MRI Performance in Detecting pCR After Neoadjuvant Chemotherapy by Molecular Subtype of Breast Cancer

Neoadjuvant chemotherapy for breast cancer in Italy: A Senonetwork analysis of 37,215 patients treated from 2017 to 2022

BACKGROUND

Adoption of neoadjuvant chemotherapy (NACT) in the "real world" has been poorly investigated. Aim of this study was to examine the rate of NACT in Italy, trends over time and determinants of therapeutic choices.

METHODS

Senonetwork, the recognized network of Breast Centers in Italy, has developed a voluntary national data warehouse with the aim to monitor and improve treatments quality. A retrospective analysis was conducted among 58,661 breast cancer (BC) patients treated between 2017 and 2022 by 24 high-volume Breast Centers participating in the project.

RESULTS

After subset exclusion, 37,215 primary BC patients were analysed, 32,933 underwent primary-breast-surgery and 4,282 underwent NACT. From 2017 to 2022, the overall NACT incidence increased particularly for HR-/HER2+, Triple-Negative, and HR+/HER2+ BC (p < 0.001). In cN + patients the recommendation to axillary lymph-node dissection after NACT decreased over time along with an increase of <4 lymph-nodes removed (p < 0.001). Immediate breast reconstruction and indication for nipple sparing mastectomy increased significantly over time (OR = 1.10, p = 0.011 and OR 1.14, p < 0.001, respectively). On multivariate analysis, there was a trend towards an increased adoption of conservative treatment for HR-/HER2+ (p = 0.01) and Triple Negative tumors (p = 0.06). Implementation of NACT varied significantly among Breast-Centers from 3.8 to 17.7 % (p < 0.001).

CONCLUSION

The impact of NACT on the subsequent surgical management is substantial and continues to evolve over time, resulting in less-extensive surgery. Even among high-volume Centers NACT implementation rate is still highly variable. Although we registered a significant increase in its use during the study period, these results need to be further improved.

Role of oedema and shrinkage patterns for prediction of response to neoadjuvant chemotherapy and survival outcomes in luminal breast cancer

AIMS

To explore the independent and additional value of oedema and shrinkage patterns for predicting the disease-free survival (DFS) and neoadjuvant chemotherapy (NAC) response in luminal breast cancer (BC).

MATERIALS AND METHODS

Patients with luminal BC who underwent NAC were enrolled in this study from 2017 to 2022. Traditional MRI features include BI-RADS-based MRI descriptors, tumor size, and ADC values, while emerging MRI features include oedema and shrinkage patterns, all of which were evaluated before, early, and after NAC. The changes in features during NAC were also evaluated. The value of features was evaluated through univariate, multivariate analyses.

RESULTS

A total of 258 patients were enrolled in this study, of which 77 responded to NAC. Diffuse oedema, stable or increased oedema during early NAC were adverse predictors for treatment response, while a greater reduction in tumor size and increase in ADC value were favorable predictors (all P<0.05). Furthermore, 20 of 60 patients who were followed up experienced recurrence. Diffuse oedema, pre-pectoral or subcutaneous oedema, and non-concentric shrinkage patterns after NAC were risk factors for DFS, whereas a greater increase in ADC value was a protective factor. Incorporating oedema and shrinkage patterns into traditional MRI features improved the predictive performance for treatment response (AUC from 0.76-0.78 to 0.80-0.83) and DFS (C-index from 0.67-69 to 0.75-0.80).

CONCLUSIONS

Oedema is an unfavorable predictor for treatment response and survival outcomes, while shrinkage patterns contribute more to the prognostic value, both of which could offer supplementary benefits for clinical outcomes in luminal BC.

Low breast density and peritumoral edema on MR predict worse overall survival of breast cancer patients after neoadjuvant chemotherapy

OBJECTIVES

To investigate the relationship of pre-treatment MR image features (including breast density) and clinical-pathologic characteristics with overall survival (OS) in breast cancer patients receiving neoadjuvant chemotherapy (NAC).

