Neoadjuvant Chemotherapy In Breast Cancer Research Articles

Correlation between late gadolinium enhancement sequences in MRI and pathologic response after neoadjuvant chemotherapy in breast cancer

ObjectivesTo study the correlation between radiologic response observed by late enhancement sequences in MRI and pathologic response after neoadjuvant chemotherapy in patients with breast cancer. Material and methodsRetrospective observational study of 132 patients with 136 tumors (4 with bilateral disease), treated consecutively with neoadjuvant chemotherapy at our institution between 2011 and 2017. In all cases, we performed 3 breast MRI's, using late enhancement gadolinium sequences: the first prior to neoadjuvant chemotherapy, the second half way through treatment, and the third at the completion of therapy. Following treatment, contrast medium uptake in tumor bed was evaluated based on the Response Evaluation Criteria for Solid Tumors (RECIST).All patients underwent conservative or radical surgery. We compared the radiologic response estimated by MRI, with the pathologic response observed in the surgical specimen, according to Miller and Payne grading system. We calculated the sensitivity, specificity, and predictive values of the test, and used the Spearman correlation coefficient to stablish correlations between the parameters analyzed. ResultsComplete pathologic response (pCR) was observed in 58.1% (79/136). The percentage of global radio-pathologic correlation was 88.97%. MRI showed a sensitivity of 78.9%, a specificity of 79.7%, a positive-predictive value (PPV) of 73.8% and a negative-predictive value (NPV) of 84%. In patients with partial response, the Spearman correlation was positive (rho = 1, P < .001). According to surrogate subtypes of breast cancer, we observed moderate correlation for luminal tumors (rho = 0.63, P < .001) and poor correlation for non-luminal types (rho = 0,4, P < .01). ConclusionsBreast MRI, using late enhancement sequences, accurately predicts tumor response to neoadjuvant chemotherapy, especially in cases of partial response to therapy and in luminal surrogate tumoral subtypes.

Role of Doppler US and elastography prior to biopsy to identify candidates for avoidance of surgery following neoadjuvant chemotherapy for breast cancer.

This study aimed to evaluate the role of Doppler ultrasound (US) and elastography to identify residual breast cancer for patients showing near complete response following chemotherapy on magnetic resonance imaging (MRI). Between September 2016 and January 2018, 40 breast cancer patients who showed near complete response (either tumor size ≤0.5 cm or lesion-to-background parenchymal signal enhancement ratio ≤1.6) on MRI following neoadjuvant chemotherapy were prospectively enrolled. After excluding seven women who did not undergo Doppler US and elastography, 33 women (median age, 49 years; range, 32 to 67 years) were analyzed. On the day of surgery, women underwent Doppler US and elastography for tumor bed prior to US-guided core needle biopsy. Histopathologic results of biopsy and surgery were evaluated. Negative predictive value (NPV) and false negative rate (FNR) of biopsy and the combined Doppler US and elastography were analyzed, respectively. After surgery, nine women had residual cancers and 24 women had pathologic complete response. The NPV and FNR of biopsy were 92% (24 of 26) and 22% (2 of 9), respectively. The NPV and FNR of combined Doppler US and elastography were 100% (14 of 14) and 0% (0 of 9), respectively. All of nine women with residual cancers had positive vascularity or elasticity. Two women with false-negative biopsy results, having 0.3 cm or 2.5 cm ductal carcinoma in situ at surgery, showed positive vascularity or elasticity. Tumor bed showing positive vascularity or elasticity indicates residual breast cancer for patients showing near complete response on MRI following chemotherapy.

Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation.

Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer. To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer. Retrospective, longitudinal. A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC. A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence. Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed. Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis. Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR=1.206) compared to grade II patients (OR=1.067). Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade. 4 TECHNICAL EFFICACY: Stage 2.

Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.

The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.

Development and External Validation of a Machine Learning Model to Predict Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.

Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables. The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital. A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833-0.972). External validation confirmed an AUC of 0.833 (95% CI, 0.800-0.865). Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.

Changes in Automated Mammographic Breast Density Can Predict Pathological Response After Neoadjuvant Chemotherapy in Breast Cancer.

