Neoadjuvant Chemotherapy In Breast Cancer Patients Research Articles

Nomogram Based on US and Clinicopathologic Characteristics: Axillary Nodal Evaluation Following Neoadjuvant Chemotherapy in Patients With Node-Positive Breast Cancer

BackgroundTo develop a convenient modality to predict axillary response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Materials and MethodsIn this multi-center study, a total of 1019 breast cancer patients with biopsy-proven positive lymph node (LN) receiving NAC were randomly assigned to the training and validation groups at a ratio of 7:3. Clinicopathologic and ultrasound (US) characteristics of both primary tumors and LNs were used to develop corresponding prediction models, and a nomogram integrating clinicopathologic and US predictors was generated to predict the axillary response to NAC. ResultsAxillary pathological complete response (pCR) was achieved in 47.79% of the patients. The expression of estrogen receptor, human epidermal growth factor receptor -2, Ki-67 score, and clinical nodal stage were independent predictors for nodal response to NAC. Location and radiological response of primary tumors, cortical thickness and shape of LNs on US were also significantly associated with nodal pCR. In the validation cohort, the discrimination of US model (area under the curve [AUC], 0.76) was superior to clinicopathologic model (AUC, 0.68); the combined model (AUC, 0.85) demonstrates strong discriminatory power in predicting nodal pCR. Calibration curves of the nomogram based on the combined model demonstrated that substantial agreement can be observed between the predictions and observations. This nomogram showed a false-negative rates of 16.67% in all patients and 10.53% in patients with triple negative breast cancer. ConclusionNomogram incorporating routine clinicopathologic and US characteristics can predict nodal pCR and represents a tool to aid in treatment decisions for the axilla after NAC in breast cancer patients.

Predicting pathological complete response based on weakly and semi-supervised joint learning in breast cancer multi-parametric MRI

Neoadjuvant chemotherapy (NAC) is the primary treatment used to reduce the tumor size in early breast cancer. Patients who achieve a pathological complete response (pCR) after NAC treatment have a significantly higher five-year survival rate. However, accurately predicting whether patients could achieve pCR remains challenging due to the limited availability of manually annotated MRI data. This study develops a weakly and semi-supervised joint learning model that integrates multi-parametric MR images to predict pCR to NAC in breast cancer patients. First, the attention-based multi-instance learning model is designed to characterize the representation of multi-parametric MR images in a weakly supervised learning setting. The Mean-Teacher learning framework is then developed to locate tumor regions for extracting radiochemical parameters in a semi-supervised learning setting. Finally, all extracted MR imaging features are fused to predict pCR to NAC. Our experiments were conducted on a cohort of 442 patients with multi-parametric MR images and NAC outcomes. The results demonstrate that our proposed model, which leverages multi-parametric MRI data, provides the AUC value of over 0.85 in predicting pCR to NAC, outperforming other comparative methods.

Predictive value of stromal tumor-infiltrating lymphocytes in patients with breast cancer treated with neoadjuvant chemotherapy: A meta-analysis.

The predictive value of tumor-infiltrating lymphocytes (TILs) in response to neoadjuvant chemotherapy (NAC) for breast cancer (BC) has received increasing attention. Here, a meta-analysis was conducted to evaluate the correlation between the expression of stromal TILs and pathological complete response (pCR) after NAC in BC patients. The PubMed, Embase, Cochrane Library, and Web of Science databases were searched online by using a combination of keywords and free words to screen literature on the expression of stromal TILs and pCR after NAC in patients with BC. The data were extracted and evaluated for quality. Relative risk (RR) was used to evaluate the relationship between the expression of stromal TILs before NAC and pCR in BC patients. Meta-analysis was performed with Review Manager 5.3 and STATA 14.0 software. Eleven studies involving 6039 BC patients were included in the meta-analysis. The results showed a generally high expression of stromal TILs in BC patients, and the pCR rate after NAC in BC patients with a high expression of stromal TILs was significantly higher than that in BC patients with a low expression of stromal TILs [RR = 1.83, 95% confidence interval (CI): 1.69-1.97]. Subgroup analysis based on the molecular subtypes of BC showed that the pCR rate was significantly higher in patients with a high expression of stromal TILs in hormone receptor (HR)-positive BC [RR = 3.23, 95% CI: 2.43-4.30], human epidermal growth factor receptor 2 (HER-2)-positive BC [RR = 1.41, 95% CI: 1.25-1.60], and triple-negative BC [RR = 1.70, 95% CI: 1.53-1.90] than in those with a low expression of stromal TILs. Subgroup analysis based on expression threshold showed that the pCR rate was higher in patients with a high expression of stromal TILs than in patients with a low expression of stromal TILs at different expression thresholds (10% [RR = 1.99, 95% CI: 1.55-2.55], 20%/30% [RR = 1.57, 95% CI: 1.37-1.81], 50%/60% [RR = 1.91, 95% CI: 1.73-2.11]. TILs can be used as a predictor of pCR after NAC in patients with BC, and the appropriate high expression threshold of stromal TILs should be selected as the predictive value according to the molecular subtype of BC.

Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

To establish a model combining radiomic and clinicopathological factors based on magnetic resonance imaging to predict pathological complete response (pCR) after neoadjuvant chemotherapy in breast cancer patients. MRI images and clinicopathologic data of 329 eligible breast cancer patients from the Affiliated Hospital of Qingdao University from August 2018 to August 2022 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and imaging examinations were performed before and after NAC. A total of 329 patients were randomly allocated to a training set and a test set at a ratio of 7:3. We mainly studied the following three types of prediction models: radiomic models, clinical models, and clinical-radiomic models. All models were evaluated using subject operating characteristic curve analysis and area under the curve (AUC), decision curve analysis (DCA) and calibration curves. The AUCs of the clinical prediction model, independent imaging model and clinical combined imaging model in the training set were 0.864 0.968 and 0.984, and those in the test set were 0.724, 0.754 and 0.877, respectively. According to DCA and calibration curves, the clinical-radiomic model showed good predictive performance in both the training set and the test set, and we found that we had developed a more concise clinical-radiomic nomogram. We have developed a clinical-radiomic model by integrating radiomic features and clinical factors to predict pCR after NAC in breast cancer patients, thereby contributing to the personalized treatment of patients.

Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer

To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. We enrolled 327 BC patients with axillary lymph node (ALN) metastases receiving axillary operations after NAC. The deep learning features were extracted by ResNet34, which was pretrained by a large, well-annotated dataset from ImageNet. Then we identified deep learning radiomics on magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI) in predicting axillary response after NAC in BC patients. The extraction of 128 deep learning radiomics (DLR) features relied on the DCE-MRI for each patient. After the least absolute shrinkage and selection operator regression analysis, 13, 8, and 21 features remained from the pre-treatment, post-treatment, and combined DCE-MRI, respectively. The DLR signature established based on the combined DCE-MRI achieved good capacity in ALN response after NAC. The support vector machineachieved the best performance with an 0.99area under the curve (AUC) of (95% confidence interval (CI), 0.98-1.00) and 0.83 (95% CI, 0.73-0.92) in the training and test sets, respectively. The LR model established with clinical parameters represented the best performance with 0.73 AUC (95% CI, 0.62-0.84), 0.73 sensitivity, 0.73 specificity, 0.63 PPV, and 0.81 NPV in the test set, respectively. Finally, the integration of radiomic signature and clinical signature resulted in establishing a predictive radiomic nomogram, with an AUC of 0.99 (95%CI, 0.99-1.00). In conclusion, our current study constructed a predictive nomogram through the deep learning method, demonstrating favorable performance in the training and test cohort. The present prognostic model furnishes a precise and objective foundation for directing the surgical strategy toward ALN management in BC patients receiving NAC.

