Accuracy of ultrasonography and mammography in predicting pathologic response after neoadjuvant chemotherapy for breast cancer

Breast Tumor-Bed Biopsy for Pathological Complete Response Prediction: The NRG-BR005 Nonrandomized Clinical Trial

Importance

Use of modern neoadjuvant chemotherapy (NAC) regimens has markedly increased rates of pathologic complete response (pCR) in breast cancer, raising the question of whether surgical removal of the primary tumor is required for patients with pCR. For surgery to be omitted, one must be able to accurately predict pCR before surgery.

Objective

To investigate if adding post-NAC core needle biopsy of the tumor bed to trimodality imaging in patients who have clinical complete response (cCR) will predict pCR (resolution of both invasive disease and ductal carcinoma in situ) in 90% or more cases.

Design, Setting, and Participants

This was a phase 2, prospective, nonrandomized clinical trial. Patients were enrolled from August 2017 to June 2019. This is the final analysis, which was completed in December 2023. The setting included academic and community hospital center members of NRG (ie, the National Surgical Adjuvant Breast and Bowel Project, the Radiation Therapy Oncology Group, and the Gynecologic Oncology Group) in the US and Canada. Patients with operable (T1-T3, stage I-III) invasive ductal carcinoma who completed NAC and achieved cCR and radiological complete response (rCR) or near rCR by mammography (mass ≤1 cm and no malignant microcalcifications), ultrasound (mass ≤2 cm), and magnetic resonance imaging (no mass with rapid rise or washout kinetics).

Interventions

Patients underwent marker-directed stereotactic multiple-core needle biopsy of the tumor bed with marker placement before breast-conservation surgery.

Main Outcomes and Measures

End points were negative predictive value (NPV) and sensitivity of the biopsy.

Results

A total of 105 patients were enrolled with 101 evaluable (mean [SD] age, 52.8 [10.5] years); 77 patients (76.2%) were younger than 60 years, and all breast cancer subtypes were represented with 32 (31.7%) triple-negative breast cancer, 21 (20.8%) hormone receptor-positive/epidermal growth factor receptor 2 (ERBB2; formerly HER2)-negative (ERBB2-) breast cancer, and 46 (45.5%) ERBB2-positive (ERBB2+) breast cancer. In 101 evaluable patients, 36 had residual disease at surgery (pCR = 64%). With imaging criteria, NPV of the biopsy was 78.3% (95% CI, 67.9%-86.6%), and the sensitivity of the biopsy was 50% (95% CI, 32.9%-67.1%). In an exploratory subset analysis, the NPV in patients with ERBB2+ breast cancer was 90% (95% CI, 76.3%-97.2%). On retrospective central review, 62 of 101 enrolled patients met imaging eligibility criteria. In this exploratory post hoc analysis, NPV in these patients was 86.8% (95% CI, 74.7%-94.5%).

Conclusions and Relevance

These findings do not support breast conservation treatment without surgery based on the study criteria for cCR and rCR/near rCR by trimodality imaging and negative tumor-bed biopsy. Strict adherence to imaging criteria may be required to achieve acceptable predictive values.

TRIAL Registration

ClinicalTrials.gov Identifier: NCT03188393.

The role and potential of digital breast tomosynthesis in neoadjuvant systemic therapy evaluation for optimising breast cancer management: a pictorial essay

Neoadjuvant therapy (NT) has become the gold standard for treating locally advanced breast cancer (BC). The assessment of pathological response (pR) post-NT plays a crucial role in predicting long-term survival, with contrast-enhanced MRI currently recognised as the preferred imaging modality for its evaluation. Traditional imaging techniques, such as digital mammography (DM) and ultrasonography (US), encounter difficulties in post-NT assessments due to breast density, lesion changes, fibrosis, and molecular patterns. Digital breast tomosynthesis (DBT) offers solutions to prevalent challenges in DM, such as tissue overlap, and facilitates a comprehensive assessment of lesion morphology, dimensions, and margins. Studies suggest that DBT correlates more accurately with pathology than DM and US, showcasing its potential advantages. This pictorial essay demonstrates the potential of DBT as a complementary tool to DM for assessing pR after NT, including instances of true- and false-positive assessments correlated with histopathological findings. In conclusion, DBT emerges as a valuable adjunct to DM, effectively addressing its limitations in post-NT assessment. The technology's potential to diminish tissue overlap, improve discrimination, and provide multi-dimensional perspectives demonstrates promising results, indicating its utility in scenarios where MRI is contraindicated or inaccessible.

Correlation on metabolic complete response on positron emission tomography and pathological complete response in patients with breast cancer after neoadjuvant chemotherapy

PURPOSE

The use of neoadjuvant chemotherapy in treating breast cancer has expanded in recent years. There was increased interest in using positron emission tomography (PET) for the evaluation of treatment response. We aimed to study the accuracy of metabolic complete response (mCR) on PET scan in predicting pathological complete response (pCR) after neoadjuvant treatment.

METHODS AND RESULTS

Between January 1, 2014 and June 30, 2019, 356 consecutive patients who completed neoadjuvant treatment underwent PET scan before surgery. 207 patients (58.1%) achieved mCR and 128 patients (36.0%) achieved pathologic CR. Among mCR patients, 101 (48.8%) had pCR. Among pCR patients, 27 (21%) did not achieve mCR on PET. The overall sensitivity of predicting pCR with mCR was 78.9% and specificity of 53.5%. The overall accuracy was 0.691 by area under the receiver operating characteristic curve (AUC). Analysis using mCR to predict breast/axilla pCR had a sensitivity of 76.2%/67.9%, specificity of 54%/62.1%, and AUC of 0.682/0.675, respectively. Sensitivity and specificity were highest among HR-/HER2+ (87.1% and 57.1%), followed by HR+/HER2- (85% and 59.6%) and triple negative (82.1% and 54.1%) and the lowest were HR+/HER2+ (triple positive) (69.4% and 40.3%). There was little difference in sensitivity and specificity among the high and low Ki67 proliferation index (78.3% vs. 75% and 52.1% vs. 62.5%).

CONCLUSION

PET was useful in evaluation of tumor response to neoadjuvant chemotherapy especially in the HR-HER2+ subtype. However, its accuracy was not high enough to replace surgery.

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images

Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical diagnosis and treatment of patients. In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. The BLFFM can effectively mine the highly complex correlation and complementary feature information between gray-scale ultrasound (GUS) and color Doppler blood flow imaging (CDFI). The THAM is able to focus on key features of lesion progression before and after one cycle of NAC. The GUS and CDFI videos of 101 patients collected from cooperative medical institutions were preprocessed to obtain 3000 sets of multistage bimodal ultrasound image combinations for experiments. The experimental results show that the proposed model is effective and outperforms the compared models. The code will be published on the https://github.com/jinzhuwei/BLTA-CNN .

