A Nomogram to Predict the Pathologic Complete Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Based on Simple Laboratory Indicators

Predicting pathological complete response after neoadjuvant chemotherapy in breast cancer by clinicopathological indicators and ultrasound parameters using a nomogram

The study explored the impact of pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) and changes in tumor blood supply on pathological complete response (pCR) in breast cancer (BC) patients following neoadjuvant chemotherapy (NACT). Additionally, a nomogram for predicting pCR was established and validated. The study included BC patients undergoing NACT at Yongchuan Hospital of Chongqing Medical University from January 2019 to October 2023. We analyzed the correlation between pCR and clinicopathological factors, as well as tumor ultrasound features, using chi-square or Fisher's exact test. We developed and validated a nomogram predicting pCR based on regression analysis results. The study included 176 BC patients. Logistic regression analysis identified AAPR [odds ratio (OR) 2.616, 95% confidence interval (CI) 1.140-5.998, P = 0.023], changes in tumor blood supply after two NACT cycles (OR 2.247, 95%CI 1.071-4.716, P = 0.032), tumor histological grade (OR 3.843, 95%CI 1.286-10.659, P = 0.010), and HER2 status (OR 2.776, 95%CI 1.057-7.240, P = 0.038) as independent predictors of pCR after NACT. The nomogram, based on AAPR, changes in tumor blood supply after two NACT cycles, tumor histological grade, and HER2 status, demonstrated a good predictive capability.

Predictive value of pretreatment circulating inflammatory response markers in the neoadjuvant treatment of breast cancer: meta-analysis

BACKGROUND

Systemic inflammatory response markers have been found to have a prognostic role in several cancers, but their value in predicting the response to neoadjuvant chemotherapy in breast cancer is uncertain. A systematic review and meta-analysis of the literature was carried out to investigate this.

METHODS

A systematic search of electronic databases was conducted to identify studies that explored the predictive value of circulating systemic inflammatory response markers in patients with breast cancer before commencing neoadjuvant therapy. A meta-analysis was undertaken for each inflammatory marker where three or more studies reported pCR rates in relation to the inflammatory marker. Outcome data are reported as ORs and 95% confidence intervals.

RESULTS

A total of 49 studies were included, of which 42 were suitable for meta-analysis. A lower pretreatment neutrophil-to-lymphocyte ratio was associated with an increased pCR rate (pooled OR 1.66 (95% c.i. 1.32 to 2.09); P < 0.001). A lower white cell count (OR 1.96 (95% c.i. 1.29 to 2.97); P = 0.002) and a lower monocyte count (OR 3.20 (95% c.i. 1.71 to 5.97); P < 0.001) were also associated with a pCR. A higher lymphocyte count was associated with an increased pCR rate (OR 0.44 (95% c.i. 0.30 to 0.64); P < 0.001).

CONCLUSION

The present study found the pretreatment neutrophil-to-lymphocyte ratio, white cell count, lymphocyte count, and monocyte count of value in the prediction of a pCR in the neoadjuvant treatment of breast cancer. Further research is required to determine their value in specific breast cancer subtypes and to establish optimal cut-off values, before their adoption in clinical practice.

The Relationship Between Body Composition and Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

Background The pathological response rate in operable breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC) is postulated to be related to body composition. The success of complete pathological response (pCR) is a known prognostic factor in BC patients treated with NAC. We aimed to accurately measure body composition through BMI and skeletal muscle mass and observe their effects on pCR. Materials and methods Patients diagnosed with operable BC who had a positron emission tomography-computed tomography (PET-CT) or chest/abdominal CT taken at the time of diagnosis were retrospectively screened and enrolled in this study. Muscle mass was defined by third lumbar vertebra (L3) level transverse CT images, and data, including weight and height, were collected from the chemotherapy records. All these data were evaluated together with the postoperative pathological results. Results Sixty-nine operable BC patients with a median age of 46 (range: 29-72) years were included in the study. In all patients, regardless of sarcopenia, 23% (n = 16) achieved pCR to NAC. The pCR rate was 37.5% (n=6) in sarcopenic patients and 62.5% (n=10) in non-sarcopenic patients (p = 0.530). Overweight (n=4; 25%) and obese (n=2; 12.5%) patients also had a lower pathological response than normal-weight (n=10; 62.5%) BC patients (p=0.261). Conclusion Both sarcopenia and obesity independently and synergistically contribute to poorer pathological responses after NAC. Addressing these conditions through tailored interventions, such as nutritional support, exercise programs, and careful monitoring of body composition, could improve treatment outcomes. Further research with larger patient populations and comprehensive body measurements is essential to fully understand these relationships and develop effective strategies to mitigate their impact.

Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients

Background

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is associated with favorable outcomes in breast cancer patients. Identifying reliable predictors for pCR can assist in selecting patients who will derive the most benefit from NAC. The prognostic nutritional index (PNI) serves as an indicator of nutritional status and systemic immune competence. It has emerged as a prognostic biomarker in several malignancies; however, its predictive value for pCR in breast cancer remains uncertain. The objective of this study is to assess the predictive value of pretreatment PNI for pCR in breast cancer patients.

Methods

A total of 1170 patients who received NAC in two centers were retrospectively analyzed. The patients were divided into three cohorts: a training cohort (n=545), an internal validation cohort (n=233), and an external validation cohort (n=392). Univariate and multivariate analyses were performed to assess the predictive value of PNI and other clinicopathological factors. A stepwise logistic regression model for pCR based on the smallest Akaike information criterion was utilized to develop a nomogram. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a high PNI (≥53) had a significantly increased pCR rate (OR 2.217, 95% CI 1.215-4.043, p=0.009). Tumor size, clinical nodal status, histological grade, ER, Ki67 and PNI were identified as independent predictors and included in the final model. A nomogram was developed as a graphical representation of the model, which incorporated the PNI and five other factors (AIC=356.13). The nomogram demonstrated satisfactory calibration and discrimination in the training cohort (C-index: 0.816, 95% CI 0.765-0.866), the internal validation cohort (C-index: 0.780, 95% CI 0.697-0.864) and external validation cohort (C-index: 0.714, 95% CI 0.660-0.769). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusion

The pretreatment PNI is a reliable predictor for pCR in breast cancer patients. The PNI-based nomogram is a low-cost, noninvasive tool with favorable predictive accuracy for pCR, which can assist in determining individualized treatment strategies for breast cancer patients.

Systematic Review of Nomograms Used for Predicting Pathological Complete Response in Early Breast Cancer

Pathological complete response (pCR) is an important surrogate outcome to assess the effects of neoadjuvant chemotherapy (NAC). Nomograms to predict pCR have been developed with local data to better select patients who are likely to benefit from NAC; however, they were never critically reviewed regarding their internal and external validity. The purpose of this systematic review was to critically appraise nomograms published in the last 20 years (2010-2022). Articles about nomograms were searched in databases, such as PubMed/MEDLINE, Embase and Cochrane. A total of 1120 hits were found, and seven studies were included for analyses. No meta-analysis could be performed due to heterogeneous reports on outcomes, including the definition of pCR and subtypes. Most nomograms were developed in Asian centers, and nonrandomized retrospective cohorts were the most common sources of data. The most common subtype included in the studies was triple negative (50%). There were articles that included HER2+ (>80%). In one study, scholars performed additional validation of the nomogram using DFS and OS as outcomes; however, there was a lack of clarity on how such endpoints were measured. Nomograms to predict pCR cannot be extrapolated to other settings due to local preferences/availability of NAC. The main gaps identified in this review are also opportunities for future nomogram research and development.

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT

OBJECTIVES

The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).

INTRODUCTION

NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.

METHODS

This article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.

RESULTS

Pathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.

CONCLUSION

It was concluded that deep learning methods can predict breast cancer treatment.

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

PURPOSE

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

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.