METHODS

This retrospective study obtained an approval of the institutional review board and the written informed consents of patients were waived. From October 2013 to April 2019, 130 patients (mean age, 47.6 ± 9.4 years) were included. The univariable and multivariable Cox proportional hazards regression models were applied to analyze factors associated with OS, including MR image parameters and clinical-pathologic characteristics.

RESULTS

Among the 130 included patients, 11 (8.5%) patients (mean age, 48.4 ± 11.8 years) died of breast cancer recurrence or distant metastasis. The median follow-up length was 70 months (interquartile range of 60-85 months). According to the Cox regression analysis, older age (hazard ratio [HR] = 1.769, 95% confidence interval [CI]): 1.330, 2.535), higher T stage (HR = 2.490, 95%CI:2.047, 3.029), higher N stage (HR = 1.869, 95%CI:1.507, 2.317), low breast density (HR = 1.693, 95%CI:1.391, 2.060), peritumoral edema (HR = 1.408, 95%CI:1.078, 1.840), axillary lymph nodes invasion (HR = 3.118, 95%CI:2.505, 3.881) on MR were associated with worse OS (all p < 0.05).

CONCLUSIONS

Pre-treatment MR features and clinical-pathologic parameters are valuable for predicting long-time OS of breast cancer patients after NAC followed by surgery. Low breast density, peritumoral edema and axillary lymph nodes invasion on pre-treatment MR images were associated with worse prognosis.

Correlation of Mammographic Microcalcifications with Final Surgical Pathology After Neoadjuvant Chemotherapy for Breast Cancer

INTRODUCTION

Imaging guidelines for post-neoadjuvant chemotherapy (NAC) breast cancer patients lack specificity on appropriateness and utility of individual modalities for surgical planning. Microcalcifications confound mammographic interpretation. We examined the correlation between the mammographic extent of microcalcifications present post-NAC, corresponding magnetic resonance imaging (MRI) lesions, and definitive surgical pathology.

METHODS

In this retrospective cohort study, patients with calcifications on mammography were collected from a database of consecutive breast cancer patients receiving NAC. The primary objective was to determine the correlation between maximum dimension of post-NAC calcifications with surgical pathology (invasive disease, tumor bed, and ductal carcinoma in situ [DCIS]), stratified by tumor receptor subgroup. Secondarily, we examined the correlation of residual disease with MRI mass enhancement (ME) and non-ME (NME). Pearson's correlation coefficient was used to evaluate statistical significance (strong: R² ≥70%; moderate: R²=25-70%; weak: R² ≤25%).

RESULTS

Overall, 186 patients met the inclusion criteria. Mammographic calcifications correlated poorly with invasive disease (R² = 10.8%), overestimating by 57%. In patients with calcifications on mammography, MRI ME and NME correlated weakly with the maximum dimension of invasive disease and DCIS. In triple-negative breast cancer (TNBC) patients, invasive disease correlated strongly with the maximum dimension of calcifications (R² = 83%) and moderately with ME (R² = 37.7%) and NME (R² = 28.4%).

CONCLUSION

Overall, current imaging techniques correlate poorly and overestimate final surgical pathology. This poor correlation may lead to uncertainty in the extent of required surgical excision and the exclusion of potential candidates for non-surgical management in ongoing trials. TNBCs would be good candidates for these trials given the stronger observed correlations between pathology and imaging.

Additional effect of anthracycline in preoperative chemotherapy with a sequential anthracycline‑containing regimen preceded by pertuzumab, trastuzumab and docetaxel combination therapy