Amplitude-Modulation Frequency Optimization for Enhancing Harmonic Motion Imaging Performance of Breast Tumors in the Clinic

OBJECTIVE

Elastography images tissue mechanical responses and infers the underlying properties to aid diagnosis and treatment response monitoring. The estimation of absolute or relative tumor properties may vary with dimensions even when the mechanical properties remain constant. Harmonic motion imaging (HMI) uses amplitude-modulated (AM) focused ultrasound to interrogate the targeted tissue's viscoelastic properties. In this study, effects of AM frequencies on HMI were investigated in terms of inclusion relative stiffness and size estimation.

METHODS

AM frequencies from 200 to 600 Hz in steps of 100 Hz were considered using a 5.3-kPa phantom with cylindrical inclusions (Young's modulus: 22, 31, 44, 56 kPa, and diameter: 4.8, 8.1, 13.6, 19.8 mm) to optimize the performance of HMI in characterizing tumors with the same mechanical properties and of different dimensions.

RESULTS

Consistent displacement ratios (DRs) (17.5% variation) of the inclusion to background were obtained with 200-Hz AM for breast-tumor-mimicking inclusions albeit a suboptimal inclusion size estimation obtained. 400-Hz was otherwise used for small and low-contrast inclusions (4.8 mm, 22 or 31 kPa). A linear relationship (R2 = 0.9043) was found between the inverse DR at these frequencies and the Young's modulus ratio. 400 Hz obtained the most accurate inclusion size estimation with an overall estimation error on the lateral dimension of 0.5 mm. In vivo imaging of breast cancer patients (n = 5) was performed at 200 or 400 Hz.

CONCLUSION

The results presented herein indicate that the HMI AM frequency could be optimized adaptively in cases of different applications, i.e., at 200 or 400 Hz, depending on whether aimed for consistent DR measurement for tumor response assessment or tumor margin delineation for surgical planning. HMI may thus be capable of predicting the pathologic endpoint of tumors in response to neoadjuvant chemotherapy (NACT) as early as 3 weeks into treatment.

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular tissue. To address this, we propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and then incorporates these structures into a deep-learning-based prediction model via an attention mechanism. Our topology-informed deep learning model, TopoTxR, leverages topology to provide enhanced insights into tissues critical for disease pathophysiology and treatment response. We empirically validate TopoTxR using the VICTRE phantom breast dataset, showing that the topological structures extracted by our model effectively approximate the breast parenchymal structures. We further demonstrate TopoTxR's efficacy in predicting response to neoadjuvant chemotherapy. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-naïve imaging, in patients who respond favorably to therapy as achieving pathological complete response (pCR) versus those who do not. In a comparative analysis with several baselines on the publicly available I-SPY 1 dataset (N = 161, including 47 patients with pCR and 114 without) and the Rutgers proprietary dataset (N = 120, with 69 patients achieving pCR and 51 not), TopoTxR demonstrates a notable improvement, achieving a 2.6% increase in accuracy and a 4.6% enhancement in AUC compared to the state-of-the-art method.

Dynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer

Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model. This retrospective study included 304 EBC patients recruited from multiple centers. All enrollees had completed NACT regimens, and underwent US examinations at baseline and at each NACT cycle. We subsequently determined that percentage reduction of tumor maximum diameter from baseline to third cycle of NACT serves to independent predictor for pCR, enabling creation of a nomogram ([Formula: see text]). Our predictive accuracy further improved ([Formula: see text]) by combining dynamic US data and clinicopathological features in a machine learning model. Such models may offer a means of accurately predicting NACT responses in this setting, helping to individualize patient therapy. Our study may provide additional insights into the US-based response prediction by focusing on the dynamic changes of the tumor in the early and full NACT cycle.

Predictors of mastectomy in breast cancer patients with complete remission of primary tumor after neoadjuvant therapy: A retrospective study

INTRODUCTION

Neoadjuvant therapy (NAT) should increase the rate of breast-conserving surgery (BCS) in non-metastatic breast cancer (BC) patients, especially in those achieving tumor shrinkage. Still, the conversion from a pre-planned mastectomy to BCS in patients responding to NAT is not a widespread standard. We aimed to identify factors influencing surgical choices in this setting.

MATERIALS AND METHODS

We retrospectively collected data of BC patients with complete remission of primitive tumor (ypT0) after NAT, treated with BCS or mastectomy in two Italian breast units. Predictors of mastectomy were explored using logistic regression. Distant recurrence and event-free survival were assessed in the BCS and mastectomy cohort.

RESULTS

243 patients were included, 147 (60.5 %) treated with BCS and 96 (39.5 %) treated with mastectomy. In the mastectomy group, there were more centrally-located, multiple and larger tumors. At univariate regression analysis, central location, baseline tumor extension on ultrasound (US) and magnetic resonance imaging (MRI), multiple foci and clinical stage were significantly associated with the chance of receiving mastectomy. At multivariate analysis, only baseline focality on US and extension on MRI retained significance as predictors of mastectomy. Distant recurrence and event-free survival were significantly longer in patients undergoing BCS.

CONCLUSION

Baseline tumor extension and focality were the main predictors of mastectomy in patients with ypT0 after NAT. However, BCS did not negatively affect survival outcomes in our cohort. An effort should be made to avoid potentially unnecessary mastectomy in this population, aiming at minimizing surgery-associated toxicities and improving patients' quality of life.

Unlocking Chemotherapy Success: The Role of Diffusion Tensor Imaging in Breast Cancer Treatment

Background: This study investigates the role of Diffusion Tensor Imaging (DTI) in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. Methods: A Diffusion Tensor Imaging magnetic resonance imaging (DTI MRI) sequence, evaluating water diffusion along tissue structures, was performed before and after two chemotherapy cycles. This study included 23 patients with 27 malignant masses, comparing changes in DTI parameters with Residual Cancer Burden (RCB) scores. Results: We found a significant correlation between changes in specific DTI parameters (e.g., λ2, FA, RA) and pathological response, suggesting that DTI could serve as a sensitive marker for early chemotherapy response. However, differences in sensitivity were observed between DTI sequences with 6 and 12 directions, indicating that 12-direction DTI may provide better diagnostic accuracy. The percentage change in DTI parameters, particularly FA, demonstrated a strong ability to predict pathological complete response (pCR) with high sensitivity. Conclusions: As a non-invasive tool, DTI has the potential to assess chemotherapy efficacy, although larger studies with standardized protocols are necessary to validate its clinical utility.

Ultrasound and diffuse optical tomography-transformer model for assessing pathological complete response to neoadjuvant chemotherapy in breast cancer

Significance

We evaluate the efficiency of integrating ultrasound (US) and diffuse optical tomography (DOT) images for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The ultrasound-diffuse optical tomography (USDOT)-Transformer model represents a significant step toward accurate prediction of pCR, which is critical for personalized treatment planning.

Aim

We aim to develop and assess the performance of the USDOT-Transformer model, which combines US and DOT images with tumor receptor biomarkers to predict the pCR of breast cancer patients under NAC.

Approach

We developed the USDOT-Transformer model using a dual-input transformer to process co-registered US and DOT images along with tumor receptor biomarkers. Our dataset comprised imaging data from 60 patients at multiple time points during their chemotherapy treatment. We used fivefold cross-validation to assess the model's performance, comparing its results against a single modality of US or DOT.

