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Effects of contrast-enhanced ultrasound treatment on neoadjuvant chemotherapy in breast cancer.

ABSTRACT: Purpose: Preclinical and clinical data indicate that contrast-enhanced ultrasound can enhance tumor perfusion and vessel permeability, thus, improving chemotherapy accumulation and therapeutic outcome. Therefore, we investigated the effects of high mechanical index (MI) contrast-enhanced Doppler ultrasound (CDUS) on tumor perfusion in breast cancer. Methods: In this prospective study, breast cancer patients were randomly assigned to receive either 18 minutes of high MI CDUS during chemotherapy infusion (n = 6) or chemotherapy alone (n = 5). Tumor perfusion was measured before and after at least six chemotherapy cycles using motion-model ultrasound localization microscopy. Additionally, acute effects of CDUS on vessel perfusion and chemotherapy distribution were evaluated in mice bearing triple-negative breast cancer (TNBC). Results: Morphological and functional vascular characteristics of breast cancer in patients were not significantly influenced by high MI CDUS. However, complete clinical tumor response after neoadjuvant chemotherapy was lower in high MI CDUS-treated (1/6) compared to untreated patients (4/5) and size reduction of high MI CDUS treated tumors tended to be delayed at early chemotherapy cycles. In mice with TNBC high MI CDUS decreased the perfused tumor vessel fraction (p < 0.01) without affecting carboplatin accumulation or distribution. Higher vascular immaturity and lower stromal stabilization may explain the stronger vascular response in murine than human tumors. Conclusion: High MI CDUS had no detectable effect on breast cancer vascularization in patients. In mice, the same high MI CDUS setting did not affect chemotherapy accumulation although strong effects on the tumor vasculature were detected histologically. Thus, sonopermeabilization in human breast cancers might not be effective using high MI CDUS protocols and future applications may rather focus on low MI approaches triggering microbubble oscillations instead of destruction. Furthermore, our results show that there are profound differences in the response of mouse and human tumor vasculature to high MI CDUS, which need to be further explored and considered in clinical translation.

SUBMITTER: Rix A

PROVIDER: S-EPMC8490514 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe primary objective was to determine whether mid-treatment ultrasound measurements of index breast tumors and index axillary nodes of different cancer subtypes associate with residual cancer burden (RCB).MethodsPatients with invasive breast cancer who underwent neoadjuvant chemotherapy and had pre-treatment and mid-treatment breast and axillary ultrasound were included in this single-institution, retrospective cohort study. Linear regression analysis assessed associations between RCB with (a) change in index breast tumor size, (b) change in index node size, and (c) absolute number of abnormal nodes at mid-treatment. Multivariate linear regression was used to calculate best-fit models for RCB.ResultsOne hundred fifty-nine patients (68 triple negative breast cancer [TNBC], 45 hormone receptor [HR]+/human epidermal growth factor receptor 2 [HER2]-, and 46 HR-/HER2+) were included. Median age at diagnosis was 50 years, range 30-76. Median tumor size was 3.4 cm, range 0.9-10.4. Pathological complete response/RCB-I rates were 36.8% (25/68) for TNBC patients, 24.4% (11/45) for HR+/HER2- patients, and 71.7% (33/46) for HR-/HER2+ patients. Linear regression analyses demonstrated associations between percent change in tumor ultrasound measurements at mid-treatment with RCB index score in TNBC and HR+/HER2- (p < .05) but not in HR-/HER2+ (p > .05) tumors and an association between axillary ultrasound assessment of number of abnormal nodes at mid-treatment with RCB index score across all subtypes (p < .05).ConclusionPerformance characteristics of breast ultrasound associated with RCB vary by cancer subtype, whereas the performance characteristics of axillary ultrasound associated with RCB are consistent across cancer subtype. Breast and axillary ultrasound may be valuable in monitoring response to neoadjuvant therapy. The Oncologist 2017;22:394-401 IMPLICATIONS FOR PRACTICE: The differential performance characteristics of breast ultrasound by molecular subtype and the consistent performance characteristics of axillary ultrasound across molecular subtypes can have clinical utility in monitoring response to neoadjuvant therapy.

Project description:Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development (n = 61) dataset and a testing dataset (n = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models (p = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.

Project description:BackgroundNeoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects.MethodsThe B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses.ResultsStatistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91.ConclusionsQuantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

Project description:Locally advanced breast cancer (LABC) is a severe type of cancer with a poor prognosis, despite advancements in therapy. As the disease is often inoperable, current guidelines suggest upfront aggressive neoadjuvant chemotherapy (NAC). Complete pathological response to chemotherapy is linked to improved survival, but conventional clinical assessments like physical exams, mammography, and imaging are limited in detecting early response. Early detection of tissue response can improve complete pathological response and patient survival while reducing exposure to ineffective and potentially harmful treatments. A rapid, cost-effective modality without the need for exogenous contrast agents would be valuable for evaluating neoadjuvant therapy response. Conventional ultrasound provides information about tissue echogenicity, but image comparisons are difficult due to instrument-dependent settings and imaging parameters. Quantitative ultrasound (QUS) overcomes this by using normalized power spectra to calculate quantitative metrics. This study used a novel transfer learning-based approach to predict LABC response to neoadjuvant chemotherapy using QUS imaging at pre-treatment. Using data from 174 patients, QUS parametric images of breast tumors with margins were generated. The ground truth response to therapy for each patient was based on standard clinical and pathological criteria. The Residual Network (ResNet) deep learning architecture was used to extract features from the parametric QUS maps. This was followed by SelectKBest and Synthetic Minority Oversampling (SMOTE) techniques for feature selection and data balancing, respectively. The Support Vector Machine (SVM) algorithm was employed to classify patients into two distinct categories: nonresponders (NR) and responders (RR). Evaluation results on an unseen test set demonstrate that the transfer learning-based approach using spectral slope parametric maps had the best performance in the identification of nonresponders with precision, recall, F1-score, and balanced accuracy of 100, 71, 83, and 86%, respectively. The transfer learning-based approach has many advantages over conventional deep learning methods since it reduces the need for large image datasets for training and shortens the training time. The results of this study demonstrate the potential of transfer learning in predicting LABC response to neoadjuvant chemotherapy before the start of treatment using quantitative ultrasound imaging. Prediction of NAC response before treatment can aid clinicians in customizing ineffectual treatment regimens for individual patients.

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Effects of contrast-enhanced ultrasound treatment on neoadjuvant chemotherapy in breast cancer.

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