The proper use of anthracycline-containing regimens in combination with anti-HER2-targeted therapy in a neoadjuvant setting for patients with HER2-positive breast cancer has not been resolved. Regimens preceded by anthracyclines have become the standard of care, and although the order has no significant impact on HER2-negative breast cancer, it is inconclusive as to whether a taxane-first sequence would have a similar effect on HER2-positive breast cancer. The present study aimed to investigate the benefit of a taxane-first sequence and of adriamycin and cyclophosphamide (AC) in patients with non-clinical complete response (non-cCR) to pertuzumab, trastuzumab and docetaxel (PTD). The present single-center prospective observational study was performed to investigate PTD followed by AC, and aimed to clarify the cCR rate after PTD alone and the pathological clinical response (pCR) rate after subsequent AC in patients without cCR after PTD alone. A total 24 patients were analyzed; of these, 14 achieved pCR (pCR rate, 58.3%). While four of 14 patients (28.6%) in the intention-to-treat population achieved pCR, nine of 14 patients (64.3%) achieved pCR with AC but not cCR after PTD. The median tumor reduction rate after four cycles of PTD was 58.9% (range, 20.8-100%) in all 24 patients, whereas the reduction rate after PTD-AC was 76.9% (range, 31.1-100%). Cardiac serious adverse events occurred in three patients (12.5%). In conclusion, a high pCR rate was observed for the taxane-first sequence. Patients were highly responsive to PTD, but some cases achieved additional antitumor effects after AC, which resulted in pCR without cCR after PTD alone. Since cardiotoxicity remains a significant problem, a higher risk-benefit treatment strategy is required to aim for AC omission. Trial registration number: UMIN000046338, name of registry: UMIN-CTR, date of registration: December 10, 2021.

MRI to assess response after neoadjuvant chemotherapy in breast cancer subtypes: a systematic review and meta-analysis

This meta-analysis aimed to estimate and compare sensitivity, specificity, positive- (PPV) and negative predictive value (NPV) of magnetic resonance imaging (MRI) for predicting pathological complete remission (pCR) after neoadjuvant chemotherapy (NAC) in patients with early-stage breast cancer. We stratified for molecular subtype by immunohistochemistry (IHC) and explored the impact of other factors. Two researchers systematically searched PUBMED and EMBASE to select relevant studies and extract data. For meta-analysis of sensitivity and specificity, we used bivariate random-effects models. Twenty-six included studies contained 4497 patients. There was a significant impact of IHC subtype on post-NAC MRI accuracy (p = 0.0082) for pCR. The pooled sensitivity was 0.67 [95% CI 0.58-0.74] for the HR-/HER2-, 0.65 [95% CI 0.56-0.73] for the HR-/HER2+, 0.55 [95% CI 0.45-0.64] for the HR+/HER2- and 0.60 [95% CI 0.50-0.70] for the HR+/HER2+ subtype. The pooled specificity was 0.85 [95% CI 0.81-0.88] for the HR-/HER2-, 0.81 [95% CI 0.74-0.86] for the HR-/HER2+, 0.88[95% CI 0.84-0.91] for the HR+/HER2- and 0.74 [95% CI 0.63-0.83] for the HR+/HER2+ subtype. The PPV was highest in the HR-/HER2- subtype and lowest in the HR+/HER2- subtype. MRI field strength of 3.0 T was associated with a higher sensitivity compared to 1.5 T (p = 0.00063). The accuracy of MRI for predicting pCR depends on molecular subtype, which should be taken into account in clinical practice. Higher MRI field strength positively impacts accuracy. When intervention trials based on MRI response evaluation are designed, the impact of IHC subtype and field strength on MR accuracy should be considered.

Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains

In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information. Next, a newly designed spatial attention module integrates such feature maps with original pathology images in multiple stains for enhanced PCR prediction in a dedicated prediction module. We compare it with baseline models that either use a single-stained slide or have no spatial attention module in place. Our proposed system yields 78.3% and 87.5% of accuracy for patch-, and patient-level PCR prediction, respectively, outperforming all other baseline models. Additionally, the heatmaps generated from the spatial attention module can help pathologists in targeting tissue regions important for disease assessment. Our system presents high efficiency and effectiveness and improves interpretability, making it highly promising for immediate clinical and translational impact.