Mammographic density is an independent risk factor for breast cancer that can change after neoadjuvant chemotherapy (NCT). This study aimed to evaluate percent changes in volumetric breast density (ΔVbd%) before and after NCT measured automatically and determine its value as a predictive marker of pathological response to NCT. A total of 357 patients with breast cancer treated between January 2014 and December 2016 were included. An automated volumetric breast density (Vbd) measurement method was used to calculate Vbd on mammography before and after NCT. Patients were divided into three groups according to ΔVbd%, calculated as follows: Vbd (post-NCT - pre-NCT)/pre-NCT Vbd × 100 (%). The stable, decreased, and increased groups were defined as -20% ≤ ΔVbd% ≤ 20%, ΔVbd% < -20%, and ΔVbd% > 20%, respectively. Pathological complete response (pCR) was considered to be achieved after NCT if there was no evidence of invasive carcinoma in the breast or metastatic tumors in the axillary and regional lymph nodes on surgical pathology. The association between ΔVbd% grouping and pCR was analyzed using univariable and multivariable logistic regression analyses. The interval between the pre-NCT and post-NCT mammograms ranged from 79 to 250 days (median, 170 days). In the multivariable analysis, ΔVbd% grouping (odds ratio for pCR of 0.420 [95% confidence interval, 0.195-0.905; P = 0.027] for the decreased group compared with the stable group), N stage at diagnosis, histologic grade, and breast cancer subtype were significantly associated with pCR. This tendency was more evident in the luminal B-like and triple-negative subtypes. ΔVbd% was associated with pCR in breast cancer after NCT, with the decreased group showing a lower rate of pCR than the stable group. Automated measurement of ΔVbd% may help predict the NCT response and prognosis in breast cancer.

A Case of Aortitis after Administration of G-CSF during Neoadjuvant Chemotherapy for Breast Cancer

A Case of Aortitis after Administration of G-CSF during Neoadjuvant Chemotherapy for Breast Cancer

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography.

To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer. This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance. Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively. CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Determinants of pathological complete response to neoadjuvant chemotherapy in breast cancer: A single-institution experience.

Neoadjuvant chemotherapy (NACT) is routinely used in all cases of locally advanced breast cancer and some cases of early breast cancer. We previously reported a pathological complete response (pCR) rate of 8.3%. With the increasing use of taxanes and human epidermal growth factor receptor 2 (HER2)-directed NACT, we conducted this study to understand the current pCR rate and its determinants. A prospective database of breast cancer patients who underwent NACT followed by surgery between January and December 2017 was evaluated. Of the 664 patients, 87.7% were cT3/T4, 91.6% were grade III, and 89.8% were node-positive at presentation (54.4% cN1, 35.4% cN2). The median age was 47 years; median pre-NACT clinical tumor size was 5.5 cm. Molecular subclassification was 30.3% hormone receptor positive (HR+) HER2-, 18.4% HR+HER2+, 14.9% HR-HER2+, and 31.6% triple negative (TN). Both anthracyclines and taxanes were given preoperatively in 31.2% patients whereas 58.5% of HER2 positive patients received HER2-targeted NACT. The overall pCR rate was 22.4% (149/664), 9.3% in HR+HER2-, 15.6% in HR+HER2+, 35.4% in HR-HER2+, and 33.4% in TN. On univariate analysis, duration of NACT ( P < 0.001), cN stage at presentation ( P = 0.022), HR status ( P < 0.001), and lymphovascular invasion ( P < 0.001) were associated with pCR. On logistic regression, HR negative status (Odds ratio [OR] 3.314, P < 0.001), longer duration of NACT (OR 2.332, P < 0.001), cN2 stage (OR 0.57, P = 0.012), and HER2 negativity (OR 1.583, P = 0.034) were significantly associated with pCR. Response to chemotherapy depends on molecular subtype and duration of NACT. A low rate of pCR in the HR+ subgroup of patients warrants reconsideration of neoadjuvant strategies.

