Prediction of pathological complete response in breast cancer patients during neoadjuvant chemotherapy: Is shear wave elastography a useful tool in clinical routine?

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

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Prediction of clinical response to neoadjuvant therapy in advanced breast cancer by baseline B-mode ultrasound, shear-wave elastography, and pathological information

Background

Neoadjuvant therapy (NAT) is the preferred treatment for advanced breast cancer nowadays. The early prediction of its responses is important for personalized treatment. This study aimed at using baseline shear wave elastography (SWE) ultrasound combined with clinical and pathological information to predict the clinical response to therapy in advanced breast cancer.

Methods

This retrospective study included 217 patients with advanced breast cancer who were treated in West China Hospital of Sichuan University from April 2020 to June 2022. The features of ultrasonic images were collected according to the Breast imaging reporting and data system (BI-RADS), and the stiffness value was measured at the same time. The changes were measured according to the Response evaluation criteria in solid tumors (RECIST1.1) by MRI and clinical situation. The relevant indicators of clinical response were obtained through univariate analysis and incorporated into a logistic regression analysis to establish the prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the prediction models.

Results

All patients were divided into a test set and a validation set in a 7:3 ratio. A total of 152 patients in the test set, with 41 patients (27.00%) in the non-responders group and 111 patients (73.00%) in the responders group, were finally included in this study. Among all unitary and combined mode models, the Pathology + B-mode + SWE model performed best, with the highest AUC of 0.808 (accuracy 72.37%, sensitivity 68.47%, specificity 82.93%, P<0.001). HER2+, Skin invasion, Post mammary space invasion, Myometrial invasion and Emax were the factors with a significant predictive value (P<0.05). 65 patients were used as an external validation set. There was no statistical difference in ROC between the test set and the validation set (P>0.05).

Conclusion

As the non-invasive imaging biomarkers, baseline SWE ultrasound combined with clinical and pathological information can be used to predict the clinical response to therapy in advanced breast cancer.

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

OBJECTIVES

To investigate the diagnostic performance of strain and shear wave elastography for the response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Shear wave elastography as a supplemental tool in the assessment of unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination

Objectives:

To define reference values for shear wave elastography (SWE) in unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination.

Methods:

In total, 177 clinically and sonographically unsuspicious axillary lymph nodes were prospectively evaluated with SWE using Virtual Touch Tissue Imaging Quantification (VTIQ) in 175 women. Mean values of tissue stiffness for axillary fatty tissue, lymph node cortex, and lymph node hilus were measured. Additionally, test-retest reliability of SWE in the assessment of axillary lymph node stiffness was evaluated by repeating each measurement three times.

Results:

In 177 axillary lymph nodes, the mean stiffness of lymph node cortex, hilus, and surrounding fatty tissue as quantified by SWE was 1.90 m/s (SD: 0.34 m/s), 2.02 m/s (SD: 0.37 m/s), and 1.75 m/s (SD: 0.38 m/s), respectively. The mean stiffness of cortex and hilus was significantly higher compared to fatty tissue (p < 0.0001). SWE demonstrated good test–retest reliability in the assessment of stiffness of the lymph node hilus, cortex, and the surrounding fatty tissue with an intraclass correlation of 0.79 (95% CI: 0.75; 0.83), 0.75 (95% CI: 0.70; 0.79), and 0.78 (95% CI: 0.74; 0.82), respectively, (p < 0.0001).

Conclusions:

Reference values for SWE in unsuspicious axillary lymph nodes are determined. These results may help to better identify axillary lymph node metastasis for breast cancer patients when combined with other lymph node features. SWE is a reliable method for the objective quantification of tissue stiffness of axillary lymph nodes.

Advances in knowledge:

This study presents physiological reference values for tissue stiffness by examining the axillary lymph nodes with SWE in 175 women with sonomorphologically unsuspicious lymph nodes.

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

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

Accuracy of ultrasound elastography for predicting breast cancer response to neoadjuvant chemotherapy: A systematic review and meta-analysis

BACKGROUND

Several studies have reported the prognostic value of ultrasound elastography (UE) in patients receiving neoadjuvant chemotherapy (NACT) for breast cancer. However, the assessment of parameters differed between shear-wave elastography and strain elastography in terms of measured elasticity parameter and mode of imaging. It is important, therefore, to assess the accuracy of the two modes of elastography.

AIM

To assess the accuracy of UE for predicting the pathologic complete response (pCR) in breast cancer patients following NACT.

METHODS

A comprehensive and systematic search was performed in the databases of MEDLINE, EMBASE, SCOPUS, PubMed Central, CINAHL, Web of Science and Cochrane library from inception until December 2020. Meta-analysis was performed using STATA software “Midas” package.

RESULTS

A total of 14 studies with 989 patients were included. The pooled sensitivities were 86% [95% confidence interval (CI): 76%-92%] for UE, 77% (95%CI: 68%-84%) for shear-wave elastography, and 92% (95%CI: 73%-98%) for strain-wave elastography. The pooled score specificities were 86% (95%CI: 80%-90%) for UE, 84% (95%CI: 72%-91%) for shear-wave elasticity, and 87% (95%CI: 81%-92%) for strain-wave elastography. A significant heterogeneity was found among studies based on the chi-square test results and an I² statistic > 75%.

CONCLUSION

Strain-wave type of UE can accurately predict the pCR following NACT amongst breast cancer patients. Studies exploring its accuracy in different ethnic populations are required to strengthen the evidence.

Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

Complete response after neoadjuvant chemotherapy (rNACT) elevates the surgical outcomes of patients with breast cancer, however, non-rNACT have a higher risk of death and recurrence.

AIM

To establish novel machine learning (ML)-based predictive models for predicting probability of rNACT in breast cancer patients who intends to receive NACT.