Genetic polymorphisms of GGT1 gene (rs8135987, rs5751901 and rs2017869) are associated with neoadjuvant chemotherapy efficacy and toxicities in breast cancer patients

BackgroundOur previous study illustrated the predictive value of serum gamma-glutamyl transpeptidase (GGT) for neoadjuvant chemotherapy (NAC) sensitivity in breast cancer patients. In this study we aim to determine whether single nucleotide polymorphisms (SNPs) in the gamma-glutamyltransferase 1 (GGT1) gene are related to the NAC response and adverse events and to find out a genetic marker in predicting NAC sensitivity.MethodsThree SNP loci (rs8135987, rs5751901, rs2017869) of GGT1 gene were selected and tested among breast cancer patients reciving NAC. Four genotype models were used in SNP analysis: co-dominant model compared AA vs. Aa vs. aa; dominant model compared AA vs. Aa + aa; recessive model compared AA + Aa vs. aa; over-dominant model compared AA + aa vs. Aa. Chi-squared test and multivariable logistic regression analysis were performed between SNP genotypes, haplotypes and pathological complete response(pCR), adverse events as well as serum GGT level.ResultsA total of 143 patients were included in the study. For SNP rs8135987 (T > C), the TC genotype in over-dominant model was inversely related with pCR (adjusted OR = 0.30, 95% CI 0.10–0.88, p = 0.029) as well as the risk of peripheral neuropathy (adjusted OR = 0.39, 95% CI 0.15–0.96, p = 0.042). The TC genotype in dominant model was significantly associated with elevated serum GGT level (OR = 3.11, 95% CI 1.07–9.02, p = 0.036). For rs2017869 (G > C), the occurrence of grade 2 or greater neutropenia (OR = 0.39, 95% CI 0.08–0.84, p = 0.025) and leukopenia (OR = 0.24, 95% CI 0.08–0.78, p = 0.017) were both significantly reduced in patients with CC genotypes. For rs5751901(T > C), the CC genotype could significantly reduce the risk of grade 2 or greater neutropenia (OR = 0.29, 95% CI 0.09–0.96, p = 0.036) and leukopenia (OR = 0.27, 95% CI 0.09–0.84, p = 0.024) in recessive model.ConclusionsThe GGT1 gene SNPs might be an independent risk factor for poor response of NAC in breast cancer patients, providng theoretical basis for further precision therapy.

Is Ki-67 Really Useful as a Predictor for Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer?

Neoadjuvant chemotherapy (NACT) is routinely offered to operable locally advanced breast cancer (LABC) patients desirous of breast conservation surgery and inoperable LABC patients. Pathological complete response (pCR) following chemotherapy is recognized as a surrogate for survival outcomes in high grade tumour subtypes. Many biological and tumor characters have been shown to predict pCR. The current study was performed with the aim of investigating the ability of Ki-67 in predicting pCR with NACT in breast cancer patients. A total of 105 patients with locally advanced breast cancer who completed NACT followed by surgery were included in this study from January 2020 till December 2022. Patients with advanced metastatic breast carcinoma, who did not give consent for NACT, who did not complete NACT and who did not undergo surgery were excluded. All patients were assessed for Ki-67 score on core-needle biopsy samples and response rate was assessed clinically and by histopathological examination of resected specimen. Quantitative variables were compared using unpaired t-test or Mann-Whitney 'U' test and for categorical variables Chi-square or Fisher's exact test were used. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive potential of Ki-67 expression levels in predicting pCR. To identify the predictive factors associated with pCR, univariate analysis was performed. The P value < 0.05 was considered as statistically significant. Mean age was 51.57 ± 10.8years. 51 patients achieved clinical complete response (cCR) and 33 achieved pCR after NACT. Mean Ki-67 index in overall study population, in pCR group and no pCR group was 46.44 ± 22.92%, 51.60 ± 22.3% and 44.06 ± 22.7%, respectively. On univariate analysis, ER negativity, PR negativity and Her 2neu positivity were found predictive of pCR. On subgroup analysis, TNBC and Her 2neu positive sub groups were associated with higher cCR and pCR rate. We found no significant association between Ki-67 and pCR. This result may be confounded by the fact that a significant duration of the study was in the COVID-19 pandemic. Validation of this data is required in a large prospective study.

Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.

This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients. We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis. For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses. MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings. Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients. • MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.

The Role of MRI in Assessing Residual Breast Cancer After Neoadjuvant Chemotherapy.