Use of artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer

Introduction: Breast cancer is the object of thousands of studies worldwide. Nevertheless, few tools are available to corroborate prediction of response to neoadjuvant chemotherapy. Artificial intelligence is being researched for its potential utility in several fields of knowledge, including oncology. The development of a standardized Artificial intelligence-based predictive model for patients with breast cancer may help make clinical management more personalized and effective. We aimed to apply Artificial intelligence models to predict the response to neoadjuvant chemotherapy based solely on clinical and pathological data. Methods: Medical records of 130 patients treated with neoadjuvant chemotherapy were reviewed and divided into two groups: 90 samples to train the network and 40 samples to perform prospective testing and validate the results obtained by the Artificial intelligence method. Results: Using clinicopathologic data alone, the artificial neural network was able to correctly predict pathologic complete response in 83.3% of the cases. It also correctly predicted 95.6% of locoregional recurrence, as well as correctly determined whether patients were alive or dead at a given time point in 90% of the time. To date, no published research has used clinicopathologic data to predict the response to neoadjuvant chemotherapy in patients with breast cancer, thus highlighting the importance of the present study. Conclusions: Artificial neural network may become an interesting tool for predicting response to neoadjuvant chemotherapy, locoregional recurrence, systemic disease progression, and survival in patients with breast cancer.

Integrative review of clinical trials and meta-analysis of the main studies of neoadjuvant chemotherapy in the treatment of breast cancer in the past 30 years

Neoadjuvant chemotherapy (NAC) has become a common treatment strategy for early-stage breast cancer. In this study, we conducted a systematic research in the PubMed database using the following terms: breast cancer, neoadjuvant chemotherapy, randomized clinical trials, complete pathological response, overall survival, and disease-free survival. The research has been limited to articles published in the past 30 years (1993–2023). We included only randomized clinical trials that evaluated the use of NAC in breast cancer and data on PCR rates and survival outcomes. Our research resulted in a total of 13 randomized clinical trials and two meta-analyses. The PCR rates ranged from 13% to 58%, with higher rates observed in patients with triple-negative breast cancer (TNBC) and human epidermal growth factor 2 (HER-2+) disease. Several trials reveal a significant association between PCR and better survival results, including overall survival and disease-free survival. However, the impact of PCR on survival results was less consistent in patients with hormone receptor-positive breast cancer. The use of taxanes in combination with anthracyclines has been the most common NAC scheme evaluated in these trials. The PCR rates have been associated with better survival outcomes, in patients with TNBC and HER-2+ disease. However, the impact of PCR on survival outcomes in patients with hormone receptorpositive breast cancer is less clear. Additional studies are needed to determine the optimal NAC regimen for each subtype of breast cancer and to identify biomarkers that can predict the NAC response.

Correlation of Before and After Invasive Breast Cancer Neoadjuvant Chemotherapy for NFkB, Cyclin D1, and Survivin Expression.

Patients undergoing neoadjuvant chemotherapy (NC) for invasive breast cancer (IBC) therapy need biomarkers to track their progress. Because of the relationship between NFkB, Survivin, and Cyclin D1 with NC resistance, the different expression levels of each of these biomarkers can be different between pre- and post-NC in IBC. However, no research has examined the correlation between these biomarkers before and after the NC expression. This study aimed to determine the correlation among them. Biomarkers expression (low and high) was used to classify 30 samples. ER, PR, HER2, Ki-67 status, tumor grade, age, and NC response were assessed. The amounts of Survivin, Cyclin D1, and NFkB were evaluated using immunohistochemistry, and the samples were classified based on the cut-off. Chi-square and linear regression were used to evaluate the data. No significant association was found with the changes in the expression of Survivin, Cyclin D1, and NFkB, both before and after the NC. Significant moderate correlations were shown between before and after the NC Survivin expression (r = 0.513) and Cyclin D1 expression (r = 0.543). The correlation between expression of NFkB before and after the NC was not significant. The high potential of these proteins as prognostic indicators was demonstrated by the strong positive association between the expression of Survivin and Cyclin D1 before and after the NC. This upregulation of biomarkers indicates chemoresistance in developing IBC in the presence of NC.