An Integrative Clinical Model for the Prediction of Pathological Complete Response in Patients with Operable Stage II and Stage III Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

Simple Summary

Neoadjuvant chemotherapy (NAC) is widely used to treat stage II and III primary, operable triple-negative breast cancer (TNBC). The response to NAC critically affects the subsequent treatment plan, including not only curative surgical planning but also adjuvant therapy. There is no standard prediction model that accurately predicts NAC response. Therefore, the development of an easy-to-apply and cost-effective clinical prediction model for NAC treatment response would improve clinical practice. We propose an integrative clinical prediction model for the prediction of pathologically complete response in patients with operable stage II and stage III TNBC receiving NAC based on findings from tumor ultrasound and blood tests. All included parameters were readily available during and before NAC. This clinical prediction model could provide a reference to guide clinicians’ decisions in planning a patient’s NAC treatment as early as after the first cycle of NAC.

Abstract

Triple-negative breast cancer (TNBC) is treated with neoadjuvant chemotherapy (NAC). The response to NAC, particularly the probability of a complete pathological response (pCR), guides the surgical approach and adjuvant therapy. We developed a prediction model using a nomogram integrating blood tests and pre-treatment ultrasound findings for predicting pCR in patients with stage II or III operable TNBC receiving NAC. Clinical data before and after the first cycle of NAC collected from patients between 2012 and 2019 were analyzed using univariate and multivariate analyses to identify correlations with pCR. The coefficients of the significant parameters were calculated using logistic regression, and a nomogram was developed based on the logistic model to predict the probability of pCR. Eighty-eight patients were included. Five parameters correlated with the probability of pCR, including the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte (PLR) ratio, percentage change in PLR, presence of echogenic halo, and tumor height-to-width ratio. The discrimination performance of the nomogram was indicated by an area under the curve of 87.7%, and internal validation showed that the chi-square value of the Hosmer–Lemeshow test was 7.67 (p = 0.363). Thus, the integrative prediction model using clinical data can predict the probability of pCR in patients with TNBC receiving NAC.

Prognostic Value of Salivary Biochemical Indicators in Primary Resectable Breast Cancer

Despite the fact that breast cancer was detected in the early stages, the prognosis was not always favorable. In this paper, we examined the impact of clinical and pathological characteristics of patients and the composition of saliva before treatment on overall survival and the risk of recurrence of primary resectable breast cancer. The study included 355 patients of the Omsk Clinical Oncology Center with a diagnosis of primary resectable breast cancer (T_1-3N_0-1M₀). Saliva was analyzed for 42 biochemical indicators before the start of treatment. We have identified two biochemical indicators of saliva that can act as prognostic markers: alkaline phosphatase (ALP) and diene conjugates (DC). Favorable prognostic factors were ALP activity above 71.7 U/L and DC level above 3.93 c.u. Additional accounting for aspartate aminotransferase (AST) activity allows for forming a group with a favorable prognosis, for which the relative risk is reduced by more than 11 times (HR = 11.49, 95% CI 1.43-88.99, p = 0.01591). Salivary AST activity has no independent prognostic value. Multivariate analysis showed that tumor size, lymph nodes metastasis status, malignancy grade, tumor HER2 status, and salivary ALP activity were independent predictors. It was shown that the risk of recurrence decreased with menopause and increased with an increase in the size of the primary tumor and lymph node involvement. Significant risk factors for recurrence were salivary ALP activity below 71.7 U/L and DC levels below 3.93 c.u. before treatment. Thus, the assessment of biochemical indicators of saliva before treatment can provide prognostic information comparable in importance to the clinicopathological characteristics of the tumor and can be used to identify a risk group for recurrence in primary resectable breast cancer.

Development and Validation of a Nomogram to Predict the Probability of Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy: A Retrospective Cohort Study

Background

The methods used to predict the pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have some limitations. In this study, we aimed to develop a nomogram to predict breast cancer pCR after NAC based on convenient and economical multi-system hematological indicators and clinical characteristics.

Materials and Methods

Patients diagnosed from July 2017 to July 2019 served as the training group (N = 114), and patients diagnosed in from July 2019 to July 2021 served as the validation group (N = 102). A nomogram was developed according to eight indices, including body mass index, platelet distribution width, monocyte count, albumin, cystatin C, phosphorus, hemoglobin, and D-dimer, which were determined by multivariate logistic regression. Internal and external validation curves are used to calibrate the nomogram.

