experienced radiologist to assess intraobserver agreement.

At each time point, tumor ROIs were redefined according to their locations in previous examinations. After treatment in tumors without residual enhancement on dynamic contrast-enhanced MRI, ROIs were defined in the same tissue region as previously examined [17]. We also calculated differences in c and φ between pre-treatment and post-treatment.

Response assessment and Follow-up

Indications for NAC were established in accordance with the National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology [18]. NAC regimens were either anthracycline-based, taxane-based, or anthracycline- and taxane-based. Patients with human epidermal growth factor receptor-2 (HER2) positivity received trastuzumab and/or pertuzumab. pCR was defined as the absence of residual invasive carcinoma (residual ductal carcinoma in situ could be present) and the absence of axillary lymph node invasion (ypT0/isN0).

Patients were followed up after surgery with physical examinations, annual mammograms, and bilateral whole-breast ultrasound, CT of the thorax every 6 months, bone scan and abdomen CT or ultrasound every year. DFS events were defined as follows: the first recurrence of invasive breast cancer at a local, regional, or distant site; the incidence of contralateral breast cancer; and death from any cause. Patients without DFS events were censored at the last follow-up.

Histopathologic assessment

Histopathology slides obtained from pre-NAC needle biopsy samples were interpreted under the supervision of one pathologist (with 15 years of experience), who was blinded to MRI findings. To validate multifrequency MR elastography parameters, we quantified stroma fraction and pathological cellularity using hematoxylin and eosin-stained whole-slide images collected by panoramic digital image scanning technology (Ningbo Jiangfeng Bio-information Technology Co., Ltd.). The tumor stroma fraction was calculated as the percentage of selected stroma area relative to the entire depicted breast tumor area. Nuclei in each whole-slide image were segmented using HoVer Network [19].

Statistical analysis

Statistical analyses were carried out by Y.H. using SPSS software (version 26) and MedCalc software (version 20.022). Continuous and categorical variables are reported as means with SDs and frequencies with percentages, respectively. Categorical variables were compared by the chi-square test or Fisher’s exact test. Numerical data were analyzed by independent t-tests or the Mann-Whitney U tests. Correlations of multifrequency MR elastography–derived parameters with stroma fraction and pathologic cellularity were assessed using the Pearson correlation coefficient (r). Generalized estimating equations were used to compare temporal changes in biomechanical parameters at four time points. Logistic regression was used for pCR analysis and Cox proportional hazards regression was used for survival analysis. Survival curves were estimated using Kaplan-Meier analysis, and statistically significance was determined with log-rank tests. Predictive performance was estimated using the area under the receiver operator characteristic curve (AUC) and C-index. The two-way random effects intraclass correlation coefficient with absolute agreement was used to assess the observer reliability, with values of ≥ 0.80 indicating excellent agreement. The significance threshold was set at two-tailed P <.05.

Results

Characteristics of the study sample

In total, 964 MRI scans of 235 participants (mean age, 50.55 years ± 7.86 [standard deviation]) were enrolled (Table 1). Among the 62 participants who experienced pCR, the HER2-enriched subtype was most common (approximately 45%). Notably, pCR was significantly associated with the estrogen receptor (ER), progesterone receptor (PR), and HER2 statuses, as well as the molecular subtype and NAC cycle (all P <.05). During the follow-up (median, 25.2 mouths; IQR, 16.5–32.6 mouths), 23 women (10%) had recurrence, which were significantly associated with the ER, PR, and HER2 statuses, molecular subtype, cN stage, clinical stage, as well as NAC response (all P <.05).

Table 1 Clinicopathologic characteristics of participants
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Correlation and observer reliability analysis

Correlation between multifrequency MR elastography–derived parameters and pathologic stroma fraction (c: r =.76, P <.001; φ: r =.49, P =.008) and cellularity (c: r =.58, P =.001; φ: r =.40, P =.035) showed a positive correlation in a subset of 28 samples (Fig. 2).

Fig. 2
figure 2

Graph showing correlations between multifrequency MR elastography–derived biomechanical parameters and pathology-based properties in 28 participants. (A) Hematoxylin and eosin (H&E)–stained slices (magnification ×40) were used for automated analysis of the stroma fraction. (B, C) Graphs showing correlations of multifrequency MR elastography–derived c (stiffness) and φ (viscosity) with the stroma fraction. (D) Nuclei were segmented using HoVer Network. (E, F) Graphs showing correlations of multifrequency MR elastography–derived c (stiffness) and φ (viscosity) with pathologic cellularity

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The reliability was excellent for all measurements, with intraobserver intraclass correlation coefficient values for c and φ were 0.85 (95%confidence interval [CI]: 0.80, 0.89) and 0.82 (95%CI: 0.80, 0.90), interobserver intraclass correlation coefficient values for c and φ were 0.83 (95%CI: 0.79, 0.91) and 0.81 (95%CI: 0.78, 0.87), respectively.