Diagnostic Value of Contrast-Enhanced Digital Mammography Versus Contrast-Enhanced MRI for Detecting Residual Disease After Neoadjuvant Chemotherapy for Breast Cancer

Purpose: Preoperative evaluation of breast cancer using contrast-enhanced digital mammography (CEDM) has gained acceptance as a possible alternative to contrast-enhanced magnetic resonance imaging (CEMRI). We aimed to compare the diagnostic performance of CEDM and CEMRI for chemotherapeutic response in breast cancer patients who underwent neoadjuvant chemotherapy (NAC).Methods: Fifty patients with invasive carcinoma who underwent both CEDM and CEMRI from November 2017 to October 2018 were included. The residual malignancy sizes after NAC were compared with the histopathological results. The diagnostic performance for detecting residual cancers was compared using Lin concordance and Pearson correlation coefficients.Results: Fifty patients were included in this analysis. The mean tumor size after NAC was 1.22 cm (range: 0–7.0) for CEDM and 1.13 cm (range: 0–5.1) for CEMRI compared with 1.89 cm (range: 0–12.0) at the final pathology measurement. The sensitivity for identifying residual lesions for CEDM and CEMRI were 62.5% (95% confidence interval [CI]: 40.6–81.2) and 66.7% (95% CI: 44.7–84.4), respectively. The positive predictive values for residual lesions were 93.8% (95% CI: 69.8– 99.8) for CEDM and 88.9% (95% CI: 65.3–98.6) for CEMRI. The mean difference in CEDM was 0.668 cm, with a concordance coefficient of 0.202 and Pearson correlation coefficient of 0.231 (&lt;i&gt;p&lt;/i&gt;=0.220).Conclusion: The diagnostic value of CEDM for detecting the residual tumor extent after NAC was comparable to that of CEMRI.

Patient perspectives on repeated contrast-enhanced mammography and magnetic resonance during neoadjuvant chemotherapy of breast cancer.

The burden perceived by the patient of repeated imaging required for neoadjuvant chemotherapy (NAC) monitoring warrants attention due to the increased use of NAC and imaging. To evaluate and compare the experienced burden associated with repeated contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) during NAC for breast cancer from the patient perspective. Approval from the ethics committee and written informed consent were obtained. In this prospective study, CEM and MRI were performed on 38 patients with breast cancer before, during, and after NAC in a tertiary cancer center. The experienced burden was evaluated with a self-reported questionnaire addressing duration, comfort, anxiety, positioning, and intravenous contrast administration, each measured on a 5-point Likert scale. The participants were asked their preference between CEM or MRI. Statistical comparisons were performed and P<0.05 was considered significant. Most participants (n = 29, 76%) preferred CEM over MRI (P = 0.0008). CEM was associated with a significantly shorter duration (P < 0.001), greater overall comfort (P < 0.01), more comfortable positioning (P = 0.01), and lower anxiety (P = 0.03). Intravenous contrast administration perception revealed no significant difference. Only 4 (10%) participants preferred MRI over CEM, due to the absence of breast compression. In the hypothetical scenario of equal diagnostic accuracy, most participants preferred CEM and compared CEM favorably to MRI in all investigated features at repeated imaging required for NAC response assessment. Our results indicate that repeated examinations with CEM is well tolerated and constitutes a patient-friendly alternative for NAC imaging monitoring in breast cancer.

A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer

BackgroundMedical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer.MethodsData on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced breast MRI were included. Response to NAC was assessed using the Miller–Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine learning model was used to classify response to NAC.Results74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity < 90%, n = 44) and an excellent response (> 90% reduction in cellularity, n = 30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor status improved the accuracy of the model with an AUC of 0.811.ConclusionThis study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

Detection of pathological response of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer using multiphoton microscopy.

Neoadjuvant treatment is often considered in breast cancer patients with axillary lymph node involvement, but most of patients do not have a pathologic complete response to therapy. The detection of residual nodal disease has a significant impact on adjuvant therapy recommendations which may improve survival. Here, we investigate whether multiphoton microscopy (MPM) could identify the pathological changes of axillary lymphatic metastasis after neoadjuvant chemotherapy in breast cancer. And furthermore, we find that there are obvious differences in seven collagen morphological features between normal node and residual axillary disease by combining with a semi-automatic image processing method, and also find that there are significant differences in four collagen features between the effective and no-response treatment groups. These research results indicate that MPM may help estimate axillary treatment response in the neoadjuvant setting and thereby tailor more appropriate and personalized adjuvant treatments for breast cancer patients.