Diagnostic Accuracy of MRI in Evaluating Response After Neoadjuvant Systemic Therapy in Operable Breast Cancer

Background

Neoadjuvant chemotherapy (NAC) is an important step in the treatment of various types of breast cancer by downsizing the tumor to make it operable. Determining disease extent after NAC is essential for accurate surgical planning. MRI has been the gold standard for detecting tumors that are usually difficult to detect on ultrasound or mammography. However, the use of MRI after NAC is controversial. Therefore, we aimed to evaluate the diagnostic accuracy of post-NAC MRI in the detection of residual disease preoperatively and to investigate the factors associated with pathological complete response (pCR).

Methodology

This retrospective review study was approved by the institutional review board with waiving of the informed consent. A total of 90 charts between January 2016 and January 2019 were reviewed. Baseline lesion size was measured as the maximal diameter in a single dimension by pretreatment MRI. To assess the diagnostic accuracy of MRI in detecting residual disease, we used two different definitions of pCR in the breast. The first is the resolution of both invasive disease and ductal carcinoma in situ. The second is the resolution of the invasive disease only. As a secondary objective of the study, we assessed the association between different patients’ characteristics and both MRI and pathologic response using univariate and multivariate analysis.

Results

A total of 52 women (mean age: 47.4 years; range: 28-74) with 56 breast masses were eligible for the study. Complete MRI response was noted in 22 (39%) masses. pCR was achieved in 14 (25%) and 25 (44.6%) masses using the first and second pCR definitions, respectively. The negative predictive value (NPV) and overall accuracy of MRI for detecting residual disease were 50% and 75%, respectively, using the first pCR definition. With the second pCR definition, NPV and accuracy were 77.3% and 76.8%, respectively. Positive axillary lymph nodes were the only significant factor associated with incomplete MRI and pathological responses.

Conclusions

MRI NPV for residual disease was higher with the second pCR definition; however, overall accuracy was not different. MRI accuracy in detecting residual disease after NAC is not adequate to replace pathological assessment.

The Way of the Future: Personalizing Treatment Plans Through Technology

Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

Neoadjuvant breast cancer treatment response; tumor size evaluation through different conventional imaging modalities in the NeoDense study

BACKGROUND

Neoadjuvant chemotherapy (NACT) is offered to an increasing number of breast cancer (BC) patients, and comprehensive monitoring of treatment response is of utmost importance. Several imaging modalities are available to follow tumor response, although likely to provide different clinical information. We aimed to examine the association between early radiological response by three conventional imaging modalities and pathological complete response (pCR). Further, we investigated the agreement between these modalities pre-, during, and post-NACT, and the accuracy of predicting pathological residual tumor burden by these imaging modalities post-NACT.

MATERIAL AND METHODS

This prospective Swedish cohort study included 202 BC patients assigned to NACT (2014-2019). Breast imaging with clinically used modalities: mammography, ultrasound, and tomosynthesis was performed pre-, during, and post-NACT. We investigated the agreement of tumor size by the different imaging modalities, and their accuracy of tumor size estimation. Patients with a radiological complete response or radiological partial response (≥30% decrease in tumor diameter) during NACT were classified as radiological early responders.

RESULTS

Patients with an early radiological response by ultrasound had 2.9 times higher chance of pCR than early radiological non-responders; the corresponding relative chance for mammography and tomosynthesis tumor size measures was 1.8 and 2.8, respectively. Post-NACT, each modality, separately, could accurately estimate tumor size (within 5 mm margin compared to pathological evaluation) in 43-46% of all tumors. The diagnostic precision in predicting pCR post-NACT was similar between the three imaging modalities; however, tomosynthesis had slightly higher specificity and positive predictive values.

CONCLUSION

Breast imaging modalities correctly estimated pathological tumor size in less than half of the tumors. Based on this finding, predicting residual tumor size post-NACT is challenging using conventional imaging. Patients with early radiological non-response might need improved monitoring during NACT and be considered for changed treatment plans.

Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer

Objectives:

To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers.

Methods:

152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller–Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors.

Results:

20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85–0.96, 0.86–0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86–1.00, 0.82–1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64–0.84, 0.46–0.82) in terms of NAC sensitivity and pCR.

Conclusions:

The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens.