Breast cancer is the most common malignancy among women in the world. The role of neoadjuvant chemotherapy (NAC) in the management of breast cancer is increasing. The decision about the management after NAC depends mainly on the tumor response to NAC. The role of the current study is to evaluate the role of the MRI scan in assessing the residual disease after NAC, which would help in decision making regarding the best treatment plan for the patient. We did this retrospective review for all patients who were diagnosed with breast cancer in our center and had NAC over four years. All patients in our study had a post-NAC magnetic resonance imaging (MRI) scan to assess the residual tumor size. A 2×2 table was used to calculate the diagnostic accuracy, and SPSS software version 25 was used to get the correlation coefficients between the post-NAC MRI measurements and pathological size. 28 female patients were included in our study. The average age was 45.25 ± 10years. We utilized the tumor size on histology as the standard for comparison. We calculated MRI sensitivity, specificity, PPV, and NPV rates of 90.9%, 100%, 100%, and 94.4%, respectively. The correlation coefficient was strong (r = 0.859, P = 0.01). Magnetic resonance imaging is a good test to assess the residual tumor disease after NAC in breast cancer patients. However, cases of under- and overestimation are still seen, which require more caution when making a decision regarding the management of such cases.

Predicting pathological complete response based on weakly and semi-supervised joint learning from breast cancer MRI.

Neoadjuvant chemotherapy (NAC) is the standard treatment for breast cancer patients. Patients achieving complete pathological response (pCR) after NAC usually have a good prognosis. However, automatic pCR prediction has been a challenging problem due to lacking well annotations in 3D MRI. Thus far, unifying different annotation information to predict the tumor's early response to NAC has not been systematically addressed. This paper proposes a weakly and semi-supervised joint learning method that integrates attentional features from multi-parametric MRI with radiomic features for predicting pCR to NAC in breast cancer patients. The attention-based multi-instance learning (MIL) is first developed to generate informative MRI bag-level features and mine key instances. The mean-teacher framework is then employed to segment tumor regions in a semi-supervised setting for extracting radiomic features. We perform experiments on 442 patients' data and show that our method achieves an AUC value of 0.85 in pCR prediction, which is superior to comparative methods. It is also shown that learning from multi-parametric MRI outperforms that of single-parameter MRI in pCR prediction.

Can peritumoral edema evaluated by Magnetic Resonance Imaging before neoadjuvant chemotherapy predict complete pathological response in breast cancer?

The complete pathological response (pCR) following neoadjuvant chemotherapy (NAC) in breast cancer is essential for the accurate prediction of prognosis. We aimed to evaluate the efficacy of the presence and type of peritumoral edema detected by magnetic resonance imaging (MRI) in predicting pCR to NAC in breast cancer patients. One hundred five patients with the diagnosis of invasive carcinoma were evaluated by MRI before NAC. Edema was evaluated in fat-suppressed T2-weighted images. The patients were categorized into three groups: patients with no peritumoral edema, patients with peritumoral edema, and patients demonstrating subcutaneous edema. The cases were categorized as being pCR and non-pCR. Molecular subtypes, lymphovascular invasion (LVI), tumor size, and apparent diffusion coefficient (ADC) were evaluated. A positive relationship was found between the presence of edema and tumor size. Subcutaneous edema was found to be statistically higher in non-pCR patients. While the number of pCR patients with subcutaneous edema was 17 (30.4%), the number of non-pCR patients with subcutaneous edema was 26 (53.1%) (p = 0.018). LVI was found to be statistically higher in patients with edema. The number of edema-negative and LVI (+) patients was 4 (15.4%), while the number of edema-positive and LVI (+) patients was 28 (35.4%) (p = 0.042). Intratumoral and peritumoral ADC values were significantly higher in tumors with edema. The presence of subcutaneous edema and LVI may be utilized for the prediction of pCR outcomes in breast cancer patients scheduled for NAC treatment.

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.

Clinical significance and prognostic value of receptor conversion after neoadjuvant chemotherapy in breast cancer patients.