Results

The USDOT-Transformer model demonstrated excellent predictive performance, with a mean area under the receiving characteristic curve of 0.96 (95%CI: 0.93 to 0.99) across the fivefold cross-validation. The integration of US and DOT images significantly enhanced the model's ability to predict pCR, outperforming models that relied on a single imaging modality (0.87 for US and 0.82 for DOT). This performance indicates the potential of advanced deep learning techniques and multimodal imaging data for improving the accuracy (ACC) of pCR prediction.

Conclusion

The USDOT-Transformer model offers a promising non-invasive approach for predicting pCR to NAC in breast cancer patients. By leveraging the structural and functional information from US and DOT images, the model offers a faster and more reliable tool for personalized treatment planning. Future work will focus on expanding the dataset and refining the model to further improve its accuracy and generalizability.

Is Sentinel Lymph Node Biopsy a Viable Alternative to Axillary Lymph Node Dissection in Breast Carcinoma Patients Who Have Received Neo-Adjuvant Chemotherapy?

Background Sentinel lymph node biopsy (SLNB) is based on the hypothesis that lymph from a primary solid neoplasm drains into one or more sentinel nodes, which are the first nodes at risk for harbouring occult metastatic disease. Sentinel lymph node biopsy has replaced axillary lymph node dissection (ALND) as the standard method for axillary staging in clinically node-negative patients. It avoids the complications associated with ALND and allows assessment of nodal status in patients with clinically node-negative breast cancer. Aims and objectives The aim of this study is to determine the false negative rate and identification rate of SLNB in breast cancer patients who received neoadjuvant chemotherapy (NACT). Materials and methods We conducted a hospital-based prospective study that included 19 patients who presented with early breast cancer and were node-positive. Post NACT, intraoperatively, methylene blue and radiocolloid dye were injected in the subareolar region. During the surgery, the blue and hot nodes identified were dissected, sent for frozen section analysis, and subsequently submitted for histopathological evaluation. This was followed by a standard-level I/II/III axillary clearance with histopathological examination. Results The false-negative rate of SLNB is 25%. Sentinel lymph node biopsy was more accurate with stage II than stage III tumours, and in patients who downstaged from stage II to any stage following NACT, it was more accurate than downstaging from stage III. The average number of sentinel nodes identified was 1.9, with the maximum being seven and the minimum being one. A total of 25 sentinel lymph nodes were identified in 13 patients, with an identification rate of 68.42%. Conclusions The main clinicopathological factors that influence the false negative rate of SLNB after NACT are axillary lymph node status, stage of the tumour at presentation, and tumour downstaging. For patients for whom sentinel nodes cannot be harvested, ALND should be done.

Towards detection of early response in neoadjuvant chemotherapy of breast cancer using Bayesian intravoxel incoherent motion

Introduction

The early identification of good responders to neoadjuvant chemotherapy (NACT) holds a significant potential in the optimal treatment of breast cancer. A recent Bayesian approach has been postulated to improve the accuracy of the intravoxel incoherent motion (IVIM) model for clinical translation. This study examined the prediction and early sensitivity of Bayesian IVIM to NACT response.

Materials and methods

Seventeen female patients with breast cancer were scanned at baseline and 16 patients were scanned after Cycle 1. Tissue diffusion and perfusion from Bayesian IVIM were calculated at baseline with percentage change at Cycle 1 computed with reference to baseline. Cellular proliferative activity marker Ki-67 was obtained semi-quantitatively with percentage change at excision computed with reference to core biopsy.

Results

The perfusion fraction showed a significant difference (p = 0.042) in percentage change between responder groups at Cycle 1, with a decrease in good responders [-7.98% (-19.47-1.73), n = 7] and an increase in poor responders [10.04% (5.09-28.93), n = 9]. There was a significant correlation between percentage change in perfusion fraction and percentage change in Ki-67 (p = 0.042). Tissue diffusion and pseudodiffusion showed no significant difference in percentage change between groups at Cycle 1, nor was there a significant correlation against percentage change in Ki-67. Perfusion fraction, tissue diffusion, and pseudodiffusion showed no significant difference between groups at baseline, nor was there a significant correlation against Ki-67 from core biopsy.

Conclusion

The alteration in tumour perfusion fraction from the Bayesian IVIM model, in association with cellular proliferation, showed early sensitivity to good responders in NACT.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT03501394, identifier NCT03501394.

Evaluation of Treatment Response in Patients with Breast Cancer

Breast cancer (BC) remains one of the leading causes of death among women. The management and outcome in BC are strongly influenced by a multidisciplinary approach, which includes available treatment options and different imaging modalities for accurate response assessment. Among breast imaging modalities, MR imaging is the modality of choice in evaluating response to neoadjuvant therapy, whereas F-18 Fluorodeoxyglucose positron emission tomography, conventional computed tomography (CT), and bone scan play a vital role in assessing response to therapy in metastatic BC. There is an unmet need for a standardized patient-centric approach to use different imaging methods for response assessment.

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer

RATIONALE AND OBJECTIVES

To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

MATERIALS AND METHODS

In this prospective study, 255 breast cancer patients who received NAC between September 2016 and December 2021 were included. Radiomics models were designed using a support vector machine classifier based on US images obtained before treatment, including BUS and SWE. And CNN models also were developed using ResNet architecture. The final predictive model was developed by combining the dual-modal US and independently associated clinicopathologic characteristics. The predictive performances of the models were assessed with five-fold cross-validation.

RESULTS

Pretreatment SWE performed better than BUS in predicting the response to NAC for breast cancer for both the CNN and radiomics models (P < 0.001). The predictive results of the CNN models were significantly better than the radiomics models, with AUCs of 0.72 versus 0.69 for BUS and 0.80 versus 0.77 for SWE, respectively (P = 0.003). The CNN model based on the dual-modal US and molecular data exhibited outstanding performance in predicting NAC response, with an accuracy of 83.60% ± 2.63%, a sensitivity of 87.76% ± 6.44%, and a specificity of 77.45% ± 4.38%.

CONCLUSION

The pretreatment CNN model based on the dual-modal US and molecular data achieved excellent performance for predicting the response to chemotherapy in breast cancer. Therefore, this model has the potential to serve as a non-invasive objective biomarker to predict NAC response and aid clinicians with individual treatments.

Accuracy of Breast Ultrasonography and Mammography in Comparison with Postoperative Histopathology in Breast Cancer Patients after Neoadjuvant Chemotherapy

INTRODUCTION

Nowadays chemotherapy in breast cancer patients is optionally applied neoadjuvant, which allows for testing of tumor response to the chemotherapeutical treatment in vivo, as well as allowing a greater number of patients to benefit from a subsequent breast-conserving surgery.

MATERIAL AND METHODS

We compared breast ultrasonography, mammography, and clinical examination (palpation) results with postoperative histopathological findings after neoadjuvant chemotherapy, aiming to determine the most accurate prediction of complete remission and tumor-free resection margins. To this end, clinical and imaging data of 184 patients (193 tumors) with confirmed diagnosis of breast cancer and neoadjuvant therapy were analyzed.