Results

The area under the receiver operating characteristic curve was 0.942 (95% confidence interval 0.892–0.992), and the concordance index indicated that the nomogram had good discrimination. The Hosmer–Lemeshow test and calibration curve showed that the model was well-calibrated.

Conclusion

The nomogram developed in this study can help clinicians accurately predict the possibility of patients achieving the pCR after NAC. This information can be used to decide the most effective treatment strategies for patients.

Construction and Validation of a Serum Albumin-to-Alkaline Phosphatase Ratio-Based Nomogram for Predicting Pathological Complete Response in Breast Cancer

Background

Breast cancer patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have favorable outcomes. Reliable predictors for pCR help to identify patients who will benefit most from NAC. The pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) has been shown to be a prognostic predictor in several malignancies, but its predictive value for pCR in breast cancer is still unknown. This study aims to investigate the predictive role of AAPR in breast cancer patients and develop an AAPR-based nomogram for pCR rate prediction.

Methods

A total of 780 patients who received anthracycline and taxane-based NAC from January 2012 to March 2018 were retrospectively analyzed. Univariate and multivariate analyses were performed to assess the predictive value of AAPR and other clinicopathological factors. A nomogram was developed and calibrated based on multivariate logistic regression. A validation cohort of 234 patients was utilized to further validate the predictive performance of the model. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a lower AAPR (<0.583) had a significantly reduced pCR rate (OR 2.228, 95% CI 1.246-3.986, p=0.007). Tumor size, clinical nodal status, histological grade, PR, Ki67 and AAPR were identified as independent predictors and included in the final model. The nomogram was used as a graphical representation of the model. The nomogram had satisfactory calibration and discrimination in both the training cohort and validation cohort (the C-index was 0.792 in the training cohort and 0.790 in the validation cohort). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusions

Pretreatment serum AAPR is a potentially valuable predictor for pCR in breast cancer patients who receive NAC. The AAPR-based nomogram is a noninvasive tool with favorable predictive accuracy for pCR, which helps to make individualized treatment strategy decisions.

A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Purpose

Clinical responses of neoadjuvant chemotherapy (NACT) are associated with prognosis in patients with breast cancer. The selection of suitable variables for the prediction of clinical responses remains controversial. Herein, we developed a predictive model based on ultrasound imaging and clinical indices to identify patients most likely to benefit from NACT.

Patients and Methods

We recruited a total of 225 consecutive patients who underwent NACT followed by surgery and axillary lymph node dissection at the Sixth Hospital of Ning Bo City of Zhe Jiang Province between January 1, 2018, and March 31, 2021. All patients had been diagnosed with breast cancer following the clinical examination. First, we created a training cohort of patients who underwent NACT+surgery (N=180) to develop a nomogram. We then validated the performance of the nomogram in a validation cohort of patients who underwent NACT+ surgery (N=45). Multivariate logistic regression was then used to identify independent risk factors that were associated with the response to NACT; these were then incorporated into the nomogram.

Results

Multivariate logistic regression analysis identified several significant differences as to clinical responses of NACT, including neutrophil–lymphocyte ratio (NLR), body mass index (BMI), pulsatility index (PI), resistance index (RI), blood flow, Ki67, histological type, molecular subtyping, and tumor size. The performance of the nomogram score exhibited a robust C-index of 0.89 (95% confidence interval [CI]: 0.83 to 0.95) in the training cohort and a high C-index of 0.87 (95% CI: 0.81 to 0.93) in the validation cohort. Clinical impact curves showed that the nomogram had a good predictive ability.

Conclusion

We successfully established an accurate and optimized nomogram incorporated ultrasound imaging and clinical indices that could be used preoperatively to predict clinical responses of NACT. This model can be used to evaluate the risk of clinical responses to NACT and therefore facilitate the choice of personalized therapy.