Changes in Biomechanical parameters during treatment

During NAC, both c (total, P <.001; luminal B, P <.001; TNBC, P =.01; HER2-enriched, P <.001) and φ (total, P <.001; luminal B, P <.001; TNBC, P =.002; HER2-enriched, P <.001) significantly reduced, especially in the pCR group (Fig. 3). From T2 to T4, the pCR group had lower c (m/sec) (T2: 1.77 ± 0.40 vs. 2.42 ± 0.63; T3: 1.63 ± 0.50 vs. 2.14 ± 0.68; T4: 1.43 ± 0.48 vs. 1.94 ± 0.62) and lower φ (radian) (T2: 0.97 ± 0.19 vs. 1.28 ± 0.25; T3: 0.94 ± 0.26 vs. 1.15 ± 0.26; T4: 0.89 ± 0.30 vs. 1.08 ± 0.34) values compared with the non-pCR group (all P <.001).

Fig. 3
figure 3

Trajectories of biomechanical parameters derived from multifrequency MR elastography during neoadjuvant chemotherapy. Individual panels show changes in c (stiffness) and φ (viscosity) among patients in pCR (green) and non-pCR (red) groups. Time 1, before NAC; Time 2, after 2 cycles of NAC; Time 3, after 4 cycles of NAC; Time 4, after 6 cycles of NAC. P-values represent comparisons between groups by conducting generalized estimating equations. *P <.05 and **P <.001 represent comparisons between groups at the same time point. pCR = pathologic complete response, HER2 = human epidermal growth factor receptor-2, TNBC = triple-negative breast cancer

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Among patients with the luminal B subtype, the pCR group had lower c (m/sec) (T2: 1.68 ± 0.38 vs. 2.40 ± 0.59, P <.001; T3: 1.51 ± 0.39 vs. 2.14 ± 0.64, P <.001; T4: 1.43 ± 0.52 vs. 1.97 ± 0.66, P =.001) and lower φ (radian) values (T2: 1.04 ± 0.19 vs. 1.29 ± 0.26, P <.001; T3: 0.89 ± 0.22 vs. 1.17 ± 0.26, P <.001) compared with the non-pCR group. Among patients with the TNBC subtype, the pCR group had lower c (m/sec) values at T2 (1.90 ± 0.43 vs. 2.57 ± 0.90, P =.02) and T3 (1.60 ± 0.61 vs. 2.14 ± 0.77, P =.04), and lower φ (radian) values at T2 (1.00 ± 0.19 vs. 1.23 ± 0.24, P =.004) and T4 (0.81 ± 0.22 vs. 1.10 ± 0.37, P =.02), compared with the non-pCR group. Among patients with the HER2-enriched subtype, the pCR group also had lower c (m/sec) values at T2 (1.74 ± 0.40 vs. 2.31 ± 0.28, P <.001) and T4 (1.35 ± 0.35 vs. 1.89 ± 0.63, P =.003), and φ (radian) values at T2 (0.89 ± 0.16 vs. 1.40 ± 0.21, P <.001) and T3 (0.98 ± 0.30 vs. 1.17 ± 0.26, P =.02), compared with the non-pCR group.

Representative dynamic contrast-enhanced MRI and multifrequency MR elastography maps of two patients (one each in the pCR and non-pCR groups) are shown in Fig. 4.

Fig. 4
figure 4

Axial dynamic contrast-enhanced MRI, c map (stiffness) and φ map (viscosity) at four time points in two breast cancer patients receiving neoadjuvant chemotherapy. (A) Patient 1: A 63-year-old woman who received neoadjuvant treatment for clinical stage IIIc triple-negative breast cancer and did not achieve pathologic complete response. (B) Patient 2: A 57-year-old woman who received neoadjuvant treatment for clinical stage IIIb human epidermal growth factor receptor-2–enriched cancer and achieved pathologic complete response. DCE = dynamic contrast-enhanced, NAC = neoadjuvant chemotherapy

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Associations of features with pCR

To construct a model for response prediction during the early stage of NAC, we focused on biomechanical parameters at T1 and T2, along with differences in these parameters. Multivariable analysis demonstrated that PR status (odds ratio [OR], 0.05 [95%CI: 0.01, 0.34]; P =.002), HER2 status (OR, 7.47 [95%CI: 2.26, 24.71]; P =.001), T2-c (OR, 0.10 [95%CI: 0.04, 0.29]; P <.001), and T2-φ (OR, 0.11 [95%CI: 0.04, 0.30]; P <.001) were independently associated with pCR (Table 2).