RESULTS

After chemotherapy, tumors could be assessed by palpation in 91.7%, by sonography in 99.5%, and by mammography in 84.5% (chi-square p < 0.0001) of cases. Although mammography proved more accurate in estimating the exact neoadjuvant tumor size than breast sonography in total numbers (136/163 (83.44%) vs. 142/192 (73.96%), n.s.), 29 tumors could be assessed solely by means of breast sonography. A sonographic measurement was feasible in 192 cases (99.48%) post-chemotherapy and in all cases prior to chemotherapy.

CONCLUSIONS

We determined a superiority of mammography and breast sonography over clinical palpation in predicting neoadjuvant tumor size. However, neither examination method can predict either pCR or tumor margins with high confidence.

ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer: 2022 Update

Imaging plays a vital role in managing patients undergoing neoadjuvant chemotherapy, as treatment decisions rely heavily on accurate assessment of response to therapy. This document provides evidence-based guidelines for imaging breast cancer before, during, and after initiation of neoadjuvant chemotherapy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI

Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.

Classifying Breast Cancer Metastasis Based on Imaging of Tumor Primary and Tumor Biology

The molecular classification of breast cancer has allowed for a better understanding of both prognosis and treatment of breast cancer. Imaging of the different molecular subtypes has revealed that biologically different tumors often exhibit typical features in mammography, ultrasound, and MRI. Here, we introduce the molecular classification of breast cancer and review the typical imaging features of each subtype, examining the predictive value of imaging with respect to distant metastases.

Comparison of automated breast volume scanning with conventional ultrasonography, mammography, and MRI to assess residual breast cancer after neoadjuvant therapy by molecular type

AIM

To compare the accuracy of hand-held ultrasonography (US), mammography (MG), magnetic resonance imaging (MRI), and automated breast volume scanning (ABVS) in defining residual breast cancer tumour size after neoadjuvant therapy (NAT).

MATERIALS AND METHODS

Patients diagnosed breast cancer and who received NAT at the Breast Center, Peking University People's Hospital, were enrolled prospectively. Imaging was performed after the last cycle of NAT. The residual tumour size, intraclass correlation coefficients (ICCs), and receiver operating characteristic (ROC) to predict pathological complete response (pCR) were analysed.

RESULTS

A total of 156 patients with 159 tumours were analysed. ABVS had a moderate correlation with histopathology residual tumour size (ICC = 0.666), and showed high agreement among triple-positive tumours (ICC = 0.797). With 5 mm as the threshold, the coincidence rate reached 64.7% between ABVS and pathological size, which was significantly higher than that between US, MG, MRI, and pathological size (50%, 45.1%, 41.4%; p=0.009, p=0.001, p<0.001, respectively). For ROC analysis, ABVS demonstrated a higher area under the ROC curve, but with no statistical difference, except for MG (0.855, 0.816, 0.819, and 0.788, respectively; p=0.183 for US, p=0.044 for MG, and p=0.397 for MRI, with ABVS as the reference).

CONCLUSIONS

The longest tumour diameter on ABVS had a moderate correlation with pathological residual invasive tumour size. ABVS was shown to have good ability to predict pCR and would appear to be a potential useful tool for the assessment after NAT for breast cancer.

Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting

Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.

Mid-treatment Ultrasound Descriptors as Qualitative Imaging Biomarkers of Pathologic Complete Response in Patients with Triple-Negative Breast Cancer

This study aimed to investigate mid-treatment breast tumor ultrasound characteristics that may predict eventual pathologic complete response (pCR) in triple-negative breast cancer; specifically, we examined associations between pCR and two parameters: tumor response pattern and tumor appearance. Ultrasound was performed at mid-treatment, defined as the completion of four cycles of anthracycline-based chemotherapy and before receiving taxane-based chemotherapy. Consensus imaging review was performed while blinded to pathology results (i.e., pCR/non-pCR) from surgery. Tumor response pattern was described as "complete," "concentric," "fragmented," "stable" or "progression." Tumor appearance was designated as "mass," "architectural distortion," "flat tumor bed" or "clip only." Univariate and multivariate regression analyses of 144 participants showed significant associations between mid-treatment response pattern and pCR (p = 0.0348 and p = 0.0173, respectively), with complete and concentric response patterns more likely to achieve pCR than other patterns. Univariate and multivariate regression analyses further showed significant associations between mid-treatment tumor appearance and pCR (p < 0.0001 for both), with persistent appearance of mass less likely than other appearances to achieve pCR. To conclude, our study demonstrated strong associations between pCR and both tumor response pattern and tumor appearance, thereby suggesting that these parameters have potential as qualitative imaging biomarkers of pCR in triple-negative breast cancer.

Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.

Assessment of Cone-Beam Breast Computed Tomography for Predicting Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer: A Prospective Study

Background

Response surveillance of neoadjuvant chemotherapy is needed to facilitate treatment decisions. We aimed to assess the imaging features of cone-beam breast computed tomography (CBBCT) for predicting the pathologic response of breast cancer after neoadjuvant chemotherapy.

Methods

This prospective study included 81 women with locally advanced breast cancer who underwent neoadjuvant chemotherapy from August 2017 to January 2021. All patients underwent CBBCT before treatment, and 55 and 65 patients underwent CT examinations during the midtreatment (3 cycles) and late-treatment phases (7 cycles), respectively. Clinical information and quantitative parameters such as the diameter, volume, surface area, and CT density were compared between pathologic responders and nonresponders using the T–test and the Mann–Whitney U test. The performance of meaningful parameters was evaluated with the receiver operating characteristic curve, sensitivity, and specificity.

Results

The quantitative results for the segmented volume, segmented surface area, segmented volume reduction, maximum enhancement ratio, wash-in rate and two-minute enhancement value in the mid- and late-treatment periods had predictive value for pathologic complete response. The area under the curve for the prediction model after multivariate regression analysis was 0.874.

Conclusion

After comparing the outcomes of each timepoint, mid- and late-treatment parameters can be used to predict pathologic outcome. The late-treatment parameters showed significant value with a predictive model.

A Clinical Assessment of a Magnetic Resonance Computer-Aided Diagnosis System in the Detection of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer

Purpose

This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Besides, to investigate whether tumor molecular typing is associated with the efficiency of diagnosis of the CAD systems.

Methods

470 patients were identified with breast cancers who underwent NAC and post MR imaging between January 2016 and March 2019. The diagnostic performance of radiologists of different levels and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the tumor molecular typing.

Results

Among 470 patients, 123 (26%) underwent pCR. The CAD system showed a comparable specificity as the senior radiologist (83.29% vs. 84.15%, p=0.488) and comparable area under the curve (AUC) (0.839 vs. 0.835, p =0.452). The performance of all radiologists significantly improved when aided by the CAD system (P<0.05), And there were no statistical differences in terms of sensitivity, specificity and accuracy between the two groups with CAD assistance(p>0.05).The AUC values for identifying pCR in TN patients were significant (0.883, 95%CI: 0.801-0.964, p < 0.001).

Conclusion

The CAD system assessed in this study improves the performance of all radiologists, regardless of experience. The molecular typing of breast cancer is potential influencer of CAD diagnostic performance.