A nomogram based on clinicopathological features and serological indicators predicting breast pathologic complete response of neoadjuvant chemotherapy in breast cancer

A single tumor marker is not enough to predict the breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. We aimed to establish a nomogram based on multiple clinicopathological features and routine serological indicators to predict bpCR after NAC in breast cancer patients. Data on clinical factors and laboratory indices of 130 breast cancer patients who underwent NAC and surgery in First Affiliated Hospital of Xi'an Jiaotong University from July 2017 to July 2019 were collected. Multivariable logistic regression analysis identified 11 independent indicators: body mass index, carbohydrate antigen 125, total protein, blood urea nitrogen, cystatin C, serum potassium, serum phosphorus, platelet distribution width, activated partial thromboplastin time, thrombin time, and hepatitis B surface antibodies. The nomogram was established based on these indicators. The 1000 bootstrap resampling internal verification calibration curve and the GiViTI calibration belt showed that the model was well calibrated. The Brier score of 0.095 indicated that the nomogram had a high accuracy. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.941 (95% confidence interval: 0.900-0.982) showed good discrimination of the model. In conclusion, this nomogram showed high accuracy and specificity and did not increase the economic burden of patients, thereby having a high clinical application value.

Prognostic nomogram for the severity of acute organophosphate insecticide self-poisoning: a retrospective observational cohort study

OBJECTIVE

To develop a convenient nomogram for the bedside evaluation of patients with acute organophosphorus poisoning (AOPP).

DESIGN

This was a retrospective study.

SETTING

Two independent hospitals in northern China, the First Hospital of Jilin University and the Lequn Hospital of the First Hospital of Jilin University.

PARTICIPANTS

A total of 1657 consecutive patients admitted for the deliberate oral intake of AOPP within 24 hours from exposure and aged >18 years were enrolled between 1 January 2013 and 31 December 2018. The exclusion criteria were: normal range of plasma cholinesterase, exposure to any other type of poisonous drug(s), severe chronic comorbidities including symptomatic heart failure (New York Heart Association III or IV) or any other kidney, liver and pulmonary diseases. Eight hundred and thirty-four patients were included.

PRIMARY OUTCOME MEASURE

The existence of severely poisoned cases, defined as patients with any of the following complications: cardiac arrest, respiratory failure requiring ventilator support, hypotension or in-hospital death.

RESULTS

440 patients from one hospital were included in the study to develop a nomogram of severe AOPP, whereas 394 patients from the other hospital were used for the validation. Associated risk factors were identified by multivariate logistic regression. The nomogram was validated by the area under the receiver operating characteristic curve (AUC). A nomogram was developed with age, white cells, albumin, cholinesterase, blood pH and lactic acid levels. The AUC was 0.875 (95% CI 0.837 to 0.913) and 0.855 (95% CI 0.81 to 0.9) in the derivation and validation cohorts, respectively. The calibration plot for the probability of severe AOPP showed an optimal agreement between the prediction by nomogram and actual observation in both derivation and validation cohorts.

CONCLUSION

A convenient severity evaluation nomogram for patients with AOPP was developed, which could be used by physicians in making clinical decisions and predicting patients' prognosis.

Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors

BACKGROUND

Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice.

OBJECTIVE

The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables.

METHODS

Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value.

RESULTS

The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646.

CONCLUSION

We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.

Laboratory indicators predict axillary nodal pathologic complete response after neoadjuvant therapy in breast cancer

The purpose was to integrate clinicopathological and laboratory indicators to predict axillary nodal pathologic complete response (apCR) after neoadjuvant therapy (NAT). The pretreatment clinicopathological and laboratory indicators of 416 clinical nodal-positive breast cancer patients who underwent surgery after NAT were analyzed from April 2015 to 2020. Predictive factors of apCR were examined by logistic analysis. A nomogram was built according to logistic analysis. Among the 416 patients, 37.3% achieved apCR. Multivariate analysis showed that age, pathological grading, molecular subtype and neutrophil-to-lymphocyte ratio were independent predictors of apCR. A nomogram was established based on these four factors. The area under the curve (AUC) was 0.758 in the training set. The validation set showed good discrimination, with AUC of 0.732. In subtype analysis, apCR was 23.8, 47.1 and 50.8% in hormone receptor-positive/HER2-, HER2+ and triple-negative subgroups, respectively. According to the results of the multivariate analysis, pathological grade and fibrinogen level were independent predictors of apCR after NAT in HER2+ patients. Except for traditional clinicopathological factors, laboratory indicators could also be identified as predictive factors of apCR after NAT. The nomogram integrating pretreatment indicators demonstrated its distinguishing capability, with a high AUC, and could help to guide individualized treatment options.