The hormone receptor (HR) status and human epidermal growth hormone receptor 2 (HER2) status of patients with breast cancer may change following neoadjuvant chemotherapy (NAC). We retrospectively analyzed the clinical data of 294 patients with stage II/III breast cancer to evaluate the clinical significance and prognostic value of receptor transformation after NAC in breast cancer patients. Pathological complete response after NAC was achieved in 10.7% of patients. HR, estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 conversion rates were 9.2%, 6.5%, 13.0%, 4.4%, and 33.7%, respectively. Patients with stable HR (P = 0.01) and HER2 (P = 0.048) expression had more favorable overall survival (OS). Low or reduced Ki-67 expression was associated with better disease-free survival (DFS) (P < 0.001) and OS (P < 0.01). Multivariate analysis showed that the number of lymph nodes after NAC, HR conversion, and radiotherapy were independent prognostic factors for overall survival. HR conversion implied a higher risk of death [hazard ratio, 2.56 (95% confidence interval: 1.19-5.51); P = 0.016]. Patients with HR conversion after NAC who received endocrine therapy had better DFS (P = 0.674) and OS (P = 0.363) than those who did not receive endocrine therapy, even if the HR changed from positive to negative. In conclusion, pathological testing should be performed before and after NAC, and even patients with HR conversion after NAC might benefit from endocrine therapy.

Factors predicting pathological complete response in patients with localized breast cancer receiving neoadjuvant chemotherapy.

165 Background: Pathological Complete Response (pCR) after Neoadjuvant Chemotherapy (NAC) is associated with improved survival outcomes in early-stage breast cancer. However, data regarding the factors predicting pCR are limited. We analyzed the clinical and pathological factors associated with the attainment of pCR after NAC. Methods: We queried National Cancer Database for non-metastatic breast cancer patients who received NAC between 2010 and 2016. All associations were compared using Kruskal-Wallis, Pearson’s Chi-Squared, and Fisher’s Exact Tests. Multivariate logistic models were used to analyze association of race, sub-type, age, clinical stage, grade, histology, insurance, comorbidity index with pCR. Odds ratios, 95% confidence intervals, and p-values for each predictor were recorded and adjusted for confounders. All analyses were conducted in RStudio v4.0.2 at a significance level of 0.05. Results: A total of 137140 female and 741 male patients were identified. Majority of the patients were Whites (n = 95909) followed by Blacks (n = 23736), and Hispanics (n = 11023). 58.4% (n = 80556) patients were &gt; 55 years. 64% of the patients had private insurance, 31.1% (n = 42930) had government insurance (Medicare and Medicaid) and 3.6% (n = 4904) were uninsured. Majority of the patients had Charlson comorbidity index (CCI) equal to 0 (87%). Hormone-receptor positive and HER2 negative (HR+HER2-) comprised 37.9% (n = 51283), HER2+ were 35.2% (n = 47794) and triple-negative breast cancer (TNBC) were 26.9% (n = 36401). Compared to TNBC, HER2+ had higher and HR+HER2- had lower chance of attaining pCR. Blacks had lower (OR = 0.95, p &lt; 0.001) and Hispanics had higher chance of pCR (OR = 1.1 p &lt; 0.001) when compared to Whites in the overall population which includes all subtypes. Younger patients had lower chance of attaining pCR compared to elderly. Stage I and II patients had more chance of PCR compared to stage III. Patients with government insurance had lower odds of attaining PCR compared to those with private insurance. Patients with CCI 0 had higher chance of pCR when compared to those with CCI &gt; = 3 (OR = 1.15, 95% CI = 1.08-1.2, p &lt; 0.001). Conclusions: Patient and tumor factors play an important role in response to NAC in breast cancer patients. Identifying modifiable factors associated with odds of lower pCR rates such as, government insurance would help us intervene to improve the quality of care of breast cancer patients.[Table: see text]

Association of serum reproductive hormones changes after neoadjuvant chemotherapy with hormone receptors expression alterations and survival outcomes in breast cancer.