Predictors of axillary node response in node-positive patients undergoing neoadjuvant chemotherapy for breast cancer

Background:

The ability to accurately predict which patients will achieve a pathologic complete response (pCR) after neoadjuvant chemotherapy could help identify those who could safely be spared the potential morbidity of axillary lymph node dissection. We performed a retrospective analysis of a cohort of clinically node-positive patients managed with neoadjuvant chemotherapy with the goal of identifying predictors of axillary pCR.

Methods:

Eligible patients were aged 18 years or older, had clinical T1–T4, N1–N3, M0 breast cancer and received neoadjuvant chemotherapy followed by surgical axillary lymph node staging between 2001 and 2017 at Misericordia Hospital, Edmonton, Alberta. Patient data, including tumour characteristics, details of neoadjuvant chemotherapy, imaging results before and after neoadjuvant chemotherapy, and final pathologic analysis, were collected from the appropriate provincial electronic data repositories. We summarized the data using descriptive statistics. We characterized associations between clinical/tumour characteristics and pCR using univariate and multivariate regression analysis.

Results:

Of the 323 patients included in the study, 130 (40.2%) achieved axillary pCR. Absence of residual disease in the breast was associated with axillary pCR (odds ratio 6.74, 95% confidence interval 2.89–15.67). HER2-positive, triple-negative and ER-positive/PR-negative/HER2-negative tumours were significantly associated with a pCR on univariate analysis; the association trended toward significance on multivariate analysis.

Conclusion:

Our findings support the routine use of neoadjuvant chemotherapy and sentinel lymph node biopsy in patients with an absence of residual disease in the breast, and potentially in those with HER2-positive or triple-negative subtypes, and highlight the ER-positive/PR-negative biomarker subtype as a potential predictor of nodal response.

Multivariable Models Based on Baseline Imaging Features and Clinicopathological Characteristics to Predict Breast Pathologic Response after Neoadjuvant Chemotherapy in Patients with Breast Cancer

Abstract Introduction Currently, the accurate evaluation and prediction of response to neoadjuvant chemotherapy (NAC) remains a great challenge. We developed several multivariate models based on baseline imaging features and clinicopathological characteristics to predict the breast pathologic complete response (pCR). Methods We retrospectively collected clinicopathological and imaging data of patients who received NAC and subsequent surgery for breast cancer at our hospital from 2014 June till 2020 September. We used mammography, ultrasound and magnetic resonance imaging (MRI) to investigate the breast tumors at baseline. Results A total of 308 patients were included and 111 patients achieved pCR. The HER2 status and Ki-67 index were significant factors for pCR on univariate analysis and in all multivariate models. Among the prediction models in this study, the ultrasound-MRI model performed the best, producing an area under curve of 0.801 (95%CI=0.749-0.852), a sensitivity of 0.797 and a specificity of 0.676. Conclusion Among the multivariable models constructed in this study, the ultrasound plus MRI model performed the best in predicting the probability of pCR after NAC. Further validation is required before it is generalized.

Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram

Background and Aims

Prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC.

Methods

This retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC and after two cycles of NAC; and the ultrasound, clinicopathological features and feature changes after two cycles of NAC were recorded. A multivariate logistic regression model was combined with bootstrapping screened for informative features associated with pCR. Then, we constructed two nomograms: an initial-baseline nomogram and a two-cycle response nomogram. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were analyzed. The C-index was used to evaluate predictive accuracy.

Results

Sixty (60/282, 21.28%) patients achieved pCR. Triple-negative breast cancer (TNBC) and HER2-amplified types were more likely to obtain pCR. Size shrinkage, posterior acoustic pattern, and elasticity score were identified as independent factors by multivariate logistic regression. In the validation cohort, the two-cycle response nomogram showed better discrimination than the initial-baseline nomogram, with the C-index reaching 0.79. The sensitivity, specificity, and NPV of the two-cycle response nomogram were 0.77, 0.77, and 0.92, respectively.

Conclusion

The two-cycle response nomogram exhibited satisfactory efficiency, which means that the nomogram was a reliable method to predict pCR after NAC. Size shrinkage after two cycles of NAC was an important in dependent factor in predicting pCR.

A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer

The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.

Breast cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?

Chemotherapy is an essential treatment for breast cancer, inducing cancer cell death. However, chemoresistance is a problem that limits the effectiveness of chemotherapy. Many factors influence chemoresistance, including drug inactivation, changes in drug targets, overexpression of ABC transporters, epithelial-to-mesenchymal transitions, apoptotic dysregulation, and cancer stem cells. The effectiveness of chemotherapy can be assessed clinically and pathologically. Clinical response evaluation is based on physical examination or imaging (mammography, ultrasonography, computed tomography scan, or magnetic resonance imaging) and includes tumor size changes after chemotherapy. Pathological response evaluation is a method based on tumor residues in histopathological preparations. We should be suspicious of chemoresistance if there are no significant changes clinically according to the Response Evaluation Criteria in Solid Tumors and World Health Organization criteria or pathological changes according to the Miller and Payne criteria, especially after 2–3 cycles of chemotherapy treatments. Chemoresistance is mostly detected after the administration of chemotherapy drugs. No reliable parameters or biomarkers can predict chemotherapy responses appropriately and effectively. Well-known parameters such as cancer type, grade, subtype, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, and MDR-1/P-gP have been used for selecting chemotherapy regimens. Some new methods for predicting chemoresistance include chemosensitivity and chemoresistance assays, multigene expressions, and positron emission tomography assays. The latest approaches are based on evaluation of molecular processes and the metabolic activity of cancer cells. Some methods for preventing chemoresistance include using the right regimen, using some combination of chemotherapy methods, conducting adequate monitoring, and using drugs that could prevent the emergence of multidrug resistance.

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Chemotherapy is an essential treatment in the management of breast cancer.

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Chemotherapy is carried out based on the selection of regimens for the specific individual and tumor characteristics.

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Combination therapy, monitoring, and evaluation are used to prevent chemoresistance.

The use of breast ultrasound for prediction of pathologic complete response in different subtypes of early breast cancer within the WSG-ADAPT subtrials

OBJECTIVE

We assessed the value of breast ultrasound (US) performed at week 3 and 6 and at the end (EOT) of neoadjuvant therapy (NAT) for prediction of pathologic complete response (pCR, ypT0/is ypN0) in patients with HR+/HER2+, HR-/HER2-or HR-/HER2+ early breast cancer enrolled in the WSG-ADAPT subtrials.

METHODS

US was performed at week 3 and 6 of NAT and at EOT in 401, 517, and 553 patients, respectively. Tumors with complete or partial response by US (RECIST 1.1) were classified as responders and those with stable or progressive disease as non-responders.