Advances in knowledge:

Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.

Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.

The diagnostic accuracy of magnetic resonance imaging in predicting pathologic complete response after neoadjuvant chemotherapy in patients with different molecular subtypes of breast cancer

Background

Patients treated with neoadjuvant chemotherapy (NAC) who achieve a pathologic complete response (pCR) can be identified preoperatively and can potentially be spared the morbidity of surgery. The objective of this retrospective study was to estimate the diagnostic accuracy of preoperative magnetic resonance imaging (MRI) in predicting pCR in patients with different molecular subtypes of breast cancer and to provide a basis for the selection of surgical methods.

Methods

We retrospectively reviewed breast MRI data from August 2015 to December 2018 of patients who underwent four or more cycles of NAC. Factors associated with radiological complete response (rCR) and pCR were analyzed in univariable and multivariable settings. The accuracy of MRI and the correlation between rCR and pCR were also analyzed in each tumor subtype.

Results

A total of 177 women with a primary tumor fulfilled the study criteria; 18 of these patients (10.2%) achieved rCR, and 21 (11.9%) achieved a pCR. MRI diagnosis of rCR was significantly correlated with pCR with a Spearman's correlation coefficient of 0.686 in the entire population. The sensitivity, specificity, accuracy, pCR predictive value (PPV), and non-pCR predictive value (NPV) were estimated to be 66.67%, 97.44%, 93.79%, 77.78%, and 95.60%, respectively. Statistically significant correlations between rCR and pCR were found in Luminal B high Ki67% (P<0.001), HER2-positive (P=0.0035), and triple-negative (P<0.001) subtypes, but not in Luminal A and Luminal B low Ki67% subtypes. On univariate analysis, the tumor characteristics significantly associated with both rCR and pCR were small tumor, lymph node metastasis (LNM) negativity, early clinical stage, high grade, high Ki67% index, and different molecular subtype. On multivariate logistic regression analysis, grade 3 tumors (P=0.013), Ki67% ≥40% (P<0.000), and stage I tumor (P=0.006) were independently associated with rCR. However, grade 3 tumors (P=0.001), triple-negative breast cancer (TNBC), and clinical stages I and II tumors (P=0.003; P=0.030) were independently associated with the likelihood of attaining a pCR.

Conclusions

The overall accuracy of MRI in predicting pCR in invasive breast cancer patients who received NAC was 93.8%. The performance of MRI differed among molecular subtypes, and the highest PPV was found in TNBC (100%) and Luminal B high Ki67% (75%) subtypes.

MRI does not predict pathologic complete response after neoadjuvant chemotherapy for breast cancer

BACKGROUND

This study assessed whether magnetic resonance imaging (MRI) could accurately predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) for patients receiving standardized treatment, pre- and post-NAC MRI on the same instrumentation using a consistent imaging protocol, interpreted by a single breast fellowship-trained radiologist.

METHODS

A single-institution retrospective analysis was performed including clinical, radiographic, and pathologic parameters for all patients with breast cancer treated with NAC from 2015 to 2018. Radiographic complete response (rCR) was defined as absence of suspicious MRI findings in the ipsilateral breast or lymph nodes. pCR was defined as the absence of invasive cancer or ductal carcinoma in-situ in breast or lymph nodes after operation (ypT0N0M0).

RESULTS

Data for 102 consecutive patients demonstrated that 44 (43.1%) had rCR and 41 (40.1%) had pCR. pCR occurred in 12 (25.0%) of 48 estrogen receptor positive (ER+) patients, 29 (53.7%) of 54 ER- patients, and 25 (52.1%) of 48 human epidermal growth factor receptor 2 positive patients. The positive predictive value for MRI after NAC was 84.5% and the negative predictive value was 72.7%. The accuracy rate for MRI was 78.6%. Of the 44 patients with rCR, 12 (27.3%) had residual cancer on the pathologic specimen after surgical excision.

CONCLUSION

rCR is not accurate enough to serve as a surrogate marker for pCR on MRI after NAC.