Neoadjuvant Chemotherapy In Breast Cancer Research Articles

Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.

Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

Quantification of Extracellular Volume in CT in Neoadjuvant Chemotherapy in Breast Cancer: New Frontiers in Assessing the Cardiotoxicity of Anthracyclines and Trastuzumab.

Breast cancer patients undergoing neoadjuvant chemotherapy with anthracyclines or trastuzumab can suffer cardiotoxic issues. Nowadays, the markers of cardiac damage are still not reliable, and extracellular volume (ECV) calculated from CT could be a promising cardiotoxic marker. Eighty-two patients, treated with two different chemotherapy regimens based on doxorubicin (DOX) or epirubicin-trastuzumab (EPI-TRAS), were retrospectively selected and the variations in extracellular volume (ECV) values were measured and analyzed. Whole Body CT (WB-CT) scans were acquired after 1 min, in the portal phase (PP), and after 5 min, in the delayed phases (DP), at the baseline (T0), after one year (T1) and after five years (T5) from the end of chemotherapies. The values measured by two radiologists with different levels of experience were evaluated in order to assess the inter-reader reproducibility assessment (ICC = 0.52 for PP and DP). Further, we performed a population-based analysis and a drug-oriented subgroup analysis in 54 DOX-treated and 28 EPI-TRAS-treated patients. In the general cohort of women treated with any of the two drugs, we observed in the lapse T0-T1 a relative increase (RI) of 25% vs. 20% (PP vs. DP, p < 0.001) as well as in the lapse T0-T5 an RI of 17% vs. 15% (PP vs. DP, p < 0.01). The DOX-treated patients reported in the lapse T0-T1 an RI of 22% (p < 0.0001) in PP and an RI of 16% (p = 0.018) in the DP, with ECV values remaining stably high at T5 both in PP (RI 14.0%, p < 0.0001) and in DP (RI 17%, p = 0.005) highlighting a possible hallmark of a persisting CTX sub-damage. On the other hand, ECV measured in EPI-TRAS-treated women showed an RI in T0-T1 of 18% (p = 0.001) and 29% (p = 0.006) in PP and DP, respectively, but the values returned to basal levels in T5 both in the PP (p = 0.12) and in DP setting (p = 0.13), suggesting damage in the first-year post-treatment and a possible recovery over time. For the 82 patients, an echocardiography was performed at T0, T1= 12 m + 3 m and T5 = 60 m + 6 m with LVEF values at T0 (64% ± 5%), T1 (54% ± 6%) and T5 (53% ± 8%). WB-CT-derived ECV values could provide a valid imaging marker for the early diagnosis of cardiotoxic damage in BC patients undergoing oncological treatments. We detected different patterns during the follow-up, with stably high values for DOX, whereas EPI-TRAS showed a peak within the first year, suggesting different mechanisms of cardiac damage.

Tumor immune microenvironment components and the other markers can predict the efficacy of neoadjuvant chemotherapy for breast cancer.

Breast cancer is an epithelial malignant tumor that occurs in the terminal ducts of the breast. Neoadjuvant chemotherapy (NACT) is an important part of breast cancer treatment. Its purpose is to use systemic treatment for some locally advanced breast cancer patients, to decrease the tumor size and clinical stage so that non-operable breast cancer patients can have a chance to access surgical treatment, or patients who are not suitable for breast-conserving surgery can get the opportunity of breast-conserving. However, some patients who do not respond to NACT will lead deterioration in their condition. Therefore, prediction of NACT efficacy in breast cancer is vital for precision therapy. The tumor microenvironment (TME) has a crucial role in the carcinogenesis and therapeutic response of breast cancer. In this review, we summarized the immune cells, immune checkpoints, and other biomarkers in the TME that can evaluate the efficacy of NACT in treating breast cancer. We believe that the detection and evaluation of the TME components in breast cancer are helpful to predict the efficacy of NACT, and the prediction methods are in the prospect. In addition, we also summarized other predictive factors of NACT, such as imaging examination, biochemical markers, and multigene/multiprotein profiling.

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

Model based on ultrasound and clinicopathological characteristics for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

Model based on ultrasound and clinicopathological characteristics for early prediction of pathological complete response to neoadjuvant chemotherapy in 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.