RESULTS

pCR rate was higher in US responders than in non-responders. US tended to yield the highest positive predictive value in HR-/HER2+ (69%) and HR-/HER2-tumors (65%) at week 3, and the highest negative predictive value in HR+/HER2+ tumors at week 6 and at EOT (88.9% and 86.9%, respectively) and in HR-/HER2-tumors at EOT (87.9%). Multivariable analysis of patients with US at week 3 and 6 identified tumor subtype (HR-/HER2+ vs HR+/HER2+; odds ratio (OR) 2.77, 95%CI 1.45-5.29, and OR 4.17, 95%CI 2.26-7.68, respectively) and each 10% change in lesion dimension on US from baseline (OR 1.15, 95%CI 1.08-1.24, and OR 1.25, 95%CI 1.16-1.35, respectively) as parameters associated with pCR.

CONCLUSIONS

Our data support the use of week 3 and EOT US for prediction of pCR in response-guided NAT and in planning of breast-conserving surgery. Change in tumor diameter on US as a continuous variable could be a valuable alternative to categorical RECIST 1.1 criteria.

Feasibility of ABUS as an Alternative to Handheld Ultrasound for Response Control in Neoadjuvant Breast Cancer Treatment

INTRODUCTION

The Invenia Automated Breast Ultrasound Screening (ABUS) is indicated as an adjunct to mammography for breast cancer screening in asymptomatic women with high-density breast tissue. ABUS provides time-efficient evaluation of the 3-dimensional recordings within 3 to 6 minutes. The role and advantages of ABUS in everyday clinical practice, especially in routine examination during neoadjuvant chemotherapy (NACT), is not clear. The aim of this monocentric, noninterventional retrospective study is to evaluate the use of ABUS in patients who are under NACT treatment for response control.

METHODS

Regular sonographic response check with handheld ultrasound (HHUS) examination and with ABUS were conducted in 83 women who underwent NACT. The response controls were conducted every 3 to 6 weeks during NACT. The handheld sonography was performed with GE Voluson S8. Handheld sonographic measurements and ABUS measurements were compared with the final pathologic tumor size.

RESULTS

There was no statistical difference between the measurements with HHUS examination or ABUS compared with final pathologic tumor size (P = .47). The average difference from ABUS measured tumor size to final pathologic tumor size was 9.8 mm. The average difference from handheld measured tumor size to final pathologic tumor size was 9/3 mm. Both the specificity of ABUS and HHUS examination in predicting pathologic complete remission was 100%.

CONCLUSION

ABUS seems to be a suitable method to conduct response control in neoadjuvant breast cancer treatment. ABUS may facilitate preoperative planning and offers remarkable time saving for physicians compared with HHUS examination and thus should be considered for clinical practice.

Early response by MR imaging and ultrasound as predictor of pathologic complete response to 12-week neoadjuvant therapy for different early breast cancer subtypes: Combined analysis from the WSG ADAPT subtrials

We evaluated the role of early response after 3 weeks of neoadjuvant treatment (NAT) assessed by ultrasound (US), magnetic resonance imaging (MRI) and Ki-67 dynamics for prediction of pathologic complete response (pCR) in different early breast cancer subtypes. Patients with HR+/HER2+, HR-/HER2- and HR-/HER2+ tumors enrolled into three neoadjuvant WSG ADAPT subtrials underwent US, MRI and Ki-67 assessment at diagnosis and after 3 weeks of NAT. Early response was defined as complete or partial response (US, MRI) and ≥30% proliferation decrease or <500 invasive tumor cells (Ki-67). Predictive values and area under the receiver operating characteristic (AUC) curves for prediction of pCR (ypT0/is ypN0) after 12-week NAT were calculated. Two hundred twenty-six had MRI and 401 US; 107 underwent both MRI and US. All three methods yielded a similar AUC in HR+/HER2+ (0.66-0.67) and HR-/HER2- tumors (0.53-0.63), while MRI and Ki-67 performed better than US in HR-/HER2+ tumors (0.83 and 0.79 vs 0.56). Adding MRI+/-Ki-67 increased AUC of US in HR-/HER2+ tumors to 0.64 to 0.75. MRI and Ki-67 demonstrated highest sensitivity in HR-/HER2- (0.8-1) and HR-/HER2+ tumors (1, both). Negative predictive value was similar for all methods in HR+/HER2+ (0.71-0.74) and HR-/HER2- tumors (0.85-1), while it was higher for MRI and Ki-67 compared to US in HR-/HER2+ subtype (1 vs 0.5). Early response assessed by US, MRI and Ki-67 is a strong predictor for pCR after 12-week NAT. Strength of pCR prediction varies according to tumor subtype. Adding MRI+/-Ki-67 to US did not improve pCR prediction in majority of our patients.

Early Assessment Window for Predicting Breast Cancer Neoadjuvant Therapy using Biomarkers, Ultrasound, and Diffuse Optical Tomography

PURPOSE

The purpose of the study was to assess the utility of tumor biomarkers, ultrasound (US) and US-guided diffuse optical tomography (DOT) in early prediction of breast cancer response to neoadjuvant therapy (NAT).

METHODS

This prospective HIPAA compliant study was approved by the institutional review board. Forty one patients were imaged with US and US-guided DOT prior to NAT, at completion of the first three treatment cycles, and prior to definitive surgery from February 2017 to January 2020. Miller-Payne grading was used to assess pathologic response. Receiver operating characteristic curves (ROCs) were derived from logistic regression using independent variables, including: tumor biomarkers, US maximum diameter, percentage reduction of the diameter (%US), pretreatment maximum total hemoglobin concentration (HbT) and percentage reduction in HbT (%HbT) at different treatment time points. Resulting ROCs were compared using area under the curve (AUC). Statistical significance was tested using two-sided two-sample student t-test with P < 0.05 considered statistically significant. Logistic regression was used for ROC analysis.

RESULTS

Thirty-eight patients (mean age = 47, range 24-71 years) successfully completed the study, including 15 HER2 + of which 11 were ER + ; 12 ER + or PR + /HER2-, and 11 triple negative. The combination of HER2 and ER biomarkers, %HbT at the end of cycle 1 (EOC1) and %US (EOC1) provided the best early prediction, AUC = 0.941 (95% CI 0.869-1.0). Similarly an AUC of 0.910 (95% CI 0.810-1.0) with %US (EOC1) and %HbT (EOC1) can be achieved independent of HER2 and ER status. The most accurate prediction, AUC = 0.974 (95% CI 0.933-1.0), was achieved with %US at EOC1 and %HbT (EOC3) independent of biomarker status.

CONCLUSION

The combined use of tumor HER2 and ER status, US, and US-guided DOT may provide accurate prediction of NAT response as early as the completion of the first treatment cycle.

CLINICAL TRIAL REGISTRATION NUMBER

NCT02891681. https://clinicaltrials.gov/ct2/show/NCT02891681 , Registration time: September 7, 2016.

Contrast enhanced ultrasound quantitative parameters for assessing neoadjuvant chemotherapy response in patients with locally advanced breast cancer

OBJECTIVES

To evaluate the role of contrast-enhanced ultrasound (CEUS) quantitative parameters in predicting neoadjuvant chemotherapy (NACT) response in patients with locally advanced breast cancer (LABC).