Table 2 Univariable and multivariable analyses of the associations of pretreatment clinicopathological characteristics and Biomechanical parameters with pathologic complete response
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Predictive performance regarding pCR

For the prediction of pCR to NAC among all patients, the clinicopathologic model including PR and HER2 statuses achieved an AUC of 0.79 (95%CI: 0.73, 0.84). The MRI model, inclusion of T2-c and T2-φ resulted in the highest AUC (0.92 [95%CI: 0.87, 0.95]). Among patients with different molecular subtype, the MRI model achieved good predictive performance in TNBC patients (AUC = 0.84 [95%CI: 0.71, 0.96]), and excellent predictive performances in patients with luminal B subtype (AUC = 0.90 [95%CI: 0.83, 0.97] and HER2-enriched subtype (AUC = 0.98 [95%CI: 0.96, 1.00], respectively) (Fig. 5A).

Fig. 5
figure 5

(A) Predictive performances of the MRI model across three molecular subtypes. (B) Performances of the three models concerning prediction of pathologic complete response to neoadjuvant therapy in all breast cancer patients. (C) Nomogram constructed based on the combined model. Each point that corresponds to each variable is on the uppermost point scale. The sum of all points is the total points. The point total projected at the bottom scale indicates the probability of pCR in the breast. The clinicopathologic model includes progesterone receptor and human epidermal growth factor receptor-2 statuses.The MRI model includes T2-c and T2-φ. The combined model includes progesterone receptor status, human epidermal growth factor receptor-2 status, T2-c, and T2-φ. AUC = area under the receiver operating characteristic curve, HER2 = human epidermal growth factor receptor-2, TNBC = triple-negative breast cancer

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The combined model, integrating PR and HER2 statuses along with T2-c and T2-φ, exhibited the highest performance with AUC of 0.95 (95%CI: 0.92, 0.98), outperforming the clinicopathologic model (P <.001) (Fig. 5B; Table 3). Then, we constructed a nomogram based on the combined model (Fig. 5C). The combined model consistently achieved the highest AUC across the following subgroups: premenopausal patients (AUC = 0.93, [95%CI: 0.88, 0.98]), postmenopausal patients (AUC = 0.97, [95%CI: 0.93, 0.99]), patients aged ≤ 45 years (AUC = 0.91, [95%CI: 0.82, 1.00]), patients aged > 45 years (AUC = 0.96, [95%CI: 0.93, 0.98]), patients with cT stage 1–2 (AUC = 0.96, [95%CI: 0.93, 0.98]), patients with cT stage 3–4 (AUC = 0.93, [95%CI: 0.85, 1.00]), patients with cN stage 0–1 (AUC = 0.95, [95%CI: 0.89, 0.99]), and patients with cN stage 2–3 (AUC = 0.95, [95%CI: 0.91, 0.99]).

Table 3 Diagnostic performances of models concerning prediction of pathologic complete response to neoadjuvant chemotherapy
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Associations of features with DFS

Considering that the overall biomechanical changes during the NAC process may be related to survival, we included biomechanical parameters and differences at all time points for survival analysis. In the multivariate Cox analysis, the ER status (hazard ratio [HR], 0.13 [95%CI: 0.05, 0.36]; P <.001), HER2 (HR, 0.25 [95%CI: 0.06, 0.94]; P =.04), clinical stage (HR, 11.07 [95%CI: 1.37, 89.15]; P =.02) and the difference in φ between T1 and T2 (HR, 0.25 [95%CI: 0.07, 0.67]; P =.03) remained independent prognostic factors with DFS (Table 4).

Table 4 Univariable and multivariable analyses of the associations of pretreatment clinicopathological characteristics and mechanical parameters for predicting disease-free survival
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Survival analysis

Patients were stratified as low risk and high risk by the prediction model which integrating ER, HER2, clinical stage, and the difference in φ between T1 and T2. Kaplan-Meier curves showed different DFS between the low- and the high-risk groups (Fig. 6; log-rank, P <.001). The predictive model also showed good discrimination ability for DFS (C-index = 0.82, 95%CI: 0.72, 0.90).