PurposeThis study aimed to determine the effect of neoadjuvant chemotherapy (NAC) on circulating levels of reproductive hormones and evaluate the correlation of hormone changes after NAC with hormone receptors expression alterations and relapse-free survival (RFS) outcomes in breast cancer.MethodsInformation from 181 breast cancer patients who received NAC was retrospectively analyzed. For hormones parameters, the median and interquartile range (IQR) were provided at baseline and the end of NAC then was compared by Wilcoxon signed-rank test. Categorical variables were represented as numbers and percentages and were compared via two-sided chi-square and Fisher's tests. The RFS outcomes were compared between patients according to hormone changes using the log-rank test. Univariate and multivariate survival analyses with hazard ratios (HR) and 95% confidence intervals (95% CI) were carried out using Cox regression.ResultsSex steroids including estradiol, progesterone, testosterone, and dehydroepiandrosterone sulfate (DHEAS) levels decreased significantly after NAC among both premenopausal and postmenopausal patients (all P < 0.05). Decreased estradiol levels were associated with reduced progesterone receptor (PR) expression (P = 0.030). In multivariate survival analysis, the non-decreased progesterone level was strongly associated with worse RFS (non-decreased vs. decreased, HR = 7.178, 95% CI 2.340–22.019, P = 0.001). Patients with decreased progesterone levels exhibited better 3-year RFS compared with those with non-decreased (87.6% vs. 58.3%, log-rank, P = 0.001).ConclusionMultiple reproductive hormone levels were influenced by NAC. The change in estradiol level had a positive connection with PR expression alteration. Furthermore, an inverse association between the change in progesterone level and RFS outcomes was found. These findings may provide a theoretical basis for pre-operative endocrine therapy combined with NAC in breast cancer patients.

Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer.

Breast cancer (BC) is the most prevalent cancer in women worldwide. A systematic approach to BC treatment, comprising adjuvant and neoadjuvant chemotherapy (NAC), as well as hormone therapy, forms the foundation of the disease’s therapeutic strategy. The extracellular matrix (ECM) is a dynamic network that exerts a robust biological effect on the tumor microenvironment (TME), and it is highly regulated by several immunological components, such as chemokines and cytokines. It has been established that the ECM promotes the development of an immunosuppressive TME. Therefore, while analyzing the ECM of BC, immune-related genes must be considered. In this study, we used bioinformatic approaches to identify the most valuable ECM-related immune genes. We used weighted gene co-expression network analysis to identify the immune-related genes that potentially regulate the ECM and then combined them with the original ECM-related gene set for further analysis. Least absolute shrinkage and selection operator (LASSO) regression and SurvivalRandomForest were used to narrow our ECM-related gene list and establish an ECM index (ECMI) to better delineate the ECM signature. We stratified BC patients into ECMI high and low groups and evaluated their clinical, biological, and genomic characteristics. We found that the ECMI is highly correlated with long-term BC survival. In terms of the biological process, this index is positively associated with the cell cycle, DNA damage repair, and homologous recombination but negatively with processes involved in angiogenesis and epithelial–mesenchymal transition. Furthermore, the tumor mutational burden, copy number variation, and DNA methylation levels were found to be related to the ECMI. In the Metabric cohort, we demonstrated that hormone therapy is more effective in patients with a low ECMI. Additionally, differentially expressed genes from the ECM-related gene list were extracted from patients with a pathologic complete response (pCR) to NAC and with residual disease (RD) to construct a neural network model for predicting the chance of achieving pCR individually. Finally, we performed qRT-PCR to validate our findings and demonstrate the important role of the gene OGN in predicting the pCR rate. In conclusion, delineation of the ECM signature with immune-related genes is anticipated to aid in the prediction of the prognosis of patients with BC and the benefits of hormone therapy and NAC in BC patients.

Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PurposeTo compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer.Materials and MethodsThis retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method.ResultsThe overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; P < 0.001) and comparable to that of the image-molecular RA model (0.755, 95% CI: 0.708, 0.802; P = 0.133). The AUROC of the image-kinetic-molecular DL model was 0.83 (95% CI: 0.816, 0.847), which was higher than that of the image-kinetic and image-molecular DL models (0.707, 95% CI: 0.654, 0.761; 0.79, 95% CI: 0.768, 0.812; P < 0.001) and higher than that of the image-kinetic-molecular RA model (0.778, 95% CI: 0.735, 0.828; P < 0.001).ConclusionsThe pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.

Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods.

BackgroundThe apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods.MethodsA total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype.ResultsAmong the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001).ConclusionsThe small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

Evaluation of Monoexponential, Stretched-Exponential and Intravoxel Incoherent Motion MRI Diffusion Models in Early Response Monitoring to Neoadjuvant Chemotherapy in Patients With Breast Cancer-A Preliminary Study.

BackgroundThere has been a growing interest in exploring the applications of stretched‐exponential (SEM) and intravoxel incoherent motion (IVIM) models of diffusion‐weighted imaging (DWI) in breast imaging, with the focus on differentiation of breast lesions. However, the use of SEM and IVIM models to predict early response to neoadjuvant chemotherapy (NACT) has received less attention.PurposeTo investigate the value of monoexponential, SEM, and IVIM models to predict early response to NACT in patients with primary breast cancer.Study TypeProspective.PopulationThirty‐seven patients with primary breast cancer (aged 46 ± 11 years) due to undergo NACT.Field Strength/SequencesA 1.5‐T MR scanner, T1 ‐weighted three‐dimensional spoiled gradient‐echo, two‐dimensional single‐shot spin‐echo echo‐planar imaging sequence (DWI) at six b‐values (0–800 s mm−2).AssessmentTumor volume, apparent diffusion coefficient, tissue diffusion (D t ), pseudo‐diffusion coefficient (D p ), perfusion fraction (f), distributed diffusion coefficient, and alpha (α) were extracted, following volumetric sampling of the tumors, at three time‐points: pretreatment, post one and three cycles of NACT.Statistical TestsMann–Whitney test, receiver operating characteristic (ROC) curve. Statistical significance level was P < 0.05.ResultsFollowing NACT, 17 patients were determined to be pathological responders and 20 nonresponders. Tumor volume was significantly larger in nonresponders at each MRI time‐point and demonstrated reasonable performance in predicting response (area under the ROC curve [AUC] = 0.83–0.87). No significant differences between groups were found in the diffusion coefficients at each time‐point (P = 0.09–1). The parameters α (SEM), f, and f × D p (IVIM) were able to differentiate between response groups after one cycle of NACT (AUC = 0.73, 0.72, and 0.74, respectively).ConclusionDiffusion coefficients derived from the monoexponential, SEM, and IVIM models did not predict pathological response. However, the IVIM‐derived parameters f and f × D p and the SEM‐derived parameter α were able to predict response to NACT in breast cancer patients following one cycle of NACT.Level of Evidence2Technical Efficacy Stage2

Diagnostic Performance of Strain and Shear Wave Elastography for the Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: Systematic Review and Meta-Analysis.

To investigate the diagnostic performance of strain and shear wave elastography for the response to neoadjuvant chemotherapy (NAC) in breast cancer patients. Relevant studies were searched in the databases of PubMed, Web of Science and Cochrane Library until October 2021. The diagnostic performance of ultrasonic elastography for the response to NAC were estimated by calculating the area under the curve (AUC) with sensitivity and specificity using Stata 14.0. A total of 15 studies that comprise 1147 breast cancer patients were included in this meta-analysis. The pooled AUC of strain elastography in diagnosing responses were 0.89 (95% CI=0.86-0.91) with 87% (95% CI=75-94%) of sensitivity and 80% (95% CI=72-84%) of specificity. The pooled AUC of shear wave elastography in diagnosing response were 0.82 (95% CI=0.78-0.85) with 79% (95% CI=72-84%) of sensitivity and 81% (95% CI=71-88%). No publication bias was observed across the studies using Deek's funnel plot. Based on current evidence, this meta-analysis confirmed that strain and shear wave elastography exhibited favorable performance for predicting responses to NAC. Strain and shear wave elastography may be a useful, noninvasive method for the assessment of response to NAC in breast cancer patients.