METHODS

30 patients with histologically proven LABC scheduled for NACT were recruited. CEUS was performed using a contrast bolus of 4.8 ml and time intensity curves (TICs) were obtained by contrast dynamics software. CEUS quantitative parameters assessed were peak enhancement (PE), time-to-peak (TTP), area under the curve (AUC) and mean transit time (MTT). The parameters were documented on four consecutive instances: before NACT and 3 weeks after each of the three cycles. The gold-standard was pathological response using Miller Payne Score obtained pre NACT and post-surgery.

RESULTS

A decrease in mean values of PE and an increase in mean values of TTP and MTT was observed with each cycle of NACT among responders. Post each cycle of NACT (compared with baseline pre-NACT), there was a statistically significant difference in % change of mean values of PE, TTP and MTT between good responders and poor responders (p-value < 0.05). The diagnostic accuracy of TTP post-third cycle was 87.2% (p = 0.03), and MTT post--second and third cycle was 76.7% (p = 0.004) and 86.7% (p = 0.006) respectively.

CONCLUSION

In responders, a decrease in the tumor vascularity was reflected in the CEUS quantitative parameters as a reduction in PE, and a prolongation in TTP, MTT.

ADVANCES IN KNOWLEDGE

Prediction of NACT response by CEUS has the potential to serve as a diagnostic modality for modification of chemotherapy regimens during ongoing NACT among patients with LABC, thus affecting patient prognosis.

Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer

BACKGROUND

Heterogeneity exists in the response of triple-negative breast cancer (TNBC) to standard anthracycline (AC)/taxane-based neoadjuvant systemic therapy (NAST), with 40% to 50% of patients having a pathologic complete response (pCR) to therapy. Early assessment of the imaging response during NAST may identify a subset of TNBCs that are likely to have a pCR upon completion of treatment. The authors aimed to evaluate the performance of early ultrasound (US) after 2 cycles of neoadjuvant NAST in identifying excellent responders to NAST among patients with TNBC.

METHODS

Two hundred fifteen patients with TNBC were enrolled in the ongoing ARTEMIS (A Robust TNBC Evaluation Framework to Improve Survival) clinical trial. The patients were divided into a discovery cohort (n = 107) and a validation cohort (n = 108). A receiver operating characteristic analysis with 95% confidence intervals (CIs) and a multivariate logistic regression analysis were performed to model the probability of a pCR on the basis of the tumor volume reduction (TVR) percentage by US from the baseline to after 2 cycles of AC.

RESULTS

Overall, 39.3% of the patients (42 of 107) achieved a pCR. A positive predictive value (PPV) analysis identified a cutoff point of 80% TVR after 2 cycles; the pCR rate was 77% (17 of 22) in patients with a TVR ≥ 80%, and the area under the curve (AUC) was 0.84 (95% CI, 0.77-0.92; P < .0001). In the validation cohort, the pCR rate was 44%. The PPV for pCR with a TVR ≥ 80% after 2 cycles was 76% (95% CI, 55%-91%), and the AUC was 0.79 (95% CI, 0.70-0.87; P < .0001).

CONCLUSIONS

The TVR percentage by US evaluation after 2 cycles of NAST may be a cost-effective early imaging biomarker for a pCR to AC/taxane-based NAST.

Background parenchymal enhancement and breast cancer: a review of the emerging evidences about its potential use as imaging biomarker

OBJECTIVES

To conduct a systematic review of evidences about the relationship between background parenchymal enhancement (BPE) of the contralateral healthy breast and breast cancer: its association with clinicopathological breast cancer characteristics, its potential as predictive and prognostic biomarker and the biological linkage between BPE and breast cancer.

METHODS

A computerized literature search using PubMed and Google Scholar was performed up to June 2020. Two authors independently conducted search, screening, quality assessment, and extraction of data from the eligible studies. Studies were assessed for quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool.

RESULTS

Of the 476 articles identified, 22 articles met the inclusion criteria. No significant association was found between BPE and invasiveness, histological cancer type, T- and N-stage, multifocality, lymphatic and vascular invasion and histological tumour grade while the association between BPE and molecular subtypes is still unclear. As predictive biomarker, a greater decrease in BPE during and after neoadjuvant chemotherapy was associated with pathological complete response. Results about the role of BPE as prognostic factor were inconsistent. An association between high BPE and microvessel density, CD34 and VEGF (histological markers of vascularization and angiogenesis) was found.

CONCLUSIONS

BPE of the contralateral breast is associated with breast cancer in several aspects, therefore it has been proposed as a tool to refine breast cancer decision-making process.

ADVANCES IN KNOWLEDGE

Additional researches with standardized BPE assessment are needed to translate this emerging biomarker into clinical practice in the era of personalized medicine.

Accuracy of morphologic change measurements by ultrasound in predicting pathological response to neoadjuvant chemotherapy in triple-negative and HER2-positive breast cancer

BACKGROUND

Neoadjuvant chemotherapy (NAC) is standard therapy in triple-negative breast cancer (TNBC) and HER2-positive breast cancer (HER2 + ve BC). There are concerns about the accurate imaging modalities to measure residual tumor during or after NAC. Up to now no standard imaging method for monitoring the efficacy of NAC has been established, and few reports showed ultrasonographic change. We aimed to assess the echogenicity in ultrasonography (US) as the predictive marker of pathological complete response (pCR) for not only TNBC, but also HER2 + ve BC. Furthermore, we also investigated the change in depth (D) and width (W) of the tumor as the predictive value of pCR.

METHODS

We retrospectively reviewed a consecutive 59 patients with TNBC and 41 patients with HER2 + ve BC who received NAC. In all of 100 patients, echogenicity, D and W of the tumor were measured before (pre-NAC) and after NAC (post-NAC). The tumor echogenicity was measured at representative region of interest (ROI), and calculated as the relative comparative assessment with fat echogenicity (ROI ratio).

RESULTS

pCR was significantly associated with higher post-NAC ROI ratio in TNBC (p = 0.010), while there was no association in HER2 + ve BC (p = 0.885). pCR was significantly associated with smaller sizes of post-NAC D and W in TNBC (p = 0.001, 0.003), while no trend was observed in HER2 + ve BC (p = 0.259, 0.435). The area under the curve (AUC) for post-NAC ROI ratio and D were 0.701, 0.755, respectively. Combined with them, AUC became higher up to 0.762.

CONCLUSION

TNBC and HER2 + ve BC showed different morphologic features of residual disease. Echogenicity and tumor size after NAC were both useful to predict pCR for TNBC, but not HER2 + ve BC. In future, radiological imaging needs to be analyzed in terms of breast cancer subtypes.

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.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Association of higher axillary pathologic complete response rate with breast pathologic complete response after neoadjuvant chemotherapy

Background

To investigate the association of axillary pathologic complete response (pCR) rate among breast cancer patients with pCR after neoadjuvant chemotherapy (NCT).

Methods

The retrospective clinical data of 1,903 patients who were treated with NCT between March, 2010 and December, 2018, were collected from one Chinese database and analyzed. The correlations between clinicopathological characteristics and breast pCR with axillary pCR were calculated by χ² test. Binary logistic regression analysis was used for multivariate analysis. The relative risk of positive axillary nodes after NCT in patients with and without breast pCR was analyzed using a Cochran-Mantel-Haenszel (CMH) test stratified by initial N stage and tumor subtype.