Association between Skeletal Muscle Loss and the Response to Neoadjuvant Chemotherapy for Breast Cancer

Simple Summary

The loss of skeletal muscle mass is known to be associated with poor treatment outcome, treatment-related toxicity, and high mortality. The association between loss of skeletal muscle mass and the response to treatment is not well-defined yet. In this study, we evaluated the impact of loss of skeletal muscle mass on responsiveness to neoadjuvant chemotherapy in breast cancer. The prediction of response to neoadjuvant chemotherapy could be helpful to guide the treatment direction.

Abstract

There are no means to predict patient response to neoadjuvant chemotherapy (NAC); the impact of skeletal muscle loss on the response to NAC remains undefined. We investigated the association between response to chemotherapy and skeletal muscle loss in breast cancer patients. Patients diagnosed with invasive breast cancer who were treated with NAC, surgery, and radiotherapy were analyzed. We quantified skeletal muscle loss using pre-NAC and post-NAC computed tomography scans. The response to treatment was determined using the Response Evaluation Criteria in Solid Tumors. We included 246 patients in this study (median follow-up, 28.85 months). The median age was 48 years old (interquartile range 42–54) and 115 patients were less than 48 years old (46.7%). Patients showing a complete or partial response were categorized into the responder group (208 patients); the rest were categorized into the non-responder group (38 patients). The skeletal muscle mass cut-off value was determined using a receiver operating characteristic curve; it showed areas under the curve of 0.732 and 0.885 for the pre-NAC and post-NAC skeletal muscle index (p < 0.001 for both), respectively. Skeletal muscle loss and cancer stage were significantly associated with poor response to NAC in locally advanced breast cancer patients. Accurately measuring muscle loss to guide treatment and delaying muscle loss through various interventions would help enhance the response to NAC and improve clinical outcomes.

Neoadjuvant Chemotherapy of Triple-Negative Breast Cancer: Evaluation of Early Clinical Response, Pathological Complete Response Rates, and Addition of Platinum Salts Benefit Based on Real-World Evidence

Simple Summary

Neoadjuvant chemotherapy (NACT) is the standard treatment for early-stage triple-negative breast cancer (TNBC). Achieving pathological complete response (pCR) is considered an essential prognostic factor with favorable long-term outcomes. The administration of NACT regimens with platinum salts is associated with a higher pCR rate. However, with unclear treatment guidelines and at the expense of a higher incidence of adverse events. Identifying patients and circumstances in which the benefits of platinum NACT outweigh inconveniences is still an ongoing challenge. Considering early clinical response (ECR) after the initial standard NACT cycles together with other suitable predictors could be useful to decide about the administration of platinum salts in clinical practice. The results of this large single institutional retrospective study of consecutive patients showed the significant role of adding platinum salts in older patients with high-proliferative early responded tumors and persisted lymph nodes involvement regardless of BRCA1/2 status.

Abstract

Pathological complete response (pCR) achievement is undoubtedly the essential goal of neoadjuvant therapy for breast cancer, directly affecting survival endpoints. This retrospective study of 237 triple-negative breast cancer (TNBC) patients with a median follow-up of 36 months evaluated the role of adding platinum salts into standard neoadjuvant chemotherapy (NACT). After the initial four standard NACT cycles, early clinical response (ECR) was assessed and used to identify tumors and patients generally sensitive to NACT. BRCA1/2 mutation, smaller unifocal tumors, and Ki-67 ≥ 65% were independent predictors of ECR. The total pCR rate was 41%, the achievement of pCR was strongly associated with ECR (OR = 15.1, p < 0.001). According to multivariable analysis, the significant benefit of platinum NACT was observed in early responders ≥45 years, Ki-67 ≥ 65% and persisted lymph node involvement regardless of BRCA1/2 status. Early responders with pCR had a longer time to death (HR = 0.28, p < 0.001) and relapse (HR = 0.26, p < 0.001). The pCR was achieved in only 7% of non-responders. However, platinum salts favored non-responders’ survival outcomes without statistical significance. Toxicity was significantly often observed in patients with platinum NACT (p = 0.003) but not for grade 3/4 (p = 0.155). These results based on real-world evidence point to the usability of ECR in NACT management, especially focusing on the benefit of platinum salts.

Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer

Objective: To develop and validate a prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NCT) of triple-negative breast cancer (TNBC). Methods: We systematically searched Gene Expression Omnibus, ArrayExpress, and PubMed for the gene expression profiles of operable TNBC accessible to NCT. Molecular heterogeneity was detected with hierarchical clustering method, and the biological profiles of differentially expressed genes were investigated by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses, and Gene Set Enrichment Analysis (GSEA). Next, machine-learning algorithms including random-forest analysis and least absolute shrinkage and selection operator (LASSO) analysis were synchronously performed and, then, the intersected proportion of significant genes was undergone binary logistic regression to fulfill variables selection. The predictive response score (pRS) system was built as the product of the gene expression and coefficient obtained from the logistic analysis. Last, the cohorts were randomly divided in a 7:3 ratio into training cohort and validation cohort for the introduction of a robust model, and a nomogram was constructed with the independent predictors for pCR rate. Results: A total of 217 individuals from four cohort datasets (GSE32646, GSE25065, GSE25055, GSE21974) with complete clinicopathological information were included. Based on the microarray data, a six-gene panel (ATP4B, FBXO22, FCN2, RRP8, SMERK2, TET3) was identified. A robust nomogram, adopting pRS and clinical tumor size stage, was established and the performance was successively validated by calibration curves and receiver operating characteristic curves with the area under curve 0.704 and 0.756, respectively. Results of GSEA revealed that the biological processes including apoptosis, hypoxia, mTORC1 signaling and myogenesis, and oncogenic features of EGFR and RAF were in proactivity to attribute to an inferior response. Conclusions: This study provided a robust prediction model for pCR rate and revealed potential mechanisms of distinct response to NCT in TNBC, which were promising and warranted to further validate in the perspective.

Development and validation of a prognostic nomogram for patients with triple-negative breast cancer with histology of infiltrating duct carcinoma

Background

The purpose of this study was to develop prognostic nomograms from a cohort of patients with triple-negative breast cancer (TNBC) with histology of infiltrating duct carcinoma (IDC) by correlating their clinical and pathological parameters with the rates of disease-free survival (DFS) and overall survival (OS).

Methods

We retrospectively analyzed TNBC patients with histology of IDC at our institution between 2009 and 2012. Age, family history, menopausal status, surgery type, T stage, N stage, histological grade, vascular invasion, perineural invasion, cytokeratin 5/6 status, Ki-67 expression, and epithelial cadherin (E-cadherin) status were analyzed. Predictors were used in multivariable logistic regression analysis to develop a nomogram to predict DFS and OS rates. The nomograms were then subjected to internal validation, with external validation of the nomogram for predicting OS using separate cohorts of TNBC patients known from the Cancer Genome Atlas (TCGA) database. Using the concordance index (C-index) with calibration curves, the predictive accuracy and discriminative ability were calculated.

Results

A total of 242 eligible TNBC patients were included for analysis. The median follow-up time was 70.73 months. Of the patients, 32.6%, 42.6%, and 24.8% had stage I, II, and III disease, respectively. The 3- and 5-year survival rates were 81.0% and 76.5% for DFS, and 86.5% and 81.1%, for OS, respectively. Age, T stage, N stage, and E-cadherin status were found to be risk factors. The nomograms based on those risk factors accurately predicted the 3- and 5-year survival rates. The C-index was 0.798 and 0.821 for DFS and OS, respectively. Besides, the nomogram for OS showed relatively reliable performance in stratifying different risk groups of patients in training and validation cohorts identified from the TCGA database. The C-index reached 0.843. DFS validation was not completed, as there was insufficient data.