Socioeconomic Disparities in Neoadjuvant Chemotherapy for Early-Stage Breast Cancer.

Neoadjuvant chemotherapy (NCT) is often used for patients with early-stage breast cancer. Disparities in the use of NCT based on clinical, demographic, and socioeconomic factors have not been evaluated. Data from the National Cancer Database was analyzed for patients with T1-2, N0-1 breast cancer from 2006 to 2015. Univariate and multivariate analysis determined which factors predicted for the receipt of NCT. We found 159946 eligible patients. Factors associated with receipt of NCT included T2 vs. T1 disease, N1 vs. N0, and treatment at an academic facility. Race itself was not significant; however, a higher level of education amongst Black populations correlated with the receipt of NCT. Clinical factors are the greatest determinants for receipt of NCT in early-stage breast cancer. Disparities exist that cannot be explained by race alone; socioeconomic and demographic factors are important. Cancer care should be evaluated in the context of the intersectionality of these health determinants.

Oncologic necessity for the complete removal of residual microcalcifications after neoadjuvant chemotherapy for breast cancer

The surgical range of breast cancer that shows pathologic complete response (pCR) without change in microcalcifications after neoadjuvant chemotherapy (NAC) is controversial. This study examined whole breast specimens to evaluate the necessity of mastectomy in those cases. The viability of cancer cells around the residual microcalcification was assessed using prospectively collected breast samples to confirm the presence or absence of cancer cells. A total of 144 patients with breast cancer and diffuse microcalcifications were classified into the reduced mass with no change in residual microcalcification (RESMIN, n = 49) and non-RESMIN (n = 95) groups. Five specimens were prospectively evaluated to assess the presence of viable cancer cells around the microcalcification. Tumor responses to NAC were significantly better with high pCR rates in the RESMIN group (p = 0.005 and p = 0.002). The incidence of human epidermal growth factor receptor 2-positive and triple-negative breast cancers was significantly high in the RESMIN group (p = 0.007). Although five (10.2%) patients had locoregional recurrence in the RESMIN group, no local recurrence in the breast was reported. Although pCR was highly estimated, residual cancers, including ductal carcinoma in situ, remained in 80% cases. Therefore, given the weak scientific evidence available currently, complete removal of residual microcalcifications should be considered for oncologic safety.

Contrast-enhanced ultrasonography for early prediction of response of neoadjuvant chemotherapy in breast cancer.

Neoadjuvant chemotherapy (NAC) is widely accepted as a primary treatment for inoperable or locally advanced breast cancer before definitive surgery. However, not all advanced breast cancers are sensitive to NAC. Contrast-enhanced ultrasonography (CEUS) has been considered to assess tumor response to NAC as it can effectively reflect the condition of blood perfusion and lesion size. Therefore, this study aimed to evaluate the diagnostic performance of CEUS to predict early response in different regions of interest in breast tumors under NAC treatment. This prospective study included 82 patients with advanced breast cancer. Parameters of TIC (time-intensive curve) between baseline and after the first cycle of NAC were calculated for the rate of relative change (Δ), including Δpeak, ΔTTP (time to peak), ΔRBV (regional blood volume), ΔRBF (regional blood flow) and ΔMTT (mean transit time). The responders and non-responders were distinguished by the Miller-Payne Grading (MPG) system and parameters from different regions of tumors were compared in these two groups. For ROI 1(the greatest enhancement area in the central region of the tumor), there were significant differences in Δpeak1, ΔRBV1 and ΔRBF1 between responders and non-responders. For ROI 2 (the greatest enhancement area on edge of the tumor), there were significant differences in Δpeak2 and ΔRBF2 between the groups. The Δpeak1 and ΔRBF2 showed good prediction (AUC 0.798-0.820, p ≤ 0.02) after the first cycle of NAC. When the cut-off value was 0.115, the ΔRBF2 had the highest diagnostic accuracy and the maximum NPV. Quantitative TIC parameters could be effectively used to evaluate early response to NAC in advanced 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.