Results

The rate of axillary pCR was increased in the cases with initial cN0, Ki67 high expression, HR+HER2+/HR-HER2+/TN subtypes, and breast pCR. After NCT, the relative risk of nodal disease burden was 4.81 in patients without breast pCR compared with patients with breast pCR. The relative risk of positive nodal status in patients with cN0, cN1, cN2, and cN3 disease without vs. with breast pCR was 6.45, 4.88, 5.69 and 6.24, respectively. The relative risk of positive nodal status in patients with HR+HER2−, HR+HER2+, HR−HER2+, and TN disease was 4.02, 4.50, 3.82 and 4.18, respectively. Of cN0 patients with breast pCR, only 4 out of 44 (9%) with HER2-positive disease had 1 or 2 axillary lymph node metastases at final surgical pathology compared to 30 out of 98 (31%) of those without breast pCR. There was no evidence of positive nodal residue among all 21 patients (100%) with TN disease compared to 65% (36 of 55) of patients without breast pCR.

Conclusions

Nodal status is strongly correlated with breast pCR after NCT. Patients with initial cN0/1 TN/HER2 positive disease who achieve breast pCR at surgery have a low risk of nodal metastasis. These results suggest that the failure rate of missing positive lymph nodes among those patients was very low and that it is safe for such patients to undergo sentinel lymph node biopsy (SLNB) after NCT. This study also provides a theoretical basis for clinical trials focused on the avoidance of axillary surgery in selected patients.

Defining radiologic complete response using a correlation of presurgical ultrasound and mammographic localization findings with pathological complete response following neoadjuvant chemotherapy in breast cancer

PURPOSE

Breast cancer affects a significant number of patients younger than 40 years in the Gulf and breast conservative treatment is highly preferred. Pathological complete response (pCR) following neoadjuvant chemotherapy is increasingly being observed with the new chemotherapy agents. Although MRI is more accurate in such evaluations, digital mammography and high-resolution ultrasound (US) which are less expensive may accurately predict pCR which is the focus of this study.

METHODS

A 6-year retrospective study of 93 breast cancer cases who had neoadjuvant chemotherapy and had presurgical radiological localization was carried out. Forty-five had US localization while 48 underwent mammographic localization when US failed to define any residual mass. Radiologic complete response (rCR) was defined as absence of mass with only postbiopsy clip overlying normal breast parenchyma pattern in US and in mammography (clip sign). Mass or abnormal parenchymal pattern was considered as residual tumor. The pathology reports of pCR or not with background changes were recorded.

RESULTS

Ultrasound localization correctly predicted 42 out of 43 pathologic masses with 98 % accuracy. Mammographic localization correctly predicted 40 out of 43 pCR with 93 % accuracy. The best responders were triple negative and HER2 positive hormone negative breast cancer.

CONCLUSION

The study defines radiologic complete response (rCR) as absence of a mass with the postbiopsy tissue marker overlying a normal-looking breast parenchyma in both ultrasound and mammographic evaluation. A correlation of 93 % was found with pCR. The few false negative cases were associated with overlying dense breast and possibly post treatment reaction. Allocation of a BI-RADS category for rCR is suggested.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Early metabolic response of breast cancer to neoadjuvant endocrine therapy: comparison to morphological and pathological response

Background

Neoadjuvant endocrine therapy (NET) has shown efficacy in terms of clinical response and surgical outcome in postmenopausal patients with estrogen receptor-positive / HER2-negative breast cancer (ER+/HER2- BC) but monitoring of tumor response is challenging. The aim of the present study was to investigate the value of an early metabolic response compared to morphological and pathological responses in this population.

Methods

This was an ancillary study of CARMINA 02, a phase II clinical trial evaluating side-by-side the efficacy of 4 to 6 months of anastrozole or fulvestrant. Positron Emission Tomography/Computed Tomography using 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG-PET/CT) scans were performed at baseline (M0), early after 1 month of treatment (M1) and pre-operatively in 11 patients (74.2 yo ± 3.6). Patients were classified as early “metabolic responders” (mR) when the decrease of SUVmax was higher than 40%, and “metabolic non-responders” (mNR) otherwise. Early metabolic response was compared to morphological response (palpation, US and MRI), variation of Ki-67 index, pathological response according to the Sataloff classification and also to Preoperative Endocrine Prognostic Index (PEPI) score. It was also correlated with overall survival (OS) and recurrence-free survival (RFS).

Results

Tumor size measured on US and on MRI was smaller in mR than mNR, with the highest statistically significant difference at M1 (p = 0.01 and 7.1 × 10^− 5, respectively). No statistically significant difference in the variation of tumor size between M0 and M1 assessed on US or MRI was observed between mR and mNR. mR had a better clinical response: no progressive disease in mR vs 2 in mNR and 2 partial response in mR vs 1 partial response in mNR. One patient with a pre-operative complete metabolic response had the best pathological response. Pathological response did not show any statistically significant difference between mR and mNR. mR had better OS and RFS (Kaplan-Meier p = 0.08 and 0.06, respectively). All cancer-related events occurred in mNR: 3 patients died, 2 of them from progressive disease.

Conclusions

FDG-PET/CT imaging could become a “surrogate marker” to monitor tumor response, especially as NET is a valuable treatment option in postmenopausal women with ER+/HER2- BC.

Monitoring the response to neoadjuvant chemotherapy in patients with breast cancer using ultrasound scattering coefficient: A preliminary report

Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10-29% (G2), 30-90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.

Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10–29% (G2), 30–90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.

Evaluation of the response of breast cancer patients to neoadjuvant chemotherapy by combined contrast-enhanced ultrasonography and ultrasound elastography

The purpose of the present study was to investigate whether contrast-enhanced ultrasonography (CEUS) in combination with ultrasound elastography (UE) is able to accurately predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 65 breast cancer patients who received NAC at the First Affiliated Hospital of Zhejiang University (Hangzhou, China) between February 2016 and August 2017 and were recruited for the present study. Prior to and after NAC, examination by CEUS, UE or their combination was performed. Pathological results were obtained at the end of each chemotherapy cycle, based on which 41 cases were assigned to the response group and 24 to the non-response group. Kappa values were 0.710, 0.434 and 0.836 for CEUS, UE and CEUS+UE, respectively. The area under the receiver operating characteristic curves for CEUS, UE and CEUS+UE for determining the response to NAC was 0.864 [95% confidence interval (CI), 0.765–0.964], 0.715 (95% CI, 0.579–0.850) and 0.910 (95% CI, 0.826–0.993), respectively. It was identified that the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of CEUS+UE were higher than those of CEUS and US individually. The prediction accuracy was 89.2, 90.8 and 100% for CEUS, UE and their combination, respectively. CEUS and UE have their own advantages in evaluating the clinical efficacy of NAC in breast cancer, and a higher accuracy was achieved when the two techniques were applied in combination. Therefore, a combination of CEUS and UE may be a preferred method for the clinical assessment of the efficacy of NAC in breast cancer patients.

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.