Conclusions

Using clinicopathological information, we produced a prognostic nomogram that accurately predicts the 3- and 5-year DFS and OS for patients with TNBC with histology of IDC. More external confirmation is required.

Construction of Nomograms for Predicting Pathological Complete Response and Tumor Shrinkage Size in Breast Cancer

Purpose

Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NAC) for the HER2-positive and triple-negative subtypes of breast cancer and is related to survival benefit; however, luminal breast cancer is not sensitive to NAC, and the size of tumor shrinkage is a more meaningful clinical indicator for the luminal breast cancer subtype. We wanted to use a nomogram or formula to develop and implement a series of prediction models for pCR or tumor shrinkage size.

Patients and Methods

We developed a prediction model in a primary cohort consisting of 498 patients with invasive breast cancer, and the data were gathered from July 2016 to September 2018. The endpoint was pCR and tumor shrinkage size. In the primary cohort, the HER2-positive cohort, and the triple-negative cohort, multivariate logistic regression analysis was used to screen the significant clinical features and clinicopathological features to develop nomograms. In the luminal group, multivariate linear regression analysis was used to test the risk factors that affect tumor shrinkage size. The area under the receiver operating characteristic curve (AUC) and calibration curves were adopted to evaluate and analyze the discrimination and calibration ability of nomograms. Furthermore, we also performed internal validation and independent validation in the primary cohort.

Results

ER status, KI67 status, HER2 status, number of NAC cycles, and tumor size were independent predictive factors of pCR in the primary cohort. These indicators had good discrimination and calibration in the primary and validation cohorts (AUC: 0.873, 0.820). The nomogram for HER2-positive and triple-negative breast cancer (TNBC) had an AUC of 0.820 and 0.785, respectively. Both the HER2 positive and TNBC nomogram calibration curves indicated significant agreement. Moreover, the luminal subtype prediction model was Y (tumor shrinkage size) = -0.576 × (age at diagnosis) + 2.158 × (number of NAC cycles) + 0.233 × (pre-NAC tumor size) + 51.662.

Conclusion

Utilizing this predictive model will enable us to identify patients at high probability for pCR after NAC. Clinicians can stratify these patients and make individualized and personalized recommendations for therapy.

Correlation between peripheral blood inflammatory indicators and pathologic complete response to neoadjuvant chemotherapy in locally advanced breast cancer patients

The immune system plays a fundamental role in the response to neoadjuvant chemotherapy (NAC) of locally advanced breast cancer (LABC) patients. Patients with pathological complete response (pCR) after NAC have a higher survival rate. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) are peripheral blood indicators of inflammatory response. This investigates the correlation between NLR, PLR, LMR, and other clinicopathological features of breast cancer patients before receiving NAC and pCR.Data of LABC patients who underwent NAC between 2009 and 2018 were retrospectively reviewed. Each patient's peripheral complete blood count was recorded before starting NAC. The cut-off values for neutrophils, lymphocytes, monocytes, and platelets in the peripheral blood and NLR, PLR, and LMR were determined by receiver operating characteristic curve analyses.The records of 131 patients were analyzed and divided into two groups, pCR (+ve) and pCR (-ve), and their clinicopathological features and laboratory findings were compared. pCR was achieved in 23.6% of patients. The cut-off values of neutrophils, lymphocytes, monocytes, and platelets at the time of diagnosis and NLR, PLR, and LMR were, respectively, 4150 μL, 2000 μL, 635 μL, 271 × 10 μL, 1.95, 119, and 3.35. The pCR rate was higher in patients with low neutrophil count, low NLR, and high lymphocyte count (P = .002, <.001, and .040, respectively).As per the findings of multivariate logistic regression analysis, the independent predictive factors of pCR were clinical tumor size T1 and T2, grade 3, ER negativity, and low NLR (P = .015, .001, .020, .022, and .001, respectively).While NLR was found to be an independent predictive factor of pCR in LABC patients receiving NAC, a similar result was not observed for PLR and LMR. NLR can be a useful biomarker for predicting the response of patients receiving NAC.