Fig. 6
figure 6

Kaplan-Meier survival curves stratified by the prediction model. The prognosis was stratified as low risk and high risk according to the prediction model scores

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Discussion

In this longitudinal cohort study of 235 participants (964 MRI scans), we demonstrated that multifrequency MR elastography–derived stiffness and viscosity were positively correlated with stroma fraction and pathologic cellularity (r =.40–0.76; P <.05). Our key finding was that the substantial reduction in stiffness and viscosity were greater in patients with pCR throughout the course of NAC. The highest performance concerning prediction of pCR was achieved using the combination of progesterone receptor status, human epidermal growth factor receptor-2 (HER2) status, and biomechanical parameters at T2 (AUC = 0.95) superior to the clinicopathologic model (AUC = 0.79, P <.001). The predictive model, which incorporated estrogen receptor, HER2, clinical stage, and difference in viscosity at the early stage of NAC, showed good discrimination ability for DFS (C-index = 0.82, 95%CI: 0.72, 0.90) and stratified prognosis into low-risk and high-risk groups (log-rank, P <.001).

Stiffness and viscosity are sensitive imaging biomarkers for collagen deposition [10]. Higher stiffness might be attributable to increased cellularity and collagen deposition. The elevated viscosity might be associated with high blood vessel density and increased intracellular fluid mobility of tumor cells [12]. We confirmed that multifrequency MR elastography–derived stiffness and viscosity were positively correlated with stroma fraction and pathologic cellularity (r =.40–0.76). In prior investigations regarding the clinical utilization of multifrequency MR elastography in prostate cancer [12] and endometrial cancer [20], there was insufficiency explanation for biomechanical parameters. The findings of our research serve as a crucial complement to the histopathological validation of multifrequency MR elastography in tumor related applications. Moreover, this can endow predictive models constructed from longitudinal MR elastography parameters with robust biological interpretability.

Our results are consistent with previous studies [21, 22], demonstrating that tumor stiffness substantially decreases during NAC. Chemotherapy can reduce cancer cellularity and vascularity, leading to tumor necrosis and decreased stiffness [23]. The slower decrease in stiffness within the non-pCR group may be caused by hypoxia, which is associated with increased matrix stiffness in the non-necrotic regions of tumors [7]. Strengths of our study include its longitudinal design and large sample size, as well as the ability to objectively quantify both stiffness and viscosity.

Patient with higher stiffness and viscosity is less likely to achieve pCR [17, 21, 24]. Mesenchymal stromal cells can trigger lysyl oxidase production by CD44-expressing breast cancer cells, contributing to ECM stiffening via catalysis of collagen fiber cross-linking and facilitation of ECM-induced drug resistance [25]. Excessive ECM accumulation and the formation of a physical barrier can hinder chemotherapy drug access, resulting in a poor treatment response among patients with breast cancer [26]. Additionally, we only observed significant difference in biomechanical parameters between the pCR and non-pCR groups after 2 cycles of NAC, and the patients with higher biomechanical parameters were less prone to achieving a pCR. Moreover, between the T1 and T2, the more pronounced the reduction in tumor stiffness and viscosity, the higher the likelihood that breast cancer patients will achieve a pCR. This finding highlights the need for dynamic monitoring of tumor biomechanical parameters during early stages of treatment, especially at pretreatment and after 2 cycles of NAC.

A meta-analysis [27] revealed that pooled AUCs of 0.89 (95% CI, 0.86–0.91) for strain elastography and 0.82 (95% CI, 0.78–0.85) for shear wave elastography in terms of predicting NAC responses. In our study, the AUC of multifrequency MR elastography–based parameters at T2 with respect to predicting pCR was 0.92, slightly better than ultrasound elastography. Ultrasound elastography is operator-dependent and has low spatial resolution, and can considerably influence image quality and interpretation [28]. MR elastography has a wider measurement range, multifrequency data acquisition and wave-number-based inversion methods yield parameter maps with extensive anatomical details. NAC resistance is considered a complex process that involves both intrinsic and acquired characteristics [29], combinations of clinicopathological characteristics and imaging biomarkers exhibit robust performance [30, 31]. In our study, the highest performance was achieved with the combination of PR, HER2 status, and biomechanical parameters at T2 (AUC = 0.95), outperforming the baseline clinicopathologic model.