METHODS

A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for rNACT by multiple ML-based algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance.

RESULTS

Analysis identified several significant differences between the rNACT and non-rNACT groups, including total cholesterol, low-density lipoprotein, neutrophil-to-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine (SVM) model with twelve variables introduced was identified as the best predictive model.

CONCLUSION

By incorporating pretreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of rNACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of rNACT in patients with breast cancer.

Usefulness of new shear wave elastography in early predicting the efficacy of neoadjuvant chemotherapy for patients with breast cancer: where and when to measure is optimal?

BACKGROUND

The aim of this study was to investigate the diagnosis performance of new shear wave elastography (sound touch elastography, STE) in the prediction of neoadjuvant chemotherapy (NAC) response at an early stage in breast cancer patients and to determine the optimal measurement locations around the lesion in different ranges.

METHODS

One hundred and eight patients were analyzed in this prospective study from November 2018 to December 2020. All patients completed NAC treatment and underwent STE examination at three time points [the day before NAC (t0); the day before the second course (t1); the day before third course (t2)]. The stiffness of the whole lesion (G), 1-mm shell (S1) and 2-mm shell (S2) around the lesion was expressed by STE parameters. The relative changes (∆stiffness) of STE parameters after the first and second course of NAC were calculated and shown as the variables [Δ(t1) and Δ(t2)]. The diagnostic accuracy of STE was evaluated by means of receiver operating characteristic curve analysis.

RESULTS

The ∆stiffness (%) including ∆G_mean(t2), ∆S1_mean(t2) and ∆S2_mean(t2) all showed significant differences between pathological complete response (pCR) and non-pCR groups. ∆S2_mean(t2) displayed the best predictive performance for pCR (AUC = 0.842) with an ideal ∆stiffness threshold value - 26%.

CONCLUSIONS

Measuring the relative changes in the stiffness of surrounding tissue or entire lesion with STE holds promise for effectively predicting the response to NAC at its early stage for breast cancer patients and ∆stiffness of shell 2 mm after the second course of NAC may be a potential prediction parameter.

Assessment of breast cancer response to neoadjuvant chemotherapy based on ultrasound backscattering envelope statistics

PURPOSE

Neoadjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment.

METHODS

The method assumes that ultrasound scattering is different for responsive and non-responsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov-Smirnov statistics (KSS) and the symmetrical Kullback-Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells (RMC) was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC).

RESULTS

For responding tumors a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In non-responding tumors the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not responding to NAC after the first dose of the drug, with AUC equal 0.83±0.06 and 0.84±0.07 respectively. After the third dose, the AUC increased to 0.90±0.05 and 0.91±0.04 respectively.

CONCLUSIONS

The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of non-responding cases. This article is protected by copyright. All rights reserved.

Predicting Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer After the Second Cycle of Chemotherapy Using Shear-Wave Elastography-A Preliminary Evaluation

ABSTRACT

The primary objective of the study was to determine whether shear wave elastography can be used to predict the response of neoadjuvant chemotherapy (NACT) in women having invasive breast cancer. A prospective study involving 28 patients having invasive breast cancer and undergoing NACT followed by surgery was done after institutional review board approval. All the patients underwent 2-dimensional B-mode ultrasound and 2-dimensional shear wave elastography before the start of chemotherapy and after 2 cycles of completion of chemotherapy, and mean stiffness was recorded. The patients were segregated to responders and nonresponders based on residual cancer burden scoring. Difference in mean elasticity was compared between the 2 groups. The results showed that the mean stiffness after 2 cycles was significantly different between the responders and nonresponders and so was the change in the mean stiffness after 2 cycles of NACT. Using a cutoff value of 45.5 kPa (20.53%), change in mean elasticity after 2 cycles of NACT, sensitivity of 76.9%, and specificity of 80% with negative predictive value of 80.1 was attained. Responders show greater change in mean stiffness after 2 cycles of NACT as compared with nonresponders on shear wave elastography; thus, it can be used to predict response to NACT after 2 cycles.

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer

OBJECTIVE

To investigate the ability of tumor stiffness, tumor blood flow, and Ki-67 expression alone or in combination in predicting the pathological response to neoadjuvant chemotherapy (NACT) in breast cancer.

PATIENTS AND METHODS

This prospective cohort study included 145 breast cancer patients treated with NACT. Tumor stiffness (maximum stiffness (Emax), mean stiffness (Emean)), blood score (BS), and their relative changes, were evaluated before (t0), during (t1-t5), and at the end of NACT (t6) by shear-wave elastography and optical imaging. Ki-67 expression was quantitatively evaluated by immunohistochemistry using core biopsy specimens obtained before NACT. Pathological responses were evaluated by residual cancer burden. The ability of tumor stiffness, BS, Ki-67, and predRCB-which combined ΔEmean (t2) (the relative changes in Emean after the second NACT cycle), BS2 (BS after the second NACT cycle), and Ki-67-in predicting tumor responses was compared using receiver operating characteristic curves and the Z-test.

RESULTS

Tumor stiffness and BS decreased during NACT. ΔEmean (t2), BS2, and Ki-67 had better predictive performance than other indexes in identifying a favorable response (AUC = 0.82, 0.81, and 0.80) and resistance responses (AUC = 0.85, 0.79, and 0.84), with no significant differences between the three (p > 0.05). PredRCB had better predictive performance than any parameter alone for a favorable response (AUC = 0.90) and resistance (AUC = 0.93).

CONCLUSION

Tumor stiffness, BS, and Ki-67 expression showed good and similar abilities for predicting the pathological response to NACT, and predRCB was a significantly better predictor than each index alone. These results may help design therapeutic strategies for breast cancer patients undergoing NACT.