There is still some controversy over the prognostic significance of pCR after NAC. Although the achievement of pCR is surrogate marker for improved disease-free survival and overall survival, it varies depending on the type of NAC and molecular subtype [4]. Our research findings indicate that pCR status is not an independent prognostic factor for DFS, possibly due to a higher proportion of luminal subtypes (59.6%) [32]. Previous studies found that stromal stiffness at ultrasound shear wave elastography was independently associated with disease-free survival [8] and breast cancer-specific survival [33]. In this longitudinal study, we first confirmed that the difference in viscosity at the early stage of NAC was an independent prognostic factor for DFS, possibly due to its ability to characterize tumor angiogenesis and cellularity [12].

Consequently, relying solely on baseline clinicopathological variables of patients is insufficient for accurately predicting the prognosis of breast cancer patients. In light of our research results, integrating baseline clinicopathological variables with the biomechanical parameters at pretreatment and after 2 cycles of NAC can effectively forecast the pCR and DFS in patients with breast cancer. This may be due to the ability of biomechanical parameters to characterize the composition of tumor tissue, such as cellularity and collagen deposition, which has also been validated by histopathology in our study. And, these parameters can reflect the alterations in tumor tissue in response to drug therapy during the early stages of treatment. This may pave the way for the formulation of customized treatment plans for individual patients, a practice that is of great clinical significance for breast cancer patients.

Limitation

This study had some limitations. First, it used a single-center design; a multicenter study is needed to validate our findings. Second, no patient in the luminal A subgroup achieved pCR; therefore, the corresponding subgroup analysis was not performed. This is consistent with previous literature: luminal A tumors exhibit the lowest rates of pCR (0.3%) [34] among molecular subtypes, and they are usually treated via surgery. Third, histopathology slides were obtained by sampling limited tumor areas, potentially reducing the precision of pathological-radiological matching. Finally, NAC treatment regimens varied. In future studies with larger sample sizes, subgroup analyses of specific therapeutic agents should be considered as a potential prognostic factor.

Conclusions

In conclusion, this longitudinal study demonstrated that combining biomechanical parameters and clinicopathologic variables showed good performance for predicting pathologic complete response and disease-free survival in breast cancer treated with neoadjuvant chemotherapy. In the future, studies involving larger cohorts and multiple centers are needed to validate our findings.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AUC:

Area under the receiver operating characteristic curve

DFS:

Disease-free survival

ER:

Estrogen receptor

HER2:

Human epidermal growth factor receptor-2

NAC:

Neoadjuvant chemotherapy

pCR:

Pathologic complete response

PR:

Progesterone receptor

TNBC:

Triple-negative breast cancer

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Acknowledgements

The authors thank all volunteers who participated in the study and the staff of the Department of Radiology, Chongqing University Cancer Hospital, for their selfless and valuable assistance.

Funding

This study has received funding by the Fundamental Research Funds for the Central Universities (2023CDJYGRH-YB04), Chongqing Medical Research Project of Combination of Science and Medicine (No. 2024MSXM171), the Chongqing University Cancer Hospital Scientific Research Capacity Improvement Project (2023nlts004), the Natural Science Foundation of Chongqing municipality (CSTB2023NSCQ-MSX0787, CSTB2024NSCQ-MSX0217, CSTB2024NSCQ-MSX0760, and CSTB2024NSCQ-MSX0334).

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Authors and Affiliations

  1. Department of Radiology, Chongqing University Cancer Hospital, Chongqing, 400030, China

    Xiaoxia Wang, Yao Huang, Jinfang Shi, Ying Cao, Huifang Chen, Lan Li, Lu Wang, Sun Tang, Xueqin Gong & Jiuquan Zhang

  2. Department of Pathology, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, 400030, China

    Haiping Huang

  3. MR Collaborations, Siemens Healthineers Ltd, Chengdu, China

    Ting Yin

  4. Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, No.181 Hanyu road, Shapingba district, Chongqing, 400030, China

    Jiuquan Zhang

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  1. Xiaoxia Wang
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Contributions

Guarantors of integrity of entire study, X.W., J.Z.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, X.W., Y.H., J.S., Y.C., H.C., L.W., S.T., J.Z.; clinical studies, X.W., J.S., L.L., X.G., H.H., J.Z.; experimental studies, X.W., T.Y., J.Z.; statistical analysis, X.W., Y.H., J.Z.; and manuscript editing, X.W., Y.H., T.Y., J.Z.

Corresponding author

Correspondence to Jiuquan Zhang.

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Wang, X., Huang, Y., Shi, J. et al. Biomechanical parameters quantified by MR elastography for predicting response to neoadjuvant chemotherapy and disease-free survival in breast cancer: a prospective longitudinal study. Breast Cancer Res 27, 72 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13058-025-02035-4

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13058-025-02035-4

Keywords

Breast Cancer Research

ISSN: 1465-542X

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